3c-model-trimming.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import gensim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "from tensorflow.keras.models import Sequential, load_model, Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.layers import Dropout, Input\n",
    "from tensorflow.keras.regularizers import l1\n",
    "\n",
    "ROOT = os.path.dirname(os.getcwd())\n",
    "#path_data = os.path.join(ROOT, 'data')\n",
    "path_data = 'C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\data'\n",
    "sys.path.insert(0, ROOT)\n",
    "sys.path.insert(0, \"C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\spec2vec_gnps_data_analysis\\\\custom_functions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data ready for training\n",
    "-Creation of this data can be seen in 3b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_training_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "# training set\n",
    "outfile = os.path.join(path_data, 'nn_prep_training_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_prep_training_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_prep_training_found_matches_s2v_2dec = find_info_matches(old_and_unique_found_matches_s2v_2dec_top20,\n",
    "                                                                old_and_unique_documents_library_s2v_2dec,\n",
    "                                                                old_and_unique_documents_query_s2v_2dec,\n",
    "                                                                max_parent_mass=max_parent_mass)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_prep_training_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_testing_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_prep_testing_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_prep_testing_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_prep_testing_found_matches_s2v_2dec = find_info_matches(new_and_unique2_found_matches_s2v_2dec_top20,\n",
    "                                                                new_and_unique2_documents_library_s2v_2dec,\n",
    "                                                                new_and_unique2_documents_query_s2v_2dec,\n",
    "                                                                max_parent_mass=max_parent_mass)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_prep_testing_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22101</th>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.909346</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.999777</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>1366</th>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.584506</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034744</td>\n",
       "      <td>0.999554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>0.336160</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.337487</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.303197</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581808</td>\n",
       "      <td>0.037051</td>\n",
       "      <td>0.000997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "22101      0.928457        0.663299          0.928457            0.663299   \n",
       "20538      0.856661        0.549896          0.856661            0.549896   \n",
       "22454      0.857507        0.549896          0.857507            0.549896   \n",
       "1366       0.389183        0.549896          0.389183            0.549896   \n",
       "5998       0.336160        0.070000          0.337487            0.195643   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass  mass_sim  \n",
       "22101   0.909346      1    1.000000     0.034743  0.999777  \n",
       "20538   0.859138      1    1.000000     0.034743  1.000000  \n",
       "22454   0.674490      1    1.000000     0.034743  1.000000  \n",
       "1366    0.584506      1    1.000000     0.034744  0.999554  \n",
       "5998    0.303197      0    0.581808     0.037051  0.000997  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_prep_testing_found_matches_s2v_2dec[2].iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add matches together for each query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add all the found matches together in one big df\n",
    "nn_training_found_matches_s2v_2dec = nn_prep_training_found_matches_s2v_2dec[0].append(\n",
    "    nn_prep_training_found_matches_s2v_2dec[1:])\n",
    "nn_training_found_matches_s2v_2dec = nn_training_found_matches_s2v_2dec.sample(frac=1)\n",
    "\n",
    "nn_testing_full_found_matches_s2v_2dec = nn_prep_testing_found_matches_s2v_2dec[0].append(\n",
    "    nn_prep_testing_found_matches_s2v_2dec[1:])\n",
    "nn_testing_full_found_matches_s2v_2dec = nn_testing_full_found_matches_s2v_2dec.sample(frac=1)\n",
    "\n",
    "\n",
    "# take 300 (randomly chosen) query matches for validation set\n",
    "np.random.seed(42)\n",
    "first_half = list(np.random.choice(range(0,1000), 250, replace=False))\n",
    "np.random.seed(42)\n",
    "second_half = list(np.random.choice(range(1000,2000), 250, replace=False))\n",
    "val_set = first_half + second_half\n",
    "nn_val_found_matches_s2v_2dec = pd.DataFrame()\n",
    "nn_testing_found_matches_s2v_2dec = pd.DataFrame()\n",
    "\n",
    "for i in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "    if i in val_set:\n",
    "        nn_val_found_matches_s2v_2dec = nn_val_found_matches_s2v_2dec.append(nn_prep_testing_found_matches_s2v_2dec[i])\n",
    "    else:\n",
    "        nn_testing_found_matches_s2v_2dec = nn_testing_found_matches_s2v_2dec.append(nn_prep_testing_found_matches_s2v_2dec[i])\n",
    "\n",
    "nn_val_found_matches_s2v_2dec = nn_val_found_matches_s2v_2dec.sample(frac=1)\n",
    "nn_testing_found_matches_s2v_2dec = nn_testing_found_matches_s2v_2dec.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_half"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nn_training_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'training set')\n",
    "plt.hist(nn_testing_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'test subset')\n",
    "plt.hist(nn_val_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'validation set')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = nn_training_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_train = nn_training_found_matches_s2v_2dec['similarity']\n",
    "X_test = nn_testing_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_test = nn_testing_found_matches_s2v_2dec['similarity']\n",
    "X_val = nn_val_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_val = nn_val_found_matches_s2v_2dec['similarity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load model from notebook 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nn function\n",
    "\n",
    "def train_nn(X_train, y_train, X_test, y_test, layers = [12, 12, 12, 12, 12, 1],\n",
    "             model_loss = 'binary_crossentropy', activations = 'relu',\n",
    "             last_activation = 'sigmoid', model_epochs = 20, model_batch_size = 16,\n",
    "             save_name = False):\n",
    "    '''Train a keras deep NN and test on test data, returns (model, history, accuracy, loss)\n",
    "    \n",
    "    X_train: matrix like object like pd.DataFrame, training set\n",
    "    y_train: list like object like np.array, training labels\n",
    "    X_test: matrix like object like pd.DataFrame, test set\n",
    "    y_test: list like object like np.array, test labels\n",
    "    layers: list of ints, the number of layers is the len of this list while the elements\n",
    "        are the amount of neurons per layer, default: [12, 12, 12, 12, 12, 1]\n",
    "    model_loss: str, loss function, default: binary_crossentropy\n",
    "    activations: str, the activation of the layers except the last one, default: relu\n",
    "    last_activation: str, activation of last layer, default: sigmoid\n",
    "    model_epochs: int, number of epochs, default: 20\n",
    "    model_batch_size: int, batch size for updating the model, default: 16\n",
    "    save_name: str, location for saving model, optional, default: False\n",
    "    \n",
    "    Returns:\n",
    "    model: keras sequential\n",
    "    history: dict, training statistics\n",
    "    accuracy: float, accuracy on test set\n",
    "    loss, float, loss on test set\n",
    "    \n",
    "    If save_name is not False and save_name exists this function will load existing model\n",
    "    '''\n",
    "    if os.path.exists(save_name) and save_name:\n",
    "        print('\\nLoading existing model')\n",
    "        nn_model = load_model(save_name)\n",
    "        with open(save_name + '_train_hist.pickle', 'rb') as hist_inf:\n",
    "            history = pickle.load(hist_inf)\n",
    "    else:\n",
    "        # define the keras model\n",
    "        nn_model = Sequential()\n",
    "        #add first layer\n",
    "        nn_model.add(Dense(layers[0], input_dim = X_train.shape[1], activation = activations))\n",
    "        #add other layers\n",
    "        for i in range(1,len(layers)-1): #skip first and last one\n",
    "            nn_model.add(Dense(layers[i], activation = activations))\n",
    "        #add last layer\n",
    "        nn_model.add(Dense(layers[-1], activation = last_activation))\n",
    "        # compile the keras model\n",
    "        nn_model.compile(loss = model_loss, optimizer='adam', metrics=['accuracy'])\n",
    "        # fit the keras model on the dataset\n",
    "        hist = nn_model.fit(X_train, y_train, epochs = model_epochs, batch_size = model_batch_size)\n",
    "        history = hist.history\n",
    "    \n",
    "    #training set\n",
    "    print('Training loss: {:.4f}\\n'.format(history['loss'][-1]))\n",
    "    \n",
    "    #test set    \n",
    "    loss, accuracy = nn_model.evaluate(X_test, y_test)\n",
    "    print('Test accuracy: {:.2f}'.format(accuracy*100))\n",
    "    print('Test loss: {:.4f}'.format(loss))\n",
    "    \n",
    "    if save_name and not os.path.exists(save_name):\n",
    "        print('Saving model at:', save_name)\n",
    "        nn_model.save(save_name)\n",
    "        with open(save_name + '_train_hist.pickle', 'wb') as hist_outf:\n",
    "            pickle.dump(history, hist_outf)\n",
    "    \n",
    "    return nn_model, history, accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading existing model\n",
      "Training loss: 0.0493\n",
      "\n",
      "30000/30000 [==============================] - 1s 37us/sample - loss: 0.0499 - accuracy: 0.1076\n",
      "Test accuracy: 10.76\n",
      "Test loss: 0.0499\n"
     ]
    }
   ],
   "source": [
    "test_layers = [10,24,1]\n",
    "#model_name = os.path.join(path_data, 'nn_2000_queries_top20_1')\n",
    "model_name = os.path.join(path_data, 'nn_2000_queries_top20_layers_opt_1') #this one tested best in notebook 3\n",
    "\n",
    "nn_2000_queries_top20_1 = train_nn(X_train, y_train, X_test, y_test, layers = test_layers,\n",
    "         model_loss = 'mean_squared_error', activations = 'relu', last_activation = None,\n",
    "         model_epochs = 50, model_batch_size = 16, save_name = model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model trimming\n",
    "- Simple model\n",
    "- L1 regularisation\n",
    "- Dropout regularistion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0621 - mae: 0.2016\n",
      "Epoch 00001: val_loss improved from inf to 0.05573, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0620 - mae: 0.2015 - val_loss: 0.0557 - val_mae: 0.1902\n",
      "Epoch 2/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0568 - mae: 0.1911\n",
      "Epoch 00002: val_loss improved from 0.05573 to 0.05571, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0569 - mae: 0.1912 - val_loss: 0.0557 - val_mae: 0.1871\n",
      "Epoch 3/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1885\n",
      "Epoch 00003: val_loss improved from 0.05571 to 0.05372, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0555 - mae: 0.1884 - val_loss: 0.0537 - val_mae: 0.1856\n",
      "Epoch 4/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0545 - mae: 0.1862\n",
      "Epoch 00004: val_loss improved from 0.05372 to 0.05331, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0545 - mae: 0.1862 - val_loss: 0.0533 - val_mae: 0.1838\n",
      "Epoch 5/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1842\n",
      "Epoch 00005: val_loss improved from 0.05331 to 0.05072, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0537 - mae: 0.1842 - val_loss: 0.0507 - val_mae: 0.1785\n",
      "Epoch 6/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1825\n",
      "Epoch 00006: val_loss did not improve from 0.05072\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0528 - mae: 0.1824 - val_loss: 0.0518 - val_mae: 0.1797\n",
      "Epoch 7/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1808\n",
      "Epoch 00007: val_loss did not improve from 0.05072\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0521 - mae: 0.1808 - val_loss: 0.0520 - val_mae: 0.1822\n",
      "Epoch 8/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1791\n",
      "Epoch 00008: val_loss improved from 0.05072 to 0.05042, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0513 - mae: 0.1791 - val_loss: 0.0504 - val_mae: 0.1786\n",
      "Epoch 9/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1782\n",
      "Epoch 00009: val_loss improved from 0.05042 to 0.04928, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0510 - mae: 0.1782 - val_loss: 0.0493 - val_mae: 0.1777\n",
      "Epoch 10/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1765\n",
      "Epoch 00010: val_loss improved from 0.04928 to 0.04881, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0503 - mae: 0.1765 - val_loss: 0.0488 - val_mae: 0.1751\n",
      "Epoch 11/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1762\n",
      "Epoch 00011: val_loss did not improve from 0.04881\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0502 - mae: 0.1763 - val_loss: 0.0489 - val_mae: 0.1737\n",
      "Epoch 12/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1755\n",
      "Epoch 00012: val_loss did not improve from 0.04881\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0497 - mae: 0.1753 - val_loss: 0.0498 - val_mae: 0.1758\n",
      "Epoch 13/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1746\n",
      "Epoch 00013: val_loss improved from 0.04881 to 0.04805, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0495 - mae: 0.1746 - val_loss: 0.0480 - val_mae: 0.1762\n",
      "Epoch 14/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1738\n",
      "Epoch 00014: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0491 - mae: 0.1738 - val_loss: 0.0492 - val_mae: 0.1782\n",
      "Epoch 15/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1735\n",
      "Epoch 00015: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0490 - mae: 0.1736 - val_loss: 0.0486 - val_mae: 0.1770\n",
      "Epoch 16/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1734\n",
      "Epoch 00016: val_loss improved from 0.04805 to 0.04751, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0490 - mae: 0.1734 - val_loss: 0.0475 - val_mae: 0.1708\n",
      "Epoch 17/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1729\n",
      "Epoch 00017: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0487 - mae: 0.1728 - val_loss: 0.0479 - val_mae: 0.1750\n",
      "Epoch 18/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1724\n",
      "Epoch 00018: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0485 - mae: 0.1724 - val_loss: 0.0475 - val_mae: 0.1674\n",
      "Epoch 19/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1716\n",
      "Epoch 00019: val_loss improved from 0.04751 to 0.04700, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0482 - mae: 0.1717 - val_loss: 0.0470 - val_mae: 0.1730\n",
      "Epoch 20/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1716\n",
      "Epoch 00020: val_loss did not improve from 0.04700\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0482 - mae: 0.1716 - val_loss: 0.0480 - val_mae: 0.1739\n",
      "Epoch 21/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1712\n",
      "Epoch 00021: val_loss improved from 0.04700 to 0.04670, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0480 - mae: 0.1713 - val_loss: 0.0467 - val_mae: 0.1667\n",
      "Epoch 22/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1714\n",
      "Epoch 00022: val_loss did not improve from 0.04670\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0481 - mae: 0.1713 - val_loss: 0.0490 - val_mae: 0.1701\n",
      "Epoch 23/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1707\n",
      "Epoch 00023: val_loss improved from 0.04670 to 0.04620, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0478 - mae: 0.1708 - val_loss: 0.0462 - val_mae: 0.1695\n",
      "Epoch 24/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1706\n",
      "Epoch 00024: val_loss did not improve from 0.04620\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0478 - mae: 0.1707 - val_loss: 0.0467 - val_mae: 0.1694\n",
      "Epoch 25/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0477 - mae: 0.1703\n",
      "Epoch 00025: val_loss did not improve from 0.04620\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0477 - mae: 0.1704 - val_loss: 0.0473 - val_mae: 0.1713\n",
      "Epoch 26/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0475 - mae: 0.1698\n",
      "Epoch 00026: val_loss did not improve from 0.04620\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0475 - mae: 0.1698 - val_loss: 0.0471 - val_mae: 0.1689\n",
      "Epoch 27/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1699\n",
      "Epoch 00027: val_loss did not improve from 0.04620\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0474 - mae: 0.1699 - val_loss: 0.0468 - val_mae: 0.1717\n",
      "Epoch 28/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1696\n",
      "Epoch 00028: val_loss did not improve from 0.04620\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0474 - mae: 0.1697 - val_loss: 0.0462 - val_mae: 0.1682\n",
      "Epoch 29/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0473 - mae: 0.1695\n",
      "Epoch 00029: val_loss improved from 0.04620 to 0.04609, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0473 - mae: 0.1695 - val_loss: 0.0461 - val_mae: 0.1658\n",
      "Epoch 30/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1690\n",
      "Epoch 00030: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0472 - mae: 0.1690 - val_loss: 0.0464 - val_mae: 0.1667\n",
      "Epoch 31/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1690\n",
      "Epoch 00031: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0471 - mae: 0.1691 - val_loss: 0.0479 - val_mae: 0.1682\n",
      "Epoch 32/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0471 - mae: 0.1690\n",
      "Epoch 00032: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0471 - mae: 0.1690 - val_loss: 0.0476 - val_mae: 0.1683\n",
      "Epoch 33/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0469 - mae: 0.1684\n",
      "Epoch 00033: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0469 - mae: 0.1684 - val_loss: 0.0466 - val_mae: 0.1674\n",
      "Epoch 34/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0468 - mae: 0.1682\n",
      "Epoch 00034: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0468 - mae: 0.1682 - val_loss: 0.0464 - val_mae: 0.1666\n",
      "Epoch 35/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0467 - mae: 0.1679\n",
      "Epoch 00035: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0467 - mae: 0.1679 - val_loss: 0.0470 - val_mae: 0.1707\n",
      "Epoch 36/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0468 - mae: 0.1682\n",
      "Epoch 00036: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0468 - mae: 0.1683 - val_loss: 0.0464 - val_mae: 0.1700\n",
      "Epoch 37/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0467 - mae: 0.1679\n",
      "Epoch 00037: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0466 - mae: 0.1678 - val_loss: 0.0463 - val_mae: 0.1674\n",
      "Epoch 38/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0465 - mae: 0.1676\n",
      "Epoch 00038: val_loss did not improve from 0.04609\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0465 - mae: 0.1676 - val_loss: 0.0467 - val_mae: 0.1693\n",
      "Epoch 39/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0465 - mae: 0.1673\n",
      "Epoch 00039: val_loss improved from 0.04609 to 0.04600, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0465 - mae: 0.1674 - val_loss: 0.0460 - val_mae: 0.1672\n",
      "Epoch 40/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0466 - mae: 0.1676\n",
      "Epoch 00040: val_loss did not improve from 0.04600\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0466 - mae: 0.1676 - val_loss: 0.0465 - val_mae: 0.1684\n",
      "Epoch 41/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0464 - mae: 0.1673\n",
      "Epoch 00041: val_loss did not improve from 0.04600\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0464 - mae: 0.1672 - val_loss: 0.0475 - val_mae: 0.1721\n",
      "Epoch 42/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0463 - mae: 0.1671\n",
      "Epoch 00042: val_loss improved from 0.04600 to 0.04583, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0463 - mae: 0.1671 - val_loss: 0.0458 - val_mae: 0.1660\n",
      "Epoch 43/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0464 - mae: 0.1670\n",
      "Epoch 00043: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0463 - mae: 0.1669 - val_loss: 0.0462 - val_mae: 0.1700\n",
      "Epoch 44/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0463 - mae: 0.1671\n",
      "Epoch 00044: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0463 - mae: 0.1670 - val_loss: 0.0478 - val_mae: 0.1668\n",
      "Epoch 45/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0463 - mae: 0.1670\n",
      "Epoch 00045: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0463 - mae: 0.1670 - val_loss: 0.0470 - val_mae: 0.1691\n",
      "Epoch 46/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0461 - mae: 0.1663\n",
      "Epoch 00046: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0461 - mae: 0.1663 - val_loss: 0.0469 - val_mae: 0.1678\n",
      "Epoch 47/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0460 - mae: 0.1664\n",
      "Epoch 00047: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0460 - mae: 0.1664 - val_loss: 0.0469 - val_mae: 0.1706\n",
      "Epoch 48/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0461 - mae: 0.1666\n",
      "Epoch 00048: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0461 - mae: 0.1667 - val_loss: 0.0470 - val_mae: 0.1721\n",
      "Epoch 49/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0459 - mae: 0.1662\n",
      "Epoch 00049: val_loss did not improve from 0.04583\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0459 - mae: 0.1662 - val_loss: 0.0486 - val_mae: 0.1703\n",
      "Epoch 50/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0459 - mae: 0.1661\n",
      "Epoch 00050: val_loss improved from 0.04583 to 0.04577, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0458 - mae: 0.1660 - val_loss: 0.0458 - val_mae: 0.1665\n",
      "Epoch 51/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0460 - mae: 0.1662\n",
      "Epoch 00051: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0460 - mae: 0.1662 - val_loss: 0.0480 - val_mae: 0.1656\n",
      "Epoch 52/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0458 - mae: 0.1656\n",
      "Epoch 00052: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0458 - mae: 0.1657 - val_loss: 0.0472 - val_mae: 0.1704\n",
      "Epoch 53/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0457 - mae: 0.1656\n",
      "Epoch 00053: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0458 - mae: 0.1658 - val_loss: 0.0464 - val_mae: 0.1680\n",
      "Epoch 54/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0458 - mae: 0.1658\n",
      "Epoch 00054: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0458 - mae: 0.1657 - val_loss: 0.0466 - val_mae: 0.1650\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0457 - mae: 0.1658\n",
      "Epoch 00055: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0457 - mae: 0.1657 - val_loss: 0.0459 - val_mae: 0.1644\n",
      "Epoch 56/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0457 - mae: 0.1656\n",
      "Epoch 00056: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0457 - mae: 0.1656 - val_loss: 0.0477 - val_mae: 0.1688\n",
      "Epoch 57/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0456 - mae: 0.1654\n",
      "Epoch 00057: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0456 - mae: 0.1654 - val_loss: 0.0467 - val_mae: 0.1659\n",
      "Epoch 58/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0456 - mae: 0.1654\n",
      "Epoch 00058: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0456 - mae: 0.1653 - val_loss: 0.0460 - val_mae: 0.1656\n",
      "Epoch 59/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0455 - mae: 0.1653\n",
      "Epoch 00059: val_loss did not improve from 0.04577\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0455 - mae: 0.1653 - val_loss: 0.0467 - val_mae: 0.1675\n",
      "Epoch 60/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0455 - mae: 0.1652\n",
      "Epoch 00060: val_loss improved from 0.04577 to 0.04568, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0455 - mae: 0.1651 - val_loss: 0.0457 - val_mae: 0.1654\n",
      "Epoch 61/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0455 - mae: 0.1651\n",
      "Epoch 00061: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0455 - mae: 0.1652 - val_loss: 0.0466 - val_mae: 0.1682\n",
      "Epoch 62/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0454 - mae: 0.1650\n",
      "Epoch 00062: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0454 - mae: 0.1650 - val_loss: 0.0482 - val_mae: 0.1683\n",
      "Epoch 63/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0454 - mae: 0.1649\n",
      "Epoch 00063: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0454 - mae: 0.1650 - val_loss: 0.0468 - val_mae: 0.1697\n",
      "Epoch 64/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0454 - mae: 0.1649\n",
      "Epoch 00064: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0454 - mae: 0.1649 - val_loss: 0.0473 - val_mae: 0.1705\n",
      "Epoch 65/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0453 - mae: 0.1647\n",
      "Epoch 00065: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0452 - mae: 0.1646 - val_loss: 0.0459 - val_mae: 0.1664\n",
      "Epoch 66/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0453 - mae: 0.1649\n",
      "Epoch 00066: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0453 - mae: 0.1649 - val_loss: 0.0460 - val_mae: 0.1669\n",
      "Epoch 67/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0452 - mae: 0.1644\n",
      "Epoch 00067: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0452 - mae: 0.1644 - val_loss: 0.0475 - val_mae: 0.1671\n",
      "Epoch 68/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0452 - mae: 0.1646\n",
      "Epoch 00068: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0451 - mae: 0.1645 - val_loss: 0.0463 - val_mae: 0.1654\n",
      "Epoch 69/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0451 - mae: 0.1643\n",
      "Epoch 00069: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0451 - mae: 0.1644 - val_loss: 0.0462 - val_mae: 0.1661\n",
      "Epoch 70/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0451 - mae: 0.1644\n",
      "Epoch 00070: val_loss did not improve from 0.04568\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0451 - mae: 0.1643 - val_loss: 0.0465 - val_mae: 0.1656\n",
      "Epoch 00070: early stopping\n"
     ]
    }
   ],
   "source": [
    "layers = [48,48,1]\n",
    "model_name = os.path.join(path_data, \"nn_2000_queries_trimming\")\n",
    "\n",
    "# define the keras model\n",
    "nn_model = Sequential()\n",
    "\n",
    "#add first layer\n",
    "nn_model.add(Dense(layers[0], input_dim = X_train.shape[1], activation = 'relu'))\n",
    "#add other layers\n",
    "for i in range(1,len(layers)-1): #skip first and last one\n",
    "    nn_model.add(Dense(layers[i], activation = 'relu'))\n",
    "#add last layer\n",
    "nn_model.add(Dense(layers[-1], activation = None))\n",
    "\n",
    "# compile the keras model\n",
    "nn_model.compile(loss = 'mean_squared_error', optimizer='adam', metrics=['mae'])\n",
    "\n",
    "# save best model\n",
    "checkpointer = ModelCheckpoint(filepath= model_name + \".hdf5\", monitor='val_loss', verbose=1, save_best_only=True)\n",
    "earlystopper = EarlyStopping(monitor='val_loss', patience=10, verbose=1)  # patience - try x more epochs to improve val_loss\n",
    "\n",
    "# fit the keras model on the dataset\n",
    "hist = nn_model.fit(X_train, y_train, epochs = 100, batch_size = 24, validation_data=(X_val, y_val),\n",
    "                    callbacks = [checkpointer, earlystopper])\n",
    "history = hist.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000/30000 [==============================] - 1s 43us/sample - loss: 0.0481 - mae: 0.1697\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04813166537086169, 0.16970219]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gAMD69eu57777WLhwIdu3by++c75p06ZyAwJHHXVU8efJyc4M5IyMDNq0aeO3/ZFHHonb7fY5ZuXKlYDzfYuIiKB3797F+4844ggaNWoU8PxZWVmkp6fTr1+/4m0REREce+yxPjNCKvv6MjIyuP/++5kzZw5bt26lsLCQvXv3lslJERsby969ewP2U9cOhqu/24FJ1tqXi56PNcYMxJkFcHfpxtbasaU23WOMOQcYCizz9gF8aa19tOj5o8aYk4u2X1KZ8x7srhnQnmsGtK/rYYiIiIjIQaxeVMWXF96KV1uycv3mETBAUqmKV8H0G4xJkyZRUFBAy5Yti7dZa4mMjGTnzp00atSI2NjYcvswxpRZh+4vAZ/3Qt/r8ccf58knn2TixIn06NGD+vXrM3bsWPbv3w9Q4XnBWTbQs2dP/vjjDyZPnszpp59O27ZtA7Zv0qQJO3furLDfQGMGGDp0KK1bt+all16iRYsWeDweunfvXjzuQCIjI4s/985gKG+ZQcn23mO87Ut/vb0CbQ9FZV/flVdeybZt25g4cSJt27YlOjqafv36lTkuMzOzTJAlnIR1DgFjTBRwDPBFqV1fAP2D7MMFxAGZJTb389Pn594+K3teY0y0MSbe+yg6r4iIiIiIFKmrilcFBQVMmTKFxx9/nOXLlxc/VqxYQdu2bXnzzTcB5852eSXxmjZtSnr6gSoI69atC+oO8Ny5cznnnHMYOXIkRx99NB06dGDdunXF+zt16kRsbGy55+7RowfHHnssL730ElOnTq1wXX6vXr3YsmVLmaBAZGQkhYWFFY55x44drF69mnvvvZfTTz+dbt26hRRgqC5du3aloKCAZcuWFW/79ddf2bVrV8BjEhISSE5OZuHChcXbCgoKWLJkSfHzYF5fVFQUQJmv19y5c7nllls4++yzOfLII4mOjmb79u1lxrFq1Sp69eoV0uutTWEdEACaAG5ga6ntW4Gkss39ugOoD0wrsS2pgj4re967gawSjz+CHGPYKCj0kFdQ8R8HEREREZHKClTxKikhpsYqXs2cOZOdO3dyzTXX0L17d5/HBRdcwKRJkwAYP348b731FuPHj2f16tWsXLmSf//738X9nHbaaTz77LMsXbqUH3/8kRtuuKHM3W1/jjjiCL788kvmz5/P6tWruf7669myZUvx/piYGMaNG8edd97JlClTWL9+PQsXLiwel9e1117LY489RmFhIcOHDy/3nL169aJp06Z8//33PtvbtWvH7Nmz/QYLSmrUqBGJiYm8+OKL/Prrr8yZM4fbb7+9wtda3bp27coZZ5zBddddx6JFi1i2bBnXXXcdsbGxAfMnANx666089thjfPjhh6xZs4Ybb7zRJ4gQzOtr1qwZsbGxzJo1i61bt5KVlQU438/XX3+d1atX88MPP3DZZZf5neUxd+5czjorfLO0hXtAwKv0XBDjZ1sZxphLgAeAi621pVNQBtNnqOd9FEgo8WhV0RjDyd0frOTI8Z8zc0XN1n0VERERESlZ8eqpET15a3Rf5o07rcYqXk2aNIkzzjiDhISEMvvOP/98li9fztKlSznllFN49913mT59Oj179uS0007zqQTw+OOP07p1a0466SQuvfRS/va3v1GvXr0Kz3/ffffRu3dvBg4cyCmnnEJSUhLnnntumTZ33HEH999/P926dePiiy8uk0n/kksuISIigksvvbTC/ABut5urr766ePZDydfw5Zdf0rp163LvXrtcLt5++22WLFlC9+7due222/jPf/5T4WutCVOmTKF58+acdNJJDB8+nNGjRxMXF1fu1+COO+5g1KhRXHnllfTr14+4uDifIEowry8iIoKnn36a//3vf7Ro0YJzzjkHgMmTJ7Nz50569erF5Zdfzi233EKzZs18jl2wYAFZWVlccMEF1fiVqF6mOtZd1JSiqft7gQtLVgAwxjwF9LTWnlzOsRcDrxQd+0mpfZuAJ621T5bYdhtOFYG2VTlvqfPEA1lZWVnEx8cHc0idemD6z7w6fwOjT2zPPwan1PVwRERERCQM5ebmkpaWRvv27UNKWCfV4/fff6ddu3YsXrzYJ8leIFu3buXII49kyZIl5eYbONj88ccftG7dmq+++ipgIsa6duGFF9KrVy/uueeeau+7vN/D7Oxsb+ArwVqbXV4/YT1DwFq7H1gCnFlq15nA/EDHFc0MeBW4tHQwoMgCP32e5e2zsuc92HVNclIerNmSU0FLERERERGpTfn5+WzatIlx48bRt2/foIIBAM2bN2fSpEllst8fbObMmcP06dNJS0tj/vz5jBgxgnbt2nHSSSfV9dD8ysvL4+ijj+a2226r66GU62CoMvAE8Lox5kecC/nrgDbACwDGmEeBltbaUUXPLwGmALcCC40x3jX/+6y1WUWfPwV8Z4wZB3wMnAOcAQwI9ryHoi4KCIiIiIiIhKXvv/+eU089lc6dO/Pee++FdKx3mvvBLD8/n3vuuYfffvuNuLg4+vfvz5tvvhlU/oa6EB0dzb333lvXw6hQ2AcErLXvGGMSgfuBZGAVcLa1dmNRk2ScC3Wv63Fe1/8VPbxeA64s6nO+MWYE8DDwELAeJ89A8eKgIM57yOncPA5jYFtOHjt255HYILquhyQiIiIiIsApp5xSLWX2DlYDBw5k4MCBdT2MQ07YBwQArLXPAc8F2HdlqeenBNnne0C5obXyznsoqh8dQZvG9di4Yy9rt+TQ/wgFBERERERERA5VYZ1DQGqfN4/Aai0bEBEREREROaQdFDMEpPaccEQTDIbWjcrW0BQREREREZFDhwIC4mNUv3aM6teurochIiIiIiIiNUxLBkREREREREQOQwoISBnWWjbv2sfuvIK6HoqIiIiIiIjUEAUEpIxLX/qBEx6bw3e/bKvroYiIiIjIocxTCGlzYeV7zkdPYV2P6JB00kknMXXq1Grrb8OGDRhjWL58OQDffPMNxhh27doV8JhXX32Vhg0bVvnc1dVPZbRr146JEyfW+HlWrlxJq1at2LNnT42fSwEBKaNN43oArEnPruORiIiIiMghK3U6TOwOrw2B969xPk7s7myvYfPnz8ftdjNo0KAaP1ddmzlzJlu2bGHEiBE1do7+/fuTnp5OQkJCtfbr7wL84osv5pdffqnW89SUygYvevToQZ8+fXjyySerf1ClKCAgZXRR6UERERERqUmp02HaKMj+03d7drqzvYaDApMnT+bmm29m3rx5bNq0qUbPBZCfn1/j5wjk6aef5qqrrsLlqrlLv6ioKJKSkjDG1Ng5vGJjY2nWrFmNn6euXXXVVTz//PMUFtbsrBkFBKSMrslOQGCtAgIiIiIiEor9ewI/8nOdNp5CmDUOsH46KNo2a5zv8oFAfVbCnj17mDZtGmPGjGHIkCG8+uqrZdpMnz6dY489lpiYGJo0acJ5551XvM8Yw0cffeTTvmHDhsX9eKfTT5s2jVNOOYWYmBjeeOMNduzYwSWXXEKrVq2oV68ePXr04K233vLpx+PxMGHCBI444giio6Np06YNjzzyCACnnXYaN910k0/7HTt2EB0dzZw5c/y+1u3bt/PVV18xbNiw4m2XXHJJmdkC+fn5NGnShFdeeQWAWbNmMWDAABo2bEhiYiJDhgxh/fr1Ab+m/pYMvPrqq7Rp04Z69eoxfPhwduzY4XPM+vXrOeecc2jevDkNGjTguOOO46uvviref8opp7Bx40Zuu+02jDHFwQZ/d92ff/55OnbsSFRUFF26dOH111/32W+M4eWXX2b48OHUq1ePTp06MX16+UGnjIwMhg4dSmxsLO3bt+fNN98s0+aJJ56gR48e1K9fn9atW3PjjTeye/fu4q/JVVddRVZWVvH4H3jgAQDeeOMNjj32WOLi4khKSuLSSy8lIyPDp++BAweyY8cOvv3223LHWVUKCEgZXZPiAdiUuVeJBUVEREQkeP9qEfgx7XKnzcb5ZWcG+LDO/o3zD2ya2MN/n5Xwzjvv0KVLF7p06cLIkSN55ZVXsPZAcOKTTz7hvPPOY/DgwSxbtozZs2dz7LHHhnyecePGccstt7B69WoGDhxIbm4uxxxzDDNnzmTVqlVcd911XH755fzwww/Fx9x9991MmDCB++67j9TUVKZOnUrz5s0BuPbaa5k6dSp5eXnF7d98801atGjBqaee6ncM8+bNo169enTr1q1422WXXcb06dOLL1wBPv/8c/bs2cP5558POEGT22+/ncWLFzN79mxcLhfDhw/H4/EE9dp/+OEHrr76am688UaWL1/OqaeeysMPP+zTZvfu3Zx99tl89dVXLFu2jIEDBzJ06NDiGRsffPABrVq14p///Cfp6emkp6f7PdeHH37Irbfeyh133MGqVau4/vrrueqqq/j666992j344INcdNFF/PTTT5x99tlcdtllZGZmBnwNV155JRs2bGDOnDm89957PPfcc2Uu2l0uF08//TSrVq3itddeY86cOdx5552As4xi4sSJxMfHF4//b3/7GwD79+/noYceYsWKFXz00UekpaVx5ZVX+vQdFRXF0Ucfzdy5cyv+gleFtVaPGnoA8YDNysqyB5vjHv7Sth030y7ZmFnXQxERERGRMLJv3z6bmppq9+3bV3bn+PjAjzcucNr89G757byPn9490O+E9v7bVEL//v3txIkTrbXW5ufn2yZNmtgvv/yyeH+/fv3sZZddFvB4wH744Yc+2xISEuwrr7xirbU2LS3NAsXnKM/ZZ59t77jjDmuttdnZ2TY6Otq+9NJLftvm5ubaxo0b23feead4W8+ePe0DDzwQsP8nn3zSdujQwWfb/v37bZMmTeyUKVOKt11yySX2wgsvDNhPRkaGBezKlSt9XuOyZcustdZ+/fXXFrA7d+4s7m/QoEE+fVx88cU2ISEh4DmstTYlJcU+88wzxc/btm1rn3zySZ82r7zyik8//fv3t6NHj/Zpc+GFF9qzzz67+Dlg77333uLnu3fvtsYY+9lnn/kdx9q1ay1gFy5cWLxt9erVFigznpKmTZtmExMTA441kEWLFlnA5uTk+GwfPny4vfLKK/0eU97vYVZWlsWZbhNvK7hmjajZcIMcrLokxZGRk8ea9Bx6t2lU18MRERERkYPBPeXc+Tdu52OD5sH1VbLd2JWVH1MJa9euZdGiRXzwwQcAREREcPHFFzN58mTOOOMMAJYvX87o0aOrfK7SswoKCwt57LHHeOedd9i8eTN5eXnk5eVRv359AFavXk1eXh6nn3663/6io6MZOXIkkydP5qKLLmL58uXFd5gD2bdvHzExMT7bIiMjufDCC3nzzTe5/PLL2bNnDx9//LFPFYL169dz3333sXDhQrZv3148M2DTpk107969wte+evVqhg8f7rOtX79+zJo1q/j5nj17ePDBB5k5cyZ//vknBQUF7Nu3L+ScDqtXr+a6667z2XbCCSfw1FNP+Ww76qijij+vX78+cXFxZe74l+wzIiLC53vYtWvXMksVvv76a/71r3+RmppKdnY2BQUF5ObmsmfPnuLvqz/Lli3jgQceYPny5WRmZvp8fVNSUorbxcbGsnfv3vK/AFWkgID4NfSoFqS0iCelRXxdD0VEREREDhZRgS+CirXtD/EtnASCfvMIGGd/2/6h9RuESZMmUVBQQMuWLYu3WWuJjIxk586dNGrUiNjY2HL7MMb4LDEA/0kDS18QPv744zz55JNMnDixeN352LFj2b9/P0CF5wVn2UDPnj35448/mDx5Mqeffjpt27YN2L5Jkybs3LmzzPbLLruMk08+mYyMDL788ktiYmL4y1/+Urx/6NChtG7dmpdeeokWLVrg8Xjo3r178VgrUvrr48/f//53Pv/8c/773/9yxBFHEBsbywUXXBD0OUoqnczQWltmW2RkZJljAi2B8I6/vCSJGzdu5Oyzz+aGG27goYceonHjxsybN49rrrmm3CSSe/bs4ayzzuKss87ijTfeoGnTpmzatImBAweWee2ZmZl07NgxYF/VQTkExK+LjmvN3X/pRs/WDet6KCIiIiJyKHG5YdCEoielL7iKng96zGlXjQoKCpgyZQqPP/44y5cvL36sWLGCtm3bFieNO+qoo5g9e3bAfpo2beqznn3dunVB3cWdO3cu55xzDiNHjuToo4+mQ4cOrFu3rnh/p06diI2NLffcPXr04Nhjj+Wll15i6tSpXH311eWes1evXmzZsqVMUKB///60bt2ad955hzfffJMLL7yQqKgowElUuHr1au69915OP/10unXr5jeoUJ6UlBQWLlzos63087lz53LllVcyfPhwevToQVJSEhs2bPBpExUVVWGW/W7dujFv3jyfbfPnz/fJmxCqbt26UVBQwI8//li8be3atT5JE3/88UcKCgp4/PHH6du3L507d+bPP31nyPgb/5o1a9i+fTuPPfYYJ554Il27dg04U2HVqlX06tWr0q8jGJohICIiIiIitStlGFw0xakmUDLBYHwLJxiQMizwsZU0c+ZMdu7cyTXXXENCQoLPvgsuuIBJkyZx0003MX78eE4//XQ6duzIiBEjKCgo4LPPPitOFnfaaafx7LPP0rdvXzweD+PGjStz99mfI444gvfff5/58+fTqFEjnnjiCbZs2VJ84RoTE8O4ceO48847iYqK4oQTTmDbtm38/PPPXHPNNcX9XHvttdx0003F2fvL06tXL5o2bcr333/PkCFDircbY7j00kt54YUX+OWXX3wS8DVq1IjExERefPFFkpOT2bRpE3fddVfFX+ASbrnlFvr378+///1vzj33XL744guf5QLer8cHH3zA0KFDMcZw3333lblj365dO7777jtGjBhBdHQ0TZo0KXOuv//971x00UX07t2b008/nRkzZvDBBx/4VCwIVZcuXRg0aBCjR4/mxRdfJCIigrFjx/rM4ujYsSMFBQU888wzDB06lO+//54XXnihzPh3797N7NmzOfroo6lXrx5t2rQhKiqKZ555hhtuuIFVq1bx0EMPlRnDhg0b2Lx5c/FSlhpTUZIBPQ7PpILWWrs9J9d+v26b3bV3f10PRURERETCRLlJBUNVWGDtb985CQR/+855XkOGDBnik2iupCVLlljALlmyxFpr7fvvv2979uxpo6KibJMmTex5551X3Hbz5s32rLPOsvXr17edOnWyn376qd+kgt6Ee147duyw55xzjm3QoIFt1qyZvffee+2oUaPsOeecU9ymsLDQPvzww7Zt27Y2MjLStmnTxv7rX//y6ScnJ8fWq1fP3njjjUG97rvuusuOGDGizPaff/7ZArZt27bW4/H47Pvyyy9tt27dbHR0tD3qqKPsN99845NMsaKkgtZaO2nSJNuqVSsbGxtrhw4dav/73//6JNhLS0uzp556qo2NjbWtW7e2zz77rD355JPtrbfeWtxmwYIF9qijjrLR0dHWuXT1n6jvueeesx06dLCRkZG2c+fOPgkTra04EaQ/6enpdvDgwTY6Otq2adPGTpkypUySwyeeeMImJyfb2NhYO3DgQDtlypQyX4cbbrjBJiYmWsCOHz/eWmvt1KlTbbt27Wx0dLTt16+fnT59epmfmX/961924MCBAcdXXUkFjQ1ifYdUjjEmHsjKysoiPv7gW4t/5hPfsi5jN69cdRyndmlW18MRERERkTCQm5tLWloa7du3L5OwTmre77//Trt27Vi8eDG9e/eusP3WrVs58sgjWbJkSbn5BiR85OXl0alTJ9566y1OOOEEv23K+z3Mzs72zoJJsNZml3cu5RCQgLomO0GMNek5dTwSEREREZHDW35+Pps2bWLcuHH07ds3qGAAQPPmzZk0aVLI2ful7mzcuJF//OMfAYMB1Uk5BCSgrklxzFgBa7eUG1QSEREREZEa9v3333PqqafSuXNn3nvvvZCOPeecc2poVFITOnfuTOfOnWvlXAoISEBdk+IAWLNFMwREREREROrSKaecElQ5P5FQaMmABORdMvBrxm72F/iv0SkiIiIiIiIHJwUEJKAWCTHExURQ4LH8tn13XQ9HRERERMKI7laL1J3q+v1TQEACMsYcWDagxIIiIiIiAkRGRgKwd+/eOh6JyOHL+/vn/X2srIMih4Ax5kbg70Ay8DMw1lo7N0DbZOBx4BigE/C0tXZsqTaRwN3AFUBLYC0wzlo7q0SbB4Dxpbrfaq1NqoaXdNC4on87zuvdimPaNqrroYiIiIhIGHC73TRs2JCMjAwA6tWrhzGmjkclcniw1rJ3714yMjJo2LAhbre7Sv2FfUDAGHMxMBG4EfgeuB74zBiTYq31VzsjGtgGPALcFqDbh4GRwGhgDTAQ+NAY099au6xEu5+BM0o8L6zCSzkoDTmqRV0PQURERETCTFKSc4/MGxQQkdrVsGHD4t/DqjDhvvbHGPMDsNRaO6bEttXAR9bauys49htguZ8ZAn8Cj1hr/6/Eto+A3dbakUXPHwDOtdb2rMLY44GsrKws4uPjK9uNiIiIiEhYKiwsJD8/v66HIXJYiYyMLHdmQHZ2NgkJCQAJ1tpya8iH9QwBY0wUztT/x0rt+gLoX4Wuo4HcUtv2AQNKbetUFDzIA34A7rHW/lbOeKOL+vaKq8IYw8ayTTtZnZ7DkKOTiY+p2hoVERERETl0uN3uKk9ZFpG6E+5JBZsAbmBrqe1bgarMj/gcuN0Y08kY4zLGnAmcg5OjwOsHYBTOcoLRReebb4xJLKffu4GsEo8/qjDGsHHzW8u458OVpP5ZbnBJREREREREDiLhHhDwKr2uwfjZFopbgXU4+QP2A88Cr1AiR4C19jNr7fvW2pXW2q+AwUW7riin30eBhBKPVlUYY9jomuQsd1iTroCAiIiIiIjIoSLcAwLbcS7SS88GaEbZWQNBs9Zus9aeC9QH2gJdgd1AWjnH7AFW4lQuCNQmz1qb7X0Ah0StPm/pwbVbD4mXIyIiIiIiIoR5QMBaux9YApxZateZwPxq6D/XWrsZJ5fC+cDHgdoW5QfoBqRX9bwHm67JTkBgdboCAiIiIiIiIoeKsE4qWOQJ4HVjzI/AAuA6oA3wAoAx5lGgpbV2lPcAY0zPok8bAE2Lnu+31qYW7T8eaAksL/r4AE5w5N8l+vgvMAPYhDMj4V4gHnitRl5lGPPOEPhlaw4ej8XlUp1ZERERERGRg13YBwSste8UJfK7Hyfp3yrgbGvtxqImyTgBgpKWlfj8GOBSYCPQrmhbDPAw0AFnqcCnwOXW2l0ljmsFvIWT2HAbsBDoW+K8h412ifWJinCxd38hv+/cS9vE+nU9JBEREREREamisA8IAFhrnwOeC7DvSj/byr2Fba39FkipoM2IEIZ4SItwu+jcvAGrNmezOj1HAQEREREREZFDwEEREJC6d+fArkS4DT1aJtT1UERERERERKQaKCAgQTmpc9O6HoKIiIiIiIhUo7CuMiAiIiIiIiIiNUMBAQmKx2P5YOkfPPrpavbtL6zr4YiIiIiIiEgVKSAgQTEGHvlkNf/77jfWZeTU9XBERERERESkihQQkKAYY+iaHAfAmnQFBERERERERA52CghI0Lo0jwdgzRYFBERERERERA52CghI0IpnCGzJruORiIiIiIiISFUpICBB65rkDQjkYK2t49GIiIiIiIhIVSggIEHr1CwOl4HMPfvZtjuvrocjIiIiIiIiVaCAgAQtNspNuyb1AVirPAIiIiIiIiIHtYi6HoAcXJ4e0YvG9aNIToip66GIiIiIiIhIFSggICHp3jKhrocgIiIiIiIi1UABAQlZoceyKC2TjJxcmsXF0Kd9Y9wuU9fDEhERERERkRAoICAhmb58M/d8uIrdeQXF25ITYhg/NIVB3ZPrcGQiIiIiIiISCiUVlKDNWpXOrW8v9wkGAGzJymXMG0uZtSq9jkYmIiIiIiIioVJAQIJS6LE8OCMV62efd9uDM1Ip9PhrISIiIiIiIuFGAQEJyqK0TNKzcgPut0B6Vi6L0jJrb1AiIiIiIiJSaQoISFAycgIHAyrTTkREREREROqWAgISlGZxMdXaTkREREREROqWqgwc7jyFsHE+7N4KDZpD2/7gcpdp1qd9Y5ITYtiSles3j4ABkhKcEoQiIiIiIiIS/hQQOJylTodZ4yD7zwPb4lvAoAmQMsynqdtlGD80hTFvLMWAT1DAFH0cPzQFt8sgIiIiIiIi4e+gWDJgjLnRGJNmjMk1xiwxxpxYTttkY8xUY8xaY4zHGDPRT5tIY8z9xpj1RX2uMMYMqsp5Dzqp02HaKN9gAEB2urM9dXqZQwZ1T+b5kb1JSvBdFpCUEMPzI3szqHtyTY5YREREREREqlHYzxAwxlwMTARuBL4Hrgc+M8akWGs3+TkkGtgGPALcFqDbh4GRwGhgDTAQ+NAY099au6yS5z14eAqdmQEBiwgamHUXdB1cZvnAoO7JnJmSxKK0TDJycmkW5ywTcBnYvGsfLRvG1sYrEBERERERkSoy1oZ33XhjzA/AUmvtmBLbVgMfWWvvruDYb4Dl1tqxpbb/CTxirf2/Ets+AnZba0dW9bwl2scDWVlZWcTHxwdzSO1ImwuvDam43RUzoX3FkyK25eRx2zvL+WVrDl/efjIJsZHVMEgREREREREJVXZ2NgkJCQAJ1trs8tqG9ZIBY0wUcAzwRaldXwD9q9B1NFC6Pt4+YEBVzmuMiTbGxHsfQFwVxlhzdm+t1nZxMRH8uWsfGTl5PPbZ6ioMTERERERERGpLWAcEgCaAGyh9ZboVSKpCv58DtxtjOhljXMaYM4FzAO8i+Mqe924gq8TjjyqMseY0aF6t7WIi3Tx6Xg8A3lr0OwvW76jsyERERERERKSWhHtAwKv0uobSie5DdSuwDid/wH7gWeAVoLCK530USCjxaFWFMdactv2dagIEqghgIL6l0y5Ix3dI5NLj2wBw9wc/kZtf+kspIiIiIiIi4STcAwLbcS7SS9+Vb0bZu/dBs9Zus9aeC9QH2gJdgd1AWlXOa63Ns9Zmex9ATmXHWKNcbqe0IOA/KGBh0GNlEgpW5K6/dKV5fDQbduxl4lfrqjxMERERERERqTlhHRCw1u4HlgBnltp1JjC/GvrPtdZuxqm2cD7wcW2cNyykDIOLpkC8v1KBLkjsGHKX8TGRPHROdwBemvsbqzZnVXGQIiIiIiIiUlPCvuwg8ATwujHmR2ABcB3QBngBwBjzKNDSWjvKe4AxpmfRpw2ApkXP91trU4v2Hw+0BJYXfXwAJzjy72DPe0hIGeaUFtw430kg2KA5/PACrJkJn/wNrvoUTKBlBf6ddWQSg3sks3ZrDp4wr2AhIiIiIiJyOAv7gIC19h1jTCJwP07Sv1XA2dbajUVNknEu1EtaVuLzY4BLgY1Au6JtMcDDQAecpQKfApdba3eFcN5Dg8vtW1qwUTtI+w5a9ILC/RARHXKX/xreg5goF9ERoS05EBERERERkdpjrO7i1pii0oNZWVlZxMfH1/VwgrdvF8Q2rLbuCgo9LN6wk4ycXJrFxdCnfWPcrtBmHoiIiIiIiEjFsrOzSUhIAEgoym0XUNjPEJA6UE3BgIJCD3979yc+W5VOXoGneHtyQgzjh6YwqLu//AUiIiIiIiJSG8I6qaDUsS2r4LWhsDW1Uoe/vfh3Plq+2ScYALAlK5cxbyxl1qr06hiliIiIiIiIVIICAhLYtxOcfAKf/h1CXFpS6LH839e/+t3n7enBGakUerRkRUREREREpC4oICCBnfUwRMTCxnmw8r2QDl2Ulkl6Vm7A/RZIz8plUVpmFQcpIiIiIiIilaGAgATWqC2ceIfz+Rf3Qm65+Sh8ZOQEDgZUpp2IiIiIiIhULwUEpHz9b4ZG7WH3FmcJQZCaxcVUazsRERERERGpXgoISPkiY+Ds/zifL3weMlYHdVif9o1JToghUHFBg1NtoE/7xtUyTBEREREREQmNAgJSsU5nQpfBYAvhhxeCOsTtMowfmgJQJijgfT5+aApuV6CQgYiIiIiIiNQkY0PMHi/BM8bEA1lZWVnEx8fX9XCqZudGWPsZHHctGAMb58PurdCgObTtDy6338NmrUrnwRmpPgkGkxNiGD80hePaNaZBTATREf6PFRERERERkdBkZ2eTkJAAkGCtLTcRnAICNeiQCgh4pU6HWeMg+88D2+JbwKAJkDLM7yGFHsuitEwycnJpFucsE1i7JYfRU35kwBFNeOz8HhijmQIiIiIiIiJVFUpAIKJ2hiSHhNTpMG0UTtHAErLTne0XTfEbFHC7DP06Jvpsy8jJJT1rH+/8+Dtdk+O46oT2NThwERERERERKU05BCQ4nkJnZkDpYAAc2DbrLqddEE7p0oy7/9INgIdmpjJ33bbqGaeIiIiIiIgERQEBCc7G+b7LBMqwkL3ZaReka09sz/m9W+GxcNPUZaRt31P1cYqIiIiIiEhQFBCQ4OzeWr3tAGMMjwzvTq82Dcnal8+1ry0mOze/kgMUERERERGRUCggIMFp0Lx62xWJiXTzv5HHkBQfw/pte/j3rDUUeiwL1u/g4+WbWbB+B4UeJb4UERERERGpbkoqKMFp29+pJpCdjv88AkB0nNMuRM3iY3hp1LE8PWcdvVo3YsCEOX7LFA7qnlzJwYuIiIiIiEhpmiEgwXG5ndKCAAQoEXjWI067SujRKoHze7fkb++u8AkGAGzJymXMG0uZtSq9Un2LiIiIiIhIWQoISPBShjmlBeNL3amPbwnnT4ZjrjiwbW9mSF0XeiwPzkgtr4YBD85I1fIBERERERGRaqIlAxKalGHQdbBTTWD3VidnQNv+vjMDVr4HM2+DCyZDpzOdUoTltQcWpWWWmRlQkgXSs3JZlJZJv46JNfTiREREREREDh8KCEjoXG5of6L/fdbCircgLxumXgQ9L4P1s31LFsa3cJYfpAwr3pSREzgYUFKw7URERERERKR8WjIg1csYGPEW9BoJ1gPLXvcNBoCTmHDaKEidXrypWVxMUN0H205ERERERETKp4CAVL+IKBjyFETHB2hQlAdg1l3OcgKgT/vGJCfEBEpXCEBSfAx92jeu1qGKiIiIiIgcrhQQkJqxaYGzbCAgC9mbndwCgNtlGD80BQhYw4DG9aOqd4wiIiIiIiKHsYMiIGCMudEYk2aMyTXGLDHGBFjADsaYZGPMVGPMWmOMxxgzMUC7sUVt9hljfjfGPGmMiSmx/wFjjC312FIDL+/QtHtryO0GdU/m+ZG9SUrwXRaQ2CCK6AgXFx/XGrervDkEIiIiIiIiEqywTypojLkYmAjcCHwPXA98ZoxJsdZu8nNINLANeAS4LUCflwGPAVcD84HOwKtFu0se8zNwRonnhZV9HYedBs0r1W5Q92TOTEliUVomGTm5NItzlgns3LufJg2ia2CgIiIiIiIih6ewDwgAtwOTrLUvFz0fa4wZCIwB7i7d2Fq7AbgVwBhzdYA++wHfW2unFj3fYIx5C+hTql2BtVazAiqjbX+nmkB2OsU5A0qLS3LaleJ2mTKlBUsGA3bt3c8nK9O57Pi21TliERERERGRw0pYLxkwxkQBxwBflNr1BVD2SjJ484BjjDF9is7TATgb+KRUu07GmD+Lliu8XdSuvPFGG2PivQ8grgpjPLi53E5pQSBgVgCPB3LSQ+o2N7+QES8u5B8fruJ/366v2hhFREREREQOY2EdEACaAG6g9IL0rUBSZTu11r4N3AfMM8bkA+uBr621j5Vo9gMwChgIjC4633xjTGLp/kq4G8gq8fijsmM8JKQMg4umQHyy7/YGSVCvKezJgFfOhl3+Vn74FxPp5pyeLQF49LM1vL1oE4Uey4L1O/h4+WYWrN9BoSfAjAQREREREREpZqwN34snY0wLYDPQ31q7oMT2fwCXW2u7VnD8N8Bya+3YUttPAd4G7sW58D8CeAp4yVr7UIC+6uMEDv5trX0iQJtonBwGXnHAH1lZWcTHByrBdxjwFDrVBHZvdXIGtO3vzAx4dQjsTIOENnDlDGjULuguH/1sNf/79jcMkFAvkl1784v3JSfEMH5oCoO6JwfuQERERERE5BCUnZ1NQkICQIK1trzSb2E/Q2A7TiK/0rMBmlF21kAoHgJet9a+bK1daa39ELgHuNsY4/drYq3dA6wEOgXq1FqbZ63N9j6AnCqM8dDhckP7E6HHBc5HlxsSWsFVn0LjjpC1CV4ZDJm/Bd3lXYO6ckLHRCz4BAMAtmTlMuaNpcxaFdpyBBERERERkcNJtQcEjDF9jDHuEs9Nqf3RxpiLgunLWrsfWAKcWWrXmTjVASqrHuApta0QZ7G73wXvRXf/uwG6yqwu8S2coECTzpD9B7x1qTObwFMIaXNh5XvOR0/Z4g4eC+u37fHbrXfOy4MzUrV8QEREREREJICaqDKwAEgGMoqeZxljelprvbd/GwJvAdOC7O8J4HVjzI9FfV8HtAFeADDGPAq0tNaO8h5gjOlZ9GkDoGnR8/3W2tSi7TOA240xyziwZOAhYLq1trCoj/8WtduEMyPhXiAeeC3IcUsw4pLgipnwzkgY9Cis+QRmjYPsPw+0iW/hJChMGVa8aVFaJluycwN2a4H0rFwWpWWWqVggIiIiIiIiNRMQKH2H3d8d9wBp58uy1r5TlMjvfpxAwyrgbGvtxqImyTgBgpKWlfj8GOBSYCPQrmjbwzjXjA8DLYFtOBf//yhxXCucwEWTov0Lgb4lzivVJa45XPMFrJ4B00ZRpkxhdrqz/aIpxUGBjJzAwYCSgm0nIiIiIiJyuKmJgEAwQprHba19DnguwL4r/WwrN+BgrS0AHix6BGozIpQxShVZjzMzwO+PhgUMzLoLug4Gl5tmcTFBdRtsOxERERERkcNNuCcVlMPFxvm+ywTKsJC92WkH9GnfmOSEmHKnmiTWj6JP+8bVOkwREREREZFDRU0FBFKMMUcZY47CWR7QtcTzI2vonHIw2x1k0Yiidm6XYfzQFCDw+pPs3HwWrN9RDYMTERERERE59NRUQGA2sLzoUQ+YWfT5MuCrGjqnHMwaNA+53aDuyTw/sjdJCb7LApISYujeIp78QsvVry3m6zUZpXsRERERERE57Blrq7csmzGmbTDtDofkfMaYeCArKyuL+Pj4uh5OePMUwsTuTgJBv3kEjFNtYOxKcLl99hR6LIvSMsnIyaVZXAx92jemwOPh5qnL+CJ1K5Fuw7OX9mbgkUm18lJERERERETqSnZ2NgkJCQAJ1trs8tpWe0AgGEVlCJfX+olrmQICIUqdXlRlAMoGBYxPlYFg5Bd6GPvOcj75KR23y/D+mP70bN2wukYrIiIiIiISdkIJCNRalQFjTAJwGXAtcDTgLv8IOeykDHMu+meN800w6I6C8yeFFAwAiHS7eOrinkS7XbhchqNaJhTv8zerwO0KuhqmiIiIiIjIQa/GZwgYY04DrgbOAzYC7wPvW2uX1eiJw4BmCFSSp9CpJrA1FdbNguNvgM4DK9+dx2Kh+IL/s5Xp/HNmKulZucVtkhNiGD80hUHdk6s6ehERERERkTpT50sGjDGtgCtxAgH1gWnADcDR1trUaj9hmFJAIPx88tOf/HVq2ViUd27A8yN7+w8KeIMUu7c6iQ3b9i+Ty0BERERERKSu1emSAWPMp8AAnMoCNwOzrLWFxpgbqvtcIqEo9Fj+8eEqv/ssTlDgwRmpnJmS5Lt8IHV62WUM8S1g0ISQlzGIiIiIiIiEi5ooO3gW8DIw3lr7ibW2sAbOIYeT9J/gi/sg87cqdbMoLZNd+/ID7rdAelYui9IyD2z0JjosGQwApxrCtFHOfhERERERkYNQTQQETgTigB+NMT8YY24yxjStgfPI4eKrB2D+07DyvSp1k5GTW3Gjku08hc7MAL9lEIu2zbrLaSciIiIiInKQqfaAgLV2gbV2NJAM/A8YAWwuOteZxpi46j6nHOJ6XOB8XPkeVCHnRbO4mNDabZxfdmaADwvZm512IiIiIiIiB5mamCEAgLV2r7V2srV2ANADeBy4C8gwxmietQSv6xBwR8P2tbDVfw6AYPRp35jkhBjKKy6YnOCUIAScBILBCLadiIiIiIhIGKmxgEBJ1tq11to7gVY4MwZqttahHFpi4qHzWc7nVVg24HYZxg9NAfAbFDDA+KEpBxIKNmgeXMfBthMREREREQkjNVFlYHIQzXZU93nlENf9Alg9A1Z9AKePB1flYlmDuifz/MjePDgjlfSsAzkFkhNiGD80hUHdk/n5zywiXC66tO0P0fGQF6hSh3GqDbTtX6mxiIiIiIiI1KVqDwgAVwIbgWX4vxELmiEgoeo8EKLiIGsT/LEI2vStdFeDuidzZkoSi9IyycjJpVmcs0zA7TL8nrmXKyYvZn9BIVPPjqL7/j0VdPYYuNyVHouIiIiIiEhdqYmAwAs4ywI6AJOBN6y1meUfIlKByFjoOhh+/aqCRH/BcbsM/TomltkeFxNB28R6rN24mbhP/gGmEFoei83+E5NT6rzHXAkpw6o8FhERERERkbpgbBWytgfs1Jho4DzgaqA/8AkwCfjC1sQJw5QxJh7IysrKIj4+vq6Hc/Dbm+lM4XfXRBzrgNz8Qr575jrOyn6PP2wTJh05hS9+3UPr3Stoxi56u37hyogv2BebROztKyAyuOoFIiIiIiIiNS07O5uEhASABGttoPXPQA0FBHxOYExbnGUEo4BIIMVau7tGTxomFBA4eBXs3cVPL17Hw1v7sdR29tkXzX6+jr6dFiaT1UffQ7fh4+polCIiIiIiIr5CCQjURpUBW/QwtXQ+OdR5PLBjfY2eIqJeQ466+W1+iUopsy+PKJ4pGA5AsxXPUZh7WMS3RERERETkEFMjF+jGmGhjzCXGmC+BtUAP4CagzeEyO0BqyM6NMLE7vHgK5OdW2Dwk+ftgxdtQNGtm8Yad7M4r8Nv03cKT2eRpSiK72PTVC9U7DhERERERkVpQE2UHn8NJKrgJeAUYYa1VmUGpHgmtnY952fDrl9BtaPX1/cW9sPhl2Pg9DHuGjJzAAYcCIvhXwWUkmUyOST6P9tU3ChERERERkVpREzMEbgCygTTgZOAlY8wHpR+hdGiMudEYk2aMyTXGLDHGnFhO22RjzFRjzFpjjMcYMzFAu7FFbfYZY343xjxpjIkp1Sbo80otcbngSGe6Pivfq75+V89wggEAKecA0Cyu/GSBszx9eLVwEE0aJlTfOERERERERGpJTQQEpgBfA7uArHIeQTHGXAxMBB4BegFzgc+MMW0CHBINbCtqvyJAn5cBjwEPAt2Aa4CLgUercF6pLT0ucD7+Mgvycqre365N8PFfnc9PuBWOOAOAPu0bk5wQgynn0Obx0fRp3xg8hc6SAxERERERkYNEjVcZqCpjzA/AUmvtmBLbVgMfWWvvruDYb4Dl1tqxpbY/C3Sz1p5eYtvjQB9r7YlVPW+J9qoyUBOshWeOgcz1MPxFOPri0PvwFMLG+ZD9J8x7ErathpbHwtWzwB1Z3GzWqnTGvLHUOa2fbprFRfPpkHyazL3fWb5w+v2VfFEiIiIiIiJVF25VBirNGBMFHAN8UWrXF0D/KnQ9DzjGGNOn6DwdgLOBT6py3qJkivHeBxBXhTFKIMYcmCWwqhLLBlKnO4kJXxsCH17nBAMw0PMSn2AAwKDuyTw/sjdJCb7LB5rFRdO4XhQZOXn8Z8Zy2P4LLHwBdm+r3GsSERERERGpZdWeVLCaNQHcwNZS27cCSZXt1Fr7tjGmKTDPGGNwvg7PW2sfq+J57wbGV3ZcEoLuF8C3E2D9HNibCfUaB3dc6nSYNoqy9/stfPI3qN8MUob57BnUPZkzU5JYlJZJRk4uzeJi6NO+MRk5uYx8+Qfe2daDUTEdOTJ/PXw/EQY+Uh2vUEREREREpEaF9QyBEkpfvRk/24JmjDkF+AdwI9AbOA8YYoy5r4rnfRRIKPFoVdkxSgWadnam51/5KcQ2Cu4YTyHMGke538JZdzntSnG7DP06JnJOz5b065iI22VITohl2vX9OLJFAhP2F81YWPyyswxBREREREQkzIX7DIHtQCFl78o3o+zd+1A8BLxurS1KK89KY0x94EVjzCOVPa+1Ng/I8z53Jh9IjTnxjtDae3MGBGQhe7PTrn1wBSUSG0QzdXRf5qxuB8u/g00LYO7jFP7lv2VmFLhd+nkQEREREZHwEdYBAWvtfmPMEuBM4MMSu84EPq5C1/UAT6lthTgzAEwNnlfq0h+Lgmu3O7RYU0JsJMN7t4bG98Krg/H8+CrDfjyGn/cdKEeYnBDD+KEpDOqeHFLfIiIiIiIiNeVgWDLwBHCtMeZqY0w3Y8yTQBvgBQBjzKPGmCklDzDG9DTG9AQaAE2LnqeUaDIDGGOMGWGMaW+MORNn1sB0a613vni555Uw8edymDEWlrwauE3ebph1D8x+KLg+GzSv3FjaDSCjaT9ctoCT93/js2tLVi5j3ljKrFXpletbRERERESkmoX1DAEAa+07xphE4H4gGVgFnG2t3VjUJBnnQr2kZSU+Pwa4FNgItCva9jDOQvKHgZbANpwgwT9COK+Egz8Ww5JXYMMRENXAuZhv2x9cbmf/mk/h079D9h/O84hYKMjFfx4BA/EtnOMrodBjuWPnBbD/ZL73HElfVyrN2EUGDVnk6YrFxYMzUjkzJUnLB0REREREpM4Zayudm08qUFR6MCsrK4v4+Pi6Hs6hadmb8PGNvtviW8CgCbDmE/jpbWdbw7Yw+AnI31tUZQB8gwJFF+gXTSlTZSBYC9bv4JKXFjLQtYjxkVNoYTKL9/1pG/Ng/ig+9/ThrdF96dcxsVLnEBERERERKU92djYJCQkACdba7PLaHgxLBkT8S50OH/+17PbsdOei3+UCVwQMuB1uXAidznAu9i+aAvGl1vLHt6hSMAAgIyeXga5FPB85kSQyffYlkcnzkRMZ6FpERk5upc8hIiIiIiJSXcJ+yYCIX+WWELSAgd++hevnQvMU390pw6DrYKeawO6tZZcZVFKz+pGMj3TSWZReEeAy4LEwPvJ1NtZ3ghjW2uJKFIUee3hWJfAUVvv3QUREREREgqOAgBycgi0huHeH/90ud9ClBYPVx70Gt8kMuN9loAU7aO5ew849DbnghfmM7NuWxPpRPPrZGtKzDswcOCyqEqROd4I6Jb+P3uUeVZipISIiIiIiwdGSATk4BVsaMMQSglXh3pMRXLvsP5i6aBPrt+3hwRmp3PL2cp9gABwGVQlSpzvLOkoHdbzLPVKn1824REREREQOIwoIyMEp2NKAlS0hWBnBnuuHF7j2xPb885wji5cWuPDQ15XKMNd8+rpSMXgAeHBGKoWeQyzxZ4XLPYBZdzntRERERESkxmjJgByc2vZ3ppdnp1MTJQRrZkyAcUO/m4iOcNOpWRyRdj9/cf3AnZHv+K9KkNWHRWmZh1ZVgmCXe2ycX+3LOkRERERE5ADNEJCDk8vtrDUHiksGFit6Puix2k1QV+GYDJz/MnQ/H3CqEvwz4hWejHye5MOpKkEYLvcQERERETkcKSAgB68aLCFYY2Pqfl5xkKJZ/UjOcc/HGDB+qhKAU5WgWf3IWhh4LQrH5R4iIiIiIochLRmQg1sNlRCsjTE5VQnyA3bjrUrgyVkGDKzhQdcSjwda9Kp4aUW9xNpd7iEiIiIichhSQEAOfjVQQrDKghhTsFUJ/m/GfC5t1pcerRKqY2R1x1r49G+w9Wc47X74aAzOUoqSQYGi50MmHgig/LkMmqVARLSTaDCcgj8iIiIiIgcxBQRE6kqQU+LT8how4sUFvHTFsfTv2KSGB1WDZj8IP04CDJx4u7OEYtY43wSD8S2c3A/e5R7Z6fDaOc4SjKNGwOIX/bSfUDfLQ0REREREDnLG2kOspFkYMcbEA1lZWVnEx8fX9XAk3HgKYWL3cisleOJacHncS3z/2y6i3C6evqQng7on+2kb5uY+DrP/6Xw+ZCIce5XzeUV3/DcugGmXw55tATouSrZQVzkjRERERETCTHZ2NgkJCQAJ1trs8toqIFCDFBCQCqVOh2mjip6UnjoPXDSF3E6DGfv2cmb9vAWXgUeG9+CSPm0o9FgWpWWSkZNLs7gY+rRvjNtVurpBGFj0krNUAODMh+CEW0I7fvc2eKoH5O8L0KCoxOTYlVo+ICIiIiKHvVACAloyIFKXvFUJAk2db9KZmJxN/N9lvbn3o5W8teh3Hp6ZissYJn71C+lZB0oSJifEMH5oSsAZBHUSQFjx9oFgwEl/Dz0YALBtTTnBAAAL2ZudmQbhlktCRERERCSMKSAgUtcCVSVY+xm8eDIk9cB91Sz+NbwHTRpE4zKGu97/qcwigy1ZuYx5YynPj+xdJigwa1U6D85IDSmAELLS0/+TjoIv73f29bkeTv1H5frdvbV624mIiIiICKCAgEh48FeVIPlocEfDH4th3hOYk+9k7BmdGTBhjt+MAxZnocGDM1I5MyWp+O7/rFXpjHljaUgBhJClTvc/y2HA7ZD9B5zxTzCVnI0QZPLFoNuJiIiIiAgArroegIgE0LA1DH7c+fybx+CPJSxKy/S5y1+aBdKzcrnwhQVM+/F3Cj2WB2ekBgwggBNAKPRUIZeINw9CyWAAOMkSZ90FrfqAqwp/atr2d4ILBAooGIhv6bQTEREREZGgKSAgEs6OuhC6nw+2ED64lh07dwR12NJNO1m/bXfQAYRFaZlld3oKIW0urHzP+egp9N9m1jj8V0ko2jbrLv/HBsvldkoLAmWDAkXPBz2mhIIiIiIiIiFSQEAk3A1+3LkDnvkbx619IqhDrh3QnoFHJpGREzgYUFKZdqnTnZKIrw2B969xPk7s7mz32vU7zHmk7MwAHyUS/lWFN/lifKmlDfUSVXJQRERERKSSFBAQCXexjeDc5wFovu4tBsetK2/yPMkJMdx9djd6t2lEs7iYoE4RUXJKf3lLAKaNcvYXFsD/HQ/zHg/uNVRHwr+UYTB2FVwxE7oVBQBaH69ggIiIiIhIJSkgIHIw6HAyDLgNTr2XYUPPBwJOnmf80JTihIJ92jcmOSEmYAABwGXgpM5NnCfBLgEwxlmz36RzcOOvroR/3uSLJ97uPP/tGyjIq56+RUREREQOMwoIiBwszngATv47A49qzfMje9MiPpK+rlSGuebT15VKi/jIMhUD3C7D+KEpQOAAws2ndSIuJhIAz4bvg18CcOk0uHFh3ST8SzoaGiRB/h7Y+H319i0iIiIicphQ2UGRg9Ag5jMw4k5M1PbibTamBcY1AfCdQj+oezLPj+zNgzNSfRIMJiXEMH5oik8AYcnPazgumAHs3nqgcsCgCc5SAgy+MwtqMOGfywWdz4KlU+CXz6HjadXTr6fQCXbs3urMamjbX8kKRUREROSQdVDMEDDG3GiMSTPG5BpjlhhjTiynbbIxZqoxZq0xxmOMmeinzTfGGOvn8UmJNg/42b+lhl6iSPCWvgHvXY3Zu91nsym5xr+UQd2TmTfuNN4a3ZenRvTkrdF9mTfuNJ9gAIArLimoIRTWb3bgSaCEf/EtajbhX6eBzse1n4GtQtlEr2ASKYqIiIiIHELCfoaAMeZiYCJwI/A9cD3wmTEmxVq7yc8h0cA24BHgtgDdngdElXieCKwA3i3V7mfgjBLPq1A7TaQaeArhm0cC7LSAcdb4dx1c5s6222Xo1zGx3O73t+zLHhtNfeN/Xb7HwhYS2VjYlX4ld6QMc85Zm3fXO5wC7ijYtRG2/wJNu1S+L28ixdK5E7xBFlUyEBEREZFD0MEwQ+B2YJK19mVr7Wpr7Vjgd2CMv8bW2g3W2luttVOArABtMq21W7wP4ExgL2UDAgUl21lrt1XbqxKpjI3za7TMX8aefP6efx3WOhf/JXmfP5h/ORl78rGl7soX4mKBJ4WPC/uxwJNCYU3/eYluAO1OBOOG9J8q30+wiRQ9igeKiIiIyKElrGcIGGOigGOAx0rt+gKozixl1wBvW2v3lNreyRjzJ5AH/ADcY639rZzxRuPMUPCKq8YxigRfvi/UMn9FF/fN4mL41NOPG/LdjI+cQgsyi5tsIZEH8y/nc08fzo1wceK/v+a8Xi256LjWrNqcVSZHQbKfHAXV7uz/QL1EiG1Y+T5CCbK0D7haSURERETkoBPWAQGgCeAGSl/dbAWCW+xcAWNMH6A7TlCgpB+AUcAvQHPgXmC+MeZIa+2OAN3dDYyvjnGJ+BVs+b5Qy/x9PxF2/U6fQRNITojhi6w+fJl3LH1ca2jGLjJoyCJPVywukhNi2LhjD3/s3MfTc37l6Tm/+u1yS1YuY95YWqbyQbVK7Fj1PmoqyCIiIiIiEuYOhiUDUHYub+l05lVxDbDKWrvI54TWfmatfd9au9Ja+xUwuGjXFeX09SiQUOLRqprGKOJo27/6y/z9/CF89QD8OAn3ulnFZQotLhZ6Upju6c9CTwq26M/F+KEpXHVCe565pBf9OzYO2K33F/TBGakUll5/UBMqO6W/poIsIiIiIiJhLtwDAttxEvmVng3QjLKzBkJmjKkHjABerqht0XKClUCnctrkWWuzvQ8gp6pjFPHhcjtl/oCyQYFSZf4K9lfc3++L4IPrnc/7XA/dhhaXKUxKiPFpmpQQU3y3PzrCzdCjW3DzaZ3L7d4C6Vm5LErLLLddlfz2Lbx8BnzkN61IxWoiyFKdPIWQNhdWvud8VC4DEREREakmYb1kwFq73xizBCfp34cldp0JfFwNp7gIZ83/GxU1LMoP0A2YWw3nFak8b5m/WeN8177Ht3CCASnDYMmrsOhluPh1aNzefz+Zv8FbI6AwDzr/BQY9WrxrUPdkzkxJYlFaJhk5uTSLi6FP+8a4Xb4XzRk5uaV79WvqD5vo3LwBiQ2ifbYXemyF56iQOxL+WAw7foXCAnCH+GfNG2SZNoqAk4+8QZbaljo9wPd5gqoeiIiIiEiVmdKZwsNNUdnB14EbgAXAdcBo4Ehr7UZjzKNAS2vtqBLH9Cz69GVgLfAfYL+1NrVU33OBzdbaEX7O+19gBrAJZ0bCvcDJQA9r7cYgxx4PZGVlZREfHx/8ixYJhqfQf5m//Fx45hjI/gNiGsIFk+GI033bR9aDL+6DzF8huSdc9SlE1Q95CAvW7+CSlxYG1TbK7WLwUcmM7NuW3m0a8vnPW6onEWFhAfynI+TugqtmQdt+FR5ShscD3/wLlr/pe/Ed0xCGPVM3F9+BSiF6ZzKoFKKIiIiI+JGdnU1CQgJAQtHM9YDCPiAAYIy5EbgTSAZWAbdZa78r2vcq0M5ae0qJ9v5e1EZrbbsSbTrjBAvOstZ+6eecbwMn4SQ23AYsBO4rHVSoYNwKCEjdyNoM0y6HzUsAAz0uhI3zymbTr5cIY+ZDXOVydBZ6LAMmzGFLVq7fpB4GSIiNpHXjWFZuPvC3qFXDWP7Ytc9veyBgIsKAMwrevxZWvgsnjIUzHwz9haydBW9dDMm94Kx/ws5N0LANtDuhbmYGeAphYvdyqh8YZ6bA2JV1Mz4RERERCVuHXEDgYKWAgNSp/Fz47O+wdEr57S56vUp3mmetSmfMG0sB33vZpS/uV/y+i9cXbmT68s3sLwz8d8fg5CuYN+40n+UDs1alB55RYL+H96+Bpt3grwdmLAS9JGHKOfDbN9D/FjjroUp8FapZ2lx4bUjF7a6YqVKIIiIiIuJDAYEwoYCA1DlPIfy7gzOd3q/qudNc7sV6qTv9X/68hdGvL6mwz3OObsHJXZpyRLMGrN+2h9vfWR5o8jwvX9SB02ecALYQbv0JGrUNfkxbU+H5fmBccOsKZ2ZASYX5Tp6C2rTyPSfAUZHzJ0GPC2p+PCIiIiJy0AglIBDWSQVFpIo2zi8nGABgIXuz064Kd5qDTUIIsDc/uCz5H6/4k49XBJoy7/AGCP4x609Oa308ZtN8WPcFs+oNYcwbS8sEELZk5TLmjaW+SxJ+eMH52HWIbzBgyavw7X+cC+7KLEOorG2/wOJJwbVVKUQRERERqQIFBEQOZbuDrM4ZbLtyuF2Gfh0TK2zXLC6mwjYAZ3Rrxu68Alan55C1L7/ctluyc/nnvq6cHAULFuUzPSfVb04DizOr4MEZqZyZkoQ7dyf89I6zs+8YnyUGR+7YzxHZfzjBkuoUKBnkrk3wzQRYMRWsp4JOimZ21FUpRBERERE5JCggIHIoC/YOci3eae7TvjHJCTHlJiJMSojhf5cfi9tl+Hj5Zm59e3mF/b6Sfwav5J9BE6LYvidwOUQLpGflsigtk35/vgYFuZB0FLOy2/HghDnFSwxamwjmRoNn81Jc+/dCVL1KvV4f5ZURzN4My4sqoHYZDO0GwOf3lBi1V9Gsi7oqhSgiIiIihwxXXQ9ARGpQ2/7OBSd+EumBsz2+Za3eaXa7DOOHpnjPXno0AIwfmlK83CDYGQVPXnQ071zXl4uOax1U+3/O/Jk/V34DwE+tLmHMm8t88g38bpuRbhvjsgUsmvd5UH2Wy1tGsHTlgOx0Z3v9ZtD9ArjmK7hkKvS70SktGF+q2kK9xio5KCIiIiLVQgEBkUOZy+3cfQYCXn7XwZ3mQd2TeX5kb5ISfC/2kxJiypQc9M4oKCekQXJCDMN6tuT49o05s1kOJ7p+qnAMq9NzuL/evRRe8Qk3/dTBz2wFw2JPFwBWfP8ZhR7/CVgLPZYF63fw8fLNLFi/w387T6EzMyDgQgbgy/vgvBeh9XEHdqUMg7GriqoJnORsO+piBQNEREREpFpoyYDIoS5lmHNH2e9U9cfq7OIy2ESE3hkFY95YisF/acPiGQVpc+k1fQhPRDWiT+6zWD9hBAM0aRDNFSe0pVtSPIs8EWzKXlimHcAiT1eGuRfQbf8qZ4lBqRwJQVcy2Di/7MwAH+Ukd3S5nW27t0Lad7BhXjn9iIiIiIgETwEBkcNByjDoOth/Mrs6FGwiQu+MgtIX30mlL75b94HI+jTN38mRZgM/2/Z+Awj/HtiEU49Kgug4Pl6+OeB5f/B0A+AY1zom/7bVZ6yzVqUHX8mgOpI7thtQdIKVsDfTWTogIoe+QIlIRUREqoECAiKHC++d5oNUUDMKIqKh46mwZiZP9trCqPXd/AYQTl17P3z5GQx5kmZxpwU856+2BQsKU1hjWxNZuK94+89/ZjHu/ZXBVTJwmepJ7hiXBE06w/ZfnIuDbkOC61NEDl7lJSLV0qGqUaBFRARQQEBEDiJBzSjoPAjWzKTTru+ZN+6hsgGEPVvhgw/Bkw9NjqBPUuCqBxYXl+TfS/1oN98N6F68fdLc38othehTyaBj4oHkjgGXDQRZRrDdiU5AYMNcBQREDnXeRKSl/zJ5E5EquWjlKdAiIlJMSQVF5NDS6Szn459Lce/JoF/HRM7p2ZJ+HROdu/WLJznBgNZ9oUWvCqseGODxC48msUF08fboyODuImXkFM1O8Enu6MuGktzxmCvgkrfhlLuDOr+Uw1MIaXNh5XvOR09hXY9I5IBgEpHOuks/t5VRUcWX1Ol1My4RkTqigICIHFrimkOLXs7nv37puy8/F36c7Hzed0zx5gqrHnRp6CTz83gAGHZ0y6CG0iwu5kDVgZRh/N7ufArwvejfSmOW9Xsq4F0pnyoGe1tR2GkQxDYM6vwSQOp0mNgdXhsC71/jfJzYXRcCEj5CSUQqwVOgRUSkDC0ZEJFDT+dB8OcyWPcl9Bp5YPuq92DvdkhoDV19p9wHzFGAhf90hH2ZcMP3kNS9uBSiv2UG4MwqSEqI4di2jRg08Tt6tEygXZP6PLnmfAzD6eNaTTOyyKAhiz1d8Xzt4vmW6b6VCQihioEET9Ow5WBQHYlIpayqVHwRETlEKSAgIoeeoy6G5J7Q/qQD26yFhS84nx93LbjL/vnzn6PAQIuesH4ObFoASd2DLoW47PddrMvYzbqM3QeGgYuFniNLn8E3CSGBqxjUy17Pr29PYfUx3eg2/K4yr6HQYyss5Vgdx9Somkr2VeHdQePcHew6WMnFpG5VRyJSKUuBFhGRMhQQEJFDT+P2zqOkbWsg42eIiIXeo0Lrr01/JyCw8XvoMxoIrhSitZYPbuzPM1/9guvXz5nn6UEeUWW69yYhvOzlhbRqVI/oCBcfLdvs97L1CLOZmyI+4refllB4zjifC/fKzCgIu1kINZnsS3cH5WBRnIg0Hf8BrCATkYov6wmunQItInIYMdb6+0cj1cEYEw9kZWVlER8fX9fDEZGdGyF9OaScE9pxG76HV8923iTesRbMgYvwYO6uf/v1LE7+9mK22QT65j1LIZW/+9yQHJZG34DLWP7e7l2O7tqFI5o1YOOOvdz1/k9lLh28I3l+ZG+/SxL8zUIo75gaFWg6v3dEVZ3Ov/I9J2dARc6fBD0uqPx5RKpD6nSYdnmAnUbLW0L162x49yrIyyq/XUQM3LkBomJrZVgiIjUhOzubhIQEgARrbXZ5bTVDQEQOTXsz4funnIv546+HuCTnblqjtqH31fIYcEc500gzf4PEjsW7gimFeMT22QAs9HQrNxhwRb+2JCXEsvz3XXz+8xa/bXYRxxrbhhSzkX2/fMe9awKXP4QDl9Z/f/cnVqfnYIyzeqLQ4+HV+RvKmzxfZhmDV40sMaiN6fyahh1+VAs+sG5DoWEb2LXJd7s7Gs5/WcGAYFkL30+E2f90Zgg07gCZad6dZdsX5ML7V8OFr0JEdNn9IiKHGAUEROTQ9OtXzptAgA8WOx8rO/U8MsYJCmxa4Fy8lAgIVMhaWmz+HIDPCo/328SbhPD+oUfidhkWrN8RMCAAsMCTQoprIxc33cDeRuewanMWGTl55Q4jJ6+Ap2avC37YOMsYFv62gxOOaFK8vTJLDIIKINTGdP7iadiBzqNp2LVKteDLt/ZTJxgQWR/Oewl2bYTP74bCfC1p8cdfcCl/H3x8I6R+7LTpPQrO/i/88rmfn72W0PNSmP+M8zUWETlMKCAgIoee1OnwwXVlt1clk3zb/k5AYNMC6B1oGq8f6SswuzZS4I7la0/PcpMQei+SK6pi8IOnG9fwGQMi1nDilcfx8fLN3Pr28gqH0r9jIu2b1McY2LB9L/N+3V7hMde+tphTuzbjpE5NiYl0c9s7y8uMaUtWLmPeWBpwWUJQAYTaSPblcjsXm+UtSxj0mO5Q1wZVeyiftfDNo87nx18P3QY7ny99zcmH8utsLWspyV9wqUGS87ucvRlckXD2f+DYq5x9KcOc2Ub+Zqd0/gs0P/LgmB2gGTZSXfSzdFhTQEBEDi01NfW82zCIbQwdTgltPKkfARDR5SyeTOlXbhJCr4qqGPzg6YrFYHasg+x0msXFBDWUm0/rVLy8YcH6HUEFBPble/h05RZmrdpC07jokJYYBMpR4DeAUFvT+VOGORebfu9MP3Z4X4TWFlV7qFhBLrQ70QmQ9LvpwPbOg5yAwNpPFRDwChRc2r3V2RbTEC57F1r38d3vcvufadHqmAOfWws/ToajR0BU/WoeeBVpho1UF/0sHfaUVLAGKamgSB1ImwuvDam43RUza37arbXwdC/YmQYXvALdzwtp/X25d9e/v9iZTnzxmxS26c+ACXMCzijwLkmYN+604nMVemxQxzx9SS++/3U7qzZn8dXqjApf8vHtG3FM28bccVYXBkyY4zP2cseUnwePtYLC/X7bWwwmvgWMXVm5i8ScrbDyXTj+BqfkpL+7IQCegoPjzuDBLJx+R8Nd/j6ILJHcbtNCmDwQohPgzvXgjqy7sYUDTyFM7F7+MqC45nBbauX+bnz9L/h2ArQ9AUZMhS0rw+MOak0nYJXDh36WDllKKigih69wqjO9ba0TDIiIgU5nAcElIfQa1D2ZM1OS/AcQ2r0H9ZqAy4Ubyp1RAL5LErzj8B7jxsNxrjU0YxcZNGSxpyseXIwfmsJx7RpzXLvGfLx8c1ABgR/SdpK1r4ATOzUNGAyAAzkKXpufxtUDOkBkNH+0/AstN36MBUrGSDzWOeLnI0bTvbJvwD/7u7OOOGM1nPt/Ze8OrvsSPr8Hel4GA8ZW7hwSnHD6HQ13JYMBAK2Og3qJsHeHs3yp/UnVc56DdbpwMLlHcrZUPvfIEWfCwuedkrP/7eQbsKyrO6iaYSPVRT9LUsRV1wMIhjHmRmNMmjEm1xizxBgT8K+6MSbZGDPVGLPWGOMxxkz00+YbY4z18/iksucVkTBRk1PPd2fA8qmw4p3g2jfrCjcvhfNehOgGoZ+PAwGEc3q2pF/HxAMX9Q2agevAn/BB3ZN5fmRvkhJ8lw8kJcQELB84qHsyH5y6nfkxt/J21MM8HfUsb0c9zPyYW/ng1O0+xwS7LOGKfm25+oT2ZOQEDgaU9OJ3vwHOjIULt17BuPzRbKGxT5stJPJ+4Ul0WfowhanTg+rXR+p0Jxhg3M567KLzLVi/g4+Xb2bB+h14dm+D7b84lSlyyw2kS1UF+7uXvsK5Q16Sp9CZYbDyPeejp7D6x1eXrIVP74Q/fvS/3+WG48fAKfdAo3bVc87U6c5d9teGOGU5XxviPK/M71ptq+ngUuvj4MQ7nM9Lz17y5ruo7a9TKAlYRcqjnyUpEvYzBIwxFwMTgRuB74Hrgc+MMSnW2k1+DokGtgGPALcF6PY8IKrE80RgBfBuFc4rIuGgOJN8Ov6j3lXIJP/7D/DRGGjaDY6+OLhjEjuGVpUgVNY6pbRc7vJnFPiTOp1eC27Flvo6NSOT5gtuhdaNiu9+VZTo0F+lhIpEs58H630MucexaHMB6Vm5TONU3ss7mT4lZiws8nTl7oipRFJAwYdjnIRfpb6mAZdi7NsJnxS9oR8wFpKP8rsUo2V8Ez6Pa0+DnDT44QU4+c7g+pfQBVPtISIG5j8Ny96AY66E466FzUsO/XWuaz+FRf+D5W/C7ashxs9yw5P/Xn3nO9iTO9Z07hFPofP98KuO7qBqho1UF/0sSZGDYYbA7cAka+3L1trV1tqxwO/AGH+NrbUbrLW3WmunAFkB2mRaa7d4H8CZwF5KBARCPa+IhAlvJnngwIR5fJ9XNpN8m37Ox22rYW9mZUdYfb79Nzx5JPw0rXhTwBkFpZWYKlj2q1R0cTDrruI7sN4lBs7+0u0d/iolBLpkjiafyfWeYeCud+Dty8jIPnAn2IOLhZ4Upnv6s9CTggcXEwpGsNjTmYj83TDtCp87x7NWpTNgwhwueWkht769nEteWsiACXOYtSodPr8X9mRAk85w0p3FiQ5LL2f4Mzufu3YUrWuf/4zP97fc/iV0Pr+jpRX9xHQ/DxJaw75MmPcEPNkdpl1eNohQV3dpa0LpygL+ggHVqcLpwvj8DQhL3uBSQMYpJ1jZUqLheAe1thKwysGtvNlUhfnO9m8D/R0uRT9Lh7ywDggYY6KAY4AvSu36AqjOQtHXAG9ba/dU5bzGmGhjTLz3AcRV4xhFJFjeTPLxpabJx7eo2h2v+k2gSRfn800Lym/73X/h7ctgw/eVO1cw9u923oxumBf6sZV4o+tdltAiPpK+rlSGuebT15VKi/jIMssSSgYQ3Hh82sewn2cjn+YEzxKIiIWT76RZfGyZEZRUQAQ37b+F/OhE2LqS3Ol3MPbtZTzySarfC/wtWbm8OfU1WP4GYGDYMxS6o3lwRmrAy59PPcfzq2kLedmw4FmAgAEEb6WEQEGB0ksSCj1K4Ouj1bFg/LwF8f6Onvsc3LIcLnod2vQHPAE6OkguXIOx9lMnaV1UA9/KAv7kZsPPH8Evpd+mhCAcL3ZD5XJDxzMC7KyGUqLheAe1OAgSKNxaxSCIHPwCLQNa9iZ89x+Y2MPZvv2XCjrSz9LhItyXDDQB3EDpv7RbgaTqOIExpg/QHScoUNXz3g2Mr45xiUgVlVdnuira9oPta51+uw7238ZaWPE27FgHKedW7XzlaXeSs+Z9w3ehHxvsG9hvJzhrZ9ufBO5IBrkWMzBmHGb/gQsJG9MC45oA+AZavDkKWix4kOYcWEKQSxQx7HemhV/6NrQ/iT4eW+GSBBPfAteFk+HN4cSsfJPI/Pq8VHhKgIF7eCDiVQD+6DSS11bGs+HrJeUmOvTg4t955/Fi1JN4FjzP9iOv5oHpa0MqtQgVVIfwk8vhsLT9F6eMZ5POcOo9/n9H3RHO73FsowqqEpS4cK2OqgR1kWCv9OyAeo3Lb//TO/Dp35xgSeezKnfOcLzYDdXODbDqfefzmATILTExtDpKiYbj3XjvDJtpo/zsrIYgiBzcylsG9PGNB543aA7HXuP8nky/uWijn/92+lk6LIR7QMDLXy2M6rrdcg2wylq7qBrO+yjwRInnccAfVRueiFRaoDrTVdH2BFjyqpN1OpCM1U4wwB0NnQdW7/lLatMXXBFO+cGdG6FR2+CPDfYN7Ia5ziO2ESQdBWnfll0yEGi9cYAcBTEUJefqfwt0OAXwrXoQqFLCA8NScB+R7FxAznmYR6JeZWFuN363ZV+LxcX1+bdxW8R7zHNfyltz04J6uV94juUnT3uOKkjjuWcmsKVwUMC23koJP/y2g/5HNAEOzCgo/Y/CO6MgUILHyjio8xp0OAVu+9lZztGwTflta/PCta7qcYcyOwCcvyuf/g1+X+gsb6kogOBPbV7s1lSQZdsvzkyTtgPg8o+cr0e1BoBrMCdNVaQMg+7nw6r3fLfHt4AeF8Ifi8M794PUjGCqBrijYOhTzs9PRFE6tZiEsn/3AE65Wz9Hh4lwDwhsBwope1e+GWXv3ofMGFMPGAHcXx3ntdbmAXkl+q/qEEUk3Hjf+KX/BHk5EO1nZVDqR87HI06v2XXA0Q2gRW/4Y5Fz0R5KQKBtf6jfzLkg88s45c26DoY1n8De7ZD2bYC2RW8+PrkdEo9wXnNEDHzmP0dBseVvwil3Fb9p9y5JKH13Pan03fUBd8DmZayN7MHvPzYL+BJ/ta34a/5YbmzYmOtPasS23Xl8sHRzwPbe1/18zLWYgn18WtitgraOUZMX0a5JfVo3iuWHtMyQZxR4hXKBX5lZCGEXQIiMqTgYALV34VpXCfZCnR0AzteteXfYusopmRlsktOSautityaDLJ3Pgr8udC6EIiKrPwDsczfe3z0hW3d3UHtd5vwPat4dmqc4P//1m8FzxzvjSj4aelxQ++OSuhPMMqDCPEhodSAYAGVnVK58H375FP5cWuNDlvAQ1gEBa+1+Y8wSnKR/H5bYdSbwcTWc4iKcqgRv1PJ5ReRgldAKEtpA1iYnKNDuhLJtUov+TKScU/PjaX9iUUBgHvQaGfxxLrfzWvwGBIouEoc86bxRGPyEk3n/i3+U3+eebfB8v+DH4Gead1CVElwuGPEmu3/LhB8XOpvw0Me1hmS2E00B0zyn4ClKk3Nip6b065hYvK6/okoJz477K26XYd66bYyc5G/ymK8Cj+XXjN38mrG73HbeGQWL0jLp1zHRZ18oF/iVmYUQNssYPB74bQ50OM2nbGa5KrxwperrXOuyHrf1wDFXwaIXg5sd4NV5kBMQ+OWzygUEKrzYpeoXu7URZEloVbXjK+LNSePvDmpUfeh4Ws2eP5COp/k/94l3wNz/woxboUWvmq1yUx3qYonOoaoqs6lKzqhM7un8XfllFmxfB006VdsQJTyFdVLBIk8A1xpjrjbGdDPGPAm0AV4AMMY8aoyZUvIAY0xPY0xPoAHQtOh5ip++rwE+stb6q49V7nlF5DB20Wvw9/X+gwEZa2DbGnBFQpe/1PxY2g1wPqbNde40BmvTwqLovwsalLrLXjr5ojsC4oJM2xJZ35mSGCw/b0yCqpRgTHElg3Nd8/gx+gbejnqYJ6Ne4LGol1kePZpBrkUkJzgBBW+/oVRK6NexCV3i82niv2ANBueies4dJ/P6NX246NjgLkz+OeNnXvruN1L/zMbjsSElLiz02HITI4IzC6FkEsPKJkasEeu+gDfOh5dPD/7ntdzKIUX6XFe1i4i6TLDncsNx18CNC0Ob+u/9+/LrbCjYX7lzB0rA6oqAv/y7ahfrNVXFwFr4+Cb49avKjy1UKcNg7Cq4YiacPwlGTYdGHWD/Hvhxcu2NIxin3O3kltjvrcgSOG9KnQuU/O5QqBpSFzJWB9euotlUTY448Pdl4XNVG5McFMJ6hgCAtfYdY0wizrT+ZGAVcLa1dmNRk2ScC/WSlpX4/BjgUmAj0M670RjTGRgA+M3GE8R5ReRw1bJ34H3e2QEdT3PW5dW01n2daH6bfk7yv4jo4I5r0ByOPM9Z8jDkyYrv0AQ7HfvSd5y7DL9+DW+cG9w4KsntMvyvx1p6LC77hiWOfTwXOZEVvTv4BBSCXpYAuFe9x0zPWD6IOJa7Cq7zm9dg/NAUOjRtQIemDYhwuZj2Y8VpY1ZvyeGRT503bo3qRbIvv7DcC/x7P/qZ+lER7NqXzx+Ze8tNjOidhfDfz9dyYqcmHN8hsdwAQkXLGKrdwv9zPrY7AUJZVhfoLm1ELBTsg2WvQ5/Rzh3bygiHBHuhLjNs0fvAsp+N30PHU0M/5/o5zt+OsasO/A0oyHP+NkSVX/mjQqEEWUKZ6r/8Tef7vfJduPUniKulhH6lc9KceDtMv8mpSNLnOmcJTG3YvwcW/B8cdbH/ZWLuCLhgErwwALauhM/vgSFPlG1X1+pqic6hqGC/s2Rv2esVNAxhGVC/vzq5TTb94ATtNGvjkBb2AQEAa+1zgN8QlbX2Sj/bKvyvaq39hcA1Wyo8r4iIX43bQ6vj4MjhtXO+qHpwfaC1/eVo3B4ufOXAP/qK3pCHut64w0k1vz7ZU8hRvzyDNX7u+BvnHL1+ngBnjvR5MxPUsgSARm2JLNjNhZFzea/ehSzOblS8y18AwTtjobwlCU0aRDP6pPYs/C2Thb/tYOfe/Apf5vbdeVw+2Vm60L5JcBe8z3+7nu/WbePewSlBBRD8LWOo9pwDW1ZC2ndg3NDn+tCP91c5pGlX+N9JsONX58Jn6FOVG1tdJNjL2eLcXe51GRw1wrmQC4XL5ayhX/YGbF4SekAgNxumXemsKR49p/rX39dEkCU7HWbd43x+yt21Fwzw56iL4ZvHIPsPp7zpcdfWznlTP4avH3Eq2dy8xH8gKb4FDH8R3jwffpzkzCTrfl7tjC8YdblE51DkjnSW7GGcykbePEb+wtjBLgNqe4KTqLP9SfoeHAYOioCAiEjYWfi8Ez0/7X5ofdyB7Udd5DxCmb5fl4L9R1/uemM/bzRCbV8ZRXcgA1fjDnwH0rssoVyt+0Cns3Ct+4J3unzLDz0fK/fiOJhKCQ+deySDuidz3Ukd2V/g4f++/pWnZq+r8KUmxUfTNrE+DetFkrZ9T4Xte7SMp2frRmTkHAgGePMsNGMXGTRkkadrcZ6FpZt2+nw9aiTnwMLnnY8pw6Bh60p1UYiLRZ4UMgo70MwTQ596jXGf9z94bZhT/eOIM6FbeSUKAygOeJVzR7s66nH7S7C3aYGTKb/npaH3d+IdcNKdoSUU9VryKuRlQZMu0NRPAs3CfPjhf06As83xofdf3UEWa2Hmbc6YW/QOLd9CTYiIghNuhc/+DvOegt5XOBdmNW3Zm87Hoy8pf1ZJpzNgwG0w70lIXxFeAYGamj1ysKhM3oTSx7TpB1jnZ84YOPd52Pqz8/UKmMgzhDKcxlRu1pEclBQQEBGpjE0LnLudG77zDQh41XaVkfx9zl3CthVMxf5pmrP29tR7oFG70M4RaNp2oDcaobYPVW1M8z71Hlj3Ba6V0+jX4RRnSYarOdAfKPsGLpQlCVERLvp2SAwqIPDkxb2KEyMOmDCHLVm5GD8X+BYXSQkxfPTXAbhdhgXrnRQ5A12LGB85hRYms7jPP21jHswfxeeePnz3SwZ/PfUIoGqlEwPOKsjZ6kzxBuj71wpfrz/lBilOuAW+fwqWvFK5gECZ2u5+qg7XVII9LHx0o1NyMNTficYdKjeWgv0H1gafcIv/BI/fPOYkp0s+GkZ/HfprDyYZJAT/d2jlu06iM1cknPN/oc+oqAm9L4fv/u0kmV35buWCOqHITION8wADPS+puP2p9zp3eOsq8WEg4bBEp65UpuqGv2MiY53A2JWfOP/z6zU+EDzxN5uqKskaC/Ig64/wT1AplRYGf01FRA5CbU9wpm5unO/cpQPnn3b7EyG2UfnHVrfCAvhvF+fO2U0/Bs4IXJAHsx9y3rw26+bcPQpVqG80qvuNSUm1Mc27RS9oeSxs/hE+vvHA9nLewAW9JIHglhkk+UmM+NHUF7jfzwX+P/NHce7QG4rP1ad9Y0Y0WM6/8ieW6TuJTJ6PnMitntv5S7/rAOeC/t6PVlUq50C5F+wZk50cF62O8wmgBbssoaIgxQuXXsPAv7SEY6/2M/Jy5GyBGWMPVNTwF8CKS67hBHtFqjpF2trgA5Er34WcdOe19bjQf5vjb4BFLzl3l5e9DsdcGdp4jAsGPgrvXon/WUIWel4e3GyRnK3w2Z3O5yePc8rshYPIWGet9Yp3oF6Tmj/firecjx1OCa66gjvCNxhQWOAEs+s6o39tLtEJJ5XJmxDomPx9Tu6Qhc85P4OlBbMUMBi/L4K3L4P6TWDM/Nq/2SG1QgEBEZHKaFNUXs+bcCczDaZd7iQ5u3N95ZObVYY7ApKPgg1znVkLgQICP052ggFxyZVbw+0V6huN6npjUlpt1FFPne4EA0qrIPFVUEsSCG6ZQcnKBwCDXIsZGPUUttRrTjKZPB/1FMZ1DOCMyY2H8ZFTIB9KX2e7DHgs/LvBVGK73wvAorRMtu8OnLG+dM6B/QUeIlyGL1K3lHvBvqjtfJoC9D0QVAl2WUJFlRUM8MAnv3LGuOvKBBPKDTjs2Q5TznGqgny0D0Z9XHEAa/0caH186L/fNTlFetta+PJ+KMh1XkNFPB6Y/7Tzed8xgRORNmgKp97tBCpm/9MpoxpKsHPBs/DbN85U5jn/rHiW0Oalziyn4651LjpKTpHetAD27YSkHjBgbPBjqA19/wr9bw2+jGZleTywvCggEEqJWa8lr8Gnf3MCc14V3ZmuKbXxtzvcBJs3odNAwOMEm4IJJC74Pyd4V1OBnSadnUSWezLgt6/Db7aJVIuDoeygiEj4aX4kRCfA/hwnWZo3iU/bfrUbDPDylh/cMM///rwc+O4/zucnj3OSER7syi1HVw15CorfjPlThbJppXiXGSQl+GYpT0qIKTs9v2hMBlvmH7iLorwJM245MKb1XxO7b0uZYEDxMQZi920pLqdXMudAebztpizYQJd7P+Ovby4rt1LCsJ23UXjFp9DNufAIphSix2NZv203/5m1JsjEiEUVhAv2w+x/8v28rxkwYQ6XvLSQW99eziUvLWTAhDlOmcW9mTDlXCcYENfCJxlhIS4WeFL4uLAfCzwpFHq/0kunwOvnOXe8CytOBumjJqdIRzVw6oX/9q0T5KjIui+c1x0dX/Fd/+OudRI37t0B30wov21Jv33jBCl+/QpsoW/JvitmwtiVvheh+/c4Zec+/Ru8famzTr5kObrFLzvBiB4X1846/VBERNV8MACc5WlZm5z/O10Hh3Zs6nTn70JhqWCfN7BZ22X+iv92B5oXRdWX6ISbYIOCjzSDt0YEeQw1Vw7VK7ahszQGYP6zNXceqVOaISAiUhkut5Noa90Xzj9jb0Ag5dy6GU+7E4FHnYCAv6nD85913tQnHgG9Lq+TIdaImsxTUIuJr4JeZhDMG8R9Ow+MaV9m+W29ii5Em8UFVzrN2+7PXbnke8pPoGmB9Ow8Jv3egdzf0mjbuB4PfVL+Hf/b3llBpPsnsnMLghs/cN3rSzgrJYkx+1/liF8n08TzDpn7Hwaiittsycrlzjfm0S95Igk7Vzll+66YUbyOvdxZC027QkSM8zs/41ZnHXuw02drcop0QktIOgq2/OSMraJ17JnrwR0Nx15VcWlUdyQMehReHw6LXoRjrnCWG5Vn1yZ49yqwHuh5mfMwpvzfkch6Tum+L+93krWu/bRsm3274Mv7nASK4ViObv8eJ2jULAU6nFz9/e/c6AR/up/n3D0OVoWBzTrK6J8yDJr3cEojlhQZ6wQumnapvbGUVpmkfxUJJdiXvy+0Y2o618LxNzi//+tnw9bU8FmyI9VGAQERkcpq2995A77of7BzA+CCzn+pm7G0Ota5WNmT4Uwhbtb1wL7d25zpuwCn3RceybiqU03lKajlN2NBLTMIdUxxQVYEKLoQLZnToLykhd6cBnef3ZXWjWN5cEaq325bmQx22Qbsph7z1m3ju3UV38G2wL78QvblQ3SEi3aJ9Vi7dXeFx+XkFvD+0j/4mj58Hv0+XVx/cHfEVGZ5+hS/hlWedkyO+jcJO3/B1kvEXDEdmoSQTPHCV5072MvfhLgkOP3+8pclZKfDH4udn8/4FtjsdGcWR5nXbDBVmSLd5S9OQGDtZxUHBPr91ckbYIL8/eh4GnQdAmtmwuf/gMs/CNw2f5+z3nhfJiT3hMFPBBc0McZZvtC6L0w6Azz+AkFhXo5u7hNOEsbWxzuJ/Kp7rfUxV0D38w9cLAYrXDP6F+RBdJzz+fkvAwYaNIP5/wfrZjkVJbwJ82pTZZL+BSPYYN+Itw5Myw+XXAuN2zt/A1ZPh4X/5wRD5ZByiL0rFBGpRdYDmKJgAIAHXjq5btZkRkQ7b0TTvnVyCZQMCCx4BvbvdhLkpZxTu+OqLTWRpyBc3oxV5lzedqGs1f3xFdy/fsUT/Ufy2ueLgkpaGOl20TUpPuAwHomYTG/XOu7Iv4GubS6heXwMP27cGVTpxL+d1YXrT+6Ay5jiygqBJhg3T4jhv+cfxdxft/PpynT+lnUDr0VN4MqIL7iSL4rb7rVR1DP7ybL1eKvN47TLaEjTfTtp0iCKB6aXP2vhwRmpnDluIO6hE2H6zTD3cVJzYhmdejStd68oDjr83uBo7hvanUH5X8Hn9zpr+2+Yx7Ij7+Lo+bdg8c3n4EywsCw/chy9/FzkBpV4sfMg+HaCk+OgIC9wXgCvBs3K31/awEecu7Zn/jNwG29ZwC0/Qb1EuPgNiAxuxkmx/bsDBAOKTxK+5ej6jIb5z8DvPzjJ3rzLuKpTdAPnEYpwuctcWkQ0XP2Z8/+zZKWJRu3g/75zvobL36xcvoTKqkzSv2AF+7e488ADwa5wyrXQ/2YnIPDTNDh9fOh/QySsKSAgIlIZqdOdjP018cahstqfeCAg0Gf0ge0n/s2ZPdBugDIEhyKYsmnVUZs+WHk5ob9B9CmnFyBt4aDHnIzw85+BzPX0WzOTvlGU4S9pIQSulHCE+YOT3T/hsYYdDTpx6+mdi0shXvLSwgpf7jFtGxHpdtZmV5R48YGhKQzo3JQBnZuS0iKeT6d963flTAz7sRaeKxjG/5ZHwfKlFY4DDuQp+HptBqf3uhyTsxW+fpiuyx5mJg1oFHVgBkNGXgI7psWD+3dnQ4veFFrLjUtbcVT+WKf8IwcCLVtI5J/5l7NiaSvmnWl9LvaDTbxIck9okAS7t8CGeRR2OK1sECFrk3PnvkWvoF6zj0bt4LJ3i5/6DVIsfsnJgm/ccOGrPtUDgq0mEbYXr8GIS3IuXn+cBN/9t3oDAhlrnCn0lfn7HY6BzZJKl51s2AZOudtZHvLFfc6su/oVJ2itsmCT/lV2dorLDWf9C967inL/FpfsO9i/37UxW6Z1H6dKzB+LnZmRtRGoqYmlG+KXAgIiIqGq6TcOldV1iFPloOOpvttj4uHUe2pvHIeKct+MFek2rOa/x4X5zhvjX7+E0XNCf4MYbJ6FS96CeRNhxdQyKRqhRBbiUj/bgSolXO3+DIAvPMcyethpPqUQQym1CAcSL5a+OE7yc3HcrH4k4yOnFN/Z93kNRZUVroj4kuWtRpJvXWTk5JG+K5dCW34uBIBrX/uRelFu7j37QtqbmfRjOQ2t73KGpmTRzJ1Fno3gfxEj6THgLmJyokjP+pV0+vBl3rFllmJ4cEFWLq/MS+Pakzo4X+ZgljB4X7fL5dxZXPoaGxe8x4h3KRNEmNbiLVqnveskFS319yDoC/aicU38eCEN9/zqMyvi3yd25ISENnD89c6U+RLtgwpqQPhfvFbkhFtgyatONvbNS6DlMVXvc9sv8NzxTp6I0XNCT6oYTneZvbb+7CxnqtfY//6+Y+Cnd2DrKicwcO5zNT+m2lhasfM3J6GvO8rJ9eJVXs6bmsyTE6pBE5xZP82PrNzxoVzg19TSDfFLAQERkVCF65rMZt2cEkEb58PK95wEVEeccejlDKhNgd6MRcc5d+yXvuZkai+5RKOy/L1Z2rvDyWq/8XunzS9fwFEXhv4GMZg8C027OOvPV0wtZ5D+f7ZLX7A3Ipvz3E7Fi0an3crxJS78KlNq0XuOYBIv9nGvwV1iqUNpLgMt2MHUgRZ3hxMAWLB+O5e89EM5r/uAvfsL2ZK1l1M9m5ygQ6nrZmOc2fO7aMDEPWfwcE4+9fd7ivd7cLHQ4z8p17bdeYBzgX73BysrXsKQknTg9XcbSuamVJ5ZE0d6oW9VhsKsdJrlfugcWKpsWCgX7J+v2Ej0e5fzqWsFrhKzSP7Ma8w/PxvFvgvf54yeB8qehhTUgPC8eA1Fo3Zw1EXOTIm5T8CIN6ve5/KiPuJbVK7CQjjdZQbnl+OjMc6sh4tfdwJZpbkjYciTMOks5/X3vLRmlmCUVNOzU3asdyp1FObBOf9xZkIEe+e7pvLkhKpVFQJcoVzg1+TSDfFL7xJFREIVrtNa/f3DdUXCGQ9A/5tqdyyHEn9vxlofD29e4CzRmDbKuXMX6trekvx97+o3hcICyN0JUXEw/AXoNiTwmCp6gxhMnoVgf2Z//6FMX4O6J3Nm16as+eFzGv78ITF/5mOTe3L8KUPKHB7KHf+Sgkm86N6TEdRLKNmuT/vEoGYtfHHbSWzLyWPDj5+TXE7QwRhozi7+fdxuzuiRxOr0nKDGdEzbRgAsSstk597ApQ0PlFrMpF/HRFb+kcXS7Z2YmHknO/2URLwy4nOiTQE/mS4c2ep4vD8loVywF3osX894nUddK8oEQZLI5LnIidzzSRSnHn0PbuO0f3BGEHkZSgY1avHiNZRZESEZcBuseNtJwljVjOyeQudOOVScLLI84XSXefMSSF/hVLpoeWzgdq37OMHW1I+dChM1rSZnp1jrVCYpzIMOpzrfy1CXf9REnpyqyNnq/I8KpuRmKBf44ToD8xCngICISKjCcVproH+4nnz44l7nboQi6pXn783Y+S/DCyfC9rUwcyyc91Ll1vgG+t7t2eZ8bJAMV0yHpp0rHlNVBfszO+chp8b88Tc4S1XcEZA6HfescRxZ4oLD7NoIq2f4/dkLutRiTb2GEu2CnbUQFxNJXEwkBZHZQZ3iqIRcGtaLCnqZxOndnDFl5OT6aVWWt93XazN44stf/LZpwF5Gur8C4Jm8wRw/L41te/JoEB3BS9/9Vu4F+30f/Uzmnv3kF1p+Sd/JLfmT/J7DuxTjlvxJXPLCScTXj2H77jyfYI+/c5QMahSr5MVrqMsegl7GEKqmXZzkrZ4CJzdH2tzK39VdPwdy0iG2cdUr2AQKIloPLPg/6HQWNOlUcT9Vtfhl52P38yrODXDmg05lnNrIIdC2v1OGMzcrQIMqzE5Z9rqT2yci1pn5cLDn8vlsHCye5MyA8TfDo6QKL/CB6TfB5h9hb6ZTJSkcZ2Ae4hQQEBEJVbhNay33H24RRdSrX4NmcOEr8OoQZz1oQW5o9cEhuO+dMZDYsUpDDVowP9uR9ZzXummB80ho7Rz307Syx+zbVe4Uz6BKLdbEa/Dz+xnKrIWOHTrCvIqH0rGD830LdZlEs7jgsvN727VrUp/uLeNZtTmbxmTTy7WO2R5neu8I99fEm72s9yTzlac3EZt28tmqLRX2bXGWMNzz4SoA+rpSaRFV8VIM1x8L+SrAkgh/tmT7CRqkDKOw89ms+eFz9u3cTGyjlnQ9fiDuCP9vW0O5wA95GUNlnD8J1n4Kbwyv2hroZW84H3tcCBF+Mn2Gyl8Q8ZNxsPglJ8A38oOavVjdswNWFZWtPO7aitvHJNTcWErzzk756AY/O6swOyVnqxOUByd3R+P2VRpmWHBHOjcb5jzsLJ0rL9hV4RJLnCDM90+FNoZwTCx6EAtinoeIiPjwvnEAyqYtq4M1maHkNJDq1bY/XD0LLn039GAABPdmKefP2vveBfOzPfwFuO1nOOnvUK8JZP1eNK25nDtAs+5ygh+1oQq/n4O6JzNv3Gm8NbovT43oyVuj+zJv3GllLhDd7U5gX2xSUcnAsjwW9sUm4W53gk/fz4/sTVKC78V+UkJMmYtQ74yCQJdmBueC15t4cdjRLfjH2Sk0JIdF0TfyUuQTNCGLSAq4JsJJ7vi/wiFYXPRs3ZBrB7Snb/sACd1KObJFPIN7JHNiUnDfv4u6RPDYeT24vig5YkUenvkzT3yxlk079hZvm7UqnQH/+ZbBM+CCeS0ZPAMG/OdbZq1KL3O89wK/9GwE7wV+yWMqWsYAzjKGQj/f2EKPZcH6HXy8fDML1u/w26bY2k+dQFjp323vFOnU6X4PK3mOxanrsWs/dXb0uizwuaqq7xgnyd36OU5ZuZq07HVn2nzy0aElXLQWfv4IPhzjfF5Tel4CF7zqBG5Kim9xIKjp8fg9NKBZ45wL3uSjoe+N1TbUOtW46Hd7y0/w/jXw2hCY2P3Az7W1sGUlfPtv+PivwfXZ8XQ49V7oG2T76pqB6Sl0ZvGsfM/5WFv/p8KMZgiIiFRGOK3JDNecBoeL1n18nxfsD/5uXjh+74L92T7tXqek5Tf/quDuTh1M8azC72dQsxZcbmKH/gc7bRQerM/dFQ9gjCF26H/KBB2CXSZRmcSLfdo3JjahKT/va8fRrt+4JuITMm0cUeSTYRP4uHAAyQkxXHtih+LyjwuDKP947+AU+nVMpPC33TClwuacc+IxuDu0odBjmb7iz4DLJLyvZceefJ6e8ytPz/mVfh0S6ZrcgFe/3xh0XoOKLvD//t5PfL12G+f2bAkQ1DKGT376k2FF7SHEJQaVXANd+hwj3HM4LnI/2QldiU8+OuCYqyyxI5xwK3z3H5h1j5OINqp+9Z/HUwg/TnY+P250aDMRctLhg+ucYMIRp0OPC6pvXHOfgIho6Fd0Idp9uPP3wV9+lkUvOTkNLnvPybZfkcICiG0ErggY+vShkeA3dTrMvL3sdm+w64JXYM4/IfO30PodcJvz/8FTCKkfll/ut15i9czAVCWDYofAT6aISB0Jl8y/4ZjT4HC0fy98+ndn+cCINyt+w7tzIyx/K7i+a/t7F+zPdmSMUw4tGLUdkKrp38+UYRg/QQcT3xJTTtAh2GUSoSZe9AYRNr7dnKNdvzEmYmbxvm02gVNdyzh36A2VLv/onRURvXcL/pbmeyzk/T97dx4fVXn9cfxzEgJhScIiIQEREFGJKIuAgrsWoSi4VqsiotZWrfvPirZWxFqX1iouFa1L3a0oLuACuAuyKZtgQAUDIgQCRJKwhEDm+f1xZ8JkMpPMZCHb9/165ZXMvc/c+0wyhNxzz3NOizSa+7MioglqPPzbPpgZk75ey6yVm5nz4xa+XpNb7gX+rZOXsm7rTrYVFpPSvEm5F/gABYV7eO2rtRzQtgX7t4kui2deVm5JQOC1r35i7OSlZcZEXGJQiS404ZYxTCo+kQ2uLbbJcf6y7KovYyjPsTfBktcg7yf44gH41bjqP8fGb73vS2IK9Dontucmd/Qykj69Gz641QtYFG2v+r/p2Y/Cx+O9r/cfCJ0HeF+HW1qxLQc+/hvsyvO6JJzzTMUF9eKbeDUDjr0JWneu3BzrkmiCXTP+4mUQ5Gd7HU0OHgqf3uP//R/FEq5o2v3u+MVbotbngsq/FnUyKMVcTabeNHJmlgzk5eXlkZycXNvTEZGGylfspetVtGb6hqWqIVCTsr+Bp0+B4iIYchd07Bf+QjQ/G2Y+AAue99Zhlqse/OyyZnopoxW55N2GWQQqlt7alRBTNfzMKbhJF5dZauADDPMCGEF/5AYuRCH8BXuZi93MKbhJo3HhsiLCHD9wjmjurq/bupMJH37P6wt+Lvf7Eey0w9N4b2nF9RCGZnTgiuMPZHex44IosiIeu6Avp/fuSLHP0eeuGRQU7gk7LhA0mTX25L0/k6VveGnUFTnnGTj8XIp9jsH3fczG/F3Rn8Mv1k4J5Y5f8R7870KvM83Vc0oKDFbrObbleIGB7idV/P0JtWcXPNzbyxYIVtk7unOf8F/cAif+GU4cW/FzfvwcXjrH+719zA1e0cPGJNrf9ec84wVjA8voSi6+IexvmnAX32Hv3neCNt1gjb+Ay9B7YVAllmGU/M0UKXBXD/7fjUJ+fj4pKSkAKc65civhKkNARKS+q2t9phur9CNg2L3w3v/Bh3eU3pfcEYb8DdYv8qps7/FfHB14InQ7AT6+yz+wHv7s6lqRzX2thtuBRV140X/3LtylWsnFe0iqesztHyuRFRHtMolOrZtzbI/9ogoI9Oncmp7pyXRukxhVQGDMMd3o37UtxT4XVVbErw/3Xvf8rNyIwQAo2ynh0xU5FKwqJppL00/mL+Sfn3Rg9eZt7NztrUuPw8fAuBWkspUcWjPfdyg+4sJ2Y4i1U0KF4w8Z7nUa+GEGTLsNRr1R/edolep9BIk64PD99LLBAIjujm5o0G7T8r3BgOP/FF0wAODAE+CMx+CtP8CXE7zuPQPCBH/Wzvfuip/+4N719g1BLFlewTV1KrOEK1KGVyALYe7jMP02r0tGrG2VK5HFU6KGA8C1RQEBEZGGoC7VNGjMWrYPvz0/Gyb/zqvIv6cQOh/ltdMK/LHR7qD6+7NTQKpuqOQfuTG3f8wYiYX8oW4V/FEcbVAj2u4KY4cd6tU18DlenPtT9MseYqzNEGv7x3e/yeathS3o36wtaeSGXVrhnLeaaMm2tizP3nvTbmjcfMYlvEBH29vJYYNrw7jdlzDdN5CbXlvMOUfuz5CMDqz7ZSd/fCX6TglRd1b49f3eBc+Qu2LuxlDe+Ftf+gJGHR+240NUAYeSVPVwKuhNH+5Oc8Ax18NJf4lw3Ah6/xa2/gSf/h3evxlS9vfqLgT+PTRv581l8wqY9RCMfDS249dlVVmeWJklXJGCrUPv8eoIzHsCDh62d3s0F+u+Yvh+RnSvI2d56fM34JoDWjJQg7RkQET2uQYava4XoklDbNEOzvi3t64ytMZAff/ZRUrxrA9BjYYgxlT1uqjY5zj2/k8qvMAPTp+PedkD0V+Izlm1JaolBq9ecTSDurfj1fk/8frXa2n/8wwmJkwAKBUUCDQmeK34RDadeD+Hd27Llm272PPWNZwf/xkuwvirdt/AdN/e4qVxRsQOF6Hfo51FxZz4wKcRlyQAtG/VjJd+N5CmTeKJNwOD3zwxO+plDIGfW7h6Ds0oYnaza1kd14U+N7xBfIr3/Y0UQAj7c4s2Vf34sV5RwLbdvcKu/lR1hwvfseO8FyDjjDKbK8xacA6mXOO1hYxvBs1bl7173jQJbvgGWkTXzaNeqGvLE3fk7v3+RnOxvmEp/O8i2Lom+nOkHeFlzzRt6c/8i/COrYM1B2JZMqCAQA1SQEBEpBFp7Gvpof4HNeqzBvL+q8kL/GDRpKpXJkARCCKEu+O/3rVj/O6Lme4bWBJEKN5ZAPd3Jj5CRXWfgxxrx5enf8LH323hk+U5FO6puPVd84Q4dhc79pTXHrECbcknl8h/v+7fJpG2LZvRq2Myr8xfG3bM2XFf8GDTJ1jn2vH+SdP4Va9OHNC2RcQAAoT5vkYb7AqIawJtDoS8n3B7CsMGAxyGhbl4jfq9VLwb/nMSbFwayFEo67wXw14kxlqXoU6pTD2AfTKni8PsCJnTrm3wYE9vuyv2ClNG+pfdpNnepX0Vqps1B1RDQEREZF+riy0E97UaXk8v5WggtRxirmtAJZY9EN0yhsq2f0xPSWRG3kA+3NW/TE0ARxzpwcsYshcS/ufliTNIYwvntFvLORcdxxsL1nLz69+UO2+gpC5BtFo2jSfOf7e/eE8Rt8a9xG/jP2VE0d3sZ/ll6hoA/PxLIT//Ukjr5gkRj3txk48AeHnPKTw+7Qe+27STc/rtH1X7x6dm/siVJ3SnuGUq0Vxmuf0OwfLXQ1EBbPkeiHChDliU3R4gwjIJi4OdWyIGAxyGRdFeEioOXkE1F3esirq2PNFXDB/cEmGn/ycZ+Dk0awWj3oQOh8HKj8pf5nb2U3DAIK+mxsIXYG15mUK10Fq3mtWLgICZXQ38CUgHvgVucM7NjDA2HfgXcCTQA3jEOXdDmHGtgb8DZwNtgCzg/5xz7/v33wmE9l3Z6JxLq/orEhGRBkftH6U2NaBaDjV1gV/ZuVSm/aN3YRnHXF9Gyb6wQYTtm6KbiD+Q2Kl1i6iG/+u83gzu3o5v1+Xzuxe+rnD805cMKPn+zVm5mR3PP0hzK+K9pn+hme3thrLetWX87tFM9w3k9tN6cmD7lmwq2MUXP2wuc8xe9iN941ZS5OKZVHwSnds0p3ObFlHXZvh4+UauPKE784sPpYuLXJfB52AD7Vgz9F0GHdSeoty1fPnqfZy0+eUKz+Er2MCiNb+QEG/89e1vy2uox/ipmQzJSPN+dv6aHTUWcAhS7cUdw4gpgFDJlq41EqRYMzt8sclgwT+HQGvJaAMbfS/ysgXKDQj41eNgf50PCJjZ+cAE4GrgS+APwAdmluGc+ynMU5oBm/Au9m+McMymwIdADnAu8DPQGSgIGfot8Kugx8WVfiEiItKwNZA7tFKP1bW7d1VQUxf4lRFrgCKmIEKMgcRABkJFyxjO7NOJ+DgjNSkxqvGBjAWAgQe248Fm/Tl596JSwQCANHKZmDCBPyfcwqXHDC+pITDhox/KnOPieC874APfUSSkdOCzP51EfJwxZ9WWqF7ykQe0ASBn+26e2z2aiQkT8LnwdRbG776Y4dt3gxlFrTry5PpunNS04nNk5jfnnFdmVzgukLVw2+RvODgtiSN+yWRghc+CjevXsLV5H3YU7eEvby2LPuDgV53FHSMFHSoVQCCO+b4McooPJNWXyEDiys3iqGxmRIWqkpkXbWCjEQT763xAALgJeMY597T/8Q1mNhS4CrgtdLBzbjVwPYCZXRbhmJcBbYHBzrnAb7pwFSb2OOcq7mcjIiLSgO7QSj1Wybt3Ur5YAxRRBxFiDCTGuoyhMsse4vFxTcJUKB0LAPYWNByX8ALx3ArEhz1HMts4I/5LAF7cM6TUOaINavxp2KGA131ium8gV+2+wavLQFAnBvbWZRjj71LRJM5o1/ME1q98vMKsghWJvejaLost24vKbTEZMMnfFvPouHz+F0XA4fr3spnr+6LCcYGAw68e/JyDO7SiY+vmpCUnMvHzVRUGEX7VswNmRtEeH3dOyYwp6LAvAgiVzYyISlUv1qNZ5tYIgv11uqig/07+DuA3zrm3grY/DPRxzp1QwfM/AxaHLhkws/eBXP+xz8DLKHgFuN85V+wfcyfeMoU8YBcwD/izc+7Hcs7XDC9DISAJ+FlFBUVEGhFV2xeRWFSiUFuNppFXskDltGXZ/G3KUjpvW8K5cZ9zbpOZ/MABrDpnOsMO71hmPtEWjwwu7mj4wtZlCFfc8blnHim328NVu29gzOXXMah7u6g7Sgw9rANNm8TzQ/YvPJt3eYUBh1P2PELzxGb4fI6tO8NEWGpB386t6bpfS3p0aMWLc9ZEX9yRGLtD4P3sBt/3cdQdK4JFtcTA3/nA5Wd7yzRCRCoeGdM5oG4WU6xAQyoquB8QD4TmeWwEqrKW/0DgZOBlYDherYF/430/7vKPmQeMBr4HOgC3A7PN7DDnXKRcp9soW3dAREQaE92hFZFYVGKpR2WWMUQ9Pto07KwvYP8BkODdmR8W9xVDE8diRXtfw0HNt9Ej/mug9GuIZVlFzHUZ8LIQbko6nqsL4I4wWQV37b6Yb5KOL1kqEW3WwuMXHVmy7GH8MxUvY3j28kExBRxuPvVgkpsnsG7rTr7KymXhT1srfE6sFq3dyqK1W+mZnhRVcccxz87npENTuWRwV8ZPjZyBAHDr5KU0T4jnhENSAZiRuaHclpeBc8zPyi2VgRN1ACsunkWH3Urv2ddFaNvpWHzYWPqG+f83piCZ/9+omzbWK1wZmH9yR6wBBPvrekAgIFwgqiqpDXF49QN+788IWGBmHfEyAu4CcM59EDR+qZnNAVYBlwAPRjjuvSH7kvDqE4iISGOiavsiEotKBBJjXcYQ9fho07C/+AfMfhS6HQ9JabDwhTJ3aW3nL96d1TB3UGMJUlS+uGMhH+7qz4CgrIKv/J0SJlZhaUVNBRyuOvGgMi0sK/LkqH4cdWA7vl7zC797vuICkr8/vhv7tWrG2tydLM8OLZ9W1syVmyl2jp7pyeUGEAC27tzNY5+uLAkIFEXZ7SKnoJDtu/bwz+nf0axJHP/54seolhgU+xxXL9yfIyIsJ7lr98UsWbg/s4a4KtVmAJjmG8DfCh+mc9GSkvfS2sLe/NV3OMOiepV1V10PCGzGK+QXmg2QStmsgVhkA7sDywP8lgNpZtbUOVcU+gTn3HYzW4qXTRCWc24X3vICAMzqSU9REREREalddSWQGM2a6RbtIK4JbNsAP0wv52D+1ethWvBBbEGNqhR3nJu3N6sg0l3gymUt1EzAAaIPIvzKXxPgpENSoxo/dljPkiyHF+eGK6FW2m8HdGZA17ZRd4do32rv6unU5MSonpOalMhXq3N5bvbqiGOC6yA0T4jnudmryc4r9D4I3+bTRxzkFXLTa4vp3bk1+yU1o22LBG5/O7YCj8EBhHUEZajk7656HYQ6oE4HBJxzRWa2ABgCvBW0awjwThUO/SVwoZnFOecCoauDgexwwQAoqQ/QEwjb7lBEREREpN6LpkDq6Q9BzxGw8VuY/x9Y+Hw5B6y+Pu01VtyxEuNrMuAQeK01WUAy2oDD3886PKbuEBcP6lrydbTnGNitLd9vLGBIzw58uDzyPd+SJQarc/n0u9ItO30hy0mCvbNkPe8sWR92X6Rz/OHFrzkoNYkWTeN4emZWzB0i6pM6XVQQStoOvghcCcwBfg9cARzmnFtjZvcCnZxzo4Oe08f/5dPAd8A/gSLnXKZ/f2cgE3gOeBTvrv+zwCPOub/7xzwATAV+wstIuB04ATjcOVdxOM07RjKQp6KCIiIiIlKvRFsgdekbMPnyio93zjNw+LnVP89aFnVhukqOr8kCkpUt7ljexX1ogcBYzvHO4nVc/7/Fkb4VJf56ek+SEhNYv3UnEz76ocLxQw/rQJP4ODYX7CJr83ZyCiLXNaisV684us60SoWGVVQQ59xrZtYOuANIB5YBw4MuytOBA0Ketijo6yOBC/HaCnb1H3OtmZ0KPAR8A6wDHgbuD3re/sCreIUNNwFzgaOjDQaIiIiIiNRb6tMelRqr5eC3r7IcYinuGO2yh1jPkZoU3RKDjPQUBnVvR7HP8dpXa6MuBgnR12Y4u18n2rRoyrfr8piblVvh+GiXVNRFdT5DoD5ThoCIiIiINGj+1m8V9mmP0PpNal8sWQuxZizEco7KZCHEkoFQmXNEG0CozxkCCgjUIAUERERERKTBq4d92qXyYl32EItYL/ADz4l1WUVNL5WobQoI1BEKCIiIiIhIoxBtzQGRClQmC6EmazNUJkhR2xQQqCMUEBARERGRRsNXXHHNAZEo1GQWQmXOUdmlErVFAYE6QgEBERERERGR+m9fBCmqS4PqMiAiIiIiIiJSm2LtEFFfxNX2BERERERERERk31NAQERERERERKQRUkBAREREREREpBFSQEBERERERESkEVJRwX0gP7/cwo4iIiIiIiIi1SKW60+1HaxBZtYJ+Lm25yEiIiIiIiKNzv7OuXXlDVBAoAaZmQEdgYLanksFkvACF/tT9+cqVaOfdeOgn3PjoZ9146Cfc+Ohn3XjoJ9z41GbP+skYL2r4IJfSwZqkP+bX25Epi7w4hYAFDjntL6hAdPPunHQz7nx0M+6cdDPufHQz7px0M+58ajln3VU51NRQREREREREZFGSAEBERERERERkUZIAQEB2AWM93+Whk0/68ZBP+fGQz/rxkE/58ZDP+vGQT/nxqPO/6xVVFBERERERESkEVKGgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIiIiISCOkgICIiIiIiIhII6SAgIiIiIiIiEgjpICAiIiIAGBmY8zM+T9ODLPfzGylf/9nIfvamdm9ZpZpZtvNLM/MVpjZi2Z2RIRzhPsoc96Q8zxnZqur4/WKiIg0dk1qewIiIiJS5xQAlwOfhWw/Aeju31/CzFoBc4FWwD+BJUBz4GDgbKAP8E3IsS4FVoQ5d2aVZi4iIiJRU0BAREREQr0GXGRmf3TO5QdtvxyYAySHjP8NcBBwsnPu05B9D5pZuIzEZc65r6ttxiIiIhIzLRkQERGRUK/6P18Q2GBmKcA5wLNhxrfzf84OdzDnnK9aZxfCzBL9yxWyzKzIzNaZ2b/NrHXIuJPN7DMz22JmO83sJzObbGYtgsZcZWZLzGybmRX4lz3cU5PzFxERqS0KCIiIiEiofOAN4LKgbRcAPrzsgVBz/J9fMLMzzaxdmDGh4s2sSchHfKwTNTMD3gZuBl4ETgMeBC4BPjGzZv5xXYH3gCL/6xoG3ApsB5r6x/wWeBz4HDgLOBN4CGgZ67xERETqAy0ZEBERkXCeBT41s8Occ9/iXUS/7pwr8K7B93LOfWlmdwC3A28BmFkWMB2Y6JwLrR8AXs2BUMXE/rfJqcBQ4Bbn3D/92z40s7V4wYvRwFPAkUAi8Cfn3JKg578S9PUxwFbn3HVB2z6OcT4iIiL1hjIEREREJJzPgVXAZWZ2ODCA8MsFAHDO/Q04AC9w8CSwDbgSWGBmF4R5ymj/MYM/jqrEPE/2f34uZPvreHf/T/E/XoyXHfAfM7vEzA4Mc6z5QGsze9XMzjCz/SoxHxERkXpDAQEREREpwznngP8Co/Au7L93zs2s4DkbnXP/dc5d6Zw7Aq8rQRHwcJjhy51zX4d8LKjEVNsBe5xzm8LMf4N/P865VcCvgBzg38AqM1tlZtcHPedFvIBGF2AykGNm88xsSCXmJSIiUucpICAiIiKRPAfshxcQ+G+sT3bOfQHMANqbWWr1Tq3EFqCJmbUP3uivLZAGbA6az0zn3AggBTgar/bBBH/tgMCY/zrnBvvHnAYY8K6Zdamh+YuIiNQaBQREREQkLOfcOuCfwFTg+UjjzKxDuNaC/iKBPYAdwNYammZgjf+okO3n4BUDLFMDwDlX7JybB/zRv6lfmDHbnXMfAH/HKzp4WLXNWEREpI5QUUERERGJyDl3axTDLgb+YGavAF8BecD+wO/wLqTvcs4VhTynl5mF+ztkVWj6fwU+xCteeL+ZJQNfAkcA44FFeJ0HMLMr8eoNvAf8hFdgMNBF4SP/mKeAnf5jZONlGNzmfz1fxTAnERGRekEBAREREamq9/AunocDVwFtgALgG+Bi59xLYZ4TaQnCFcDT0Z7YOefM7EzgTuBS4C94ywReBP7snNvlH7oYryPBeP9ctwHLgJHOuRn+MTOBMcB5/tewGZgFjI4xSCEiIlIvmFdzR0REREREREQaE9UQEBEREREREWmEFBAQERERERERaYQUEBARERERERFphBQQEBEREREREWmEFBAQERERERERaYQUEBARERERERFphJrU9gQaMjMzoCNeL2YRERERERGRfSEJWO+cc+UNUkCgZnUEfq7tSYiIiIiIiEijsz+wrrwBCgjUrAKAtWvXkpycXNtzERERERERkQYuPz+fzp07QxSZ6rUeEDCzq4E/AenAt8ANzrmZ5Yw/AXgQOAxYD/zDOfdEyJjWwN+Bs4E2QBbwf8659/37b/PvOxTYCcwGxjrnvgs6xnPAJSGnn+ecOzrW15icnKyAgIiIiIiIiNQptVpU0MzOBybgXbz3BWYCH5jZARHGdwPe94/rC9wDPGJm5wSNaQp8CHQFzgUOAa6gdKrECcC/gaOBIXiBkRlm1jLklNPwAhWBj+GVfrEiIiIiIiIidYhVUGOgZk9uNg9Y6Jy7KmjbcuBt59xtYcbfD4x0zvUM2vYE0Ns5N8j/+Eq8jINDnXO7o5xHeyAHOME594V/23NAa+fcmZV8eZhZMpCXl5enDAERERERERGpcfn5+aSkpACkOOfyyxtbaxkC/jv5RwIzQnbNAAZHeNqgMOOnA/3NLMH/eCQwB/i3mW00s2Vm9mcziy9nOin+z7kh2080sxwz+97MnjKz1ApeUzMzSw584FV2FBEREREREalzarOGwH5APLAxZPtGIC3Cc9IijG/iP142cCBwMvAyXop/D7zlAU2Au0IP6G8N+CAwyzm3LGjXB8DrwBqgG/A34BMzO9I5tyvC/G4DxkXYJyIiIiIijURxcTG7d0eVsCwSk4SEBOLjy7vfHb1aLyoIhK5ZsDDbKhofvD0OL/3/9865YmCBmXXEW0ZQJiAAPAYcARxb6iTOvRb0cJmZfY0XHDgNeDPC3O7FCy4EJKG2gyIiIiIijYZzjg0bNrB169banoo0YK1btyYtLQ3v/nbl1WZAYDNQTNlsgFTKZgEEbIgwfg+wxf84G9jtDwYELAfSzKypc64osNHMHsVbYnC8c67cC3fnXLaZrcHLOIg0ZhdQkj1Q1R/OvlDsc8zPyiWnoJDUpEQGdmtLfFzdn7eIiIiISF0UCAakpqbSokWLenFNIPWHc44dO3aQk5MDQHp6epWOV2sBAedckZktwKvy/1bQriHAOxGeNgcYEbLtVODroAKCXwIXmlmcc87n33YwkB0IBviXCTwKnAWc6JzLqmi+ZtYO6IwXcGgQpi3LZvzUTLLzCku2packMm5EBsN6Ve2NJSIiIiLS2BQXF5cEA9q1a1fb05EGqnnz5gDk5OSQmppapeUDtdp2EC+9/ndmdpmZ9TSzh4ADgCcAzOxeM3shaPwTQBcze9A//jLgcuCBoDETgXbAw2Z2sJmdBvwZr45AwL+BUcCFQIGZpfk/mvvP28rMHjCzQWbW1cxOBKbiZTUEBy/qrWnLsrnqpYWlggEAG/IKueqlhUxb1mDiHiIiIiIi+0SgZkCLFi1qeSbS0AXeY1WtU1GrNQScc6/577zfAaQDy4Dhzrk1/iHpeAGCwPgsMxsOPAT8EVgPXOecmxw0Zq2Zneof8w2wDngYuD/o1IE2h5+FTOlS4Dm8pQyHA6OB1nhZAZ8C5zvnCqr0ouuAYp9j/NTMsIUaHF5RhvFTMxmSkablAyIiIiIiMdIyAalp1fUeq/Wigs65x4HHI+wbE2bb50C/Co45Bzi6nP3lfvecczuBoeWNqc/mZ+WWyQwI5oDsvELmZ+UyqLtSnURERERERBqi2l4yILUgpyByMKAy40RERERERIJ17dqVCRMmRD3+s88+w8xqpTvDc889R+vWrff5eesCBQQaodSkxGodJyIiIiIi1avY55izagvvLF7HnFVbKPaV15m96k488URuuOGGajveV199xe9///uoxw8ePJjs7GxSUlKqbQ41KdaAR11V60sGZN8b2K0t6SmJbMgrDFtHwIC0FK8FoYiIiIiI7Ft1tRuYc47i4mKaNKn4MrJ9+/YxHbtp06akpYV2mJeapgyBRig+zhg3IgPwLv6DBR6PG5GhgoIiIiIiIvtYbXQDGzNmDJ9//jkPP/wwZoaZsXr16pI0/unTp9O/f3+aNWvGzJkzWbVqFWeccQYdOnSgVatWDBgwgI8++qjUMUPvoJsZTz/9NGeddRYtWrSgR48eTJkypWR/6JKBQBr/9OnT6dmzJ61atWLYsGFkZ+99/Xv27OG6666jdevWtGvXjrFjx3LJJZdw5plnlvt6n3vuOQ444ABatGjBWWedxZYtW0rtr+j1nXjiiaxZs4Ybb7yx5PsFsGXLFi644AL2339/WrRoweGHH86rr74ay49in1NAoJEa1iudiaP6kZZSellA+6RmTBzVr1YjjyIiIiIiDc2Ooj0RPwp3FwMVdwMDuHNqZqnlA5GOGYuHH36YQYMGccUVV5CdnU12djadO3cu2X/LLbdw7733snz5co444gi2bdvG8OHD+eijj1i0aBFDhw5lxIgR/PTTT+WeZ/z48Zx33nl88803DB8+nIsuuojc3NyI43fs2MEDDzzAiy++yBdffMFPP/3EzTffXLL//vvv5+WXX+a///0vX375Jfn5+bz99tvlzmHevHlcdtllXH311SxevJiTTjqJu+++u9SYil7fm2++yf77789dd91V8v0CKCws5Mgjj+Tdd99l2bJl/P73v+fiiy9m3rx55c6pNmnJQCM2rFc6QzLSmJ+Vy/+9voT1W3dy9xm9OLWXUnVERERERKpTxh3TI+476ZD2/PfSgVF1A9sQ0g3s2Ps/JXd7UZmxq+87Leq5paSk0LRpU1q0aBE2bf+uu+5iyJAhJY/btWtH7969Sx7ffffdvPXWW0yZMoVrrrkm4nnGjBnDBRdcAMA999zDo48+yvz58xk2bFjY8bt37+aJJ56ge/fuAFxzzTXcddddJfsfffRRbrvtNs466ywAHnvsMd5///1yX+vDDz/M0KFDufXWWwE4+OCDmT17NtOmTSsZ07t373JfX9u2bYmPjycpKanU96tTp06lAhbXXnst06ZN4/XXX+eoo44qd161RRkCjVx8nDGoezv6d2kDwI9bttfyjEREREREGqe62g2sf//+pR5v376dW265hYyMDFq3bk2rVq1YsWJFhRkCRxxxRMnXLVu2JCkpiZycnIjjW7RoURIMAEhPTy8Zn5eXx8aNGxk4cGDJ/vj4eI488shy57B8+XIGDRpUalvo48q+vuLiYv7+979zxBFH0K5dO1q1asWMGTMqfF5tUoaAAHDZsd34Tf/9yUhPru2piIiIiIg0OJl3DY24L86/Br0y3cBmjT2pahOLQsuWLUs9/tOf/sT06dN54IEHOOigg2jevDnnnnsuRUVlMxWCJSQklHpsZvh8vpjGO+fKbAsWuj9URfuh8q/vX//6Fw899BATJkzg8MMPp2XLltxwww0VPq82KSAgAPTp3Lq2pyAiIiIi0mC1aFrxpVdluoFFc9xoNG3alOLi4qjGzpw5kzFjxpSk6m/bto3Vq1dXyzyilZKSQocOHZg/fz7HHXcc4N2hX7RoEX369In4vIyMDObOnVtqW+jjaF5fuO/XzJkzOeOMMxg1ahQAPp+PH374gZ49e1bmJe4TWjIgIiIiIiJSB9RmN7CuXbsyb948Vq9ezebNm8u9c3/QQQfx5ptvsnjxYpYsWcKFF15Y7viacu2113Lvvffyzjvv8N1333H99dfzyy+/lMkaCHbdddcxbdo0/vGPf/D999/z2GOPlaofANG9vq5du/LFF1+wbt06Nm/eXPK8Dz/8kNmzZ7N8+XL+8Ic/sGHDhup/4dVIAQEpMXXJeh6Y/h2bCnbV9lRERERERBqlSN3A0lISa7Qb2M0330x8fDwZGRm0b9++3HXvDz30EG3atGHw4MGMGDGCoUOH0q9fvxqZV3nGjh3LBRdcwOjRoxk0aBCtWrVi6NChJCZGXnpx9NFH8/TTT/Poo4/Sp08fZsyYwe23315qTDSv76677mL16tV0796d9u3bA/DXv/6Vfv36MXToUE488UTS0tIqbIFY2yyaNRRSOWaWDOTl5eWRnFz31+af/K/P+HHTdl64bCDHH9y+tqcjIiIiIlKvFBYWkpWVRbdu3cq9KI1Gsc8xPyuXnIJCUpO8ZQI1kRnQkPh8Pnr27Ml5553H3/72t9qeTo0q772Wn59PSkoKQIpzLr+846iGgJTokdqKHzdtZ2XONgUERERERERqUaAbmES2Zs0aZsyYwQknnMCuXbt47LHHyMrK4sILL6ztqdUbWjIgJQ5KbQXADznbankmIiIiIiIi5YuLi+O5555jwIABHHPMMSxdupSPPvqoThfxq2uUISAleqQmAbBKAQEREREREanjOnfuzJdfflnb06jXlCEgJQIZAis3KSAgIiIiIiLS0CkgICUObN8SgNztRWzZpk4DIiIiIiIiDZkCAlKiRdMm7N+mOQArtWxARERERESkQVMNASnlsQv70bZFUzr5AwMiIiIiIiLSMCkgIKX06dy6tqcgIiIiIiIi+4CWDIiIiIiIiIg0QgoISClbdxQx4aPv+fNbS2t7KiIiIiIiUo907dqVCRMmlDw2M95+++2I41evXo2ZsXjx4iqdt7qOUxljxozhzDPP3OfnrS4KCEgpZsaEj37glXk/UVC4u7anIyIiIiLSOPmKIWsmLH3D++wrru0ZxSw7O5tf//rX1XrMcBfgnTt3Jjs7m169elXruWpCbQYvwlENASklpXkCqUnNyCnYxapN21VTQERERERkX8ucAtPGQv76vduSO8Kw+yFjZO3NK0ZpaWn75Dzx8fH77FwNjTIEpIyDUlsB8MPGglqeiYiIiIhII5M5BSaNLh0MAMjP9rZnTqn2Uz755JN06tQJn89XavvIkSO55JJLAFi1ahVnnHEGHTp0oFWrVgwYMICPPvqo3OOGLhmYP38+ffv2JTExkf79+7No0aJS44uLi7n88svp1q0bzZs355BDDuHhhx8u2X/nnXfy/PPP884772BmmBmfffZZ2Lvun3/+OQMHDqRZs2akp6dz6623smfPnpL9J554Itdddx233HILbdu2JS0tjTvvvLPc11NcXMxNN91E69atadeuHbfccgvOuVJjpk2bxrHHHlsy5vTTT2fVqlUl+7t16wZA3759MTNOPPFEAL766iuGDBnCfvvtR0pKCieccAILFy4sdz7VodYDAmZ2tZllmVmhmS0ws+MqGH+Cf1yhmf1oZleGGdPazP5tZtn+ccvNbHgs5zXPnWa23sx2mtlnZnZY9bzquq2HPyCwctO2Wp6JiIiIiEgDUbQ98sfuQm+Mr9jLDMCFOYB/27SxpZcPRDpmDH7zm9+wefNmPv3005Jtv/zyC9OnT+eiiy4CYNu2bQwfPpyPPvqIRYsWMXToUEaMGMFPP/0U1Tm2b9/O6aefziGHHMKCBQu48847ufnmm0uN8fl87L///kyaNInMzEzuuOMO/vznPzNp0iQAbr75Zs477zyGDRtGdnY22dnZDB48uMy51q1bx/DhwxkwYABLlixh4sSJPPPMM9x9992lxj3//PO0bNmSefPm8Y9//IO77rqLDz/8MOJr+Ne//sWzzz7LM888w6xZs8jNzeWtt94q8zpvuukmvvrqKz7++GPi4uI466yzSoIt8+fPB+Cjjz4iOzubN998E4CCggIuueQSZs6cydy5c+nRowfDhw+noKBmb9LW6pIBMzsfmABcDXwJ/AH4wMwynHNl3llm1g14H3gKGAUcAzxuZpucc5P9Y5oCHwI5wLnAz0BnoCDoONGc9xbgJmAM8D1wO/ChmR3inGvQt84DGQIrNyogICIiIiJSLe7pGHlfj1PhotdhzeyymQGlOG//mtnQzX8/c8LhsGNL2aF35kU9tbZt2zJs2DBeeeUVTjnlFABef/112rZtW/K4d+/e9O7du+Q5d999N2+99RZTpkzhmmuuqfAcL7/8MsXFxTz77LO0aNGCww47jJ9//pmrrrqqZExCQgLjx48vedytWzdmz57NpEmTOO+882jVqhXNmzdn165d5S4RePzxx+ncuTOPPfYYZsahhx7K+vXrGTt2LHfccQdxcd598SOOOIJx48YB0KNHDx577DE+/vhjhgwZEva4EyZM4LbbbuOcc84B4IknnmD69OmlxgT2BTzzzDOkpqaSmZlJr169aN++PQDt2rUr9RpOPvnkUs978sknadOmDZ9//jmnn356xNdaVbWdIXAT8Ixz7mnn3HLn3A3AWuCqCOOvBH5yzt3gH/808CwQHFq6DGgLnOmc+9I5t8Y5N8s5tyTa85qZATcAf3fOvemcWwZcArQALqyel153HZSaBChDQERERERkn9q2sXrHxeCiiy5i8uTJ7Nq1C/Au4H/7298SHx8PeHe+b7nlFjIyMmjdujWtWrVixYoVUWcILF++nN69e9OiRYuSbYMGDSoz7oknnqB///60b9+eVq1a8dRTT0V9juBzDRo0CO+yznPMMcewbds2fv7555JtRxxxRKnnpaenk5OTE/aYeXl5ZGdnl5pzkyZN6N+/f6lxq1at4sILL+TAAw8kOTm5ZIlARa8hJyeHK6+8koMPPpiUlBRSUlLYtm1bzK89VrWWIeC/k38kcF/IrhlA2bwPzyD//mDTgcvNLME5txsYCcwB/m1mZwCbgFeA+51zxVGetxuQFnwu59wuM/vcP+bJCK+pGdAsaFNShNdRpwUyBDbkFVK0x0fTJrUdNxIRERERqef+XM6df/MuumnVIbpjBY+7oXrahY8YMQKfz8d7773HgAEDmDlzJg8++GDJ/j/96U9Mnz6dBx54gIMOOojmzZtz7rnnUlRUFNXxQ9fahzNp0iRuvPFG/vWvfzFo0CCSkpL45z//ybx582J6Lc65UsGA4PMHb09ISCg1xszK1FGI1YgRI+jcuTNPPfUUHTt2xOfz0atXrwq/T2PGjGHTpk1MmDCBLl260KxZMwYNGhT197eyanPJwH5APBAa3tqIdzEeTlqE8U38x8sGDgROBl4GhgM9gH/7x9wV5XnTgraFjulSzmu6DRhXzv56Yb9WTfn4/07ggLYtSIhXMEBEREREpMqatqx4TJfBXjeB/GzC1xEwb3+XoPun0Rw3Cs2bN+fss8/m5ZdfZuXKlRx88MEceeSRJftnzpzJmDFjOOusswCvpsDq1aujPn5GRgYvvvgiO3fupHnz5gDMnTu31JiZM2cyePBgrr766pJtwQX5AJo2bUpxcfktGDMyMpg8eXKpwMDs2bNJSkqiU6dOUc85WEpKCunp6cydO5fjjz8egD179rBgwQL69esHwJYtW1i+fDlPPvkkxx3nLemYNWtWmfkDZV7DzJkzefzxxxk+3Ct9t3btWjZv3lypucaiLlzthb7TLcy2isYHb4/Dqx/we+fcAufc/4C/U3YZQjTnjXVu9wIpQR/7lzO2zjIzurdvpWCAiIiIiMi+FBfvtRYE9l7mUPrxsPu8cTXgoosu4r333uPZZ59l1KhRpfYddNBBvPnmmyxevJglS5Zw4YUXxnQ3/cILLyQuLo7LL7+czMxM3n//fR544IEy5/j666+ZPn0633//PX/961/56quvSo3p2rUr33zzDd999x2bN29m9+7dZc519dVXs3btWq699lpWrFjBO++8w7hx47jppptK6gdUxvXXX899993HW2+9xYoVK7j66qvZunVryf42bdrQrl07/vOf/7By5Uo++eQTbrrpplLHSE1NpXnz5kybNo2NGzeSl5dX8tpffPFFli9fzrx587joootKAic1qTav+DYDxZTNBkil7J35gA0Rxu8BApU0soHvnXPBIZflQJp/uUA0593g/xzL3HDO7XLO5Qc+CCpkKCIiIiIiUqGMkXDeC5CcXnp7ckdve8bIGjv1ySefTNu2bfnuu++48MLSpdMeeugh2rRpw+DBgxkxYgRDhw4tuTMejVatWjF16lQyMzPp27cvf/nLX7j//vtLjbnyyis5++yzOf/88znqqKPYsmVLqWwBgCuuuIJDDjmkpM7Al19+WeZcnTp14v3332f+/Pn07t2bK6+8kssvv5zbb789hu9GWf/3f//H6NGjGTNmTMmShkDGBEBcXBz/+9//WLBgAb169eLGG2/kn//8Z6ljNGnShEceeYQnn3ySjh07csYZZwDw7LPP8ssvv9C3b18uvvhirrvuOlJTU6s032hYNGs5auzkZvOABc65q4O2ZQLvOOduCzP+fmCEcy4jaNtEoI9zbpD/8T14hf8OdM75/NuuB8Y65zpGc15/UcH1wEPOuX/49zfFyzwY65wLW0MgzHyTgby8vDySk5Oj/8bUAYvXbuWF2atJS0nklmGH1vZ0RERERETqvMLCQrKysujWrRuJiYlVO5iv2OsmsG2jVzOgy+AaywyQ+qe891p+fj4pKSkAKf4b1RHVdk74g8DvzOwyM+tpZg8BBwBPAJjZvWb2QtD4J4AuZvagf/xlwOVAcK7JRKAd8LCZHWxmpwF/xqsjENV5nRclmQD82czOMrNewHPADrwChQ3e1h1FvLloHR8tr/4KpiIiIiIiUoG4eK+14OHnep8VDJAaUJtFBXHOvWZm7YA7gHRgGTDcObfGPyQd70I9MD7LzIYDDwF/xLuLf51zbnLQmLVmdqp/zDfAOuBh4P6gMRWdF+AfQHPgcaANMA841TnXKJYBBDoNZG3ezp5iH01UT0BERERERKRBqdWAAIBz7nG8i+5w+8aE2fY5UO5iFefcHODoyp7Xv98Bd/o/Gp2OKc1pnhDPzt3FrMndQff2rWp7SiIiIiIiIlKNdNtXwoqLs5IsgR82bqvl2YiIiIiIiEh1U0BAIgoEBFZtUkBARERERCRatVm4XRqH6nqPKSAgEe3NEGgUZRNERERERKokISEBgB07dtTyTKShC7zHAu+5yqr1GgJSdx2U2oo4g+1FxbU9FRERERGROi8+Pp7WrVuTk5MDQIsWLfA6motUD+ccO3bsICcnh9atWxMfX7XuE6Z0lppjZslAXl5eHsnJybU9nZjt2lOMc5CYoBYnIiIiIiLRcM6xYcMGtm7dWttTkQasdevWpKWlhQ045efnk5KSApDinMsv7zjKEJCImjVRIEBEREREJBZmRnp6Oqmpqezevbu2pyMNUEJCQpUzAwIUEBAREREREalm8fHx1XbRJlJTVFRQyvXyvDWcM3E2L81dU9tTERERERERkWqkDAEp18b8XSxY8wsHtW9V21MRERERERGRaqQMASlXj0DrwRy1HhQREREREWlIFBCQch3kDwiszNmGOlKIiIiIiIg0HAoISLm67deSOIP8wj1s2rartqcjIiIiIiIi1UQBASlXYkI8B7RtAcDKjdtqeTYiIiIiIiJSXRQQkAodlJoEwMpNCgiIiIiIiIg0FAoISIV6dGhFp9bNUQkBERERERGRhsNUKK7mmFkykJeXl0dycnJtT6fSnHOYWW1PQ0RERERERCqQn59PSkoKQIpzLr+8scoQkAopGCAiIiIiItLwKCAgMVFGiYiIiIiISMOggIBE5dpXF9H/7g9Z+NMvtT0VERERERERqQYKCEhUtu4oYvO2IlbmqNOAiIiIiIhIQ6CAgETloNRWAPywUQEBERERERGRhkABAYlKj9QkAFZuUkBARERERESkIVBAQKKiDAEREREREZGGRQEBiUoPf0Bg3dad7CjaU8uzERERERERkapSQECi0qZlU9q1bArAj5u21/JsREREREREpKqa1PYEpP446sC2bN2xm2Kfq+2piIiIiIiISBXVeoaAmV1tZllmVmhmC8zsuArGn+AfV2hmP5rZlSH7x5iZC/ORGDRmdYQx/w4a81yY/XOr/ztQfzx+0ZG8csXR9O7curanIiIiIiIiIlVUqxkCZnY+MAG4GvgS+APwgZllOOd+CjO+G/A+8BQwCjgGeNzMNjnnJgcNzQcOCX6uc64w6OEAID7ocS/gQ+D1kFNOAy4NelwU9YsTERERERERqcNqe8nATcAzzrmn/Y9vMLOhwFXAbWHGXwn85Jy7wf94uZn1B24GggMCzjm3IdJJnXObgh+b2a3AKuDzkKG7yjtOY7WzqJjmTeMrHigiIiIiIiJ1Vq0tGTCzpsCRwIyQXTOAwRGeNijM+OlAfzNLCNrWyszWmNnPZvaumfWtYB6jgGedc6GL4080sxwz+97MnjKz1ApeUzMzSw58AEnlja9vtu4oYtC9H3P4ndMp2uOr7emIiIiIiIhIFdRmDYH98NL2N4Zs3wikRXhOWoTxTfzHA1gBjAFGAhcAhcCXZtYjwjHPBFoDz4Vs/wC4CDgZ+D+8ZQafmFmzCMcBL6shL+jj53LG1jspzRMoKNzDHp9j9RZ1GhAREREREanPar2oIBB6V97CbKtofMl259xc59xLzrklzrmZwHnA98C1EY53OfCBc259qZM495pz7j3n3DLn3FTg18DBwGnlzO1eICXoY/9yxtY7Zkb31FYA/LBxWy3PRkRERERERKqiNgMCm4FiymYDpFI2CyBgQ4Txe4At4Z7gnPMBXwFlMgTMrAvwK+Dp0H1hjpMNrAl3nKAxu5xz+YEPoKCi49Y3PfwBgZU5CgiIiIiIiIjUZ7UWEHDOFQELgCEhu4YAsyM8bU6Y8acCXzvndod7gpkZ0AfIDrP7UiAHeK+i+ZpZO6BzhOM0GgcFMgRyGlysQ0REREREpFGp7SUDDwK/M7PLzKynmT0EHAA8AWBm95rZC0HjnwC6mNmD/vGX4aX8PxAYYGbjzGyomR1oZn2AZ/ACAk8En9jM4vACAs875/aE7GtlZg+Y2SAz62pmJwJT8bIa3qrG11/vBDIElqzdyjuL1zFn1RaKfeWt8BAREREREZG6qFbbDjrnXvPfeb8DSAeWAcOdc2v8Q9LxAgSB8VlmNhx4CPgjsB64zjkX3HKwNfAfvKUFecAi4Hjn3PyQ0//Kf+xnw0ytGDgcGO0/XjbwKXC+c65R3xpfv3UnAGt/2cn1/1sMQHpKIuNGZDCsV3otzkxERERERERiYWU77Ul18bcezMvLyyM5Obm2p1Nl05Zlc9VLCyNWdZw4qp+CAiIiIiIiIrUoPz+flJQUgBR/bbuIanvJgNQTxT7H+KmZYds/BLaNn5qp5QMiIiIiIiL1hAICEpX5Wblk5xVG3O+A7LxC5mfl7rtJiYiIiIiISKUpICBRySmIHAyozDgRERERERGpXQoISFRSkxKrdZyIiIiIiIjULgUEJCoDu7UlPSWxpIBgKMPrNjCwW9t9OS0RERERERGpJAUEJCrxcca4ERkAEYMC40ZkEB8Xaa+IiIiIiIjUJQoISNSG9Upn4qh+pKWUXRZw2/BD1XJQRERERESkHjHn1CaupphZMpCXl5dHcnJybU+n2hT7HPOzcskpKOTV+T8x98dcBnZty2t/OBozZQiIiIiIiIjUlvz8fFJSUgBSnHP55Y1VhoDELD7OGNS9HWf06cSD5/UhMSGO+atzmbJkfW1PTURERERERKLUpLYnIPVbx9bN+eOJB5GZnU+/A9rU9nREREREREQkSgoISJVdc/JBWiogIiIiIiJSz2jJgFRZaDBgd7GvlmYiIiIiIiIi0VJAQKpNTkEhN762mKteWljbUxEREREREZEKKCAg1SZ/5x6mLlnPR8s38ul3ObU9HRERERERESmHAgKNna8YsmbC0je8z77iSh/qoNRWXHpMVwD+NjWToj1aOiAiIiIiIlJXqahgY5Y5BaaNhfygdoHJHWHY/ZAxMvLzfMWwZjZs2witOkCXwRAXD8B1p/TgrUXr+XHzdv77ZRZ/OKF7Db8IERERERERqQxzztX2HBosM0sG8vLy8khOTq7t6ZSWOQUmjQZCf/7+AoHnvRA+KBBFEOGNBT9z8+tLaNk0nk9vPpHU5MQaeQkiIiIiIiJSWn5+PikpKQApzrn88sZqyUBj5Cv2LurLBAPYu23arWWXDwSCCMHBAID8bG975hQAzu7bib4HtGZ7UTH3fbCi2qcvIiIiIiIiVaeAQGO0ZnbZi/pSHOSvg5fOgbf/CFOugz1FUQcR4uKMO0cchhnMWrmZ3G1FzFm1hXcWr2POqi0U+5SVIiIiIiIiUttUQ6Ax2rYxunE/frr3617nRhdEWDMbuh1H786teeyCfhTtKea0R2eSnVdYMjI9JZFxIzIY1iu9cvMXERERERGRKlNAoDFq1SG6cUdeBq07e0sHtkfZRjAo2BAfBzdNWlImp2BDXiFXvbSQiaP6KSggIiIiIiJSSxQQaIy6DPYKAeZnE34JgHn7T3ugpHsAWTOjO7Y/2FDsc4yfmhlxgYEB46dmMiQjjfg4i/01iIiIiIiISJWohkBjFBfvdQUASroKlPA/Hnbf3mAA7A0ilBkf9LzkTt44YH5WbqllAqEckJ1XyPys3Eq8ABEREREREamqmAMCZjbMzI4NevxHM1tsZq+YWZvqnZ7UmIyRXmvB5JCU/eSO4VsOlhtE8AsKIuQURA4GBIt2nIiIiIiIiFSvyiwZ+CcwFsDMDgf+BTwInOz/fGm1zU5qVsZIOPQ0rxDgto1eun+XwaUzA0LHn/eC120guMBgfFM455lSQYTUpMSophDtOBEREREREalelQkIdAMy/V+fA7zrnPuzmfUD3q+2mcm+ERcP3Y6LfnxwEGHTCljwPBx7A/QcUWrYwG5tSU9JZENeYdg6AuB1GxjYrW2lpy4iIiIiIiKVV5kaAkVAC//XvwJm+L/OBZJjPZiZXW1mWWZWaGYLzKzcq1MzO8E/rtDMfjSzK0P2jzEzF+YjMWjMnWH2bwg5jvnHrTeznWb2mZkdFuvra5ACQYSBV8BVs+Dwc8FKLyOIjzPGjcgAIlcduP6UHiooKCIiIiIiUksqExCYBTxoZn8FBgLv+bcfDPwcy4HM7HxgAvB3oC8wE/jAzA6IML4bXhbCTP/4e4BHzOyckKH5QHrwh3MudLH6tyFjDg/ZfwtwE3ANMADYAHxoZkmxvMbGbFivdCaO6kdaSullAfFxRpM4o31Ss1qamYiIiIiIiJhzkRK6IzzBu1h/HOgMPOKce8a//SEg3jl3XQzHmgcsdM5dFbRtOfC2c+62MOPvB0Y653oGbXsC6O2cG+R/PAaY4JxrXc557wTOdM71ibDfgPX+49zv39YM2AiMdc49GeXrSwby8vLySE6OOXmi/ti+GRa+ACn7wxHnldld7HPMz8olp6CQ1KRE0pIT2bRtl5YLiIiIiIiIVLP8/HxSUlIAUpxz+eWNjbmGgHPuJ+D0MNtvjOU4ZtYUOBK4L2TXDGBwhKcNYu8ShYDpwOVmluCc2+3f1srM1gDxwGLgr865RSHP62Fm64FdwDzgz865H/37ugFpwedyzu0ys8/9cwsbEPAHDYJvezeObIJv34KPx8N+B8Phvwm7fGBQ93altnVr37Lk6/zC3SQ1a4KZlg+IiIiIiIjsK5VpO9jP310g8PgMM3vbzO7xX+RHaz+8C/aNIds34l2Mh5MWYXwT//EAVgBjgJHABUAh8KWZ9Qh6zjxgNDAUuMJ/3NlmFrhqDZw/lrkB3AbkBX3EtISi3jrifGjaCjZ/D1mfx/TU7zYU8OsJM3n2y9U1MzcREREREREJqzI1BJ7EqxeAmR0I/A/YAfwG+Ecljhe6ZsHCbKtofMl259xc59xLzrklzrmZwHnA98C1JQdw7gPn3GTn3FLn3EfAaf5dl1RxbvcCKUEf+5cztuFITIbev/W+nv9UTE+dtXIz67bu5O73Mpn+7YaKnyAiIiIiIiLVojIBgYPx0vDBCwJ84Zy7EO+ufGhxv/JsBoope8c9lbJ35gM2RBi/B9gS7gnOOR/wFdAj3H7/mO3A0qAxgSvTWOaGc26Xcy4/8AEURBrb4Az4nff5u/chb13UT7vsmK5cdNQBOAfX/28RS9ZupdjnmLNqC+8sXsecVVso9sVW50JEREREREQqFnMNAby75IFAwq+Ad/1fr2Vv2n6FnHNFZrYAGAK8FbRrCPBOhKfNAUaEbDsV+DqofkDpyXoL0/vgXfCH5V/73xOvewFAFl5QYAiwyD+mKXACMDbii2rMUntC1+Ng9UxY8F84+faonmZmjB95GOu27uSz7zYx6pl5JCbEs6lgV8mY9JRExo3IYFiv9JqavYiIiIiISKNTmQyBr4HbzexivAvkQNvBbpRz9zyCB4HfmdllZtbT36ngAOAJADO718xeCBr/BNDFzB70j78MuBx4IDDAzMaZ2VAzO9DM+gDP4AUEngga84CZnWBm3czsKOANIBl4HsB5rRcmAH82s7PMrBfwHN7SiFdifI2NRyBLYMHzsKco6qc1iY/jsQv70al1cwoK95QKBgBsyCvkqpcWMm1ZdnXOVkREREREpFGrTIbADcDLwJnA351zK/3bzwVmx3Ig59xr/kJ+dwDpwDJguHNujX9IOl6AIDA+y8yGAw8Bf8RrDXidc25y0GFbA//BS/fPw7vDf7xzbn7QmP2BV/EyGjYBc4Gjg84LXj2E5ngtFtvgFSI81TnXeJYBxOrQ06BNV+h8NOzKhyZRJ4zQPCGe3cW+sPscXlrK+KmZDMlIIz5O3QhERERERESqyryb4dVwILNEoDhS6n5jZGbJQF5eXh7Jycm1PZ19o3g3xCfE/LQ5q7ZwwVNzKxz36hVHl2lhKCIiIiIiIp78/HxSUlIAUvy17SKqTIYAAGZ2JN66ewcsd84trOyxpAGpRDAAIKegsFrHiYiIiIiISPliDgiYWSrwGl79gK142dwpZvYp8Fvn3KZqnaHUTxu/9T6OOC+q4alJidU6TkRERERERMpXmaKCjwJJwGHOubbOuTZAL7yifI9U5+Sknlq/GCYOhqnXw86tUT1lYLe2pKckUl51gPSURAZ2a1sdMxQREREREWn0KhMQGAZc5ZxbHtjgnMvEK/L36+qamNRj6b2hfU/YvQMWR9eUIT7OGDciAyBiUKB9q6bs8YUvPCgiIiIiIiKxqUxAIA4IVzhwdyWPJw2NGQz0tyD86mmI8iJ+WK90Jo7qR1pK6WUBbVs2pUmc8c26fK54YQE7i4qre8YiIiIiIiKNTsxdBszsHbzWfhc459b7t3XCa0X4i3PurOqeZH3VKLsMBOwqgH/1hKICuPgt6H5y1E8t9jnmZ+WSU1BIapK3TGDej1u4/Pmv2bm7mKMPbMszlwygZbNK18QUERERERFpkGLpMlCZO/rX4NUQWG1mq8xsJZDl33ZtJY4nDVGzJOj9W+/r+U/H9NT4OGNQ93ac0acTg7q3Iz7OGHzQfrxw+UBaNWvC3B9z+fS7nBqYtIiIiIiISOMRc4ZAyRPNhgCH4i35znTOfVSdE2sIGnWGAEDOCnj8KMDgnGcAB606QJfBEBdfqUMuXruVRT/9wqXHdCvZFi6jID6uvPKEIiIiIiIiDVMsGQKVDghIxRp9QADg30fD5u/ABdURSO4Iw+6HjJFVPvzkBWu5f9p35BTsKtmWnpLIuBEZDOuVXuXji4iIiIiI1CfVHhAws+uiPblzTq0H/Rp9QCBzCkwaDYS+x/x37897oUpBgTcX/sxNk5aU2R7IDZg4qp+CAiIiIiIi0qjUREAgK8pzO+fcgVGObfAadUDAVwwTekH++ggDzMsUuGFppZYPFPscR9/zMZu27Qq734C0lERmjT1ZywdERERERKTRiCUgEFWZdudct4pHiQRZM7ucYACAg/x13rhux8V8+PlZuRGDAf6jk51XyPysXAZ1bxfz8UVERERERBq6ynQZEKnYto3VOy5ETkFhtY4TERERERFpbBQQkJrRqkN0436aA0U7Sm/zFUPWTFj6hvfZV1zmaalJiVEdPtpxIiIiIiIijU1USwZEYtZlsFcjID+bskUFg3z1tFd88KrZ0Kq99/W0saWXG4TpSjCwW1vSUxLZkFcY8ejpKV4LQhERERERESlLGQJSM+LivYt4YG/df4IeG/S/HFofAB0y9gYDJo0uW3sgP9vbnjmlZFN8nDFuREbYowe2jRuRoYKCIiIiIiIiESggIDUnY6TXWjA5pPVfckdv++kPwrUL4awnvWUB08YSPpvAv23araWWDwzrlc7EUf1ISym9LCA9JbGk5eDitVvZWVR2yYGIiIiIiEhjF1XbQQAzuwV41Dm30//4eGCec26X/3EScL9z7uqammx906jbDgbzFXvdBLZt9GoLdBlcttVg1kx4/vSKj3XJu2W6EhT7HPOzcskpKCQ1yVsmEB9nfPZdDn94cQEDu7Xl6Uv606xJ7O0NRURERERE6pNY2g7GkiFwL5AU9PhdoFPQ4xbAH2I4njQWcfHeRfzh53qfQ4MBUKWuBPFxxqDu7TijTycGdW9XskygVbMmxJkx84fNXPPKInYX+6ryKkRERERERBqUWAIC4RaCi1SPaLsSRDsO6N/Vywxo2iSODzM3cuNriyn2RZcRIyIiIiIi0tCphoDUDYGuBBHjTAbJnbxxMTjmoP14ctSRJMQb736Tza2Tv8HncxT7HHNWbeGdxeuYs2qLAgUiIiIiItLoqO2g1A2BrgSTRuMFBcJcoA+7L/xygwqcdGgqj/y2L398ZSGvL/iZnIJdfLexgA15hSVj0lMSGTcig2G90ss5koiIiIiISMMRS1FBH3A7sM2/6X7gn8Bm/+Mk4C7nnCq3+amoYCVkTvG6DQS3HmzeBkY84nUtqIK3Fv3Mja8tCbsvkJcQ6E4gIiIiIiJSH8VSVDCWgMBqwveEK8U51y2qAzYCCghUUqArQe4qaN0Fuh1fqcyAUMU+x4C/f0Tu9qKw+w1IS0lk1tiTSwoTioiIiIiI1CexBASiXjLgnOtaxXmJRCfQlSCkvWBVzc/KjRgMAC/alZ1XyPysXAZ1b1et5xYREREREalrVFRQ6j7nSi8hqKScgsKKB8UwTkREREREpD6LOiBgZkeZ2a9Dto02sywzyzGz/5hZs1gnYGZX+49RaGYLzKzc28JmdoJ/XKGZ/WhmV4bsH2NmLsxHYtCY28zsKzMr8M/9bTM7JOQ4z4U5xtxYX59UUc4KmHAEPHUK+HxVOlRqUmLFg2IYJyIiIiIiUp/FkiFwJ3BE4IGZHQ48A3wE3AeMAG6L5eRmdj4wAfg70BeYCXxgZgdEGN8NeN8/ri9wD/CImZ0TMjQfSA/+cM4F3/Y9Afg3cDQwBG/pxAwzaxlynGkhxxkey+uTatCmK+zMhYL1sH5RlQ41sFtb0lMSIzY2BGjbsikDu7Wt0nlERERERETqg1gCAn2Aj4Me/xaY55y7wjn3IHAdcF6M578JeMY597Rzbrlz7gZgLXBVhPFXAj85527wj38aeBa4OWScc85tCP4I2TnMOfecc+5b59wS4FLgAODIkOPsCjlOboyvT6oqIRF6nOp9vfydKh0qPs4YNyIDIGJQYOuOIl6Ys5poi22KiIiIiIjUV7EEBNoAG4Men4B3Bz3gK6BztAczs6Z4F+AzQnbNAAZHeNqgMOOnA/3NLCFoWyszW2NmP5vZu2bWt4LppPg/h17wn+hfUvC9mT1lZqnlHcTMmplZcuADrxWjVFXPEd7n5VO9egJVMKxXOhNH9SMtpfSygLSURI7q1hafgwemf8eGfNUREBERERGRhi3qLgN4wYBuwFr/xXw/YFzQ/iRgdwzH2w+Ip3SQIXCetAjPSYswvon/eNnACmAMsBRIBq4HvjSz3s65H0IPaGYGPAjMcs4tC9r1AfA6sAbvdf8N+MTMjnTO7Yowv9so/T2R6tBjCMQ3g9wfIScTOhxWpcMN65XOkIw05mflklNQSGpSIgO7tSXO4JlZWXRu24L0lOalnlPsc2XGqzWhiIiIiIjUZ7EEBKYB95nZWOBMYAfeWv6AI4BVlZhD6C1fC7OtovEl251zc4GS4n9m9iWwELgWb1lDqMfw5n5sqZM491rQw2Vm9jVecOA04M0Ic7sXL7gQkAT8XM5rkWg0S4KDToHv3veyBKoYEABv+UC41oK/O+7AUo+/Xp3L16t/4fk5q8nO25s1kJ6SyLgRGQzrlV7luYiIiIiIiNSGWJYM3A4UA58DVwBXOOeCm7pfRtl0/vJs9h8vNBsglbJZAAEbIozfA2wJ9wTnnA9vOUOP0H1m9igwEjjJOVfuhbtzLhsvIFDmOEFjdjnn8gMfQEF5x5QYBJYNZE7ZZ6dct3UnY/77FfdNW8HGvB0cHZfJyLjZHB2XSU7eDq56aSHTlmXvs/mIiIiIiIhUp6gzBJxzm4DjzCwF2OacKw4Z8htgWwzHKzKzBXhV/t8K2jUEiFQ9bg5eN4NgpwJfO+fCLlfwLwnog7eEIHjbo8BZwInOuayK5mtm7fBqJOgKsDYcPAwGXOEFBpwDq/l0/ZZN49ld7GNo3HzGJbxAR9tbYmK9a8tdu0czfmoiQzLStHxARERERETqnViWDADgnMuLsL0yFfgfBF70p+PPAX6PV+3/CQAzuxfo5Jwb7R//BHCNmT0IPIVXZPBy4ILAAc1sHN6SgR/waghchxcQ+GPQef8NXAicARSYWSDrIM85t9PMWuG1WZyMFwDoitficDOlgxeyr7RoC6c9sE9PuTy7gBN9c5mYMKHMvjRyeTxhAlcVwPysPmGXH4iIiIiIiNRlUQcEzOzZaMY55y6L9pjOudf8d97vANKBZcBw59wa/5B0vABBYHyWmQ0HHsK7wF8PXOecmxx02NbAf/CWFuQBi4DjnXPzg8YE2hp+FjKlS4Hn8JYyHA6M9h8vG/gUON85p2UADYGvGNbMhm0boVUH6DIY4uJLDcnJ3864hBcACE0AiDPwORiX8CJf5V8OKCAgIiIiIiL1i0Xbb93MfHhr6BcRuY07zrmzqmdq9Z+/9WBeXl4eycnJtT2dhmHNbMh8BwZfBymdKneMzCkwbSzkr9+7LbkjDLsfMkaWbPp21hQO++jiCg/37ZBXOOyY0yo3FxERERERkWqUn59PSkoKQIq/tl1EsSwZeAL4LXAg8CzwUiWXCYhU3kfjYe1caHsgHPWH2J+fOQUmjaZMs4r8bG/70L8DBllfkPHjZ1EdsmfSjtjnISIiIiIiUsui7jLgnLsaL4X/frzCfmvNbJKZDfUX6ROpeYE7+JXpNuAr9jIDwna1dN7H9D/D9Nvg+w+wPTujOqwldYh9LiIiIiIiIrUslraDgbZ6rzrnhgAZwLfA48AafyE+kZp16One559mw7ZNsT13zezSywQi6dgPhtwFv/sYkjviIqyQcRj5TTvwl4XJRLv0RkREREREpK6IKSAQwn9LFavicUSi16YLpPcG54Pv3o/tuds2Rjdu0B/hmOth//4w7H4MygQFApf/Y7dfwCtfrePOKd8qKCAiIiIiIvVKTBfyZtbMzC4wsw+B7/Aq8V8DHOCc21YTExQpo6d/2cDyqbE9r1WUqf3B4zJGwnkvYMnppYYYYIOu5pSzr8AMnp+zhr+/t1xBARERERERqTeiDgiY2eN47ffGAu8C+zvnfuOce98556upCYqUEQgI/PgZ7Nwa/fO6DIam5a1sMUju5I0LljESblgGl7wL5zwDfUZ527+fzrl90rjnrMMBeHpWFv+Y/p2CAiIiIiIiUi/E0mXgSuAnIAs4ATghXC1B59zZ1TM1kQjaHwz7HQK7CiD3R+jUL7rnfT8NiiIlsvjfy8Pug7j4srvj4qHbcd7XBw+F7z+ALSthyatcMPBi9hT7+Os73zLxs1U0jY/julN6MD8rl5yCQlKTEhnYrS3xcaq9KSIiIiIidYdFezfTzJ4jfHn2Upxzl1ZxTg2GmSUDeXl5eSQnJ9f2dBqWgg3QMhXiokxyyf0RnjwRduXBwcNgwzelCwwmd/KCAYEuBhWZ/RjM+AukdIZrF0CTZjwzK4u/vZsJQLtWTdmyrahkeHpKIuNGZDCsV3qkI4qIiIiIiFRZfn4+KSkpACnOufzyxkYdEJDYKSBQh3x4B3z5MHQ+Gsa8CxbndR3YttGrGdBlcPjMgEh274SnToZe53hFCBOaA3Dja4t5a9G6MsMDuQETR/VTUEBERERERGpMLAGBWJYMiNQ9xXtgVz60aFv+uF+Nh6R0yDgD4hO8bYElAJWR0Byu/LJUhkKxzzH3xy1hhwfacYyfmsmQjDQtHxARERERkVqndoFSfy17E/51MHwwtuKxZnD0VZDcsfrOH7JcYX5WLtl5hRGHOyA7r5D5Wbll9hX7HHNWbeGdxeuYs2oLxT5l7oiIiIiISM1ShoDUX8mdYMcWr1jgniJo0rT0/uwlMO9J+PU/oFl53QWqwDlY+RHMfoTcnv+I6ikvz1tD1/1akJ7iLTOYtiyb8VMzSwUTVHNARERERERqmjIEpP7af4C3/n9XPmR9Xnrfzq0waTQsfhk+vqvm5uB8MON2yPqC3mtfiuop736TzeD7PuHV+T8xbVk2V720sExmwYa8Qq56aSHTlmXXxKxFREREREQUEJB6LC4ODj3d+3r5lL3bfT5460r4ZTW0PgBOuq0G5xAPJ98OQKcV/yUjuZBI1QEMSGmewICubXAO+h3QhvFTM8O27ghsGz81U8sHRERERESkRiggIPVboE3gt2/DN5MgaybMehC+/wDim8J5L0DzNjU7h0NPh459sd3befwAL1MhNCgQeHz/OYfz+pWDmXvbKeRuL6p0zQEREREREZGqUkBA6rcdvwDmLRt48wp4/nT45G/evuH/hI59a34OZnDyXwHomvU//nt2OmkpiaWGpKUklmo5mJaSSE5B5GBAsGjHiYiIiIiIxEJFBaX+ypwCb1wKYZPuqfnMgGDdT4Yux8KaWZy44TlmjX2Y+Vm55BQUkpqUyMBubcu0GkxNSoxwsNKiHSciIiIiIhILZQhI/eQrhmljiRgMwGDabd64fcEMTvGyBFj0EvF5axjUvR1n9OnEoO7tygQDAAZ2a0t6SmLEmgMALZrG0++A1jUyZRERERERadwUEJD6ac1syF9fzgAH+eu8cfvKAUfDUVfCb56D5P29egZL3/A+hwlMxMcZ40ZkAGVrDgS0aZHAbhUVFBERERGRGqAlA1I/bdtYveOqy6/v95YyPHx46YBFckcYdv/eIoh+w3qlM3FUP8ZPzSxVYDA9JZEz+3Ri9OAutGqmf6YiIiIiIlL9dKUh9VOrDtU7rrpkToFJoymzlCE/29t+3gthgwJDMtIqrDnw8rw1HNmlDYemJdfwixARERERkcZAAQGpn7oM9u6652cTvo6Aefu7DN53cyq3roHz5jTtVjj0NIiLL7U3Ps4Y1L1dxEN/vHwjf3lrGa2aNWHiqH4c16M9xT5XYRBBREREREQkEgUEpH6Ki/dS8CeNxluBH3wR7r8oHnZfmQvvGhVLXYNux8V06P5d2nJUt7bMy8rl0v9+xQUDO/PR8pwyywzGjcgoaW0YSgEEEREREREJZs6pYFlNMbNkIC8vL4/kZKV514jMKd5d+VLr9Tt5wYCQ1Pwat/QNmHx5xePOeQYOPzfmw+/aU8ytk5fy1qJ1YfcHLu0njupXJigwbVl22DoF5QUQRERERESk/snPzyclJQUgxTmXX95YBQRqkAIC+4iv2Lvrvm2jVzOgy+B9mxkQkDUTnj+94nGXvBtzhkDAnmIffe6awbZd4dspGpCWksissSeX3P2ftiybq15aWGYhQ3kBBBERERERqZ9iCQhoyYDUf3Hxlb7Arlb7oK7BV6t/iRgMwH/W7LxC/vTGEo4+sB0dUxL56zvfllfVgPFTMxmSkablAyIiIiIijUxcbU/AzK42sywzKzSzBWZW7pWdmZ3gH1doZj+a2ZUh+8eYmQvzkRjLec1zp5mtN7OdZvaZmR1Wfa9cGpxAXQNg7/33YK7KdQ1yCgorHgS8uXAdt7zxDaOemc+mgl0RxwUCCPOzcis9JxERERERqZ9qNSBgZucDE4C/A32BmcAHZnZAhPHdgPf94/oC9wCPmNk5IUPzgfTgD+dcYdBxojnvLcBNwDXAAGAD8KGZJVX+FUuDlzHSay2YHCYFP+MM6DmiSodPTUqseBBwSs9UjuuxH6lJzaIaHzbQ4Cv2lkEsfcP77IucmSAiIiIiIvVPrdYQMLN5wELn3FVB25YDbzvnbgsz/n5gpHOuZ9C2J4DezrlB/sdjgAnOudaVPa+ZGbDef5z7/fubARuBsc65J6N8faoh0FiF1jVomQqph1T5sMU+x7H3f8KGvMJIixJK1RCYs2oLFzw1t8LjPn/ZAE44OHXvhrDFGjt6GRD7ulijiIiIiIhELZYaArWWIWBmTYEjgRkhu2YAkRZZDwozfjrQ38wSgra1MrM1Zvazmb1rZn1jPG83IC14jHNuF/B5OXPDzJqZWXLgA1A2QWMVqGtw+Lne5+BgwJ4i8Pkqddj4OGPciAyg7KKEwONxIzJK6gEM7NaW9JTEsAsYgo194xtenreGoj0+LxgwaTQupIWiy8/22jxmTqnU3EVEREREpG6pzSUD+wHxeHfdg23EuxgPJy3C+Cb+4wGsAMYAI4ELgELgSzPrEcN504K2RTs3gNuAvKCPn8sZK43RllXwzBCYN7HShxjWK52Jo/qRllJ6+UBaSmKZjgHRBBBat0hgQ/4u/vLWMn71wMfsmHIzDhdmvPOyEqbdquUDIiIiIiINQF3oMhCuG1p56xgidU9zAM65uUBJjrSZfQksBK4FrovxvLHO7V7gwaDHSSgoIMGyPofsxbDxW+hyDHTsU6nDDOuVzpCMNOZn5ZJTUEhqUiIDu7UN2ykgEEAYPzWT7Ly9tQLSUhIZNyKDEw9J5dX5P/HvT1fRMX8RLZqGxsH2Mhzkr/OWQ9SFzg4iIiIiIlJptRkQ2AwUU/aOeypl78wHbIgwfg+wJdwTnHM+M/sKCGQIRHPeDf7PaUB2lHMLLCsoKenulSIQCXLkpbDyY1jxLky+HH7/OTRrValDxccZg7q3i2psRQGES4/pxm8HHMCst1bA8oqP5yvYUCa9qNjnogpQiIiIiIhI3VBrAQHnXJGZLQCGAG8F7RoCvBPhaXOA0DLtpwJfO+d2h3uCv0BgH2BpDOfNwgsKDAEW+Y/TFDgBGBvFyxMJzwxGPgrrF8GWlfDBWDjz3/vk1BUFEJo3jafj/t2iCggsL2hOcA/Oacuyy2QgpPszEIKXMIiIiIiISN1Rq20H8dLrf2dml5lZTzN7CDgAeALAzO41sxeCxj8BdDGzB/3jLwMuBx4IDDCzcWY21MwONLM+wDN4AYEnoj2v81ovTAD+bGZnmVkv4DlgB/BKtX8XpHFp0RbO/g9gsPglr61fHbGyxeGsd23xRVgY43Ow3rXj0k+aMH7qt/ywsYBpy7K56qWFpYIBABvyCrnqpYVMW5Yd/mAiIiIiIlKrajUg4Jx7DbgBuANYDBwPDHfOrfEPSce7UA+MzwKGAyf6x/8VuM45NznosK2B/+Dd55wBdAKOd87Nj+G8AP/ACwo8DnztP86pzrmCKr1oEYCux8Lxf/K+fvdGr9hg1kwvOJA1s9aK9h20cxnP7zkVoExQINChdPzui8nZXsx/v1zNT1t2MH5qZtjCGoFt46dmUhwpwiAiIiIiIrXGnNMf6jXF33owLy8vj+Tk5NqejtQ1xXvgueGwfRMUbYdtQeUpkjvCsPshY+S+m8+Gpbj/DmfnriIe3n0WlzSZTkfLLTXknt0XMKXlb7jj9Ay++GETvz48jUue/arCQ796xdFR1zsQEREREZHKy8/PJyUlBSDFOZdf3ti60GVApHGKbwL9LoF3/kiZ5hX52TBpNJz3wr4JCvyyGl46B9uVT+F+A3ju56E8XXwaA+JWkMpWzo3/jGTbwVe+Q7lzpFcXYPgR6byzeF1Uh88pKAy/w1fsdSzYthFadYAugyEuvtpeloiIiIiIRKaAgEht8RXDp3cTvpOlAwym3QqHnlazF8nbN8OLZ3sX5R160fbSyTy80lsKMDcvA4AZviNpk5zMuPMOK1UkMDUpMapTbC7YVXZj5hSYNhby1+/dVhuZESIiIiIijZQCAiK1Zc3s0hfDZTjIX+eN63Zczcxh1zZ4+TeQuwpSDoCL3oDEFIb1Sim3TWHAwG5tSU9JZENeYdiwRsAPm7aV3pA5BSaNxuEIPqLLz8b2ZWaEiIiIiEgjVttdBkQar+CaAdUxLlZ7imDSxbB+ITRvCxe/Ccl77/4H2hSe0acTg7q384IBO7fCzH95Sxr8Y8aN8LIILOTwgccnHdKe60/pUbL9x415FL13S5lggPcc5wUWpt0atrBisc8xZ9UW3lm8jjmrtlRYrDDW8Y2Wr7hOFLUUERERkX1LGQIitaVVh+jGNW9dPecLXa+f3gfiEiChBVz0OuzXo8JD8MZlsOpjLzBw6t8AGNYrnYmj+jF+amap1oNpKYmMG5FRaokBwOS3XudP2yO3IrQImRHTlmWXOUd6hHNUZnyjpaUbIiIiIo2WugzUIHUZkHL5imFCL//d9nL+HSbvD+e/AJ2OLPv8aAvyRbroO/UeaNcd0o+Ibs7fTYNXz4emSXDjslLBimKfq3CJwe5iHy8//S/GZN9d4al8Zz9N3BG/AbyL+6teWljmuxQ4+sRR/Upd5Mc6vtHyL90o+/7zf6e0dENERESk3omly4CWDIjUlrh47y4sEDHhvnlbKCqANt1K786c4gUTnj8dJl/ufZ7Qy9seKnDRF1qvID8b3rjU6zAQrR6nQmqGN6evnym1K+wSgxAJ8XEM6JUR1akW/dKMO95ZxpOfr+LWN5dGLL0IMH5qZslygGKfY/zUzKjHN1q+Yi9IVN53KsLSDRERERFpGBQQEKlNGSO9u7DJIXerkzvCeS/CTZkw6k1o0dbb7hy8fmnkC/xJo0sHBar7oi8uDo65wft67kTYvTO65wVZ2eJwtrtm5Y4pcvFkFrTghTlruPeDFWzdsTviWAdk5xVy1D0fcez9n3D0vR+VWiYQafz8rNwy+xpVzYFYilqKiIiISIOkGgIitS1jpNdaMFL6//799479bhp8+2aEA4W0Kpw7Eb59q/o7GfQ6Gz75G+SthcWvwIDLo3ueX88tn9DSduGcN+PgRILACqamVsxvF4+mzWF/4tmCgSz8aWuFx928rSimeby56Gc6tW7OAe1aAI2w5kBtF7UUEalPYlmmJyJSjyggIFIXxMVHd0Fe+EsFA4Iu8LMXw7qvozt/LBd98Qkw+Fr44BaY/Qj0uwTio/xVsmUVPebfDsAHvoH0iVtJR/beqc+mHY/tPpPfNJtD3z2ZnL7qTo7qdgYnMYJttCAOHwPjVpDKVnJozXzfofj8iU53nXEYvTqlsPTnPMZN+bbCqbz+9c+8/vXPdG3Xgi7tWvL595vKjNmQV8hVLy1smDUHoi1qGe04EZGGSsVXRaQBU0BApD6JbxrduG0boc+F0CoN5jxa8fhYL/r6jvLaDx4wyKsn0LxNdM9rlowdcBS5W7dy3c/X4jAGBF3gf+W/wD/+7Jsg9xX47D7aZ73DtMS5vLj7JC5pMoOOtjeAsN615a7do1mSdDwXHdWF+Dij9/6teeLzVWzIK4xYqjGpWRMOTUti0dqtrN6yg9VbdoQd58+5YPzUTIZkpIWtixBNMcU6qctg7w/aiBkk5u3vMnifTktEpE6JVHw1sExPxVdFpJ5Tl4EapC4DUu2yZnoFBCtyybtexkGFnQz8F303LI099bFoBzRtEdtzAHw+2JXPtFU7K07R/2keTP4dLu8nCLPEILDEf8ngR+g79JKS7YEuA1D6VYd2GSgo3M3zs1fzwIzvK5z2nSMyuPCoLjRtsrf0Sr1fZvDtO/D66DA71GVARGTv/6EVBE4r83+oiEgNiqXLgAICNUgBAal2lbnAL7m7AWEvj/fFRV9+dtnCiUR5d337FnjsSNzOX8r0YgBwGBbmD7JoL9bfWbyO6/+3OKqXkZgQR/8ubTn6QK/I479mfF+/WxuWBJiMUu+NVh1g+AMKBohI4xZrEF5EpI6IJSCgJQMi9UmgVeGk0ZS5iAtcig67r/SdikAng7DrH++r+kXfpu/gu/fh2BvD79+6Fp44Fnqe7l1kJjQv2RVoVViunEyIEAwAsAiFEYf1SmdIRlqFAYfUpMRoXiXJiU3IL9zDrJWbmbVyc5nvfkA0ywzqjLXzvM99L4Ijfgvv/R9s/g6OuV7BABERFV8VkUZAAQGR+qYyF/gVdTKorB253sV+cRF0Pa50RwSA4t0w+XIo3AobM8Eqcb4q/EEWTcBhYLe2pKckRqw5YEBaSiIzbzmJHzdvZ+6PW3jvm/XMy4pc4DG4tWHo+WOtOVCjNQqOv9nrGoFB227QbzTM+At8Px0G/bF6ziEiUl+p+KrUFepyITVIAQGR+qgyF/jRdjKIRYu2cPhvYPHLMOsh+O3Lpfd/eo93F7pZMpz7LDSJsihisBr+gyw+zhg3IoOrXloYKeeCcSMyaBIfx8Edkji4QxIpzRPKDQgELPzpl1IBgVhrDuyTGgVtD9z79cHDvIDAmi+hMB8StdRJRBqxkuKrFSzTU/FVqUnqciE1LK7iISJSJwUu8A8/1/tcW5HiY673Pq94DzYFFedb9YkXJAAY+Yh3B7oyAn+QRVw0ACR3qtIfZMN6pTNxVD/SUkovH0hLSQxbCyDaZQb/nP4dpz0yk8++yykpdBh8cQ97WxtOW5Zdanus42O2a1vJl8U+x5xVW3jn5+bsTO4Gvj2w6uOqHV9EpL4LLNOLmD9G2WV6ItUpUAcqtLBloMtF5pTamZc0KMoQEJGqaX8IHHIafPceTL8Nel/gtUd890bAQf/L4LCzKn/8cusm+B17Y5X/IIu25gBUvMwAvAKExcWOb9fns3uPj/FTM6OuOVDscyXj4/AxMKg143zfoTjiItYoiGqJwbYcmHAEdD+Z6Rn3cuf7K0sCD39p0pMrmmSxbv5bdKrKz01EpCHIGAm/eR7evQF2BmWGVVcdnmBKC5dgvmIvM6C8vx6m3epljOp9IlWggICIVN3+/b2AwMqPvI+AlM4w9J6qHz9S3YT4pl79ggXPQ5+LKtcGMUhURQ6JbpnBhPP7cFS3dry7NJvmTePL3OkPFqg5MPKxWbx33XHMz8olO6+QoXHzGZfwAh0tt2TseteW8btHMz1vYJkaBVEvMVg2GfbsZGvOWq5csqzU/D/29eMK3idu9UymLV3PsMM7Vvj9EBFp0A47E3qOqNmLdaWFS6g1s8tpeQlEKKosEistGRCRqsmcAh/fFX5f3lr44cPqOU/GSLhhmdfe6ZxnvM/XLoSW7b1AQNH2qp/DV+y1mVr6hvfZVxxxaDTLDNq0bMrFR3dhy/aiqE7/3YYCAHIKvGDAxIQJpJFbakwauUxMmMDQuPlkb91Zsj2mJQZL/gfAMwUDy9x3+Np3ML8vupFf7XqA8e8up9hX9s5EyRKDxeuYs2pL2DGyD8TwfhWRSijaDkU7vK+Dl+kld4S5j0PWF9VzHqWFSzjqciH7iDIERKTyyk1ng2pPZwtXGHHM+9Cma+UKFgarxN2Z6m5teOOQHt74lgmMS3gBgNBs/zgDn4NxCS+yJtnrBFDsc1z/v8VRLUnYsyGTZtmLKSael7b1LzN+D02Y4RsAwPYwnRIqU+iwRjslNFa6myhS82ZNgIUvwK/vK730bcFzMPsROOJ86HZ81c6htHCJRF0uZB9RQEBEKq8upLO1P7j0451boXnr2I4RuDsT+gdZ4O7MeS9EvMiqztaGV55wkDc+fgXxlhtmpCfOoCNb6BC/Akjli+83sWuPL+L44DaIA1ZNAuCT4j78QsVdBD77LofDOiWTnJhQkoUQ+hoCWQjhCjDuk04JjU0V3q8i1aIxrHXftgnm/Bt2by/bMvfgYV5A4IcPve9FVV57Xfh/tDY1hvdSZZV0uYj0/lCXC6keWjIgIpVXl9LZivfAjNth4mDvD7loVXh3Bu/uTBXSsQM1B6Bsr4Tg1oaBu+bx23OiO65/3C87oluSkJO/gybL3gBgZfpp5Yx0XB8/mXeb/pn3Zs6jz/gZjHxsFv/3+pLyvkuMn5pZavlAZTslaElCOfbB+1WkXJlTYEIveP50mHy593lCr4aX1j7rQS8Y0LGvVz8gWOejIDEFdubCz19X7Tx16f/Rfa2xvJcqKy7eK1wZlrpcSPVRQEBEKq8upbPt2QnfT/fupLxxqRcgiEYsd2eqIKbWhjF+X9NTmkc1/KAdSyD/Z2iWwu9/dzXpKYkRmjkax8R/S6+41Zyb9C0+B9/8nMf2XZEvMgNZCI98/AOffZfDvB+3cPvby2IKIIAXRDj2/k+44Km5XP+/xVzw1FyOvf+TqrdZbCiq8n5VzQGpqsay1n3rWvjqae/rU+4AC/lNGd8Eup/iff3D9Kqdqy79P7ovNZb3UlVlnAHnvehvvxwkuaOywaTaaMmAiFReSTpbNhH7NO+rdLZmSXD+S/DUybB6JnzyN+8PuYpSEVd/Gd3xc5aHT9eMId0x6taGMaYJRrsk4dDefaDJP2D3TuKbNi+3U8InxX0ZGLeCGzqv4rwRd/HvT1fy8ryfyv0WATz88Q8VjoG9AYSpS9Yxoncn4uOsUksSAhpNnYLK3k1UzYH6q66kVDemte6f3+91sOl6HBx4UvgxBw+Fb9+E72d4/9dUVjT/jyZ1aFhp4Y3pvVQdMkZ634vQ3wNZX0D2N5B+RG3PUOo5c06pmDXFzJKBvLy8PJKTK16rK1IvlaxnhrAN+PZ1BPvbt+D1Md7Xzdt6KZ0BgQugniPgx0/hi3/BmlnRHdfioe9FcPId0Kq9t60mL7IirRP3JlPm+xq4mIawP4WIF9OR1vj/44RmHDfjNK+14y1ZzPl5Fxc8NbfCaR/aIYm4OCOnoJDN26JbytCsSRwH7teSrC3bKdwdvhZCIKgxa+zJZS70G1WdgqyZXlptRS55F3x7YMcWcA7evIKy76Va+jcq0atLgZxY3nv1ea375h/g3wPB+eDyD6HzwPDjtm+Gfx4EOLgxE1I6Vf6cmVNg0sVhdvjDtfFN4YL/wUGnVP4cdUljeS9V1Xs3wwFHewUtQwMj856ED26B/QfAZTMgrgEmfdeVYGg9lZ+fT0pKCkCKcy6/vLG1/u4xs6vNLMvMCs1sgZmV+y/fzE7wjys0sx/N7Mpyxv7WzJyZvR2yfbV/e+jHv4PGPBdmf8V/DYs0NhkjvQuK5JALr9pKZzvsLDj4197XwcEA8KciXgyP9IUXz/KCAdYEElpQdnV/kPim4Irh27chPsHbVlPpjnt2eR8l39fQNMFOYb+vMS1JCHnerLEn8+oVR/Pwb/vw6hVHM2vsyRw36Bive0NxEfz4WUkWQqTvkuFdhL93/XG8f/1xPHpBv6hebpM4Y9ceH8s3FEQMBsDejIKZP5SuD7Gv6hTUmboGgbuJEZn3HukyGGY95K3LDRsMANUcqOPqWkp1Y1nr/sMMLxhw8K8jBwMAWu7nXYw1SYSN31btnD1HhF8SkNwR0vt4v4df/S0sn1q189QVjeW9VBVr58NXT8Gbv/daOIfKOAOatoKfv4Jv/rfv51fTVF9in6rVJQNmdj4wAbga+BL4A/CBmWU458rkpppZN+B94ClgFHAM8LiZbXLOTQ4Z2wV4AJgZ5tQDgOAQUy/gQ+D1kHHTgEuDHkd3u0uksYmUzlZbaa3ZSyLs9F8A/ZIF8YnQ/1IYfA2sW+i/Gx8ueR445xlo2R62rvE6GNRUuqNz8O6NkPujt/whxu9ruUsSPrrTu8DvdY63vCJIxE4JB/8a5k2E7z8gvufp5S4xgNKFEaNdxvDZzSeyPq+Ql+et4emZWRV+i8b89ysObN+Swzqm0DM9iee+XB1Vu8XgrIJYMwrqVAZCXDwMvQ9eHx1mZ1CRKYvz3iubVlTwR3UDr2BeX9XFlOrGstZ90B/hgEFlfk+GdfaT0CoNmrao2jmdD4bcBUsnw8ArYFf+3t/3vmJ483eQ+Q5MugTOnAi9z6/a+WpbY3kvVcVn/mKCfS7w/u8OlZQGJ9wCH94BH46DQ0+HxCpkI9elu/HqpLPP1eqSATObByx0zl0VtG058LZz7rYw4+8HRjrnegZtewLo7ZwbFLQtHvgc+C9wHNDaOXdmOfOYAJwO9HD+b4iZPVfR86J4fVoyILKvRZuKeP7L0DNoXNjU3E7exVXofzw1le745SPw4V+95QkXvwUHnhB+3K4C+ORu6HQkHHFexcct2AgP9vSyHK5dCO26RzefVZ/Ci2dCy1T4v+8gLi6mi+NYljHMWbWlZElCHD4Gxq0gla3k0Jr5vkPxVSGh7dUrji4JeESqUxBpaUWs4wNqvKZB5hQvXbQgKAMi3Pv1m9e9i4mKnPMMHH5u9c1PqqYuplT7ir07dBHXugNxTeBPq2Jv/SrlK94DU6+DxS8DBqf9CwZcXtuzqrxo3kvJneCGpY0zRXztfHhmiPe3wLULoG238OP2FMHEQbBlJQy+Fk69u3Lnq0tLk0reGxXUUGqs740YxLJkoNYyBMysKXAkENpPYwYQqXLKIP/+YNOBy80swTm327/tDmCTc+6ZKJYgNMXLNnjQlY2OnGhmOcBWvADDX5xzEfuBmVkzoFnQpijCyyJSraJNMdxTOsU8prvxNZHu+P10L9IPMOzeyMEAgIUvwrwnvBoJ3U/2UlfLs+wNLxjQqX/0wQCALsdA6wOg89HeHavmraMvjMjeZQyhAYS0MAGEQEZB74IvuCPhBTra3uUe611b7to9miVJx/PW1cewYkM+367P59MVOXy95pcKX8b/TVpM786tuefswxk/NTPqjIJin4tpfEBlMwpiCiJE+35NSot4vlIa8524uqguplTHxcOQv8Hk31E2m8rv4GH1NxiQswKatoTWnSv3/OI9XveBmhDfBEY+5s1v/n/gvZugaBsMuqbu3NWNRVy8d7EZtm6C34Ar6sdrqQnB2QGRggEATZp6QeCXz4W5E6HvaGh/cGznqmt342PppKOstmpTm0sG9sNL2w/932wjEOkvmLQI45v4j5dtZscAlwN9opzHmUBr4LmQ7R/gLSFYA3QD/gZ8YmZHOud2RTjWbcC4KM8rIjWhKqmIcfHR/QdT3emOOcvhjcsBB0eOgYG/L3/8wCu8O0Ubl8G02+Ccp8ofv8S/vrD3b6ObT0CTpnD9N2VabkVcYhBGtAGE+Djj8X4/03v2hDLHSCOXxxMmsKTfgaSlJJKWksiJh6TS74A2UWUVrM8rZPO2HDLX55epNRAsUKfg0Ns/4Id7hjM/Kzeq8bN+2MQJh6QCkTMKKuqUEFUQoWi79z455rq9f/hX9H6tS51AJHp1NaV68w/QKnVvwcqA5E5w8u3Q+4K923y++lPozDl49wZYtwDO/o9XiyZay9+FT++BrsfA8H/Gfu6VH3u/y484v/wAXlwc/Pof3rrxWQ96zwm9k1qfOoccMhxa7Ac7Npfe3iTRC9jPe8L7Pyu0PlFDt3Y+rPrYyw447uaKx/cY4gXivp/mLSMaNblsm8xI6uLSpLoYDG0E6kLbwXCZmOWtY4iUuenMLAl4CbjCORfyGyaiy4EPnHOlwlHOudeCHi4zs6/xggOnAW9GONa9wINBj5OAn6Och4hUh31xAVThOYj+HNu3wCvnQ1EBdDkWfv3Piv8zj0+AkY/A07+CpZO8ZQM9hoQfm7McNnzjpfIednbF8wkV7R8W5YgqgOArpu+39+GsbHnHOAOH0ffb+2HIqJI/TKLJKljQ8jjuPasXv+zYzeZtkWK5pe3xFwzMKYgcDAiWu90rL1Psc9w0aUmlMgqiCiJ8+Qh8/wFsWg7XLKDY4ivOKPDfiXOTRuMoXUnYBxgOO+6mxnsnrq7qMhiSOkJBdK1H94kduTDn397vqnP/69VViXRnevdOePk3XoCg70X7bo6VtfIj+GmOdzHa+ajYnmtxkPOt93359T9i/5057wmvkGFhPpzy1wrOZfCrcZCQCJ/eS525q1sZmW97wYDm7eCc/8DOrd57Ke1weHaY93vutVEw5j3v9TYW0WYHBBt6jxes6xeutkw56uLd+LoaDG3gajMgsBkopmw2QCplswACNkQYvwfYAhwGdAWm2t5fyHEAZrYHOMQ5tyqww1948FdAhX8lO+eyzWwN0KOcMbuAkr84rRr+kBaRGJWkIpZTJHDYfVW7ACr3HH7D7vfGFe/xKkQHik6FFu6Z87hXrLBNVzj/Re+ufDQ6HQlHXQVz/+0VIrx6LjRrVXZcIDugx6nQMrq7+mU451XRTu4ILdpW7hgV8f9hErmLgf8Pk/lPeRcYzZKiyyrofyB9M34FeHUKovH4RV6HhNSk6P4ITUtpDsD8rFx2FEWu1h/IKJiflcug7u1YvHYrcQbjpnxbcRBhfx/xXz7s7fjVeKYt3xR9LQffAN4uur5M0GSLSyaRIpj5BEmHn1e1glSVVZcKWdUlcfHQ4bByAgKu6r/HYvXlw95Fb9rhkHFm+Xf/F70Eq2d6H/nr4dgbvQvuuvRzDrz3CrK9O/zgZV+V28UjjANPgPhmsPUn2PQdpB4a/XMLNnoZAlA6u6KieS94jjp1VzdWzsGXE7yvj74SDvpV6f0XvAL/OQnWfe11SzmpTFmxhmvQ1bB7R3TZAQHtusM1X8ee3521ZQAANWRJREFUkVMX78aX3HCpQ8HQRqDWAgLOuSIzWwAMAd4K2jUEeCfC0+YAI0K2nQp87ZzbbWYrgMND9t+Nd6f+eiC0b8elQA7wXkXzNbN2QGcgfA8rEak7Ai37whbJCVMksFrPEVLYbf6T3h2g4Q947QRDx7dKhdZdvR7TsV5sn/wXWDHV+0P0k7vh1yElWXw+WOpvnnJEFapSvzYKVrwLpz8E/S+r/HHKE+0fHNPGemN/Nc7LKlh2b9RZBdF2Pjj1MC/uHO34gd28n1u0GQWBceOnfsuin7aWOzYQRNgy5XZS9+yEzkczzTeQq16ObllCoA5Ctm8gM3b1L7WsYo0vlbeb3UGH/B9wk3+HXfDqvr2AqEuFrOqar/8LKz/0vm7etmwL1c6D9u33aFuOt3Yd4KTbK77wGPA7L4A36yH49G4vxX33jr37a+LnHEtwKdx7D4PUjNjP27QldD3WS/P+YXpsAYGlk7z6LvsPhP0Oiu45dfGubqx+/BQ2LPVa/g4IU/S07YHwm+e8wNIx1+3z6dWqg35VNkASjeB/k1HXs4jyxuW+vBtf6oZLhKTwfR0MbQRqe8nAg8CL/nT8OcDvgQOAJwDM7F6gk3MukAPzBHCNmT2I13pwEF7K/wUAzrlCYFnwCcxsq39f6PY4vIDA8865PSH7WgF3ApPxAgBdgXvwshqCgxciUlfti1aIFZ3DVwwLnvcu2F+J0A1g2ybv8+YfILVn+DGRNG3pXaS/dI7Xh/jEsdC8zd79O3Oh/aFe+u7Bw2J/fQGd+nkBge+n11xAoNyVYkFa7OfdoYSSu3sVZhX4/zCOj7OYWifGOj7ajILAuNbNE2jZNJ7t5WQVABxmWez3o/dfzw1bf8MHr5W/LOGWN75hflYuRcU+1ubuKMki8BHHXF/pC54riv6PSU3vIvGH6fDRuFJVqmu0U0IlC1nVePeGumD1LHjff3fw5Nvh2Jv2/o4p2u5Vm187FzZ9H3sBscqa9ZB3Qd+pPxw8tOLxZvCrO71lBgufLx0MgOhS26t6gR8p6BDpvYeDt6/21ujHGqg4eJgXEPh+BhxzfXTPcQ4Wv+p9HUt9l7p4VzdWgayIfqMjB8K7n+R9SPR8Plj0AnzxL7hsGqR0Cj+ueI/XUviTeyo4YC3djd8XN3WklFptOwhgZlcDtwDpeBfzNzrnvvDvew7o6pw7MWj8CcBDeMsD1gP3O+eeKOf4zxGmfaCZnYrXoeAQ59z3IfuaA28DffEKDmYDnwJ/dc6FZhmU99rUdlCksdu1DT79O8x9vJxBVWyjM/8p6DkickGqou1e8KCyNn4LEwd762tvyap6z+1Sc9vhZTfMfdz7w3BHLuXWfrhhqbdm1wyWvgGTo2i9FdJSb9qybP42ZSmdty0puVu+tlVv/jry8MoX/MO7WD32/k8qzCiYNfbkkovY4HaL4TleTfg7g+Izebt4MDfsvqbi1xujEXGzebTpY96DMx6HvhfVbKeESraVquyc6pVfVnup0jtzvZof5z5bdk36qxd4QcYRD8P+/Wt+Tnnr4JG+ULzLa4fa/eTonleV9mHVcoHv/74FBx2K98CEwyuuzRDr7+JfVsPDvb1CcLesKh2YjST7G3jyOG+5wc3fRfccqJstKSvjp3mQsn/ki9ZgzsHsR7z/59oeWPNzqw2vXwrtD4Gjr4LElModwzmv/sLauXDYOdD/0rIBtZ8XwLvXexkaAPsdApu/I+Lyx/Ne3PfZSK28Ir1hg4KYF8xo1wMOPnXfzaseiqXtYK0HBBoyBQREBNi3f8DVxJps57w/ovPWwgWvwSExZhtEmlPWFzDlWu+PaYBDT4cVgRVcYe7Hh95NjPX7GkijzJyCmzYWC7rYcMkdsXJSmKO9Mx0oEBjhFZTpMlBREOHYuKW81PReXHwi35/3KZNXwX++yKrwJZ/SM5VeHVPYmF/I/76qOI59U5NJXNfkbXbThAc7/ouJP7YvMybSawiI+oK9Ev8eIhVerGhOlVJbdQ12FcAzp0JOJqT3gUs/CB9827kVmiXvuwr+sx+DGX/x2pCOeS/6onnR/px7XwS9zvYykVq0je0Cv8KgA14gs/2hsH0zbNvgdUioSGV+Fz820LuwOvdZ6HVOxeOn3eYFQjPOhPOej/48Ja+5nKK2CS1g7Jroa9LUdZ/eC5/fB+17ene+NyytW/UoqmrtfHhmiFf895qvoy8mGE72Enjy+LLbkztCn4vgiwcA5wWghvzN27bi3TBLaIAmzeH6JZC0j5YM7MiFR4/0luCMfCR8kOzrZ726SS3awVWzo2+t2wjFEhCo7SUDIiIN375K8cycAlOvL73euDrW6pp5KbFfPeW1NoolIBDuTl9SuvcH+o+f+ufYCU6f4EX7I94ZDJMmGFW3h0570x3fuRrWLYQtP5RZZmAVpDBH22pxWK90Jo7qV+bCOC3CneyKliXM9h3GNwPu54i2xRxySAYnNdkSVUDgd8ceyKDu7Sj2OT7/flPEgANAy2bxfND6Ug7KXc/w+PkMWPtfJnJLmXHV0ikBYv73EKiDEGv3hkqpzboG8570ggGtOsBvX4mcidO8dc3OI9Tga7xlOonJsVXQj/bnvORl7wOgdRfvDmHEnzbwzh+9rgAFG2Dz9xWsp8drYZe9OMpJ+1Xmd3Hv872lX627RDe+eLeXHdDnwtjOE01R29074MM7ytaVqW3bNnnzj7VezpFjvEKKm5bDvw7xfqYBtV2PojoEOgv0jqGzQCS/rAm/PT/bCwYkd4RuJ8Cpf4OW+3n7Qpc/tmwPH46D7EXe++jsJ6s2p2h9dq/398uWldA0KfyYPhd5QYENS+HN38PFb9ef9qZ1mDIEapAyBEQE2DcZAplTYNLFEXZa1dtQ/fARvHyO1wrtpszoLgwi3ukL0v8y+NX40hXuY107PClQZqacrII9u+Af3b0q6RFVcelGkFjXutfksoRosxZycrewavLfGLPqBHbRlDh8pYoQzvcdis/fuLDrfi04tEMy+7dpTnrrRCZ+torN24rCvrYyc1rwvLcOviL+fw8VL6vwvHrF0WWCNjH9HGK5M10FEefkK4aPx8OhI6DzgIoPVLTd+8O45wivS0ldE+3vvQNP8rKPtqysubkMuhYOO8vLRpocRR2UfZVuv3OrV7MgqgJwIcIGrzp5r3PpG97yjg6VKJIYqjovjt//k1cocOjfY69HM/Mh+PjOMDuq99/nPgkKBn9Pd/7i1QypjuyAaJbpJKXDjcsq/hmuWwhPnQw4GPM+dD2m8vOKxsZMeOJYr8jm6Cle945INn0P/znBC3ydMg6Ou6lm51ZPaclAHaGAgIgAUaR4VvFCtCprdaO1uxD+cSDs3g5/+ALSe1dxTngFAm/+vup3XiL9YRyaVZD5TlDwoBy1tPY2+EIxLXEP/bu2Iz6xbCvJWJclBJ4TTcDhncXruP5/ixkaN59xIW0K17u2jN89mum+gZV6fTed0o1zdk6m46IJmNuDc5HjSoUk8OBhkzn3+H4sz87n+v8trvD4d47MYMzgvX9Mx1RzYF/8GyL2+hXlen0MfPsW9LvES6+tbvnZEN+08u1KY/29t3Or19pw1oMVH7vnCOh+ivecsBeJIQL/pmv6d/G+Fulivap1YwKq8+J4+xZ46DDYs7PiC75Q++jf5z4JCobtcIF31/6SKVU7dnXffHj3Ri/oePwtXlejmuIcvDDSW0bYcwSc/1LFz1n4Iky5xqvbcdn06IKo1aEetcrVkgERkbqk3BTPamijsy/aUCUkwrB7oPUBXrp/lecE7NhcPa2xou0oUbw7uuPVUnXuUssSpv8F3psMpz0Ihw4vNS7WZQmB5wzJSKvwbnlqUiJD4+YzMWFCmWUVaeQyMWECV+2+gSOGjKJVswR+/mUH87NyWfJzHkDErILuto7jZ/6VTnGrAFhU3J3ecatwzmsRGRC4R5Fou7lk2aWsaP0wqQdGV+E6Lii68MLs1dwx5dsyY8IuYYAq/RuKpb7E2688wesJL9Cx6d5Ay/ZdzRj7yu/hwj+G/dlFPP5RV3kBgcWvwPE3e/82q9OM270lQiMeLlWUM2qx/t5r3torWBhNQGDgH/Ze4H/1n4ov8APLhmr6d7Fz3hruXQWRf68V5nnzjaU9YSRx8eHPExwMWD3Laz972oOxva5KdgKJaP5/vGBAeh/oFmaNe3n2xf9xvmLvQr28xUnTbvX+r6ns+6O8rLmsL7z9VQk4VPfyxJP/6hU3rekA+Yp3vdcf36xUp5ty9R3lLTtcNtkrLnzlzMoXY4xWA26Vq4CAiMi+UJNtdPZVjYIjx0Q/dl+3xor0h3GwaHsp78uey8ECdx42fANzJ3qpk/Hhi4JFe4EfLJo6CAO7pHBg0xexMHfv4wx8DsY3fZH2x/2V+CbenxCBlP7ysgqasZs+casooAUzuvwf//fdoQyN+8obz97x2bTjmT3DuCj+Yw6M20DH2WNwLcaRnpzBhvxdWISAQ/OEOM7v3xnwLqDvm7Yi7OsL/BlepuZAwYZyvy8lQt6vsSz1+OztZ3k8YUKZQ7a0XTya8Ci3vd2cIRl/LlObIfLxj/LuKmb9f3t3Hh9Vdf5x/PNMSMIaFhHCpiKCsimK4l6Ximu12lZL1WKttVXrT/lpXUrrVqtttT+Xal2q1q3UfUWr4m4VBERFBBUEBGTf1xCSzPn9cWaSIZnJ3JlkksnM9/165ZXkzpk7d+ZkJvc89znPeRfevw2+F2AgHdTymf5EG+crn6cr1c+9pHVBGmGAn8nP4ujgpHRPP0CJZ8ZT8PKlsOeozM/NLlsHj50O5ev98rMn3QmLJie/utnYg+Ntm31AAPyyjKnUooCm+X+S6aBDva9pREMDDo39P65tl8wHAyq2+gA4wMEXBZ/+ZOaXXf52qu+XhR8GWxI1XY0dIMsyCgiIiDSVoFeyU5VtA91w2P+TDqIpB99BBxtfvuS/79Cv6Y4t3pWHguK667fHCFroMBUFiybRndXUSQ+ICBmUshoWTao+URzRtwuj2n/KjRW31WkfzSoYW3gZ4e9cQ4e9fkzPVa3hqw95LTyC18v3jTvAf6LqCN7Z/Xm6LngJe/0qHh58Mbd8YlwdJ+Dwh4rRnPzj8ygu9O+jKfPXsGVbVb3Pc+n6rUyZv4YD++3AfY89ydFzbyBIKbiqdt2IvluTFVK8/uQh9OjYmjkrNjFpznL+VHF/9WtYmwMuqniAxyf/hNNG7EJhQShYocbDrvABgU8ehUMvrV7CLdUaFrXbHzD1RgznK+CXDk3avt79DzqJqgHH8+Xk1yhbu5g2nXuxx/7HVAeUttNUA/xMfRbverg/zmWf+WMp6Vm3zaeP+e9xXtdG16YTfP8OePrn8NkT8MWLPjAQFe/q5toFMPX+xh0cfzLOF4vrvAsMTGPQ1BT/4zIddGiKLIdUA2qpWLcIvvoP7P+r9I4tkbXzfbCkQ0845H9Tu2/rjnDqQ/7nXsMzl87fFNkjzUwBARGRphTkSnaqMnkSUNvij/1VsJ0O8HP9als52xeMWzgpyY4a8ZiCCjLY2PUImHwPfPwIHHOjz4owy+y8wURXHqrKm/7KQxonxQVfvsR1BQ9ARd3BbjSr4JrCRwkdMgtCBYzo4OjRsTXL1m8lTIgPw9sXPjOgQ8cudB79KEx7ACbewYB+A7h75oW4Wq9Rqa3h7qLbsdBwwL9GKzbWXE2vrzDiio1bwTkfDKj8pnq6QryLl2EHK+jM5W+2YvFz79ClXREzFq+vd+WDa16cSVXYtzggNGu7aQK1hQx6sprx45/h+v8MYWjPjsxauiH5ygpXHEnBzgfDgg/8/Pvjb0qtdgJ1sxCG2DxeKn4JRwg7YmzS9sH3D+ADFj3eezdh+yYb4Gfis7hdV+i9rw+IzplQN6tq1RxY/JGf97znaY372IkM+r5f237iHdsHA6Dm6uZB/wNb1/m07egysEEE+byoqoRJd/ifD7wwvQKKQVaUKS6BnQ5Mfd9R7eoutRpXukGHpshyyNSUmC1r4K4DfVHeboMa933TbSBcOBXWzE2v7kWv4f57JtP5myKY08y0ToOISEsXPQkA6l7abYR5sbHmvA6T7vRrk8942hcxClf5ubPv/RXuOdgHAwrbwd4/jTx+ho8pFdHBRkmtgUhJT7/98Cthl0P9lfmXxsBjo3zxotuG+GJNz5zjv982xJ+ANFTQNNJw/Ve8G02qV+LCYXj2FxSXr4575Rv8YLdN2TJ/skTNUouQ8C+Da04cREFBCEacCxd8CO/cgOHqnLSEoveJeY26dWgNwDGhKbxffBGPF/2RvxXdyeNFf+T94os4NfQOxWzz7cwoP/waPu5yAr+tOAeHH/zHCjv/GO0oo2T1J8xduZmp36xla0U44cvj8FfR+3Ruw4l79eTEXYOdbvVptYGtFWGmLljL5nqyHBzRLIfVcFhkichpD/H21Omc/6+PtxusQ01WwaufL91uezQLYfn6LRwQmsVJoYlc3+pBAJ6rOohXl5fEbZ/q/oO2rzboJBjzuS9+9sMH/PcxM+o/sY8O8If+yH9P8tlSFXZMmruaFz5dzKS5q6uDNw3WP5K2PHtC3ds+/bf/vttR0L5b4zxeMuGqyPSPeCLPefK9Pgi69hsfrNhxYLB9B/m8WPqpH8i33cEvGZeOIP/jyjf4YHTQWjGxtm7w/9PqZdsvY5uqpsrkS/Y/Lp3BcdsuNQGs//wmvde4PkVtG5YxEw2q1x60RwNe9f2vDlf585jY85lYq+bAh3cFO45mqj/UGLTKQAZplQERaVJBq+03xAd/g9ev2n5bNAo/902/VvRuI/3cvk59muaY0lHfFf9w2J8AvHkdVMVfSi9p1emgGQVNsSRlKtKpDP/gsbDii+T7/uED2xWnC3ylOehrdMbT0H8kVWHH7268kRsrbgLiFy18teA7HP37F6vT3JPVQSh3hfQNLacqVMi8g25i3JYRPDQxwXrfMW4fNYzvD+tF1bz3KHgkTkZNLZVnjuebkn144P15PDZlUdL2xQVG367tuKN8LBVtunHZulOZWRa/sJYB3UqKeeHXh+BwFLcq4IS//Zc9N75X5zk7B1dVns3r7b7HKxd9h3atC2gVCnHIX96qM7iP3X/s8pJVYcfBkSUyg7RvSqlmOaRk6XS49ztQ2BYun+8LskLkvTXUX0k89SG/PGBTCPr+GXSyH7DvfKA/9no/B/BTmi6ZVbOWfX02LIGVX0G/I1I58roS/T8ZcIxfztRV+Syv0x7ZfinbZNbMh/uOgPJNEK4g/tV1ByPOg+P/En8fyYSr4JZBsClRvZJGXuGisbPatqyBO/eFLat94b+D/qdhx7doKqz8AoadCaEGXJ9uyAoUybIKNq2E/xsALnHwdzvNtEJRIlp2MEsoICAiTa45Utujg+NT7vFrKQ/54fZ51y1omZ7tLP3MnySGKxM0SHCiESR1cdsWPx+zshxeuCD5sdQaTGdUdT9D3JTT2kGQBgQ1As1Fn/G0z8wI4sJp0KUvZTcPovWWZQlrl1WFiij43dLq9OWqsOOQyOA1XuHC1mzjrrb3cnh4MgCL9hrDoZP3I2GxhYgnzxrEiN199f+ymwdRvGVZ3EyKsIPytqW0ucxPq4gGKIIqooJtFAZuD3DysJ6UffY8d0cKHdYOnDjg/IoxvBYewegDd+a4IT0CHdPRg7rzj9H7Bn4Oj517QNxaGA2tg1Dfag/xajPUt2xnSpyDWwbCxqVw5jM+GwBg7tvw6Ml+3vOls2sCBZkW9P1T+zMm4edAjB12g588AV13a/BhBpbo/8nsCX4pzorN0H0onPFU3avk9VkwEVoVw/rFdT+/W3fyUyoKiuCs8X7KXKq2boB7DoV138S5sRGXNcyk6FJ/Re19mn+8Ghn1ifbdxqXwzp/9NIEjfleT5ZSOoP9/vncr7Ptz/7Nz8MX4xCs+YDV9Me5U//u3U6FsbeL2WbhcqZYdFBHJV5mYFwvBiuq8+Qf/D7FOefoMHVOmbV1fTzAAqucNfvMB7BpZRitZJeIjxsK6BTDzBT8f89DfBDuWpiy+mOnK8DECFUZM5bmX9IQFE/0UhXrG6gXhbX5qS+TvMjqNwQ8Ut69rYEAZrdl6yoOw9G6YeAd9pt/G3W0PY8yWn1NBQZ0AgsM4u/2H7Df+Ith4JYw4lzYn3ox7cjThWlMfwoCZ0ebEm6tPJkf07VJdZyHBK0r3jq155OcjWLy2jAWrN/P2Vyt5d/bKQC9TYYGxpbycawsfAerWfjDz58zXFD7K6+X7UlgQ2q42Q302lPl04qDt47VraB2ERO2rwo7rxs9KXpshdgWKVJlB/5E+Bf/rt2oCAjOf898H/yBhMCDVIEgg6aaqJ/wc6AUHXOBrraz+Gu4/0rfb9fC6+1wzH7r0TfvQ40r0/2TA0XD2yzDuNFg+A+4/Cs582s9RjxdE+HQctC/194Oaz6dew+vWo+hzADxzth9EPn46/OLN1J7Xti1++tm6b/xgurAtbF5Rc3tjrHDRFIadAR8/7AfHE34PP/pn8PvGC5RjvphgQwRN01+7sObn6Y9HAvEBVnz4yeP+b676f3sGlivNAgoIiIhIcnlQVKeOoCca406FnsOg++CapdrqiGx7+4aaTZ12hq79m64gZCpSKdKW6bXdgwYcznvfz0VNs3jXsUN6cPeZ+9QZWJbGDiyH/hG69IOXL+W48LvsUjiPTqHN9IhJt1/pOrLWtWdA5WKoBD57Evb7BQw6CYszwLKSXlitwUBsgCLBK8q1Jw5iQPcODOjeAYDdS0uYN+dzflXwErdUnsoa4mcmPnbu/hzYryszP3iZnvOTFzp8/gTY48A9mLZgbf2vZ8Q5h/iBUrSWQzLRdl+v2MRlT0+nT6c2vPhZ3doC262uEDPID7QaQ6T9lPmrE055gNjaDGvqBKpSGqzvfx4MPdUPJKOOv9kHB7r2j3uXjE1jaEjR2fo+B/Y8DR4/A76dAuMvhl9PhVZFNYPvb6f4IHH/Y+AnjzXNYKnn3vCL1+FfP4LVc+D1a/x69bUHokXt/FKIRR3ggonQaaft9xMv6HDKvb7S/tJP/eD+nAnB1r2v3AZP/tQX/iwu8RkGpUNbZtZcKAQn/B/843D/v67XcH/8yZ5DwuxC5zMOWpekHwwJGvDqtU/Nz/PfTTINIM75TCaXK80CCgiIiEhyTVEhOdsEPdGo2gqLPvRfQew20i+vtNOB/gSrsG12XnlIJbMjkydLQQMObbv4nxtQvOvYIT0YOai0/oHfvmf7AcTjZ7AHi+qc4na19exo6yFUBEeOhQN+XZM1M+gkrNYAyxKcSAcKUMQY0bcLd7W+m6FuNhtoy3vhvWplLYQo7diaEX39QHdgm2AD/CEdtxJqFQqUtVDasTVHDuxefTxB2o/o6/vtrS+X88nCdXyycF3c44i9gr9Xn05sKKukdWGIa16cWe8V/yufncELny5m4Zoy5q7YFOg5vzt7JXv27ki7Yn+anPJgvftgIBJEmLu65m9pjxNTmsaQKAgSFShI0dCAXaLPgfbd/OD2lct98c9WRfGvAn/zHnz5ctMNmDrv4gfrr/3OH3e8gei2zf57/5HQsU+w/Ra181eL7zsSVn7ppyec/lT9qyY4B8+eC1+/4T/nz3jKB4+h5QbOe+zlgzzf/Bdei1mFJFFF/6CFc9Ndsi9owGuPE2LuczBMfyz5vmufz2RqudIsoBoCGaQaAiKSM7Kt+F1TCFpg7/QnfVG9GU/BnNeS7zdePYBsLb6YqkzXsAjyGqVaGDEdSQuE4dORL5nV4OefypXpaRPGMXziBYTd9tMAlrgu/KFiNCeffh7Hdlnu09mnP+7nWycT856ODlwhftZCoqv3Qdqv2LCV+/47j/v+Oz/pIZ06vDdPTfs2+bE3QKuQsWfvjuzYoZjXZtYNdCarOfDq50u5/sUZ9Nk0nW6sZQWdWdR+L646aWidaQypFGuM3X9KQYpZL+JevQKLef+4ONkptQX++0t4FTjyLJp6fnzSYnP4z49UPweWfAoPHudXotn/PDguSZHBqff74MSof8Nu3w3+ONkqWS2h0x72mTHrFvoCjeGqzJ87NGHdm5ZENQRERKRxNSTttKUKemWtdIj/6lAaLCAQ7wp2rlx5yGS9iKCvUaanMEDkGOoJBoC/vRGm0ASqsxAxvHd7HHVrApTaGu4uuh174zlfwyLKCnCuKm65BYdhtd7TqWYtpNK+W0lrhvQKkIINlFVU0aVdEevLKgItF3jK3r04YWgPenduw88enMLyDeUJr1e2KSygS7tCFq/byscJshWg/poDr36+lPf+fRMTCsfRrqi8evuS8i784d+j4fTzYqYxrAk0jeHd2Ss5co9u1ftPNaPg1fB+XL/1dvpsm16dObJo615cFR7KsQkeO3DQIdNXgdORdJob6U1z6zkMfnCfnyYxMDLIrC8Qut8vYPfjUy/Al42S1hIiZmAOdBsMh14SbN8NyS5swro3uUoZAhmkDAERySmpRuFzRTZdmZbgMpl1kW7l9kwKsvxWQZGfOzvoJNhnNJSth6fOigxua/5mHebf1Qne05laASDVlQnSWckgaNbCojVbGPfhAu55b17S/Q/uWcLeO3Wi347t2WWHdrzy1D/4S+XNdeqrRmMXl3Ap2/qfwFfLN9K/WwdenZkkuBTxs4N24arvDUo5oyCdlRVSuk+mVxpJR6bfo+UbobhD/M+Z1p3g2D/BsNNT3282C9rP0f913QbBwRc33dX4VDLU8uB8RhkCIiLS+HK8qE5C2XRlWoLLZNZFA+oUZEyQwp9V5TDqMdjj+JrN9ghWp9Bh/e/pVLIWUmmfat2BVNtD8KyFPl3aMrBnsIs5M5dsYOYSf74dIsz7xQ9UZxDECpkPClzOQxzy+d6ECdGxTVGgxwDYsUNx4IyCP4yfyXXfH0JV2NVbZwHg6hdmVmc5lG2rYtqCNYx97vPgqzGkWWMmnUKKgQMIDXiPBnqMaDAgXvr81nXw/Pk+ANdUAcGmELSfT7kH9hrlfw5XNd3V+Gype9MCKSAgIiLB5Upqe6qCnmjoJCO7ZGoKQzamnAY9Wa/Ysv3vWfSeDrK6wjUnDqoenKXaPipQ8UiCr5Rw3mH9CBnMXbmJggXv07My+eoN/9t/JXseeiIDurfnB3dNTBrU+M/FhxIy452vVsRpVdeqTX6qwpT5a1i+obzetis2llevrLB0fRlnPjCl3vbRoMPkeavp3bktvdt1224pzYRiBt9pTXtIJYAQeY+6DUu3y36peQ51p8Sk9BhBpkm8fjUMPqXOeyljWRGZFjTIUtKr5udsDpRn0Wdfc1NAQEREUpPJeeK5QCcZuS8bT3IbkrWQRe/pTNYpiBUkayFoBsJlx+xePaCb/cYMeD/58zxmZ2PAgB0BAgU1Orf1mQRBgxRHDyoFYMXGxNkEsaLtCgtClJYUsyxJEAHgy2UbOf3+ybRp5XiroAvdWVOnfgX4rIgVtgNfb+tP6OtVVFa51DIQSCOAECrgk8FXstfEi+rU1fBTNxyfDr6CvWPeoyk9Rpo1CjK2vGQtGQk6pBsIzeZAeRZ99jUnBQREREQam04ycl+2neRmY9ZCmoJewU+3fVDpZCD027VfoIBAv137bXf8QYMaQYMU39vLF7ELGkCItuvTpS23/njvQLUZnHMUtQpRVhnm2vBo7i68rc4KF9G6Cdds+ymvPTgt0LFEMxCOu/09+nfrQNcORTw7bXHSaQ99u7Yj7KCyylFWUcV5U3qyX8UYril8hJ7UZG0sYweuq/gp06f15v2jwrQqCFEVdlw3flZGp0mku7wkpDbAz9hUjIYEQhUoz2oqKphBKiooIiKS4zK51GKq8qBQVnNIaYAVrqLs5kEUb1mW8Gp5edtS2lxWd0nKoIO+VJZzjC5rmCyAEFuEMJX7ADz4wXz++PIXHBOa4gffVjP4XuL84Pu18AhKS4rp2KaIjVsrWFJPHYTGFiLMiNCX1asrTAnvQTgyycGADq1bUVQQYtXmbUn3deER/dh7p8703/IpO40/LWn7qtHjKdj1O2kvLwmp/f2lW0Ay1SUs0ynY2mKnSrRQqRQVVEAggxQQEBERkSaVydUV8lhKg5lZL+KeHI3DbTe3PgwYhjVCYCadQSIkDyCkc5/Y1R7qG3ynujrExd/tT8c2hXwwdxVvfpG8dkLbogLaFrWisMDYVhlmdYABfrqOHdSVq+eOorSeaRLL2IHvF/ydU0f05dD+OwZ6zg+ctS/fHZi81kJ9wZ9Mr0ABUFVZyZeTX6Ns7WLadO7FHvsfQ0GrxInnGS0gKXEpIJAlFBAQERGRJpdNWQv5ataLuFevwGICM66kF9aIgZlMp5EHvU+qWQiptk9necmg97n7jH3o370Dk+at5qrnP0/afnDPElqFjH47tmfz9Oe4u/A2IP40ifMrxvBaeAQ/PWBn9t2lMxc//mnS/e+zUyeeveBgALZVhtn7+glsLq9K2L5H5HXaUFbBDS9/wdMff5v0Mcb9Yn9CZpS0acXZD05lxcb49SISZS2k+rfUJFkLacrloIOWHRQRERHJV6ph0fwGnYTVmjNtjRyYSWX5x3TqLAS9T6ZXh0hnecmg9zl6sK8J0LdrO+56++uk7V+88JCaIMUnIzi/nhoFr4VHcOPJQzh0wI58u7Yszl7r2qlL2+qfX525tN5gAPhaC1Pmr6FPlzaBggEAC1ZvZuxzyYMf0VoOU+av5sB+Xf0xpVgHIdmyl41SQDLmsVL5+1bWQg1lCGSQMgREREREJB+kc+U4W6Y9pNo+NsvB4kyTcITSyop477IjKGzlp1g8PHE+17w4K07r7d0+ahjfHdidix77hLe+TD614s6f7M2tb8xm+YatbEoScAAoLjD26FHCbt3aM2HWcjZurUzYtlPbQn5+8C4sXFPGgtWbmb18E+vLKpI+xtBeJezZuxO9O7fh3vfmsW5L/PvkQ9ZCY9GUgSyhgICIiIiI5ItUr6Bmy7SHdNpnMuAAwac9RKdKZGoqRja6/uQhHDO4O13bFTNh1rKUBvdNWWuhObWogICZXQBcBvQAZgJjnHP/raf9YcAtwGBgCXCTc+6eBG1HAY8BLzjnTo7Zfi1wTa3my51zpTFtLNLml0BnYDLwa+fczBSemwICIiIiIiKNIJ2U7WwKUqTSPp0VIjKxAsVDZ+/H3JWbeXH6El79fFnc1yHWiF26cGj/ruzctR0byyr4XYDaDL/6zq4UtQoxce4qpi1Yl7R9VGHIaFfcinX1ZCF0KG7FWQfvgnOOk4f1YtWmbYECIXedsQ/HD+1BVdhxcOR1iqe+FSKaU4upIWBmPwZuAy4APgB+BbxiZoOccwvjtO8L/Ae4DzgTOBi4y8xWOueeqdV2Z+CvQKLgwkzgqJjfa+fMXA5cAvwMmA38HnjdzHZ3zm1M4WmKiIiIiEgDpVI3Id37ZLI2QyrtU621EN3/3WfuUyfoUBon6BB0/7uXlrB7aQmd2xYFCgj878gB1a9fVdhxZ4DaDJcfuwcFIeOguV0DDda7tCtk3ZYKKsKu3mAAwMbySu5862sAhvbqSHllOOn+AT74ehXHD+3BlPlrEgYDILbWwpqU/zazRXMXFbwEeMA5d3/k9zFmdgxwPvDbOO3PAxY658ZEfv/CzPYFfgNUBwTMrAAYh7/CfyjQKc6+Kp1zcf+qI9kBY4AbnHPPRradBSwHTgfuTXC/YqA4ZlOHeO1ERERERKTly3TAIegAP/Y+QYMOqew/neKOmSog+f4VRxJ2jn99uIDrxievs3Bo/67s1q09fbq0ZUNZ4hoIsfru0A6AFRsTBwNiBW2XjZotIGBmRcBw4M+1bpoAHJTgbgdGbo/1GnCOmRU656IhoquBlc65B8wsUZnd/ma2BCjHTwcY65ybF7mtL1Aa+1jOuXIzezdybHEDAvggRu2pCCIiIiIiIilLZ4WITGQ5pJOxEN1/Y2ctFISMAow9SoNNyb7g8N22y1oIEnQ4+5C+AHTr0DrQYwRtl42aM0OgK1CAv+oeazl+MB5PaYL2rSL7W2pmBwPnAMPqeezJwGj8VIDu+OkAE81ssHNudczjx3usnevZ75/w9Q2iOgDB1gARERERERGpJZ2pEpnYfzoZC9H75XrWQuxjtDTNPWUAiFuwsb5Kh4kKPDoz6wD8CzjXObcq4Q6ceyXm1xlmNgmYC5zF9gP6lI7NOVeOzzjwjS17CkuIiIiIiIg0RDoZC5AfWQstVXMGBFbhC/nVzgboRt0r81HLErSvBFbjVx7YBRgfMxgPAZhZJbC7c25u7Z065zab2Qygf8zjEHmspQGPTUREREREJKdlOmMhlcfItqyFlqjZAgLOuW1mNg0YCTwXc9NI4IUEd5sEnFhr29HAR865CjP7Ehha6/Y/4lP3LwYWxdtppBjgQGpWJJiPDwqMBD6JtCkCDgOuSPrkREREREREJOOyKWuhJWruKQO3AI+a2Uf4wf4vgZ2AewDM7E9AL+fc6Ej7e4ALzewW/NKDB+LrBfwEwDm3FdhusUszWxe57fOYbX8FxgML8Vf9fw+UAA9H2jozuw0Ya2ZzgDnAWGAL8O/GfAFEREREREQkfdmUtdDSNGtAwDn3hJntgF8VoAd+MH+8c25BpEkPfIAg2n6+mR0P3Ar8GlgCXOSce4bU9AYewxciXAl8CBwQ87gANwFtgLuAzvhChEc75zam+FgiIiIiIiIiWcecq69+nzSEmZUA69evX09JSbBlMURERERERETStWHDBjp27AjQ0Tm3ob62oaY5JBERERERERHJJgoIiIiIiIiIiOQhBQRERERERERE8lBzrzKQFzZsqHfahoiIiIiIiEijSGX8qaKCGWRmvYBvm/s4REREREREJO/0ds4trq+BAgIZZGYG9ASyfanCDvjARW+y/1ilYdTX+UH9nD/U1/lB/Zw/1Nf5Qf2cP5qzrzsAS1ySAb+mDGRQ5MWvNyKTDXzcAoCNyZalkJZNfZ0f1M/5Q32dH9TP+UN9nR/Uz/mjmfs60OOpqKCIiIiIiIhIHlJAQERERERERCQPKSAgAOXAdZHvktvU1/lB/Zw/1Nf5Qf2cP9TX+UH9nD+yvq9VVFBEREREREQkDylDQERERERERCQPKSAgIiIiIiIikocUEBARERERERHJQwoIiIiIiIiIiOQhBQQEM7vAzOab2VYzm2Zmhzb3MUnDmNl3zGy8mS0xM2dmJ9e63czs2sjtZWb2jpkNbqbDlTSY2W/NbKqZbTSzFWb2vJntXquN+jkHmNn5ZvaZmW2IfE0ys+Niblc/56DIe9yZ2W0x29TXOSDSh67W17KY29XPOcTMepnZv8xstZltMbNPzWx4zO3q7xbOzL6J8552Zvb3yO1Z3ccKCOQ5M/sxcBtwA7A38F/gFTPbqTmPSxqsHTAduDDB7ZcDl0Ru3w9YBrxuZh2a5vCkERwG/B04ABgJtAImmFm7mDbq59zwLXAlsG/k6y3ghZiTCfVzjjGz/YBfAp/Vukl9nTtmAj1ivobG3KZ+zhFm1hn4AKgAjgMGAZcC62Kaqb9bvv3Y/v08MrL9qcj3rO5jLTuY58xsMvCxc+78mG1fAM87537bfEcmjcXMHHCKc+75yO8GLAFuc879JbKtGFgOXOGcu7e5jlXSZ2Y7AiuAw5xz76mfc5uZrQEuA/6J+jmnmFl74GPgAuD3wKfOuTF6T+cOM7sWONk5NyzObernHGJmfwYOds7Fzb5Vf+emSGbX94D+kU1Z3cfKEMhjZlYEDAcm1LppAnBQ0x+RNJG+QCkx/e6cKwfeRf3eknWMfF8T+a5+zkFmVmBmo/BZQJNQP+eivwMvO+feqLVdfZ1b+kfSh+eb2eNmtmtku/o5t5wEfGRmT5mf3veJmZ0bc7v6O8dExldnAv90/sp71vexAgL5rStQgI9QxVqO/8OV3BTtW/V7johcYbgFeN8593lks/o5h5jZUDPbBJQD9+Czfmahfs4pkWDPcCBehp76OndMBkYDxwDn4vtvopntgPo51+wKnA/Mwff3PcDfzGx05Hb1d+45GegEPBT5Pev7uFVzH4BkhdrzRizONsk96vfccSewJ3BInNvUz7nhK2AY/iTjh8DDZnZYzO3q5xbOzPoAtwNHO+e21tNUfd3COedeifl1hplNAuYCZwEfRpvVupv6uWUKAR8558ZGfv8kUv/lfOCRmHbq79xxDvCKc25Jre1Z28fKEMhvq4Aq6kanulE3iiW5I1rJWP2eA8zsDnxK4hHOuW9jblI/5xDn3Dbn3NfOuY8i9V2mAxejfs4lw/H9Ns3MKs2sEl889KLIz9H+VF/nGOfcZmAGfr6x3tO5ZSkwq9a2L4Bo8W71dw4xs52Bo4D7YzZnfR8rIJDHnHPbgGnUVMKMGglMbPojkiYyH//hVN3vkflOh6F+bzEiS9jcCfwAONI5N79WE/VzbjOgGPVzLnkTX2l+WMzXR8C4yM/zUF/npEiBsYH4waPe07nlA2D3WtsGAAsiP6u/c8vZ+ALPL8dsy/o+1pQBuQV41Mw+wheo+iU+anlPsx6VNEikSvVuMZv6mtkwYI1zbmGk+ulYM5uDn9c2FtgC/Lupj1XS9nfgdOD7wEYzi0ae1zvnypxzTv2cG8zsRuAVYBHQARgFHA4cq37OHc65jcDnsdvMbDOwOlobRH2dG8zsr8B4YCH+KuHvgRLgYb2nc86t+PoQY4EngRH4c+1fAqi/c4eZhfABgYedc5XR7S2hjxUQyHPOuSciRWyuxq+b+TlwvHNuQf33lCy3L/B2zO+3RL4/DPwMuAloA9wFdMYXODo6ckIqLUN0qdB3am0/m5pCNurn3NAdeBT/Gb0evzb9sc651yO3q5/zh/o6N/QGHsMXd16JrxtwQMy5l/o5RzjnpprZKcCf8Ofa84ExzrlxMc3U37nhKPxF1X/GuS2r+9j8aggiIiIiIiIikk9UQ0BEREREREQkDykgICIiIiIiIpKHFBAQERERERERyUMKCIiIiIiIiIjkIQUERERERERERPKQAgIiIiIiIiIieUgBAREREREREZE8pICAiIiIiIiISB5SQEBERERaLDM73MycmXVq7mMRERFpaRQQEBEREREREclDCgiIiIiIiIiI5CEFBERERCRt5l1uZvPMrMzMppvZjyK3RdP5T4hs32pmk81saK19/NDMZppZuZl9Y2aX1rq92MxuMrNFkTZzzOycWocy3Mw+MrMtZjbRzHaPuf9eZva2mW00sw1mNs3M9s3YiyIiItJCKCAgIiIiDfFH4GzgfGAwcCvwLzM7LKbNzcBvgP2AFcCLZlYIYGbDgSeBx4GhwLXA9Wb2s5j7PwKMAi4CBgLnAZtqHccNwKXAvkAl8M+Y28YB30YefzjwZ6Ai/acsIiKSG8w519zHICIiIi2QmbUDVgFHOucmxWy/H2gL/AN4GxjlnHsiclsX/OD8Z865J81sHLCjc+7omPvfBJzgnBtsZgOAr4CRzrk34hzD4ZHHOMo592Zk2/HAy0Ab59xWM9sA/I9z7uFGfxFERERaMGUIiIiISLoGAa2B181sU/QLGA30i2lXHSxwzq3BD/AHRjYNBD6otd8PgP5mVgAMA6qAd5Mcy2cxPy+NfO8W+X4LcL+ZvWFmV5pZP0REREQBAREREUlb9DziBPzAPfo1CPhRkvtGUxQt5mditkWVBTyW2CkA0f2FAJxz1+KnM7wMHAnMMrNTAu5XREQkZykgICIiIumaBZQDOznnvq71tSim3QHRH8ysMzAA+DJmH4fU2u9BwGznXBUwA3++chgN4Jyb7Zy7NTI14Vl83QMREZG81qq5D0BERERaJufcRjP7K3CrmYWA94ES/IB+E7Ag0vRqM1sNLMcX/1sFPB+57f+AqWZ2FfAEcCBwIXBB5DG+MbOHgX+a2UXAdGBnoJtz7slkx2hmbfBFDZ8G5gO98cUFn2nYsxcREWn5FBAQERGRhrgKv3LAb4FdgXXAx8CN1GQiXgncDvTHD+hPcs5tA3DOfWxmpwF/iOxrKXC1c+6hmMc4P7K/u4AdgIWR34OoitznEaA7PhjxLHBNys9UREQkx2iVAREREcmImBUAOjvn1jXrwYiIiEgdqiEgIiIiIiIikocUEBARERERERHJQ5oyICIiIiIiIpKHlCEgIiIiIiIikocUEBARERERERHJQwoIiIiIiIiIiOQhBQRERERERERE8pACAiIiIiIiIiJ5SAEBERERERERkTykgICIiIiIiIhIHlJAQERERERERCQP/T+uT56CbzevoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,8), dpi=100)\n",
    "\n",
    "ax1.plot(hist.history['mae'], \"o--\", label='Acuracy (training data)')\n",
    "ax1.plot(hist.history['val_mae'], \"o--\", label='Acuracy (validation data)')\n",
    "ax1.set_title('MAE loss')\n",
    "ax1.set_ylabel(\"MAE\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(hist.history['loss'], \"o--\", label='training data')\n",
    "ax2.plot(hist.history['val_loss'], \"o--\", label='validation data')\n",
    "ax2.set_title('MSE loss')\n",
    "ax2.set_ylabel(\"MSE loss\")\n",
    "ax2.set_xlabel(\"epochs\")\n",
    "ax2.legend()\n",
    "\n",
    "model_name_1 = model_name + \".hdf5\"\n",
    "model_1 = load_model(model_name_1)\n",
    "model_1.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0661 - mae: 0.2113\n",
      "Epoch 00001: val_loss improved from inf to 0.06335, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0661 - mae: 0.2113 - val_loss: 0.0634 - val_mae: 0.2061\n",
      "Epoch 2/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0600 - mae: 0.1992\n",
      "Epoch 00002: val_loss improved from 0.06335 to 0.06228, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0600 - mae: 0.1992 - val_loss: 0.0623 - val_mae: 0.2019\n",
      "Epoch 3/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0584 - mae: 0.1953\n",
      "Epoch 00003: val_loss improved from 0.06228 to 0.05719, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0584 - mae: 0.1953 - val_loss: 0.0572 - val_mae: 0.1940\n",
      "Epoch 4/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0573 - mae: 0.1929\n",
      "Epoch 00004: val_loss improved from 0.05719 to 0.05599, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0573 - mae: 0.1930 - val_loss: 0.0560 - val_mae: 0.1919\n",
      "Epoch 5/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0564 - mae: 0.1911\n",
      "Epoch 00005: val_loss improved from 0.05599 to 0.05542, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0564 - mae: 0.1911 - val_loss: 0.0554 - val_mae: 0.1910\n",
      "Epoch 6/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0559 - mae: 0.1899\n",
      "Epoch 00006: val_loss did not improve from 0.05542\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0559 - mae: 0.1900 - val_loss: 0.0558 - val_mae: 0.1881\n",
      "Epoch 7/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1892\n",
      "Epoch 00007: val_loss did not improve from 0.05542\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0555 - mae: 0.1893 - val_loss: 0.0557 - val_mae: 0.1909\n",
      "Epoch 8/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1886\n",
      "Epoch 00008: val_loss improved from 0.05542 to 0.05407, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0552 - mae: 0.1886 - val_loss: 0.0541 - val_mae: 0.1877\n",
      "Epoch 9/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0550 - mae: 0.1880\n",
      "Epoch 00009: val_loss improved from 0.05407 to 0.05380, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0549 - mae: 0.1880 - val_loss: 0.0538 - val_mae: 0.1884\n",
      "Epoch 10/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0545 - mae: 0.1872\n",
      "Epoch 00010: val_loss improved from 0.05380 to 0.05344, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0546 - mae: 0.1873 - val_loss: 0.0534 - val_mae: 0.1865\n",
      "Epoch 11/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0543 - mae: 0.1869\n",
      "Epoch 00011: val_loss improved from 0.05344 to 0.05305, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0543 - mae: 0.1869 - val_loss: 0.0530 - val_mae: 0.1843\n",
      "Epoch 12/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0541 - mae: 0.1862\n",
      "Epoch 00012: val_loss did not improve from 0.05305\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0541 - mae: 0.1863 - val_loss: 0.0532 - val_mae: 0.1865\n",
      "Epoch 13/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0539 - mae: 0.1860\n",
      "Epoch 00013: val_loss did not improve from 0.05305\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0539 - mae: 0.1860 - val_loss: 0.0544 - val_mae: 0.1889\n",
      "Epoch 14/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1854\n",
      "Epoch 00014: val_loss improved from 0.05305 to 0.05280, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0536 - mae: 0.1853 - val_loss: 0.0528 - val_mae: 0.1845\n",
      "Epoch 15/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0535 - mae: 0.1850\n",
      "Epoch 00015: val_loss did not improve from 0.05280\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0535 - mae: 0.1850 - val_loss: 0.0528 - val_mae: 0.1859\n",
      "Epoch 16/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1848\n",
      "Epoch 00016: val_loss improved from 0.05280 to 0.05224, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0534 - mae: 0.1848 - val_loss: 0.0522 - val_mae: 0.1835\n",
      "Epoch 17/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1847\n",
      "Epoch 00017: val_loss improved from 0.05224 to 0.05215, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0532 - mae: 0.1846 - val_loss: 0.0522 - val_mae: 0.1826\n",
      "Epoch 18/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1844\n",
      "Epoch 00018: val_loss did not improve from 0.05215\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0531 - mae: 0.1843 - val_loss: 0.0527 - val_mae: 0.1846\n",
      "Epoch 19/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1843\n",
      "Epoch 00019: val_loss improved from 0.05215 to 0.05164, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0530 - mae: 0.1842 - val_loss: 0.0516 - val_mae: 0.1832\n",
      "Epoch 20/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1840\n",
      "Epoch 00020: val_loss did not improve from 0.05164\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0529 - mae: 0.1839 - val_loss: 0.0528 - val_mae: 0.1850\n",
      "Epoch 21/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1837\n",
      "Epoch 00021: val_loss did not improve from 0.05164\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0528 - mae: 0.1837 - val_loss: 0.0521 - val_mae: 0.1833\n",
      "Epoch 22/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0527 - mae: 0.1834\n",
      "Epoch 00022: val_loss improved from 0.05164 to 0.05147, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0527 - mae: 0.1834 - val_loss: 0.0515 - val_mae: 0.1828\n",
      "Epoch 23/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1832\n",
      "Epoch 00023: val_loss did not improve from 0.05147\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0526 - mae: 0.1832 - val_loss: 0.0526 - val_mae: 0.1848\n",
      "Epoch 24/100\n",
      "39144/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1830\n",
      "Epoch 00024: val_loss did not improve from 0.05147\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0524 - mae: 0.1830 - val_loss: 0.0518 - val_mae: 0.1839\n",
      "Epoch 25/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1830\n",
      "Epoch 00025: val_loss did not improve from 0.05147\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0524 - mae: 0.1829 - val_loss: 0.0523 - val_mae: 0.1849\n",
      "Epoch 26/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1828\n",
      "Epoch 00026: val_loss did not improve from 0.05147\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0523 - mae: 0.1829 - val_loss: 0.0521 - val_mae: 0.1859\n",
      "Epoch 27/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1827- ETA: 1s - l\n",
      "Epoch 00027: val_loss improved from 0.05147 to 0.05112, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0522 - mae: 0.1827 - val_loss: 0.0511 - val_mae: 0.1826\n",
      "Epoch 28/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1827\n",
      "Epoch 00028: val_loss did not improve from 0.05112\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0522 - mae: 0.1828 - val_loss: 0.0520 - val_mae: 0.1837\n",
      "Epoch 29/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1825\n",
      "Epoch 00029: val_loss did not improve from 0.05112\n",
      "40000/40000 [==============================] - 3s 72us/sample - loss: 0.0521 - mae: 0.1826 - val_loss: 0.0512 - val_mae: 0.1828\n",
      "Epoch 30/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1822\n",
      "Epoch 00030: val_loss did not improve from 0.05112\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0520 - mae: 0.1822 - val_loss: 0.0513 - val_mae: 0.1819\n",
      "Epoch 31/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1822\n",
      "Epoch 00031: val_loss did not improve from 0.05112\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0520 - mae: 0.1823 - val_loss: 0.0517 - val_mae: 0.1845\n",
      "Epoch 32/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1820\n",
      "Epoch 00032: val_loss improved from 0.05112 to 0.05103, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0519 - mae: 0.1821 - val_loss: 0.0510 - val_mae: 0.1823\n",
      "Epoch 33/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1820\n",
      "Epoch 00033: val_loss did not improve from 0.05103\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0519 - mae: 0.1821 - val_loss: 0.0512 - val_mae: 0.1821\n",
      "Epoch 34/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1820- ETA: 0s - loss: 0.0519 - mae: 0.1\n",
      "Epoch 00034: val_loss did not improve from 0.05103\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0519 - mae: 0.1820 - val_loss: 0.0511 - val_mae: 0.1813\n",
      "Epoch 35/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1819\n",
      "Epoch 00035: val_loss did not improve from 0.05103\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0518 - mae: 0.1819 - val_loss: 0.0537 - val_mae: 0.1851\n",
      "Epoch 36/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1820\n",
      "Epoch 00036: val_loss did not improve from 0.05103\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0518 - mae: 0.1819 - val_loss: 0.0514 - val_mae: 0.1805\n",
      "Epoch 37/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1820\n",
      "Epoch 00037: val_loss did not improve from 0.05103\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0519 - mae: 0.1820 - val_loss: 0.0511 - val_mae: 0.1822\n",
      "Epoch 38/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1820\n",
      "Epoch 00038: val_loss improved from 0.05103 to 0.05071, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0518 - mae: 0.1820 - val_loss: 0.0507 - val_mae: 0.1804\n",
      "Epoch 39/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1819\n",
      "Epoch 00039: val_loss did not improve from 0.05071\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0518 - mae: 0.1819 - val_loss: 0.0510 - val_mae: 0.1821\n",
      "Epoch 40/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1816\n",
      "Epoch 00040: val_loss did not improve from 0.05071\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0517 - mae: 0.1818 - val_loss: 0.0524 - val_mae: 0.1855\n",
      "Epoch 41/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1817\n",
      "Epoch 00041: val_loss did not improve from 0.05071\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0517 - mae: 0.1817 - val_loss: 0.0510 - val_mae: 0.1805\n",
      "Epoch 42/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1819\n",
      "Epoch 00042: val_loss did not improve from 0.05071\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0517 - mae: 0.1819 - val_loss: 0.0515 - val_mae: 0.1827\n",
      "Epoch 43/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1816\n",
      "Epoch 00043: val_loss improved from 0.05071 to 0.05057, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0517 - mae: 0.1816 - val_loss: 0.0506 - val_mae: 0.1810\n",
      "Epoch 44/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1818\n",
      "Epoch 00044: val_loss did not improve from 0.05057\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0517 - mae: 0.1819 - val_loss: 0.0506 - val_mae: 0.1815\n",
      "Epoch 45/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1816\n",
      "Epoch 00045: val_loss did not improve from 0.05057\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0516 - mae: 0.1816 - val_loss: 0.0514 - val_mae: 0.1819\n",
      "Epoch 46/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1816\n",
      "Epoch 00046: val_loss did not improve from 0.05057\n",
      "40000/40000 [==============================] - 3s 72us/sample - loss: 0.0516 - mae: 0.1816 - val_loss: 0.0510 - val_mae: 0.1812\n",
      "Epoch 47/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1816\n",
      "Epoch 00047: val_loss improved from 0.05057 to 0.05054, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0516 - mae: 0.1816 - val_loss: 0.0505 - val_mae: 0.1807\n",
      "Epoch 48/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1815\n",
      "Epoch 00048: val_loss did not improve from 0.05054\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0516 - mae: 0.1815 - val_loss: 0.0507 - val_mae: 0.1814\n",
      "Epoch 49/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1815\n",
      "Epoch 00049: val_loss did not improve from 0.05054\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0516 - mae: 0.1815 - val_loss: 0.0516 - val_mae: 0.1831\n",
      "Epoch 50/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1816\n",
      "Epoch 00050: val_loss improved from 0.05054 to 0.05047, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0516 - mae: 0.1816 - val_loss: 0.0505 - val_mae: 0.1813\n",
      "Epoch 51/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1817\n",
      "Epoch 00051: val_loss did not improve from 0.05047\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0516 - mae: 0.1817 - val_loss: 0.0510 - val_mae: 0.1803\n",
      "Epoch 52/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1814\n",
      "Epoch 00052: val_loss improved from 0.05047 to 0.05045, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0516 - mae: 0.1814 - val_loss: 0.0504 - val_mae: 0.1809\n",
      "Epoch 53/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1815\n",
      "Epoch 00053: val_loss did not improve from 0.05045\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0516 - mae: 0.1814 - val_loss: 0.0519 - val_mae: 0.1841\n",
      "Epoch 54/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1814\n",
      "Epoch 00054: val_loss did not improve from 0.05045\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0515 - mae: 0.1814 - val_loss: 0.0507 - val_mae: 0.1808\n",
      "Epoch 55/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1814\n",
      "Epoch 00055: val_loss did not improve from 0.05045\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0515 - mae: 0.1814 - val_loss: 0.0537 - val_mae: 0.1877\n",
      "Epoch 56/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1813\n",
      "Epoch 00056: val_loss did not improve from 0.05045\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0516 - mae: 0.1813 - val_loss: 0.0523 - val_mae: 0.1857\n",
      "Epoch 57/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1812\n",
      "Epoch 00057: val_loss did not improve from 0.05045\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0515 - mae: 0.1812 - val_loss: 0.0506 - val_mae: 0.1805\n",
      "Epoch 58/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1811\n",
      "Epoch 00058: val_loss did not improve from 0.05045\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0515 - mae: 0.1812 - val_loss: 0.0508 - val_mae: 0.1796\n",
      "Epoch 59/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1812\n",
      "Epoch 00059: val_loss improved from 0.05045 to 0.05039, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0515 - mae: 0.1812 - val_loss: 0.0504 - val_mae: 0.1806\n",
      "Epoch 60/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1812\n",
      "Epoch 00060: val_loss did not improve from 0.05039\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0515 - mae: 0.1813 - val_loss: 0.0511 - val_mae: 0.1823\n",
      "Epoch 61/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1811\n",
      "Epoch 00061: val_loss improved from 0.05039 to 0.05032, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_0.hdf5\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0514 - mae: 0.1812 - val_loss: 0.0503 - val_mae: 0.1800\n",
      "Epoch 62/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1812- ETA\n",
      "Epoch 00062: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0514 - mae: 0.1812 - val_loss: 0.0505 - val_mae: 0.1800\n",
      "Epoch 63/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1813- ETA: 0s - loss: 0.0514 - ma\n",
      "Epoch 00063: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 5s 113us/sample - loss: 0.0515 - mae: 0.1812 - val_loss: 0.0515 - val_mae: 0.1823\n",
      "Epoch 64/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1812\n",
      "Epoch 00064: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0515 - mae: 0.1812 - val_loss: 0.0509 - val_mae: 0.1811\n",
      "Epoch 65/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1812\n",
      "Epoch 00065: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0514 - mae: 0.1810 - val_loss: 0.0505 - val_mae: 0.1792\n",
      "Epoch 66/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1812\n",
      "Epoch 00066: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0515 - mae: 0.1811 - val_loss: 0.0508 - val_mae: 0.1804\n",
      "Epoch 67/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1808\n",
      "Epoch 00067: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0513 - mae: 0.1807 - val_loss: 0.0519 - val_mae: 0.1823\n",
      "Epoch 68/100\n",
      "39144/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1808\n",
      "Epoch 00068: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0514 - mae: 0.1808 - val_loss: 0.0521 - val_mae: 0.1836\n",
      "Epoch 69/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1810\n",
      "Epoch 00069: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0514 - mae: 0.1810 - val_loss: 0.0505 - val_mae: 0.1802\n",
      "Epoch 70/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1808\n",
      "Epoch 00070: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0513 - mae: 0.1806 - val_loss: 0.0510 - val_mae: 0.1798\n",
      "Epoch 71/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1807- ETA: 2s - loss:\n",
      "Epoch 00071: val_loss did not improve from 0.05032\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0513 - mae: 0.1807 - val_loss: 0.0511 - val_mae: 0.1817\n",
      "Epoch 00071: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0819 - mae: 0.2261\n",
      "Epoch 00001: val_loss improved from inf to 0.05993, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 112us/sample - loss: 0.0816 - mae: 0.2257 - val_loss: 0.0599 - val_mae: 0.1991\n",
      "Epoch 2/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0596 - mae: 0.1976\n",
      "Epoch 00002: val_loss improved from 0.05993 to 0.05718, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0596 - mae: 0.1976 - val_loss: 0.0572 - val_mae: 0.1948\n",
      "Epoch 3/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0580 - mae: 0.1941\n",
      "Epoch 00003: val_loss did not improve from 0.05718\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0581 - mae: 0.1941 - val_loss: 0.0575 - val_mae: 0.1941\n",
      "Epoch 4/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0573 - mae: 0.1922\n",
      "Epoch 00004: val_loss improved from 0.05718 to 0.05643, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0573 - mae: 0.1922 - val_loss: 0.0564 - val_mae: 0.1914\n",
      "Epoch 5/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0569 - mae: 0.1913\n",
      "Epoch 00005: val_loss improved from 0.05643 to 0.05533, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0569 - mae: 0.1913 - val_loss: 0.0553 - val_mae: 0.1903\n",
      "Epoch 6/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0566 - mae: 0.1909\n",
      "Epoch 00006: val_loss improved from 0.05533 to 0.05487, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0566 - mae: 0.1909 - val_loss: 0.0549 - val_mae: 0.1883\n",
      "Epoch 7/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0562 - mae: 0.1903\n",
      "Epoch 00007: val_loss improved from 0.05487 to 0.05451, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0562 - mae: 0.1903 - val_loss: 0.0545 - val_mae: 0.1882\n",
      "Epoch 8/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0558 - mae: 0.1894\n",
      "Epoch 00008: val_loss did not improve from 0.05451\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0558 - mae: 0.1893 - val_loss: 0.0556 - val_mae: 0.1917\n",
      "Epoch 9/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0553 - mae: 0.1886\n",
      "Epoch 00009: val_loss did not improve from 0.05451\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0553 - mae: 0.1885 - val_loss: 0.0546 - val_mae: 0.1870\n",
      "Epoch 10/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0549 - mae: 0.1879\n",
      "Epoch 00010: val_loss did not improve from 0.05451\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0549 - mae: 0.1878 - val_loss: 0.0547 - val_mae: 0.1896\n",
      "Epoch 11/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0542 - mae: 0.1866- ETA: 0s - loss: 0.\n",
      "Epoch 00011: val_loss did not improve from 0.05451\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0543 - mae: 0.1866 - val_loss: 0.0549 - val_mae: 0.1886\n",
      "Epoch 12/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1860\n",
      "Epoch 00012: val_loss improved from 0.05451 to 0.05257, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0539 - mae: 0.1859 - val_loss: 0.0526 - val_mae: 0.1838\n",
      "Epoch 13/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0534 - mae: 0.1848\n",
      "Epoch 00013: val_loss did not improve from 0.05257\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0534 - mae: 0.1848 - val_loss: 0.0548 - val_mae: 0.1885\n",
      "Epoch 14/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1841\n",
      "Epoch 00014: val_loss improved from 0.05257 to 0.05223, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0529 - mae: 0.1840 - val_loss: 0.0522 - val_mae: 0.1836\n",
      "Epoch 15/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1835\n",
      "Epoch 00015: val_loss did not improve from 0.05223\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0527 - mae: 0.1835 - val_loss: 0.0553 - val_mae: 0.1907\n",
      "Epoch 16/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1832\n",
      "Epoch 00016: val_loss did not improve from 0.05223\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0525 - mae: 0.1831 - val_loss: 0.0528 - val_mae: 0.1842\n",
      "Epoch 17/100\n",
      "39120/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1825\n",
      "Epoch 00017: val_loss improved from 0.05223 to 0.05086, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0523 - mae: 0.1825 - val_loss: 0.0509 - val_mae: 0.1810\n",
      "Epoch 18/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1824\n",
      "Epoch 00018: val_loss did not improve from 0.05086\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0521 - mae: 0.1822 - val_loss: 0.0509 - val_mae: 0.1812\n",
      "Epoch 19/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1821\n",
      "Epoch 00019: val_loss improved from 0.05086 to 0.05064, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0520 - mae: 0.1822 - val_loss: 0.0506 - val_mae: 0.1801\n",
      "Epoch 20/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1818\n",
      "Epoch 00020: val_loss did not improve from 0.05064\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0518 - mae: 0.1819 - val_loss: 0.0508 - val_mae: 0.1802\n",
      "Epoch 21/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1814\n",
      "Epoch 00021: val_loss did not improve from 0.05064\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0517 - mae: 0.1813 - val_loss: 0.0507 - val_mae: 0.1810\n",
      "Epoch 22/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1813\n",
      "Epoch 00022: val_loss did not improve from 0.05064\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0517 - mae: 0.1812 - val_loss: 0.0519 - val_mae: 0.1838\n",
      "Epoch 23/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1810\n",
      "Epoch 00023: val_loss did not improve from 0.05064\n",
      "40000/40000 [==============================] - 4s 112us/sample - loss: 0.0516 - mae: 0.1810 - val_loss: 0.0509 - val_mae: 0.1813\n",
      "Epoch 24/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1806\n",
      "Epoch 00024: val_loss improved from 0.05064 to 0.05053, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0514 - mae: 0.1808 - val_loss: 0.0505 - val_mae: 0.1808\n",
      "Epoch 25/100\n",
      "39192/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1812\n",
      "Epoch 00025: val_loss improved from 0.05053 to 0.05019, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0515 - mae: 0.1810 - val_loss: 0.0502 - val_mae: 0.1780\n",
      "Epoch 26/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1808\n",
      "Epoch 00026: val_loss improved from 0.05019 to 0.05013, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0515 - mae: 0.1809 - val_loss: 0.0501 - val_mae: 0.1785\n",
      "Epoch 27/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1810\n",
      "Epoch 00027: val_loss did not improve from 0.05013\n",
      "40000/40000 [==============================] - 4s 109us/sample - loss: 0.0515 - mae: 0.1809 - val_loss: 0.0506 - val_mae: 0.1802\n",
      "Epoch 28/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1807\n",
      "Epoch 00028: val_loss did not improve from 0.05013\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0514 - mae: 0.1807 - val_loss: 0.0511 - val_mae: 0.1811\n",
      "Epoch 29/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1808\n",
      "Epoch 00029: val_loss did not improve from 0.05013\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0514 - mae: 0.1808 - val_loss: 0.0514 - val_mae: 0.1815\n",
      "Epoch 30/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1806\n",
      "Epoch 00030: val_loss did not improve from 0.05013\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0513 - mae: 0.1806 - val_loss: 0.0510 - val_mae: 0.1813\n",
      "Epoch 31/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1805\n",
      "Epoch 00031: val_loss did not improve from 0.05013\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0513 - mae: 0.1804 - val_loss: 0.0505 - val_mae: 0.1807\n",
      "Epoch 32/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1804\n",
      "Epoch 00032: val_loss improved from 0.05013 to 0.04996, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0513 - mae: 0.1805 - val_loss: 0.0500 - val_mae: 0.1788\n",
      "Epoch 33/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1805\n",
      "Epoch 00033: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0513 - mae: 0.1806 - val_loss: 0.0512 - val_mae: 0.1810\n",
      "Epoch 34/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1805\n",
      "Epoch 00034: val_loss improved from 0.04996 to 0.04991, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0513 - mae: 0.1804 - val_loss: 0.0499 - val_mae: 0.1777\n",
      "Epoch 35/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1804\n",
      "Epoch 00035: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0512 - mae: 0.1804 - val_loss: 0.0500 - val_mae: 0.1766\n",
      "Epoch 36/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1803\n",
      "Epoch 00036: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0512 - mae: 0.1802 - val_loss: 0.0508 - val_mae: 0.1810\n",
      "Epoch 37/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1803\n",
      "Epoch 00037: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0513 - mae: 0.1803 - val_loss: 0.0500 - val_mae: 0.1793\n",
      "Epoch 38/100\n",
      "39192/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1799\n",
      "Epoch 00038: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0512 - mae: 0.1800 - val_loss: 0.0534 - val_mae: 0.1873\n",
      "Epoch 39/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1802\n",
      "Epoch 00039: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 4s 111us/sample - loss: 0.0512 - mae: 0.1802 - val_loss: 0.0520 - val_mae: 0.1840\n",
      "Epoch 40/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1803\n",
      "Epoch 00040: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0512 - mae: 0.1802 - val_loss: 0.0503 - val_mae: 0.1797\n",
      "Epoch 41/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1802\n",
      "Epoch 00041: val_loss did not improve from 0.04991\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0511 - mae: 0.1802 - val_loss: 0.0503 - val_mae: 0.1793\n",
      "Epoch 42/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1803\n",
      "Epoch 00042: val_loss improved from 0.04991 to 0.04964, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0511 - mae: 0.1801 - val_loss: 0.0496 - val_mae: 0.1781\n",
      "Epoch 43/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1802\n",
      "Epoch 00043: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0512 - mae: 0.1802 - val_loss: 0.0499 - val_mae: 0.1774\n",
      "Epoch 44/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1800\n",
      "Epoch 00044: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0511 - mae: 0.1801 - val_loss: 0.0505 - val_mae: 0.1811\n",
      "Epoch 45/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1802\n",
      "Epoch 00045: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0511 - mae: 0.1802 - val_loss: 0.0497 - val_mae: 0.1777\n",
      "Epoch 46/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1799\n",
      "Epoch 00046: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0510 - mae: 0.1799 - val_loss: 0.0500 - val_mae: 0.1786\n",
      "Epoch 47/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1799\n",
      "Epoch 00047: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0511 - mae: 0.1799 - val_loss: 0.0509 - val_mae: 0.1818\n",
      "Epoch 48/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1803\n",
      "Epoch 00048: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0511 - mae: 0.1802 - val_loss: 0.0498 - val_mae: 0.1771\n",
      "Epoch 49/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1799\n",
      "Epoch 00049: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0510 - mae: 0.1800 - val_loss: 0.0497 - val_mae: 0.1790\n",
      "Epoch 50/100\n",
      "39168/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1795\n",
      "Epoch 00050: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0508 - mae: 0.1794 - val_loss: 0.0498 - val_mae: 0.1774\n",
      "Epoch 51/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1795\n",
      "Epoch 00051: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0509 - mae: 0.1795 - val_loss: 0.0511 - val_mae: 0.1809\n",
      "Epoch 52/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1793\n",
      "Epoch 00052: val_loss improved from 0.04964 to 0.04917, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_1.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0509 - mae: 0.1793 - val_loss: 0.0492 - val_mae: 0.1772\n",
      "Epoch 53/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1792\n",
      "Epoch 00053: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0508 - mae: 0.1791 - val_loss: 0.0545 - val_mae: 0.1843\n",
      "Epoch 54/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1792\n",
      "Epoch 00054: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0508 - mae: 0.1792 - val_loss: 0.0493 - val_mae: 0.1771\n",
      "Epoch 55/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1792\n",
      "Epoch 00055: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0508 - mae: 0.1793 - val_loss: 0.0513 - val_mae: 0.1817\n",
      "Epoch 56/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1792\n",
      "Epoch 00056: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0508 - mae: 0.1793 - val_loss: 0.0493 - val_mae: 0.1770\n",
      "Epoch 57/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1790\n",
      "Epoch 00057: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0507 - mae: 0.1790 - val_loss: 0.0501 - val_mae: 0.1778\n",
      "Epoch 58/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1791\n",
      "Epoch 00058: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0507 - mae: 0.1792 - val_loss: 0.0503 - val_mae: 0.1779\n",
      "Epoch 59/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1792\n",
      "Epoch 00059: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0507 - mae: 0.1791 - val_loss: 0.0505 - val_mae: 0.1779\n",
      "Epoch 60/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1791\n",
      "Epoch 00060: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 4s 110us/sample - loss: 0.0507 - mae: 0.1791 - val_loss: 0.0495 - val_mae: 0.1771\n",
      "Epoch 61/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1789\n",
      "Epoch 00061: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0507 - mae: 0.1789 - val_loss: 0.0493 - val_mae: 0.1778\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1792\n",
      "Epoch 00062: val_loss did not improve from 0.04917\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0507 - mae: 0.1792 - val_loss: 0.0496 - val_mae: 0.1789\n",
      "Epoch 00062: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0660 - mae: 0.2104\n",
      "Epoch 00001: val_loss improved from inf to 0.05979, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0659 - mae: 0.2102 - val_loss: 0.0598 - val_mae: 0.2013\n",
      "Epoch 2/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0591 - mae: 0.1973\n",
      "Epoch 00002: val_loss improved from 0.05979 to 0.05733, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0590 - mae: 0.1971 - val_loss: 0.0573 - val_mae: 0.1938\n",
      "Epoch 3/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0576 - mae: 0.1940\n",
      "Epoch 00003: val_loss improved from 0.05733 to 0.05676, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0577 - mae: 0.1940 - val_loss: 0.0568 - val_mae: 0.1946\n",
      "Epoch 4/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0569 - mae: 0.1922\n",
      "Epoch 00004: val_loss improved from 0.05676 to 0.05567, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0569 - mae: 0.1922 - val_loss: 0.0557 - val_mae: 0.1923\n",
      "Epoch 5/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0563 - mae: 0.1909\n",
      "Epoch 00005: val_loss improved from 0.05567 to 0.05501, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0563 - mae: 0.1909 - val_loss: 0.0550 - val_mae: 0.1880\n",
      "Epoch 6/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0560 - mae: 0.1901\n",
      "Epoch 00006: val_loss did not improve from 0.05501\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0560 - mae: 0.1901 - val_loss: 0.0551 - val_mae: 0.1904\n",
      "Epoch 7/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1891\n",
      "Epoch 00007: val_loss did not improve from 0.05501\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0555 - mae: 0.1891 - val_loss: 0.0554 - val_mae: 0.1916\n",
      "Epoch 8/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0553 - mae: 0.1886\n",
      "Epoch 00008: val_loss improved from 0.05501 to 0.05376, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0553 - mae: 0.1886 - val_loss: 0.0538 - val_mae: 0.1881\n",
      "Epoch 9/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1882\n",
      "Epoch 00009: val_loss improved from 0.05376 to 0.05338, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0551 - mae: 0.1882 - val_loss: 0.0534 - val_mae: 0.1864\n",
      "Epoch 10/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0550 - mae: 0.1879\n",
      "Epoch 00010: val_loss did not improve from 0.05338\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0549 - mae: 0.1878 - val_loss: 0.0557 - val_mae: 0.1917\n",
      "Epoch 11/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1872\n",
      "Epoch 00011: val_loss did not improve from 0.05338\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0547 - mae: 0.1874 - val_loss: 0.0534 - val_mae: 0.1880\n",
      "Epoch 12/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0545 - mae: 0.1869- ETA: 1s \n",
      "Epoch 00012: val_loss did not improve from 0.05338\n",
      "40000/40000 [==============================] - 5s 124us/sample - loss: 0.0545 - mae: 0.1870 - val_loss: 0.0538 - val_mae: 0.1875\n",
      "Epoch 13/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0543 - mae: 0.1865- ETA: 1s - \n",
      "Epoch 00013: val_loss improved from 0.05338 to 0.05317, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0543 - mae: 0.1865 - val_loss: 0.0532 - val_mae: 0.1853\n",
      "Epoch 14/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1859\n",
      "Epoch 00014: val_loss did not improve from 0.05317\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0540 - mae: 0.1860 - val_loss: 0.0534 - val_mae: 0.1880\n",
      "Epoch 15/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1853\n",
      "Epoch 00015: val_loss did not improve from 0.05317\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0537 - mae: 0.1852 - val_loss: 0.0539 - val_mae: 0.1887\n",
      "Epoch 16/100\n",
      "39168/40000 [============================>.] - ETA: 0s - loss: 0.0535 - mae: 0.1847\n",
      "Epoch 00016: val_loss improved from 0.05317 to 0.05227, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0535 - mae: 0.1847 - val_loss: 0.0523 - val_mae: 0.1851\n",
      "Epoch 17/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1836\n",
      "Epoch 00017: val_loss improved from 0.05227 to 0.05178, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0529 - mae: 0.1836 - val_loss: 0.0518 - val_mae: 0.1823\n",
      "Epoch 18/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1828\n",
      "Epoch 00018: val_loss improved from 0.05178 to 0.05055, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0524 - mae: 0.1826 - val_loss: 0.0505 - val_mae: 0.1802\n",
      "Epoch 19/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1819\n",
      "Epoch 00019: val_loss did not improve from 0.05055\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0521 - mae: 0.1819 - val_loss: 0.0507 - val_mae: 0.1801\n",
      "Epoch 20/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1814\n",
      "Epoch 00020: val_loss did not improve from 0.05055\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0518 - mae: 0.1815 - val_loss: 0.0524 - val_mae: 0.1843\n",
      "Epoch 21/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1812\n",
      "Epoch 00021: val_loss improved from 0.05055 to 0.05000, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0517 - mae: 0.1813 - val_loss: 0.0500 - val_mae: 0.1789\n",
      "Epoch 22/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1809\n",
      "Epoch 00022: val_loss did not improve from 0.05000\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0515 - mae: 0.1809 - val_loss: 0.0502 - val_mae: 0.1805\n",
      "Epoch 23/100\n",
      "39168/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1806\n",
      "Epoch 00023: val_loss did not improve from 0.05000\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0514 - mae: 0.1806 - val_loss: 0.0507 - val_mae: 0.1791\n",
      "Epoch 24/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1804\n",
      "Epoch 00024: val_loss improved from 0.05000 to 0.04991, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0513 - mae: 0.1804 - val_loss: 0.0499 - val_mae: 0.1790\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1804\n",
      "Epoch 00025: val_loss improved from 0.04991 to 0.04984, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0513 - mae: 0.1804 - val_loss: 0.0498 - val_mae: 0.1774\n",
      "Epoch 26/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1800\n",
      "Epoch 00026: val_loss did not improve from 0.04984\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0512 - mae: 0.1801 - val_loss: 0.0512 - val_mae: 0.1836\n",
      "Epoch 27/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1801\n",
      "Epoch 00027: val_loss did not improve from 0.04984\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0511 - mae: 0.1801 - val_loss: 0.0504 - val_mae: 0.1785\n",
      "Epoch 28/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1800\n",
      "Epoch 00028: val_loss improved from 0.04984 to 0.04955, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 72us/sample - loss: 0.0512 - mae: 0.1800 - val_loss: 0.0496 - val_mae: 0.1766\n",
      "Epoch 29/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1801\n",
      "Epoch 00029: val_loss improved from 0.04955 to 0.04954, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0511 - mae: 0.1800 - val_loss: 0.0495 - val_mae: 0.1785\n",
      "Epoch 30/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1798\n",
      "Epoch 00030: val_loss did not improve from 0.04954\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0510 - mae: 0.1797 - val_loss: 0.0501 - val_mae: 0.1782\n",
      "Epoch 31/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1797\n",
      "Epoch 00031: val_loss did not improve from 0.04954\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0509 - mae: 0.1797 - val_loss: 0.0496 - val_mae: 0.1785\n",
      "Epoch 32/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1794\n",
      "Epoch 00032: val_loss did not improve from 0.04954\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0509 - mae: 0.1794 - val_loss: 0.0496 - val_mae: 0.1764\n",
      "Epoch 33/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1795\n",
      "Epoch 00033: val_loss did not improve from 0.04954\n",
      "40000/40000 [==============================] - 5s 113us/sample - loss: 0.0509 - mae: 0.1795 - val_loss: 0.0501 - val_mae: 0.1797\n",
      "Epoch 34/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1789\n",
      "Epoch 00034: val_loss did not improve from 0.04954\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0507 - mae: 0.1789 - val_loss: 0.0509 - val_mae: 0.1804\n",
      "Epoch 35/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1791\n",
      "Epoch 00035: val_loss improved from 0.04954 to 0.04944, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0507 - mae: 0.1791 - val_loss: 0.0494 - val_mae: 0.1765\n",
      "Epoch 36/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1789- ETA: 0s - loss: 0.0\n",
      "Epoch 00036: val_loss did not improve from 0.04944\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0507 - mae: 0.1790 - val_loss: 0.0522 - val_mae: 0.1834\n",
      "Epoch 37/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1789\n",
      "Epoch 00037: val_loss did not improve from 0.04944\n",
      "40000/40000 [==============================] - 4s 111us/sample - loss: 0.0507 - mae: 0.1789 - val_loss: 0.0496 - val_mae: 0.1765\n",
      "Epoch 38/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1787\n",
      "Epoch 00038: val_loss did not improve from 0.04944\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0506 - mae: 0.1787 - val_loss: 0.0509 - val_mae: 0.1825\n",
      "Epoch 39/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1792\n",
      "Epoch 00039: val_loss improved from 0.04944 to 0.04919, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0507 - mae: 0.1791 - val_loss: 0.0492 - val_mae: 0.1772\n",
      "Epoch 40/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1788\n",
      "Epoch 00040: val_loss did not improve from 0.04919\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0506 - mae: 0.1789 - val_loss: 0.0493 - val_mae: 0.1760\n",
      "Epoch 41/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1788\n",
      "Epoch 00041: val_loss did not improve from 0.04919\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0505 - mae: 0.1788 - val_loss: 0.0503 - val_mae: 0.1797\n",
      "Epoch 42/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1788\n",
      "Epoch 00042: val_loss did not improve from 0.04919\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0506 - mae: 0.1788 - val_loss: 0.0497 - val_mae: 0.1781\n",
      "Epoch 43/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1784\n",
      "Epoch 00043: val_loss did not improve from 0.04919\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0505 - mae: 0.1785 - val_loss: 0.0492 - val_mae: 0.1770\n",
      "Epoch 44/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1786\n",
      "Epoch 00044: val_loss improved from 0.04919 to 0.04879, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_2.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0506 - mae: 0.1786 - val_loss: 0.0488 - val_mae: 0.1759\n",
      "Epoch 45/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1782\n",
      "Epoch 00045: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0504 - mae: 0.1783 - val_loss: 0.0493 - val_mae: 0.1785\n",
      "Epoch 46/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1784\n",
      "Epoch 00046: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0504 - mae: 0.1784 - val_loss: 0.0493 - val_mae: 0.1769\n",
      "Epoch 47/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1782\n",
      "Epoch 00047: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0504 - mae: 0.1783 - val_loss: 0.0493 - val_mae: 0.1776\n",
      "Epoch 48/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1781\n",
      "Epoch 00048: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0504 - mae: 0.1781 - val_loss: 0.0497 - val_mae: 0.1781\n",
      "Epoch 49/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1781\n",
      "Epoch 00049: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0504 - mae: 0.1782 - val_loss: 0.0491 - val_mae: 0.1762\n",
      "Epoch 50/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1784\n",
      "Epoch 00050: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0504 - mae: 0.1784 - val_loss: 0.0489 - val_mae: 0.1758\n",
      "Epoch 51/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1783\n",
      "Epoch 00051: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0504 - mae: 0.1782 - val_loss: 0.0488 - val_mae: 0.1759\n",
      "Epoch 52/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1781\n",
      "Epoch 00052: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0503 - mae: 0.1781 - val_loss: 0.0505 - val_mae: 0.1813\n",
      "Epoch 53/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1781\n",
      "Epoch 00053: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0503 - mae: 0.1781 - val_loss: 0.0491 - val_mae: 0.1748\n",
      "Epoch 54/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1779\n",
      "Epoch 00054: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0503 - mae: 0.1778 - val_loss: 0.0493 - val_mae: 0.1771\n",
      "Epoch 00054: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0644 - mae: 0.2064\n",
      "Epoch 00001: val_loss improved from inf to 0.05927, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0644 - mae: 0.2065 - val_loss: 0.0593 - val_mae: 0.2010\n",
      "Epoch 2/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0578 - mae: 0.1939\n",
      "Epoch 00002: val_loss improved from 0.05927 to 0.05632, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0579 - mae: 0.1940 - val_loss: 0.0563 - val_mae: 0.1929\n",
      "Epoch 3/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0567 - mae: 0.1915\n",
      "Epoch 00003: val_loss improved from 0.05632 to 0.05509, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0566 - mae: 0.1914 - val_loss: 0.0551 - val_mae: 0.1901\n",
      "Epoch 4/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0559 - mae: 0.1896\n",
      "Epoch 00004: val_loss did not improve from 0.05509\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0558 - mae: 0.1894 - val_loss: 0.0553 - val_mae: 0.1889\n",
      "Epoch 5/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0554 - mae: 0.1887\n",
      "Epoch 00005: val_loss improved from 0.05509 to 0.05311, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0554 - mae: 0.1885 - val_loss: 0.0531 - val_mae: 0.1857\n",
      "Epoch 6/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0551 - mae: 0.1879\n",
      "Epoch 00006: val_loss did not improve from 0.05311\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0551 - mae: 0.1879 - val_loss: 0.0545 - val_mae: 0.1901\n",
      "Epoch 7/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1871\n",
      "Epoch 00007: val_loss did not improve from 0.05311\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0547 - mae: 0.1873 - val_loss: 0.0531 - val_mae: 0.1853\n",
      "Epoch 8/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0545 - mae: 0.1867\n",
      "Epoch 00008: val_loss did not improve from 0.05311\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0545 - mae: 0.1868 - val_loss: 0.0535 - val_mae: 0.1876\n",
      "Epoch 9/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0542 - mae: 0.1862\n",
      "Epoch 00009: val_loss improved from 0.05311 to 0.05229, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0542 - mae: 0.1862 - val_loss: 0.0523 - val_mae: 0.1838\n",
      "Epoch 10/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0538 - mae: 0.1854\n",
      "Epoch 00010: val_loss did not improve from 0.05229\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0538 - mae: 0.1854 - val_loss: 0.0529 - val_mae: 0.1826\n",
      "Epoch 11/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0535 - mae: 0.1847\n",
      "Epoch 00011: val_loss improved from 0.05229 to 0.05169, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0535 - mae: 0.1847 - val_loss: 0.0517 - val_mae: 0.1825\n",
      "Epoch 12/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1843\n",
      "Epoch 00012: val_loss improved from 0.05169 to 0.05154, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0534 - mae: 0.1844 - val_loss: 0.0515 - val_mae: 0.1830\n",
      "Epoch 13/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1834\n",
      "Epoch 00013: val_loss did not improve from 0.05154\n",
      "40000/40000 [==============================] - 3s 72us/sample - loss: 0.0531 - mae: 0.1834 - val_loss: 0.0525 - val_mae: 0.1840\n",
      "Epoch 14/100\n",
      "39168/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1832\n",
      "Epoch 00014: val_loss improved from 0.05154 to 0.05066, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0529 - mae: 0.1832 - val_loss: 0.0507 - val_mae: 0.1809\n",
      "Epoch 15/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1821\n",
      "Epoch 00015: val_loss did not improve from 0.05066\n",
      "40000/40000 [==============================] - 5s 116us/sample - loss: 0.0525 - mae: 0.1822 - val_loss: 0.0531 - val_mae: 0.1857\n",
      "Epoch 16/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1817\n",
      "Epoch 00016: val_loss improved from 0.05066 to 0.04972, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 5s 126us/sample - loss: 0.0522 - mae: 0.1818 - val_loss: 0.0497 - val_mae: 0.1780\n",
      "Epoch 17/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1799\n",
      "Epoch 00017: val_loss improved from 0.04972 to 0.04924, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0513 - mae: 0.1798 - val_loss: 0.0492 - val_mae: 0.1759\n",
      "Epoch 18/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1794\n",
      "Epoch 00018: val_loss did not improve from 0.04924\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0509 - mae: 0.1794 - val_loss: 0.0506 - val_mae: 0.1799\n",
      "Epoch 19/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1784\n",
      "Epoch 00019: val_loss did not improve from 0.04924\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0504 - mae: 0.1783 - val_loss: 0.0503 - val_mae: 0.1770\n",
      "Epoch 20/100\n",
      "39168/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1781\n",
      "Epoch 00020: val_loss improved from 0.04924 to 0.04916, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0504 - mae: 0.1780 - val_loss: 0.0492 - val_mae: 0.1781\n",
      "Epoch 21/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1777\n",
      "Epoch 00021: val_loss improved from 0.04916 to 0.04784, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_3.hdf5\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0502 - mae: 0.1778 - val_loss: 0.0478 - val_mae: 0.1740\n",
      "Epoch 22/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1775\n",
      "Epoch 00022: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0502 - mae: 0.1774 - val_loss: 0.0499 - val_mae: 0.1782\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1770\n",
      "Epoch 00023: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0499 - mae: 0.1769 - val_loss: 0.0487 - val_mae: 0.1735\n",
      "Epoch 24/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1766\n",
      "Epoch 00024: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0498 - mae: 0.1765 - val_loss: 0.0484 - val_mae: 0.1742\n",
      "Epoch 25/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1763\n",
      "Epoch 00025: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0496 - mae: 0.1762 - val_loss: 0.0488 - val_mae: 0.1737\n",
      "Epoch 26/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1764\n",
      "Epoch 00026: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0497 - mae: 0.1764 - val_loss: 0.0484 - val_mae: 0.1751\n",
      "Epoch 27/100\n",
      "39144/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1758\n",
      "Epoch 00027: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0495 - mae: 0.1757 - val_loss: 0.0479 - val_mae: 0.1742\n",
      "Epoch 28/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1756\n",
      "Epoch 00028: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0494 - mae: 0.1756 - val_loss: 0.0495 - val_mae: 0.1751\n",
      "Epoch 29/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1756\n",
      "Epoch 00029: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 71us/sample - loss: 0.0494 - mae: 0.1755 - val_loss: 0.0483 - val_mae: 0.1747\n",
      "Epoch 30/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1755\n",
      "Epoch 00030: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0493 - mae: 0.1754 - val_loss: 0.0497 - val_mae: 0.1742\n",
      "Epoch 31/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1754\n",
      "Epoch 00031: val_loss did not improve from 0.04784\n",
      "40000/40000 [==============================] - 3s 73us/sample - loss: 0.0495 - mae: 0.1754 - val_loss: 0.0501 - val_mae: 0.1762\n",
      "Epoch 00031: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0803 - mae: 0.2282\n",
      "Epoch 00001: val_loss improved from inf to 0.05803, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0802 - mae: 0.2281 - val_loss: 0.0580 - val_mae: 0.1970\n",
      "Epoch 2/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0584 - mae: 0.1957\n",
      "Epoch 00002: val_loss improved from 0.05803 to 0.05645, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0585 - mae: 0.1958 - val_loss: 0.0565 - val_mae: 0.1951\n",
      "Epoch 3/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0571 - mae: 0.1925\n",
      "Epoch 00003: val_loss improved from 0.05645 to 0.05585, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0570 - mae: 0.1924 - val_loss: 0.0559 - val_mae: 0.1908\n",
      "Epoch 4/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0561 - mae: 0.1904\n",
      "Epoch 00004: val_loss did not improve from 0.05585\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0561 - mae: 0.1904 - val_loss: 0.0565 - val_mae: 0.1931\n",
      "Epoch 5/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1888\n",
      "Epoch 00005: val_loss did not improve from 0.05585\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0552 - mae: 0.1887 - val_loss: 0.0566 - val_mae: 0.1949\n",
      "Epoch 6/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1876\n",
      "Epoch 00006: val_loss improved from 0.05585 to 0.05375, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0547 - mae: 0.1877 - val_loss: 0.0538 - val_mae: 0.1878\n",
      "Epoch 7/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0542 - mae: 0.1864\n",
      "Epoch 00007: val_loss did not improve from 0.05375\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0542 - mae: 0.1864 - val_loss: 0.0552 - val_mae: 0.1886\n",
      "Epoch 8/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0541 - mae: 0.1858\n",
      "Epoch 00008: val_loss improved from 0.05375 to 0.05116, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0540 - mae: 0.1856 - val_loss: 0.0512 - val_mae: 0.1810\n",
      "Epoch 9/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1847\n",
      "Epoch 00009: val_loss did not improve from 0.05116\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0536 - mae: 0.1847 - val_loss: 0.0512 - val_mae: 0.1810\n",
      "Epoch 10/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1839\n",
      "Epoch 00010: val_loss improved from 0.05116 to 0.05098, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0533 - mae: 0.1839 - val_loss: 0.0510 - val_mae: 0.1790\n",
      "Epoch 11/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1835\n",
      "Epoch 00011: val_loss did not improve from 0.05098\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0532 - mae: 0.1835 - val_loss: 0.0552 - val_mae: 0.1874\n",
      "Epoch 12/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1827\n",
      "Epoch 00012: val_loss did not improve from 0.05098\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0530 - mae: 0.1829 - val_loss: 0.0534 - val_mae: 0.1809\n",
      "Epoch 13/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1823\n",
      "Epoch 00013: val_loss improved from 0.05098 to 0.05025, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0529 - mae: 0.1823 - val_loss: 0.0503 - val_mae: 0.1794\n",
      "Epoch 14/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1823\n",
      "Epoch 00014: val_loss improved from 0.05025 to 0.05014, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0528 - mae: 0.1823 - val_loss: 0.0501 - val_mae: 0.1782\n",
      "Epoch 15/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1817\n",
      "Epoch 00015: val_loss improved from 0.05014 to 0.04996, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0526 - mae: 0.1817 - val_loss: 0.0500 - val_mae: 0.1773\n",
      "Epoch 16/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1818\n",
      "Epoch 00016: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0526 - mae: 0.1818 - val_loss: 0.0500 - val_mae: 0.1791\n",
      "Epoch 17/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1817\n",
      "Epoch 00017: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0526 - mae: 0.1816 - val_loss: 0.0503 - val_mae: 0.1777\n",
      "Epoch 18/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1811\n",
      "Epoch 00018: val_loss improved from 0.04996 to 0.04975, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0525 - mae: 0.1812 - val_loss: 0.0498 - val_mae: 0.1774\n",
      "Epoch 19/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1812\n",
      "Epoch 00019: val_loss did not improve from 0.04975\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0525 - mae: 0.1812 - val_loss: 0.0499 - val_mae: 0.1763\n",
      "Epoch 20/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1806\n",
      "Epoch 00020: val_loss did not improve from 0.04975\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0522 - mae: 0.1806 - val_loss: 0.0502 - val_mae: 0.1785\n",
      "Epoch 21/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1808\n",
      "Epoch 00021: val_loss did not improve from 0.04975\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0522 - mae: 0.1807 - val_loss: 0.0505 - val_mae: 0.1775\n",
      "Epoch 22/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1801\n",
      "Epoch 00022: val_loss did not improve from 0.04975\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0521 - mae: 0.1801 - val_loss: 0.0533 - val_mae: 0.1851\n",
      "Epoch 23/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1803\n",
      "Epoch 00023: val_loss improved from 0.04975 to 0.04921, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0521 - mae: 0.1803 - val_loss: 0.0492 - val_mae: 0.1761\n",
      "Epoch 24/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1801\n",
      "Epoch 00024: val_loss improved from 0.04921 to 0.04876, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0519 - mae: 0.1800 - val_loss: 0.0488 - val_mae: 0.1747\n",
      "Epoch 25/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1797\n",
      "Epoch 00025: val_loss did not improve from 0.04876\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0518 - mae: 0.1796 - val_loss: 0.0505 - val_mae: 0.1765\n",
      "Epoch 26/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1797\n",
      "Epoch 00026: val_loss did not improve from 0.04876\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0518 - mae: 0.1796 - val_loss: 0.0499 - val_mae: 0.1758\n",
      "Epoch 27/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1793\n",
      "Epoch 00027: val_loss did not improve from 0.04876\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0516 - mae: 0.1793 - val_loss: 0.0498 - val_mae: 0.1791\n",
      "Epoch 28/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1784\n",
      "Epoch 00028: val_loss improved from 0.04876 to 0.04864, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0512 - mae: 0.1785 - val_loss: 0.0486 - val_mae: 0.1769\n",
      "Epoch 29/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1778\n",
      "Epoch 00029: val_loss did not improve from 0.04864\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0508 - mae: 0.1778 - val_loss: 0.0495 - val_mae: 0.1740\n",
      "Epoch 30/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1776\n",
      "Epoch 00030: val_loss improved from 0.04864 to 0.04810, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0508 - mae: 0.1776 - val_loss: 0.0481 - val_mae: 0.1741\n",
      "Epoch 31/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1774\n",
      "Epoch 00031: val_loss did not improve from 0.04810\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0506 - mae: 0.1774 - val_loss: 0.0481 - val_mae: 0.1735\n",
      "Epoch 32/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1773\n",
      "Epoch 00032: val_loss improved from 0.04810 to 0.04793, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 5s 115us/sample - loss: 0.0506 - mae: 0.1773 - val_loss: 0.0479 - val_mae: 0.1729\n",
      "Epoch 33/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1771\n",
      "Epoch 00033: val_loss did not improve from 0.04793\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0505 - mae: 0.1771 - val_loss: 0.0493 - val_mae: 0.1756\n",
      "Epoch 34/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1769\n",
      "Epoch 00034: val_loss did not improve from 0.04793\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0504 - mae: 0.1769 - val_loss: 0.0484 - val_mae: 0.1759\n",
      "Epoch 35/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1768\n",
      "Epoch 00035: val_loss did not improve from 0.04793\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0504 - mae: 0.1767 - val_loss: 0.0480 - val_mae: 0.1722\n",
      "Epoch 36/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1763\n",
      "Epoch 00036: val_loss did not improve from 0.04793\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0502 - mae: 0.1763 - val_loss: 0.0488 - val_mae: 0.1744\n",
      "Epoch 37/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1764\n",
      "Epoch 00037: val_loss did not improve from 0.04793\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0502 - mae: 0.1764 - val_loss: 0.0481 - val_mae: 0.1741\n",
      "Epoch 38/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1764\n",
      "Epoch 00038: val_loss did not improve from 0.04793\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0502 - mae: 0.1764 - val_loss: 0.0486 - val_mae: 0.1760\n",
      "Epoch 39/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1760\n",
      "Epoch 00039: val_loss improved from 0.04793 to 0.04775, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0500 - mae: 0.1759 - val_loss: 0.0477 - val_mae: 0.1737\n",
      "Epoch 40/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1758\n",
      "Epoch 00040: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0500 - mae: 0.1759 - val_loss: 0.0486 - val_mae: 0.1761\n",
      "Epoch 41/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1757\n",
      "Epoch 00041: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0499 - mae: 0.1756 - val_loss: 0.0494 - val_mae: 0.1777\n",
      "Epoch 42/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1756\n",
      "Epoch 00042: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0499 - mae: 0.1756 - val_loss: 0.0496 - val_mae: 0.1760\n",
      "Epoch 43/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1752\n",
      "Epoch 00043: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0498 - mae: 0.1753 - val_loss: 0.0501 - val_mae: 0.1774\n",
      "Epoch 44/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1749\n",
      "Epoch 00044: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0497 - mae: 0.1750 - val_loss: 0.0481 - val_mae: 0.1725\n",
      "Epoch 45/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1750\n",
      "Epoch 00045: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0497 - mae: 0.1750 - val_loss: 0.0480 - val_mae: 0.1716\n",
      "Epoch 46/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1749\n",
      "Epoch 00046: val_loss improved from 0.04775 to 0.04768, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0497 - mae: 0.1750 - val_loss: 0.0477 - val_mae: 0.1721\n",
      "Epoch 47/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1752\n",
      "Epoch 00047: val_loss improved from 0.04768 to 0.04753, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0498 - mae: 0.1751 - val_loss: 0.0475 - val_mae: 0.1711\n",
      "Epoch 48/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1749\n",
      "Epoch 00048: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0496 - mae: 0.1748 - val_loss: 0.0484 - val_mae: 0.1722\n",
      "Epoch 49/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1746\n",
      "Epoch 00049: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0496 - mae: 0.1747 - val_loss: 0.0477 - val_mae: 0.1730\n",
      "Epoch 50/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1746\n",
      "Epoch 00050: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0495 - mae: 0.1746 - val_loss: 0.0491 - val_mae: 0.1744\n",
      "Epoch 51/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1745\n",
      "Epoch 00051: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0495 - mae: 0.1745 - val_loss: 0.0478 - val_mae: 0.1714\n",
      "Epoch 52/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1743\n",
      "Epoch 00052: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0495 - mae: 0.1743 - val_loss: 0.0479 - val_mae: 0.1747\n",
      "Epoch 53/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1742\n",
      "Epoch 00053: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0494 - mae: 0.1743 - val_loss: 0.0486 - val_mae: 0.1718\n",
      "Epoch 54/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1740\n",
      "Epoch 00054: val_loss did not improve from 0.04753\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0493 - mae: 0.1740 - val_loss: 0.0479 - val_mae: 0.1750\n",
      "Epoch 55/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1744\n",
      "Epoch 00055: val_loss improved from 0.04753 to 0.04749, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0495 - mae: 0.1743 - val_loss: 0.0475 - val_mae: 0.1712\n",
      "Epoch 56/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1737\n",
      "Epoch 00056: val_loss improved from 0.04749 to 0.04740, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0493 - mae: 0.1739 - val_loss: 0.0474 - val_mae: 0.1725\n",
      "Epoch 57/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1739\n",
      "Epoch 00057: val_loss did not improve from 0.04740\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0493 - mae: 0.1739 - val_loss: 0.0491 - val_mae: 0.1760\n",
      "Epoch 58/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1740\n",
      "Epoch 00058: val_loss did not improve from 0.04740\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0493 - mae: 0.1741 - val_loss: 0.0492 - val_mae: 0.1772\n",
      "Epoch 59/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1739\n",
      "Epoch 00059: val_loss improved from 0.04740 to 0.04727, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0493 - mae: 0.1739 - val_loss: 0.0473 - val_mae: 0.1708\n",
      "Epoch 60/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1739\n",
      "Epoch 00060: val_loss did not improve from 0.04727\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0492 - mae: 0.1739 - val_loss: 0.0476 - val_mae: 0.1712\n",
      "Epoch 61/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1737\n",
      "Epoch 00061: val_loss did not improve from 0.04727\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0491 - mae: 0.1735 - val_loss: 0.0480 - val_mae: 0.1717\n",
      "Epoch 62/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1738\n",
      "Epoch 00062: val_loss did not improve from 0.04727\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0492 - mae: 0.1738 - val_loss: 0.0481 - val_mae: 0.1741\n",
      "Epoch 63/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1738\n",
      "Epoch 00063: val_loss did not improve from 0.04727\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0492 - mae: 0.1738 - val_loss: 0.0474 - val_mae: 0.1702\n",
      "Epoch 64/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1734\n",
      "Epoch 00064: val_loss improved from 0.04727 to 0.04695, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_4.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0491 - mae: 0.1735 - val_loss: 0.0469 - val_mae: 0.1707\n",
      "Epoch 65/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1735\n",
      "Epoch 00065: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0491 - mae: 0.1734 - val_loss: 0.0472 - val_mae: 0.1706\n",
      "Epoch 66/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1737\n",
      "Epoch 00066: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0491 - mae: 0.1736 - val_loss: 0.0470 - val_mae: 0.1704\n",
      "Epoch 67/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1734\n",
      "Epoch 00067: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0492 - mae: 0.1736 - val_loss: 0.0480 - val_mae: 0.1741\n",
      "Epoch 68/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1735\n",
      "Epoch 00068: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0491 - mae: 0.1735 - val_loss: 0.0489 - val_mae: 0.1750\n",
      "Epoch 69/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1736\n",
      "Epoch 00069: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0490 - mae: 0.1735 - val_loss: 0.0481 - val_mae: 0.1712\n",
      "Epoch 70/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1738\n",
      "Epoch 00070: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0492 - mae: 0.1738 - val_loss: 0.0474 - val_mae: 0.1714\n",
      "Epoch 71/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1735\n",
      "Epoch 00071: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0491 - mae: 0.1735 - val_loss: 0.0472 - val_mae: 0.1692\n",
      "Epoch 72/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1734\n",
      "Epoch 00072: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0491 - mae: 0.1734 - val_loss: 0.0473 - val_mae: 0.1691\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 73/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1731\n",
      "Epoch 00073: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0489 - mae: 0.1733 - val_loss: 0.0478 - val_mae: 0.1747\n",
      "Epoch 74/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1730\n",
      "Epoch 00074: val_loss did not improve from 0.04695\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0490 - mae: 0.1731 - val_loss: 0.0472 - val_mae: 0.1718\n",
      "Epoch 00074: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0656 - mae: 0.2093\n",
      "Epoch 00001: val_loss improved from inf to 0.05684, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0656 - mae: 0.2091 - val_loss: 0.0568 - val_mae: 0.1955\n",
      "Epoch 2/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0575 - mae: 0.1931\n",
      "Epoch 00002: val_loss improved from 0.05684 to 0.05460, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0575 - mae: 0.1931 - val_loss: 0.0546 - val_mae: 0.1877\n",
      "Epoch 3/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0563 - mae: 0.1901\n",
      "Epoch 00003: val_loss did not improve from 0.05460\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0564 - mae: 0.1902 - val_loss: 0.0557 - val_mae: 0.1912\n",
      "Epoch 4/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0557 - mae: 0.1888\n",
      "Epoch 00004: val_loss did not improve from 0.05460\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0558 - mae: 0.1888 - val_loss: 0.0550 - val_mae: 0.1891\n",
      "Epoch 5/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1880\n",
      "Epoch 00005: val_loss did not improve from 0.05460\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0555 - mae: 0.1881 - val_loss: 0.0559 - val_mae: 0.1902\n",
      "Epoch 6/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0551 - mae: 0.1873\n",
      "Epoch 00006: val_loss improved from 0.05460 to 0.05298, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0551 - mae: 0.1873 - val_loss: 0.0530 - val_mae: 0.1854\n",
      "Epoch 7/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0547 - mae: 0.1863\n",
      "Epoch 00007: val_loss improved from 0.05298 to 0.05262, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0547 - mae: 0.1863 - val_loss: 0.0526 - val_mae: 0.1834\n",
      "Epoch 8/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0545 - mae: 0.1858\n",
      "Epoch 00008: val_loss did not improve from 0.05262\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0545 - mae: 0.1857 - val_loss: 0.0527 - val_mae: 0.1831\n",
      "Epoch 9/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0542 - mae: 0.1851\n",
      "Epoch 00009: val_loss improved from 0.05262 to 0.05248, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0542 - mae: 0.1852 - val_loss: 0.0525 - val_mae: 0.1822\n",
      "Epoch 10/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0539 - mae: 0.1843\n",
      "Epoch 00010: val_loss improved from 0.05248 to 0.05240, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0538 - mae: 0.1842 - val_loss: 0.0524 - val_mae: 0.1830\n",
      "Epoch 11/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0535 - mae: 0.1834\n",
      "Epoch 00011: val_loss did not improve from 0.05240\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0535 - mae: 0.1835 - val_loss: 0.0525 - val_mae: 0.1851\n",
      "Epoch 12/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1822\n",
      "Epoch 00012: val_loss improved from 0.05240 to 0.05145, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0530 - mae: 0.1822 - val_loss: 0.0515 - val_mae: 0.1825\n",
      "Epoch 13/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1813\n",
      "Epoch 00013: val_loss improved from 0.05145 to 0.05107, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0526 - mae: 0.1812 - val_loss: 0.0511 - val_mae: 0.1825\n",
      "Epoch 14/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1805\n",
      "Epoch 00014: val_loss improved from 0.05107 to 0.04981, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0522 - mae: 0.1804 - val_loss: 0.0498 - val_mae: 0.1766\n",
      "Epoch 15/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1796\n",
      "Epoch 00015: val_loss did not improve from 0.04981\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0520 - mae: 0.1797 - val_loss: 0.0502 - val_mae: 0.1800\n",
      "Epoch 16/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1790\n",
      "Epoch 00016: val_loss did not improve from 0.04981\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0517 - mae: 0.1791 - val_loss: 0.0508 - val_mae: 0.1787\n",
      "Epoch 17/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1787\n",
      "Epoch 00017: val_loss did not improve from 0.04981\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0516 - mae: 0.1788 - val_loss: 0.0524 - val_mae: 0.1855\n",
      "Epoch 18/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1782\n",
      "Epoch 00018: val_loss did not improve from 0.04981\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0514 - mae: 0.1782 - val_loss: 0.0504 - val_mae: 0.1783\n",
      "Epoch 19/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1786\n",
      "Epoch 00019: val_loss improved from 0.04981 to 0.04942, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0514 - mae: 0.1785 - val_loss: 0.0494 - val_mae: 0.1740\n",
      "Epoch 20/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1779\n",
      "Epoch 00020: val_loss did not improve from 0.04942\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0512 - mae: 0.1779 - val_loss: 0.0503 - val_mae: 0.1787\n",
      "Epoch 21/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1775\n",
      "Epoch 00021: val_loss did not improve from 0.04942\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0510 - mae: 0.1776 - val_loss: 0.0501 - val_mae: 0.1794\n",
      "Epoch 22/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1775\n",
      "Epoch 00022: val_loss did not improve from 0.04942\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0510 - mae: 0.1776 - val_loss: 0.0508 - val_mae: 0.1790\n",
      "Epoch 23/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1770\n",
      "Epoch 00023: val_loss did not improve from 0.04942\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0509 - mae: 0.1771 - val_loss: 0.0505 - val_mae: 0.1779\n",
      "Epoch 24/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1772\n",
      "Epoch 00024: val_loss improved from 0.04942 to 0.04875, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0508 - mae: 0.1770 - val_loss: 0.0487 - val_mae: 0.1713\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1769\n",
      "Epoch 00025: val_loss improved from 0.04875 to 0.04858, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0506 - mae: 0.1768 - val_loss: 0.0486 - val_mae: 0.1727\n",
      "Epoch 26/100\n",
      "39168/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1766\n",
      "Epoch 00026: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0506 - mae: 0.1766 - val_loss: 0.0499 - val_mae: 0.1781\n",
      "Epoch 27/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1762\n",
      "Epoch 00027: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0505 - mae: 0.1763 - val_loss: 0.0499 - val_mae: 0.1778\n",
      "Epoch 28/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1762\n",
      "Epoch 00028: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0504 - mae: 0.1762 - val_loss: 0.0497 - val_mae: 0.1785\n",
      "Epoch 29/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1761\n",
      "Epoch 00029: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0504 - mae: 0.1762 - val_loss: 0.0499 - val_mae: 0.1751\n",
      "Epoch 30/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1760\n",
      "Epoch 00030: val_loss improved from 0.04858 to 0.04824, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0504 - mae: 0.1762 - val_loss: 0.0482 - val_mae: 0.1729\n",
      "Epoch 31/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1760\n",
      "Epoch 00031: val_loss improved from 0.04824 to 0.04788, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_5.hdf5\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0503 - mae: 0.1760 - val_loss: 0.0479 - val_mae: 0.1717\n",
      "Epoch 32/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1758\n",
      "Epoch 00032: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0502 - mae: 0.1759 - val_loss: 0.0483 - val_mae: 0.1727\n",
      "Epoch 33/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1758\n",
      "Epoch 00033: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0502 - mae: 0.1758 - val_loss: 0.0482 - val_mae: 0.1717\n",
      "Epoch 34/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1761\n",
      "Epoch 00034: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0504 - mae: 0.1760 - val_loss: 0.0493 - val_mae: 0.1748\n",
      "Epoch 35/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1756\n",
      "Epoch 00035: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0502 - mae: 0.1756 - val_loss: 0.0484 - val_mae: 0.1732\n",
      "Epoch 36/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1755\n",
      "Epoch 00036: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0501 - mae: 0.1755 - val_loss: 0.0490 - val_mae: 0.1755\n",
      "Epoch 37/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1756\n",
      "Epoch 00037: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0501 - mae: 0.1756 - val_loss: 0.0503 - val_mae: 0.1777\n",
      "Epoch 38/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1754\n",
      "Epoch 00038: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0501 - mae: 0.1754 - val_loss: 0.0482 - val_mae: 0.1719\n",
      "Epoch 39/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1752\n",
      "Epoch 00039: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0499 - mae: 0.1753 - val_loss: 0.0484 - val_mae: 0.1730\n",
      "Epoch 40/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1754\n",
      "Epoch 00040: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0500 - mae: 0.1754 - val_loss: 0.0504 - val_mae: 0.1758\n",
      "Epoch 41/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1750\n",
      "Epoch 00041: val_loss did not improve from 0.04788\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0500 - mae: 0.1751 - val_loss: 0.0483 - val_mae: 0.1739\n",
      "Epoch 00041: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0630 - mae: 0.2051\n",
      "Epoch 00001: val_loss improved from inf to 0.05779, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0630 - mae: 0.2051 - val_loss: 0.0578 - val_mae: 0.1952\n",
      "Epoch 2/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0577 - mae: 0.1932\n",
      "Epoch 00002: val_loss improved from 0.05779 to 0.05553, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0577 - mae: 0.1932 - val_loss: 0.0555 - val_mae: 0.1876\n",
      "Epoch 3/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0563 - mae: 0.1902\n",
      "Epoch 00003: val_loss did not improve from 0.05553\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0564 - mae: 0.1903 - val_loss: 0.0556 - val_mae: 0.1911\n",
      "Epoch 4/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1885\n",
      "Epoch 00004: val_loss improved from 0.05553 to 0.05301, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0555 - mae: 0.1886 - val_loss: 0.0530 - val_mae: 0.1845\n",
      "Epoch 5/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0551 - mae: 0.1877\n",
      "Epoch 00005: val_loss improved from 0.05301 to 0.05254, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0551 - mae: 0.1876 - val_loss: 0.0525 - val_mae: 0.1866\n",
      "Epoch 6/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0541 - mae: 0.1857\n",
      "Epoch 00006: val_loss improved from 0.05254 to 0.05140, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0541 - mae: 0.1857 - val_loss: 0.0514 - val_mae: 0.1826\n",
      "Epoch 7/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1844\n",
      "Epoch 00007: val_loss improved from 0.05140 to 0.05073, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0536 - mae: 0.1843 - val_loss: 0.0507 - val_mae: 0.1804\n",
      "Epoch 8/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1830\n",
      "Epoch 00008: val_loss improved from 0.05073 to 0.05062, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0530 - mae: 0.1830 - val_loss: 0.0506 - val_mae: 0.1817\n",
      "Epoch 9/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1821\n",
      "Epoch 00009: val_loss did not improve from 0.05062\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0527 - mae: 0.1821 - val_loss: 0.0509 - val_mae: 0.1802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1814\n",
      "Epoch 00010: val_loss improved from 0.05062 to 0.04977, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0523 - mae: 0.1814 - val_loss: 0.0498 - val_mae: 0.1770\n",
      "Epoch 11/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1807\n",
      "Epoch 00011: val_loss improved from 0.04977 to 0.04969, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0521 - mae: 0.1807 - val_loss: 0.0497 - val_mae: 0.1778\n",
      "Epoch 12/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1800\n",
      "Epoch 00012: val_loss improved from 0.04969 to 0.04940, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0519 - mae: 0.1799 - val_loss: 0.0494 - val_mae: 0.1778\n",
      "Epoch 13/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1799\n",
      "Epoch 00013: val_loss did not improve from 0.04940\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0517 - mae: 0.1797 - val_loss: 0.0512 - val_mae: 0.1812\n",
      "Epoch 14/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1790\n",
      "Epoch 00014: val_loss improved from 0.04940 to 0.04917, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0513 - mae: 0.1789 - val_loss: 0.0492 - val_mae: 0.1764\n",
      "Epoch 15/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1784\n",
      "Epoch 00015: val_loss improved from 0.04917 to 0.04905, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0511 - mae: 0.1784 - val_loss: 0.0491 - val_mae: 0.1753\n",
      "Epoch 16/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1780\n",
      "Epoch 00016: val_loss did not improve from 0.04905\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0509 - mae: 0.1780 - val_loss: 0.0493 - val_mae: 0.1737\n",
      "Epoch 17/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1768\n",
      "Epoch 00017: val_loss did not improve from 0.04905\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0505 - mae: 0.1768 - val_loss: 0.0504 - val_mae: 0.1784\n",
      "Epoch 18/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1764\n",
      "Epoch 00018: val_loss did not improve from 0.04905\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0503 - mae: 0.1764 - val_loss: 0.0501 - val_mae: 0.1771\n",
      "Epoch 19/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1763\n",
      "Epoch 00019: val_loss improved from 0.04905 to 0.04824, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0502 - mae: 0.1763 - val_loss: 0.0482 - val_mae: 0.1730\n",
      "Epoch 20/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1756\n",
      "Epoch 00020: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0499 - mae: 0.1755 - val_loss: 0.0487 - val_mae: 0.1748\n",
      "Epoch 21/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1754\n",
      "Epoch 00021: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0498 - mae: 0.1755 - val_loss: 0.0514 - val_mae: 0.1771\n",
      "Epoch 22/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1757\n",
      "Epoch 00022: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0499 - mae: 0.1757 - val_loss: 0.0487 - val_mae: 0.1759\n",
      "Epoch 23/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1752- ETA: 0s - loss: 0.\n",
      "Epoch 00023: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0497 - mae: 0.1752 - val_loss: 0.0494 - val_mae: 0.1758\n",
      "Epoch 24/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1750\n",
      "Epoch 00024: val_loss improved from 0.04824 to 0.04813, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 5s 129us/sample - loss: 0.0496 - mae: 0.1750 - val_loss: 0.0481 - val_mae: 0.1754\n",
      "Epoch 25/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1748\n",
      "Epoch 00025: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0495 - mae: 0.1748 - val_loss: 0.0492 - val_mae: 0.1777\n",
      "Epoch 26/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1752\n",
      "Epoch 00026: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0496 - mae: 0.1751 - val_loss: 0.0494 - val_mae: 0.1767\n",
      "Epoch 27/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1747\n",
      "Epoch 00027: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0495 - mae: 0.1746 - val_loss: 0.0495 - val_mae: 0.1749\n",
      "Epoch 28/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1746\n",
      "Epoch 00028: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0495 - mae: 0.1745 - val_loss: 0.0482 - val_mae: 0.1734\n",
      "Epoch 29/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1743\n",
      "Epoch 00029: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0493 - mae: 0.1743 - val_loss: 0.0485 - val_mae: 0.1733\n",
      "Epoch 30/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1743\n",
      "Epoch 00030: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0493 - mae: 0.1742 - val_loss: 0.0486 - val_mae: 0.1732\n",
      "Epoch 31/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1738\n",
      "Epoch 00031: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0491 - mae: 0.1737 - val_loss: 0.0491 - val_mae: 0.1737\n",
      "Epoch 32/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1741\n",
      "Epoch 00032: val_loss improved from 0.04813 to 0.04764, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0492 - mae: 0.1740 - val_loss: 0.0476 - val_mae: 0.1713\n",
      "Epoch 33/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1735\n",
      "Epoch 00033: val_loss improved from 0.04764 to 0.04714, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0490 - mae: 0.1735 - val_loss: 0.0471 - val_mae: 0.1688\n",
      "Epoch 34/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1733\n",
      "Epoch 00034: val_loss did not improve from 0.04714\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0489 - mae: 0.1732 - val_loss: 0.0478 - val_mae: 0.1738\n",
      "Epoch 35/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1736\n",
      "Epoch 00035: val_loss did not improve from 0.04714\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0489 - mae: 0.1736 - val_loss: 0.0480 - val_mae: 0.1725\n",
      "Epoch 36/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1733\n",
      "Epoch 00036: val_loss did not improve from 0.04714\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0488 - mae: 0.1733 - val_loss: 0.0481 - val_mae: 0.1738\n",
      "Epoch 37/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1732\n",
      "Epoch 00037: val_loss did not improve from 0.04714\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0488 - mae: 0.1730 - val_loss: 0.0472 - val_mae: 0.1678\n",
      "Epoch 38/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1728\n",
      "Epoch 00038: val_loss improved from 0.04714 to 0.04670, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0486 - mae: 0.1727 - val_loss: 0.0467 - val_mae: 0.1681\n",
      "Epoch 39/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1726\n",
      "Epoch 00039: val_loss improved from 0.04670 to 0.04663, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_6.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0486 - mae: 0.1727 - val_loss: 0.0466 - val_mae: 0.1716\n",
      "Epoch 40/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1725\n",
      "Epoch 00040: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0485 - mae: 0.1726 - val_loss: 0.0471 - val_mae: 0.1718\n",
      "Epoch 41/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1722- ETA\n",
      "Epoch 00041: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0485 - mae: 0.1724 - val_loss: 0.0474 - val_mae: 0.1727\n",
      "Epoch 42/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1722\n",
      "Epoch 00042: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0484 - mae: 0.1722 - val_loss: 0.0477 - val_mae: 0.1724\n",
      "Epoch 43/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1720\n",
      "Epoch 00043: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0484 - mae: 0.1720 - val_loss: 0.0491 - val_mae: 0.1777\n",
      "Epoch 44/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1721\n",
      "Epoch 00044: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0483 - mae: 0.1722 - val_loss: 0.0481 - val_mae: 0.1717\n",
      "Epoch 45/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1719\n",
      "Epoch 00045: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0484 - mae: 0.1720 - val_loss: 0.0469 - val_mae: 0.1706\n",
      "Epoch 46/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1720\n",
      "Epoch 00046: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0483 - mae: 0.1720 - val_loss: 0.0477 - val_mae: 0.1738\n",
      "Epoch 47/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1716\n",
      "Epoch 00047: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0481 - mae: 0.1715 - val_loss: 0.0472 - val_mae: 0.1735\n",
      "Epoch 48/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1716\n",
      "Epoch 00048: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0481 - mae: 0.1715 - val_loss: 0.0470 - val_mae: 0.1685\n",
      "Epoch 49/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1713\n",
      "Epoch 00049: val_loss did not improve from 0.04663\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0482 - mae: 0.1714 - val_loss: 0.0466 - val_mae: 0.1715\n",
      "Epoch 00049: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0742 - mae: 0.2223\n",
      "Epoch 00001: val_loss improved from inf to 0.06270, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0741 - mae: 0.2221 - val_loss: 0.0627 - val_mae: 0.2034\n",
      "Epoch 2/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0612 - mae: 0.2010\n",
      "Epoch 00002: val_loss improved from 0.06270 to 0.05888, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0612 - mae: 0.2010 - val_loss: 0.0589 - val_mae: 0.1982\n",
      "Epoch 3/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0588 - mae: 0.1962\n",
      "Epoch 00003: val_loss improved from 0.05888 to 0.05683, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0589 - mae: 0.1963 - val_loss: 0.0568 - val_mae: 0.1936\n",
      "Epoch 4/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0572 - mae: 0.1928\n",
      "Epoch 00004: val_loss improved from 0.05683 to 0.05570, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0572 - mae: 0.1928 - val_loss: 0.0557 - val_mae: 0.1929\n",
      "Epoch 5/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0562 - mae: 0.1906\n",
      "Epoch 00005: val_loss improved from 0.05570 to 0.05473, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0562 - mae: 0.1906 - val_loss: 0.0547 - val_mae: 0.1885\n",
      "Epoch 6/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0556 - mae: 0.1892\n",
      "Epoch 00006: val_loss improved from 0.05473 to 0.05304, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0556 - mae: 0.1892 - val_loss: 0.0530 - val_mae: 0.1854\n",
      "Epoch 7/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0549 - mae: 0.1878\n",
      "Epoch 00007: val_loss improved from 0.05304 to 0.05260, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0549 - mae: 0.1878 - val_loss: 0.0526 - val_mae: 0.1852\n",
      "Epoch 8/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0543 - mae: 0.1866\n",
      "Epoch 00008: val_loss improved from 0.05260 to 0.05260, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0543 - mae: 0.1866 - val_loss: 0.0526 - val_mae: 0.1835\n",
      "Epoch 9/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1860\n",
      "Epoch 00009: val_loss did not improve from 0.05260\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0540 - mae: 0.1859 - val_loss: 0.0547 - val_mae: 0.1893\n",
      "Epoch 10/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0538 - mae: 0.1856\n",
      "Epoch 00010: val_loss improved from 0.05260 to 0.05176, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0539 - mae: 0.1857 - val_loss: 0.0518 - val_mae: 0.1827\n",
      "Epoch 11/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1851\n",
      "Epoch 00011: val_loss did not improve from 0.05176\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0537 - mae: 0.1852 - val_loss: 0.0528 - val_mae: 0.1822\n",
      "Epoch 12/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1851\n",
      "Epoch 00012: val_loss did not improve from 0.05176\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0535 - mae: 0.1850 - val_loss: 0.0531 - val_mae: 0.1855\n",
      "Epoch 13/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1844\n",
      "Epoch 00013: val_loss did not improve from 0.05176\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0533 - mae: 0.1844 - val_loss: 0.0518 - val_mae: 0.1830\n",
      "Epoch 14/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0532 - mae: 0.1843\n",
      "Epoch 00014: val_loss did not improve from 0.05176\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0532 - mae: 0.1843 - val_loss: 0.0526 - val_mae: 0.1852\n",
      "Epoch 15/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0532 - mae: 0.1842\n",
      "Epoch 00015: val_loss improved from 0.05176 to 0.05138, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0532 - mae: 0.1841 - val_loss: 0.0514 - val_mae: 0.1825\n",
      "Epoch 16/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1840\n",
      "Epoch 00016: val_loss did not improve from 0.05138\n",
      "40000/40000 [==============================] - 5s 114us/sample - loss: 0.0529 - mae: 0.1839 - val_loss: 0.0532 - val_mae: 0.1873\n",
      "Epoch 17/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1837\n",
      "Epoch 00017: val_loss did not improve from 0.05138\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0528 - mae: 0.1836 - val_loss: 0.0533 - val_mae: 0.1824\n",
      "Epoch 18/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1831\n",
      "Epoch 00018: val_loss did not improve from 0.05138\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0527 - mae: 0.1832 - val_loss: 0.0514 - val_mae: 0.1815\n",
      "Epoch 19/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0527 - mae: 0.1830\n",
      "Epoch 00019: val_loss did not improve from 0.05138\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0527 - mae: 0.1830 - val_loss: 0.0532 - val_mae: 0.1854\n",
      "Epoch 20/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1825\n",
      "Epoch 00020: val_loss improved from 0.05138 to 0.05091, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0525 - mae: 0.1825 - val_loss: 0.0509 - val_mae: 0.1790\n",
      "Epoch 21/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1820\n",
      "Epoch 00021: val_loss did not improve from 0.05091\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0523 - mae: 0.1819 - val_loss: 0.0511 - val_mae: 0.1808\n",
      "Epoch 22/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1814\n",
      "Epoch 00022: val_loss improved from 0.05091 to 0.05026, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0521 - mae: 0.1815 - val_loss: 0.0503 - val_mae: 0.1803\n",
      "Epoch 23/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1812\n",
      "Epoch 00023: val_loss improved from 0.05026 to 0.04996, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0520 - mae: 0.1812 - val_loss: 0.0500 - val_mae: 0.1777\n",
      "Epoch 24/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1809\n",
      "Epoch 00024: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0520 - mae: 0.1810 - val_loss: 0.0512 - val_mae: 0.1826\n",
      "Epoch 25/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1805\n",
      "Epoch 00025: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0517 - mae: 0.1806 - val_loss: 0.0502 - val_mae: 0.1785\n",
      "Epoch 26/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1799\n",
      "Epoch 00026: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0514 - mae: 0.1798 - val_loss: 0.0507 - val_mae: 0.1769\n",
      "Epoch 27/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1797\n",
      "Epoch 00027: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0515 - mae: 0.1798 - val_loss: 0.0501 - val_mae: 0.1787\n",
      "Epoch 28/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1796\n",
      "Epoch 00028: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0514 - mae: 0.1796 - val_loss: 0.0501 - val_mae: 0.1794\n",
      "Epoch 29/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1790\n",
      "Epoch 00029: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0512 - mae: 0.1791 - val_loss: 0.0511 - val_mae: 0.1829\n",
      "Epoch 30/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1792\n",
      "Epoch 00030: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0512 - mae: 0.1792 - val_loss: 0.0504 - val_mae: 0.1761\n",
      "Epoch 31/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1787\n",
      "Epoch 00031: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0510 - mae: 0.1786 - val_loss: 0.0513 - val_mae: 0.1821\n",
      "Epoch 32/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1786\n",
      "Epoch 00032: val_loss did not improve from 0.04996\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0510 - mae: 0.1786 - val_loss: 0.0500 - val_mae: 0.1788\n",
      "Epoch 33/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1781\n",
      "Epoch 00033: val_loss improved from 0.04996 to 0.04951, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 5s 114us/sample - loss: 0.0507 - mae: 0.1781 - val_loss: 0.0495 - val_mae: 0.1753\n",
      "Epoch 34/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1780\n",
      "Epoch 00034: val_loss improved from 0.04951 to 0.04937, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0507 - mae: 0.1780 - val_loss: 0.0494 - val_mae: 0.1747\n",
      "Epoch 35/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1777\n",
      "Epoch 00035: val_loss improved from 0.04937 to 0.04904, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0506 - mae: 0.1776 - val_loss: 0.0490 - val_mae: 0.1747\n",
      "Epoch 36/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1773- ETA: 1s - loss: 0.0503 - mae: 0.17 - ETA: \n",
      "Epoch 00036: val_loss did not improve from 0.04904\n",
      "40000/40000 [==============================] - 5s 137us/sample - loss: 0.0505 - mae: 0.1773 - val_loss: 0.0500 - val_mae: 0.1770\n",
      "Epoch 37/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1774\n",
      "Epoch 00037: val_loss improved from 0.04904 to 0.04888, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0505 - mae: 0.1775 - val_loss: 0.0489 - val_mae: 0.1760\n",
      "Epoch 38/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1767\n",
      "Epoch 00038: val_loss did not improve from 0.04888\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0503 - mae: 0.1767 - val_loss: 0.0495 - val_mae: 0.1770\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1768\n",
      "Epoch 00039: val_loss improved from 0.04888 to 0.04858, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0503 - mae: 0.1768 - val_loss: 0.0486 - val_mae: 0.1729\n",
      "Epoch 40/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1763\n",
      "Epoch 00040: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0501 - mae: 0.1763 - val_loss: 0.0507 - val_mae: 0.1809\n",
      "Epoch 41/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1760\n",
      "Epoch 00041: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0500 - mae: 0.1759 - val_loss: 0.0487 - val_mae: 0.1750\n",
      "Epoch 42/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1761\n",
      "Epoch 00042: val_loss did not improve from 0.04858\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0500 - mae: 0.1760 - val_loss: 0.0492 - val_mae: 0.1736\n",
      "Epoch 43/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1756\n",
      "Epoch 00043: val_loss improved from 0.04858 to 0.04837, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0499 - mae: 0.1757 - val_loss: 0.0484 - val_mae: 0.1737\n",
      "Epoch 44/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1757\n",
      "Epoch 00044: val_loss did not improve from 0.04837\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0499 - mae: 0.1757 - val_loss: 0.0487 - val_mae: 0.1744\n",
      "Epoch 45/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1756\n",
      "Epoch 00045: val_loss improved from 0.04837 to 0.04836, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0498 - mae: 0.1756 - val_loss: 0.0484 - val_mae: 0.1728\n",
      "Epoch 46/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1754\n",
      "Epoch 00046: val_loss did not improve from 0.04836\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0498 - mae: 0.1754 - val_loss: 0.0491 - val_mae: 0.1762\n",
      "Epoch 47/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1755\n",
      "Epoch 00047: val_loss improved from 0.04836 to 0.04832, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0498 - mae: 0.1754 - val_loss: 0.0483 - val_mae: 0.1727\n",
      "Epoch 48/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1748\n",
      "Epoch 00048: val_loss improved from 0.04832 to 0.04814, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0496 - mae: 0.1749 - val_loss: 0.0481 - val_mae: 0.1745\n",
      "Epoch 49/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1750\n",
      "Epoch 00049: val_loss did not improve from 0.04814\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0496 - mae: 0.1751 - val_loss: 0.0492 - val_mae: 0.1788\n",
      "Epoch 50/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1748\n",
      "Epoch 00050: val_loss improved from 0.04814 to 0.04780, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0495 - mae: 0.1748 - val_loss: 0.0478 - val_mae: 0.1706\n",
      "Epoch 51/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1750\n",
      "Epoch 00051: val_loss improved from 0.04780 to 0.04778, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0496 - mae: 0.1749 - val_loss: 0.0478 - val_mae: 0.1708\n",
      "Epoch 52/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1747\n",
      "Epoch 00052: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0496 - mae: 0.1747 - val_loss: 0.0481 - val_mae: 0.1711\n",
      "Epoch 53/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1749\n",
      "Epoch 00053: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0496 - mae: 0.1749 - val_loss: 0.0482 - val_mae: 0.1735\n",
      "Epoch 54/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1746\n",
      "Epoch 00054: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0494 - mae: 0.1746 - val_loss: 0.0479 - val_mae: 0.1720\n",
      "Epoch 55/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1743\n",
      "Epoch 00055: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0493 - mae: 0.1744 - val_loss: 0.0482 - val_mae: 0.1722\n",
      "Epoch 56/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1742\n",
      "Epoch 00056: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0493 - mae: 0.1742 - val_loss: 0.0486 - val_mae: 0.1747\n",
      "Epoch 57/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1744\n",
      "Epoch 00057: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0493 - mae: 0.1744 - val_loss: 0.0479 - val_mae: 0.1735\n",
      "Epoch 58/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1743\n",
      "Epoch 00058: val_loss improved from 0.04778 to 0.04740, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0493 - mae: 0.1743 - val_loss: 0.0474 - val_mae: 0.1724\n",
      "Epoch 59/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1744\n",
      "Epoch 00059: val_loss did not improve from 0.04740\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0494 - mae: 0.1744 - val_loss: 0.0474 - val_mae: 0.1702\n",
      "Epoch 60/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1741\n",
      "Epoch 00060: val_loss did not improve from 0.04740\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0492 - mae: 0.1740 - val_loss: 0.0474 - val_mae: 0.1719\n",
      "Epoch 61/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1740\n",
      "Epoch 00061: val_loss did not improve from 0.04740\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0492 - mae: 0.1740 - val_loss: 0.0482 - val_mae: 0.1732\n",
      "Epoch 62/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1742\n",
      "Epoch 00062: val_loss did not improve from 0.04740\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0492 - mae: 0.1742 - val_loss: 0.0487 - val_mae: 0.1741\n",
      "Epoch 63/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1742\n",
      "Epoch 00063: val_loss improved from 0.04740 to 0.04737, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0492 - mae: 0.1741 - val_loss: 0.0474 - val_mae: 0.1711\n",
      "Epoch 64/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1739\n",
      "Epoch 00064: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0491 - mae: 0.1737 - val_loss: 0.0479 - val_mae: 0.1706\n",
      "Epoch 65/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1741\n",
      "Epoch 00065: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0492 - mae: 0.1740 - val_loss: 0.0485 - val_mae: 0.1768\n",
      "Epoch 66/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1738\n",
      "Epoch 00066: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0490 - mae: 0.1737 - val_loss: 0.0489 - val_mae: 0.1727\n",
      "Epoch 67/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1738\n",
      "Epoch 00067: val_loss improved from 0.04737 to 0.04709, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_7.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0490 - mae: 0.1738 - val_loss: 0.0471 - val_mae: 0.1700\n",
      "Epoch 68/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1736\n",
      "Epoch 00068: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0491 - mae: 0.1736 - val_loss: 0.0477 - val_mae: 0.1721\n",
      "Epoch 69/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1738\n",
      "Epoch 00069: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0491 - mae: 0.1738 - val_loss: 0.0492 - val_mae: 0.1729\n",
      "Epoch 70/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1737\n",
      "Epoch 00070: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0490 - mae: 0.1736 - val_loss: 0.0503 - val_mae: 0.1766\n",
      "Epoch 71/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1736\n",
      "Epoch 00071: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0490 - mae: 0.1736 - val_loss: 0.0489 - val_mae: 0.1759\n",
      "Epoch 72/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1736\n",
      "Epoch 00072: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0490 - mae: 0.1736 - val_loss: 0.0477 - val_mae: 0.1709\n",
      "Epoch 73/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1738\n",
      "Epoch 00073: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0490 - mae: 0.1737 - val_loss: 0.0483 - val_mae: 0.1709\n",
      "Epoch 74/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1733\n",
      "Epoch 00074: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0490 - mae: 0.1733 - val_loss: 0.0481 - val_mae: 0.1738\n",
      "Epoch 75/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1731\n",
      "Epoch 00075: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0488 - mae: 0.1732 - val_loss: 0.0481 - val_mae: 0.1729\n",
      "Epoch 76/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1736\n",
      "Epoch 00076: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0489 - mae: 0.1736 - val_loss: 0.0480 - val_mae: 0.1734\n",
      "Epoch 77/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1731\n",
      "Epoch 00077: val_loss did not improve from 0.04709\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0488 - mae: 0.1731 - val_loss: 0.0483 - val_mae: 0.1718\n",
      "Epoch 00077: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0676 - mae: 0.2110\n",
      "Epoch 00001: val_loss improved from inf to 0.05841, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0675 - mae: 0.2108 - val_loss: 0.0584 - val_mae: 0.1961\n",
      "Epoch 2/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0587 - mae: 0.1945\n",
      "Epoch 00002: val_loss improved from 0.05841 to 0.05547, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0587 - mae: 0.1944 - val_loss: 0.0555 - val_mae: 0.1902\n",
      "Epoch 3/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0568 - mae: 0.1907\n",
      "Epoch 00003: val_loss improved from 0.05547 to 0.05513, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0568 - mae: 0.1907 - val_loss: 0.0551 - val_mae: 0.1873\n",
      "Epoch 4/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0559 - mae: 0.1889\n",
      "Epoch 00004: val_loss improved from 0.05513 to 0.05395, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0558 - mae: 0.1888 - val_loss: 0.0540 - val_mae: 0.1866\n",
      "Epoch 5/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0549 - mae: 0.1868\n",
      "Epoch 00005: val_loss improved from 0.05395 to 0.05261, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0548 - mae: 0.1868 - val_loss: 0.0526 - val_mae: 0.1831\n",
      "Epoch 6/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0542 - mae: 0.1859\n",
      "Epoch 00006: val_loss improved from 0.05261 to 0.05243, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0542 - mae: 0.1858 - val_loss: 0.0524 - val_mae: 0.1821\n",
      "Epoch 7/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1844\n",
      "Epoch 00007: val_loss improved from 0.05243 to 0.05227, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0536 - mae: 0.1845 - val_loss: 0.0523 - val_mae: 0.1832\n",
      "Epoch 8/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1834\n",
      "Epoch 00008: val_loss improved from 0.05227 to 0.05061, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0530 - mae: 0.1834 - val_loss: 0.0506 - val_mae: 0.1798\n",
      "Epoch 9/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1825\n",
      "Epoch 00009: val_loss did not improve from 0.05061\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0526 - mae: 0.1826 - val_loss: 0.0506 - val_mae: 0.1789\n",
      "Epoch 10/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1814\n",
      "Epoch 00010: val_loss did not improve from 0.05061\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0521 - mae: 0.1814 - val_loss: 0.0511 - val_mae: 0.1796\n",
      "Epoch 11/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1802\n",
      "Epoch 00011: val_loss improved from 0.05061 to 0.04918, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0516 - mae: 0.1802 - val_loss: 0.0492 - val_mae: 0.1764\n",
      "Epoch 12/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1791\n",
      "Epoch 00012: val_loss did not improve from 0.04918\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0510 - mae: 0.1791 - val_loss: 0.0505 - val_mae: 0.1761\n",
      "Epoch 13/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1784\n",
      "Epoch 00013: val_loss did not improve from 0.04918\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0507 - mae: 0.1784 - val_loss: 0.0501 - val_mae: 0.1792\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1777\n",
      "Epoch 00014: val_loss improved from 0.04918 to 0.04830, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0505 - mae: 0.1777 - val_loss: 0.0483 - val_mae: 0.1742\n",
      "Epoch 15/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1773\n",
      "Epoch 00015: val_loss did not improve from 0.04830\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0504 - mae: 0.1773 - val_loss: 0.0493 - val_mae: 0.1766\n",
      "Epoch 16/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1762\n",
      "Epoch 00016: val_loss did not improve from 0.04830\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0500 - mae: 0.1763 - val_loss: 0.0500 - val_mae: 0.1769\n",
      "Epoch 17/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1764\n",
      "Epoch 00017: val_loss improved from 0.04830 to 0.04813, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0500 - mae: 0.1762 - val_loss: 0.0481 - val_mae: 0.1724\n",
      "Epoch 18/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1757\n",
      "Epoch 00018: val_loss improved from 0.04813 to 0.04805, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0498 - mae: 0.1758 - val_loss: 0.0480 - val_mae: 0.1742\n",
      "Epoch 19/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1757\n",
      "Epoch 00019: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0498 - mae: 0.1756 - val_loss: 0.0494 - val_mae: 0.1757\n",
      "Epoch 20/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1756\n",
      "Epoch 00020: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0497 - mae: 0.1754 - val_loss: 0.0491 - val_mae: 0.1749\n",
      "Epoch 21/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1755\n",
      "Epoch 00021: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0497 - mae: 0.1755 - val_loss: 0.0521 - val_mae: 0.1788\n",
      "Epoch 22/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1751\n",
      "Epoch 00022: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0495 - mae: 0.1752 - val_loss: 0.0483 - val_mae: 0.1724\n",
      "Epoch 23/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1744\n",
      "Epoch 00023: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0494 - mae: 0.1745 - val_loss: 0.0489 - val_mae: 0.1758\n",
      "Epoch 24/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1751\n",
      "Epoch 00024: val_loss did not improve from 0.04805\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0495 - mae: 0.1751 - val_loss: 0.0481 - val_mae: 0.1730\n",
      "Epoch 25/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1747\n",
      "Epoch 00025: val_loss improved from 0.04805 to 0.04786, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0494 - mae: 0.1747 - val_loss: 0.0479 - val_mae: 0.1730\n",
      "Epoch 26/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1746\n",
      "Epoch 00026: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0493 - mae: 0.1746 - val_loss: 0.0486 - val_mae: 0.1755\n",
      "Epoch 27/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1744\n",
      "Epoch 00027: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0493 - mae: 0.1745 - val_loss: 0.0491 - val_mae: 0.1746\n",
      "Epoch 28/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1741\n",
      "Epoch 00028: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0492 - mae: 0.1741 - val_loss: 0.0481 - val_mae: 0.1715\n",
      "Epoch 29/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1742\n",
      "Epoch 00029: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 5s 131us/sample - loss: 0.0492 - mae: 0.1742 - val_loss: 0.0485 - val_mae: 0.1734\n",
      "Epoch 30/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1741\n",
      "Epoch 00030: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 4s 110us/sample - loss: 0.0491 - mae: 0.1740 - val_loss: 0.0482 - val_mae: 0.1718\n",
      "Epoch 31/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1739\n",
      "Epoch 00031: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0491 - mae: 0.1738 - val_loss: 0.0499 - val_mae: 0.1755\n",
      "Epoch 32/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1740\n",
      "Epoch 00032: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0491 - mae: 0.1740 - val_loss: 0.0486 - val_mae: 0.1746\n",
      "Epoch 33/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1740\n",
      "Epoch 00033: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0491 - mae: 0.1739 - val_loss: 0.0484 - val_mae: 0.1725\n",
      "Epoch 34/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1737\n",
      "Epoch 00034: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0490 - mae: 0.1737 - val_loss: 0.0512 - val_mae: 0.1779\n",
      "Epoch 35/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1739\n",
      "Epoch 00035: val_loss improved from 0.04786 to 0.04764, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0491 - mae: 0.1738 - val_loss: 0.0476 - val_mae: 0.1728\n",
      "Epoch 36/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1737\n",
      "Epoch 00036: val_loss did not improve from 0.04764\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0490 - mae: 0.1737 - val_loss: 0.0477 - val_mae: 0.1725\n",
      "Epoch 37/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1730\n",
      "Epoch 00037: val_loss did not improve from 0.04764\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0488 - mae: 0.1730 - val_loss: 0.0481 - val_mae: 0.1728\n",
      "Epoch 38/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1734\n",
      "Epoch 00038: val_loss did not improve from 0.04764\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0489 - mae: 0.1735 - val_loss: 0.0477 - val_mae: 0.1711\n",
      "Epoch 39/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1736\n",
      "Epoch 00039: val_loss did not improve from 0.04764\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0489 - mae: 0.1735 - val_loss: 0.0480 - val_mae: 0.1713\n",
      "Epoch 40/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1733\n",
      "Epoch 00040: val_loss improved from 0.04764 to 0.04731, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0488 - mae: 0.1732 - val_loss: 0.0473 - val_mae: 0.1704\n",
      "Epoch 41/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1734\n",
      "Epoch 00041: val_loss did not improve from 0.04731\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0488 - mae: 0.1733 - val_loss: 0.0487 - val_mae: 0.1718\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1729\n",
      "Epoch 00042: val_loss did not improve from 0.04731\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0487 - mae: 0.1730 - val_loss: 0.0500 - val_mae: 0.1775\n",
      "Epoch 43/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1723\n",
      "Epoch 00043: val_loss did not improve from 0.04731\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0485 - mae: 0.1725 - val_loss: 0.0489 - val_mae: 0.1784\n",
      "Epoch 44/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1727\n",
      "Epoch 00044: val_loss did not improve from 0.04731\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0487 - mae: 0.1728 - val_loss: 0.0483 - val_mae: 0.1757\n",
      "Epoch 45/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1730\n",
      "Epoch 00045: val_loss did not improve from 0.04731\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0488 - mae: 0.1730 - val_loss: 0.0479 - val_mae: 0.1709\n",
      "Epoch 46/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1728\n",
      "Epoch 00046: val_loss improved from 0.04731 to 0.04725, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0486 - mae: 0.1728 - val_loss: 0.0473 - val_mae: 0.1734\n",
      "Epoch 47/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1724\n",
      "Epoch 00047: val_loss did not improve from 0.04725\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0485 - mae: 0.1724 - val_loss: 0.0477 - val_mae: 0.1730\n",
      "Epoch 48/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1728\n",
      "Epoch 00048: val_loss did not improve from 0.04725\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0486 - mae: 0.1728 - val_loss: 0.0477 - val_mae: 0.1719\n",
      "Epoch 49/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1724\n",
      "Epoch 00049: val_loss did not improve from 0.04725\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0485 - mae: 0.1724 - val_loss: 0.0483 - val_mae: 0.1755\n",
      "Epoch 50/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1723\n",
      "Epoch 00050: val_loss did not improve from 0.04725\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0484 - mae: 0.1723 - val_loss: 0.0476 - val_mae: 0.1736\n",
      "Epoch 51/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1721\n",
      "Epoch 00051: val_loss improved from 0.04725 to 0.04701, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0484 - mae: 0.1721 - val_loss: 0.0470 - val_mae: 0.1693\n",
      "Epoch 52/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1723\n",
      "Epoch 00052: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0484 - mae: 0.1722 - val_loss: 0.0478 - val_mae: 0.1735\n",
      "Epoch 53/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1722\n",
      "Epoch 00053: val_loss improved from 0.04701 to 0.04684, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_8.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0483 - mae: 0.1721 - val_loss: 0.0468 - val_mae: 0.1707\n",
      "Epoch 54/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1717\n",
      "Epoch 00054: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0481 - mae: 0.1717 - val_loss: 0.0479 - val_mae: 0.1730\n",
      "Epoch 55/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1720\n",
      "Epoch 00055: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0483 - mae: 0.1721 - val_loss: 0.0486 - val_mae: 0.1745\n",
      "Epoch 56/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1719\n",
      "Epoch 00056: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0482 - mae: 0.1719 - val_loss: 0.0487 - val_mae: 0.1741\n",
      "Epoch 57/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1721\n",
      "Epoch 00057: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0483 - mae: 0.1720 - val_loss: 0.0489 - val_mae: 0.1751\n",
      "Epoch 58/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1715\n",
      "Epoch 00058: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0481 - mae: 0.1714 - val_loss: 0.0478 - val_mae: 0.1693\n",
      "Epoch 59/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1715\n",
      "Epoch 00059: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0481 - mae: 0.1715 - val_loss: 0.0493 - val_mae: 0.1750\n",
      "Epoch 60/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1715\n",
      "Epoch 00060: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0481 - mae: 0.1715 - val_loss: 0.0474 - val_mae: 0.1690\n",
      "Epoch 61/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1715\n",
      "Epoch 00061: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0480 - mae: 0.1714 - val_loss: 0.0499 - val_mae: 0.1759\n",
      "Epoch 62/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1714\n",
      "Epoch 00062: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0481 - mae: 0.1715 - val_loss: 0.0513 - val_mae: 0.1792\n",
      "Epoch 63/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0479 - mae: 0.1713\n",
      "Epoch 00063: val_loss did not improve from 0.04684\n",
      "40000/40000 [==============================] - 4s 112us/sample - loss: 0.0479 - mae: 0.1713 - val_loss: 0.0487 - val_mae: 0.1746\n",
      "Epoch 00063: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0632 - mae: 0.2047\n",
      "Epoch 00001: val_loss improved from inf to 0.05659, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 7s 168us/sample - loss: 0.0632 - mae: 0.2047 - val_loss: 0.0566 - val_mae: 0.1936\n",
      "Epoch 2/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0572 - mae: 0.1923\n",
      "Epoch 00002: val_loss did not improve from 0.05659\n",
      "40000/40000 [==============================] - 4s 108us/sample - loss: 0.0572 - mae: 0.1923 - val_loss: 0.0587 - val_mae: 0.1927\n",
      "Epoch 3/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0558 - mae: 0.1890\n",
      "Epoch 00003: val_loss did not improve from 0.05659\n",
      "40000/40000 [==============================] - 5s 119us/sample - loss: 0.0558 - mae: 0.1890 - val_loss: 0.0577 - val_mae: 0.1943\n",
      "Epoch 4/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0551 - mae: 0.1873\n",
      "Epoch 00004: val_loss improved from 0.05659 to 0.05319, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 4s 108us/sample - loss: 0.0550 - mae: 0.1872 - val_loss: 0.0532 - val_mae: 0.1847\n",
      "Epoch 5/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0543 - mae: 0.1856\n",
      "Epoch 00005: val_loss improved from 0.05319 to 0.05261, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0544 - mae: 0.1857 - val_loss: 0.0526 - val_mae: 0.1869\n",
      "Epoch 6/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0538 - mae: 0.1842\n",
      "Epoch 00006: val_loss improved from 0.05261 to 0.05199, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0538 - mae: 0.1843 - val_loss: 0.0520 - val_mae: 0.1823\n",
      "Epoch 7/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1833\n",
      "Epoch 00007: val_loss did not improve from 0.05199\n",
      "40000/40000 [==============================] - 5s 120us/sample - loss: 0.0533 - mae: 0.1832 - val_loss: 0.0535 - val_mae: 0.1873\n",
      "Epoch 8/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1818\n",
      "Epoch 00008: val_loss improved from 0.05199 to 0.05052, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0526 - mae: 0.1818 - val_loss: 0.0505 - val_mae: 0.1792\n",
      "Epoch 9/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1802\n",
      "Epoch 00009: val_loss improved from 0.05052 to 0.04980, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0519 - mae: 0.1802 - val_loss: 0.0498 - val_mae: 0.1785\n",
      "Epoch 10/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1794\n",
      "Epoch 00010: val_loss improved from 0.04980 to 0.04928, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0515 - mae: 0.1792 - val_loss: 0.0493 - val_mae: 0.1743\n",
      "Epoch 11/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1778\n",
      "Epoch 00011: val_loss did not improve from 0.04928\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0508 - mae: 0.1778 - val_loss: 0.0493 - val_mae: 0.1739\n",
      "Epoch 12/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1766\n",
      "Epoch 00012: val_loss did not improve from 0.04928\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0504 - mae: 0.1768 - val_loss: 0.0501 - val_mae: 0.1756\n",
      "Epoch 13/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1763\n",
      "Epoch 00013: val_loss improved from 0.04928 to 0.04828, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0501 - mae: 0.1763 - val_loss: 0.0483 - val_mae: 0.1743\n",
      "Epoch 14/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1759\n",
      "Epoch 00014: val_loss did not improve from 0.04828\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0500 - mae: 0.1759 - val_loss: 0.0506 - val_mae: 0.1797\n",
      "Epoch 15/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1755\n",
      "Epoch 00015: val_loss did not improve from 0.04828\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0499 - mae: 0.1754 - val_loss: 0.0484 - val_mae: 0.1727\n",
      "Epoch 16/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1749\n",
      "Epoch 00016: val_loss did not improve from 0.04828\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0496 - mae: 0.1750 - val_loss: 0.0500 - val_mae: 0.1785\n",
      "Epoch 17/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1745\n",
      "Epoch 00017: val_loss improved from 0.04828 to 0.04710, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0494 - mae: 0.1745 - val_loss: 0.0471 - val_mae: 0.1686\n",
      "Epoch 18/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1739\n",
      "Epoch 00018: val_loss did not improve from 0.04710\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0492 - mae: 0.1739 - val_loss: 0.0486 - val_mae: 0.1755\n",
      "Epoch 19/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1741\n",
      "Epoch 00019: val_loss improved from 0.04710 to 0.04660, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0492 - mae: 0.1741 - val_loss: 0.0466 - val_mae: 0.1691\n",
      "Epoch 20/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1737\n",
      "Epoch 00020: val_loss did not improve from 0.04660\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0490 - mae: 0.1736 - val_loss: 0.0470 - val_mae: 0.1695\n",
      "Epoch 21/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1735\n",
      "Epoch 00021: val_loss did not improve from 0.04660\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0490 - mae: 0.1736 - val_loss: 0.0473 - val_mae: 0.1706\n",
      "Epoch 22/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1730\n",
      "Epoch 00022: val_loss did not improve from 0.04660\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0488 - mae: 0.1729 - val_loss: 0.0474 - val_mae: 0.1697\n",
      "Epoch 23/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1728\n",
      "Epoch 00023: val_loss improved from 0.04660 to 0.04656, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_9.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0488 - mae: 0.1729 - val_loss: 0.0466 - val_mae: 0.1698\n",
      "Epoch 24/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1728\n",
      "Epoch 00024: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0487 - mae: 0.1729 - val_loss: 0.0472 - val_mae: 0.1703\n",
      "Epoch 25/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1725\n",
      "Epoch 00025: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0486 - mae: 0.1724 - val_loss: 0.0480 - val_mae: 0.1748\n",
      "Epoch 26/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1725\n",
      "Epoch 00026: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0487 - mae: 0.1726 - val_loss: 0.0479 - val_mae: 0.1711\n",
      "Epoch 27/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1721\n",
      "Epoch 00027: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0485 - mae: 0.1722 - val_loss: 0.0470 - val_mae: 0.1704\n",
      "Epoch 28/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1721\n",
      "Epoch 00028: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0485 - mae: 0.1720 - val_loss: 0.0478 - val_mae: 0.1688\n",
      "Epoch 29/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1720\n",
      "Epoch 00029: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0483 - mae: 0.1720 - val_loss: 0.0475 - val_mae: 0.1714\n",
      "Epoch 30/100\n",
      "39216/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1718\n",
      "Epoch 00030: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0483 - mae: 0.1718 - val_loss: 0.0480 - val_mae: 0.1718\n",
      "Epoch 31/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1712\n",
      "Epoch 00031: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0482 - mae: 0.1712 - val_loss: 0.0469 - val_mae: 0.1696\n",
      "Epoch 32/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1713\n",
      "Epoch 00032: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0481 - mae: 0.1712 - val_loss: 0.0471 - val_mae: 0.1684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1713\n",
      "Epoch 00033: val_loss did not improve from 0.04656\n",
      "40000/40000 [==============================] - 3s 74us/sample - loss: 0.0480 - mae: 0.1713 - val_loss: 0.0482 - val_mae: 0.1729\n",
      "Epoch 00033: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0623 - mae: 0.2018\n",
      "Epoch 00001: val_loss improved from inf to 0.05801, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0621 - mae: 0.2014 - val_loss: 0.0580 - val_mae: 0.1909\n",
      "Epoch 2/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0566 - mae: 0.1907\n",
      "Epoch 00002: val_loss improved from 0.05801 to 0.05338, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0566 - mae: 0.1907 - val_loss: 0.0534 - val_mae: 0.1859\n",
      "Epoch 3/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1878\n",
      "Epoch 00003: val_loss improved from 0.05338 to 0.05321, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0552 - mae: 0.1878 - val_loss: 0.0532 - val_mae: 0.1839\n",
      "Epoch 4/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0539 - mae: 0.1851\n",
      "Epoch 00004: val_loss did not improve from 0.05321\n",
      "40000/40000 [==============================] - 3s 75us/sample - loss: 0.0539 - mae: 0.1851 - val_loss: 0.0538 - val_mae: 0.1841\n",
      "Epoch 5/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1825\n",
      "Epoch 00005: val_loss improved from 0.05321 to 0.05049, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 76us/sample - loss: 0.0528 - mae: 0.1826 - val_loss: 0.0505 - val_mae: 0.1806\n",
      "Epoch 6/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1800\n",
      "Epoch 00006: val_loss improved from 0.05049 to 0.04963, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0517 - mae: 0.1799 - val_loss: 0.0496 - val_mae: 0.1760\n",
      "Epoch 7/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1784\n",
      "Epoch 00007: val_loss improved from 0.04963 to 0.04905, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0511 - mae: 0.1785 - val_loss: 0.0491 - val_mae: 0.1725\n",
      "Epoch 8/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1766\n",
      "Epoch 00008: val_loss improved from 0.04905 to 0.04781, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0503 - mae: 0.1767 - val_loss: 0.0478 - val_mae: 0.1745\n",
      "Epoch 9/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1758\n",
      "Epoch 00009: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0499 - mae: 0.1757 - val_loss: 0.0493 - val_mae: 0.1757\n",
      "Epoch 10/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1749\n",
      "Epoch 00010: val_loss improved from 0.04781 to 0.04741, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 5s 121us/sample - loss: 0.0497 - mae: 0.1750 - val_loss: 0.0474 - val_mae: 0.1735\n",
      "Epoch 11/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1743\n",
      "Epoch 00011: val_loss did not improve from 0.04741\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0494 - mae: 0.1743 - val_loss: 0.0503 - val_mae: 0.1769\n",
      "Epoch 12/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1736\n",
      "Epoch 00012: val_loss did not improve from 0.04741\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0493 - mae: 0.1736 - val_loss: 0.0480 - val_mae: 0.1736\n",
      "Epoch 13/100\n",
      "39192/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1739\n",
      "Epoch 00013: val_loss did not improve from 0.04741\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0493 - mae: 0.1737 - val_loss: 0.0476 - val_mae: 0.1696\n",
      "Epoch 14/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1732\n",
      "Epoch 00014: val_loss did not improve from 0.04741\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0491 - mae: 0.1732 - val_loss: 0.0509 - val_mae: 0.1752\n",
      "Epoch 15/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1723\n",
      "Epoch 00015: val_loss did not improve from 0.04741\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0487 - mae: 0.1723 - val_loss: 0.0487 - val_mae: 0.1688\n",
      "Epoch 16/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1721\n",
      "Epoch 00016: val_loss improved from 0.04741 to 0.04699, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0487 - mae: 0.1721 - val_loss: 0.0470 - val_mae: 0.1704\n",
      "Epoch 17/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1718\n",
      "Epoch 00017: val_loss did not improve from 0.04699\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0485 - mae: 0.1718 - val_loss: 0.0489 - val_mae: 0.1733\n",
      "Epoch 18/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1715\n",
      "Epoch 00018: val_loss did not improve from 0.04699\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0485 - mae: 0.1716 - val_loss: 0.0471 - val_mae: 0.1677\n",
      "Epoch 19/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1713\n",
      "Epoch 00019: val_loss did not improve from 0.04699\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0482 - mae: 0.1712 - val_loss: 0.0477 - val_mae: 0.1668\n",
      "Epoch 20/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1708\n",
      "Epoch 00020: val_loss improved from 0.04699 to 0.04652, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0482 - mae: 0.1708 - val_loss: 0.0465 - val_mae: 0.1706\n",
      "Epoch 21/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1713\n",
      "Epoch 00021: val_loss did not improve from 0.04652\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0482 - mae: 0.1712 - val_loss: 0.0474 - val_mae: 0.1688\n",
      "Epoch 22/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0479 - mae: 0.1704\n",
      "Epoch 00022: val_loss improved from 0.04652 to 0.04634, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0479 - mae: 0.1704 - val_loss: 0.0463 - val_mae: 0.1687\n",
      "Epoch 23/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1708\n",
      "Epoch 00023: val_loss did not improve from 0.04634\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0481 - mae: 0.1708 - val_loss: 0.0485 - val_mae: 0.1684\n",
      "Epoch 24/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1702\n",
      "Epoch 00024: val_loss improved from 0.04634 to 0.04627, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 7s 172us/sample - loss: 0.0478 - mae: 0.1703 - val_loss: 0.0463 - val_mae: 0.1687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1701\n",
      "Epoch 00025: val_loss improved from 0.04627 to 0.04618, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 6s 162us/sample - loss: 0.0477 - mae: 0.1700 - val_loss: 0.0462 - val_mae: 0.1656\n",
      "Epoch 26/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0477 - mae: 0.1700\n",
      "Epoch 00026: val_loss did not improve from 0.04618\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0478 - mae: 0.1701 - val_loss: 0.0464 - val_mae: 0.1693\n",
      "Epoch 27/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1702\n",
      "Epoch 00027: val_loss did not improve from 0.04618\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0478 - mae: 0.1701 - val_loss: 0.0467 - val_mae: 0.1677\n",
      "Epoch 28/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0475 - mae: 0.1695\n",
      "Epoch 00028: val_loss improved from 0.04618 to 0.04593, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0475 - mae: 0.1695 - val_loss: 0.0459 - val_mae: 0.1658\n",
      "Epoch 29/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0477 - mae: 0.1697\n",
      "Epoch 00029: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0477 - mae: 0.1698 - val_loss: 0.0464 - val_mae: 0.1688\n",
      "Epoch 30/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1695\n",
      "Epoch 00030: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0476 - mae: 0.1695 - val_loss: 0.0471 - val_mae: 0.1691\n",
      "Epoch 31/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1690\n",
      "Epoch 00031: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0473 - mae: 0.1691 - val_loss: 0.0462 - val_mae: 0.1683\n",
      "Epoch 32/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0473 - mae: 0.1692\n",
      "Epoch 00032: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0473 - mae: 0.1692 - val_loss: 0.0473 - val_mae: 0.1703\n",
      "Epoch 33/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1687\n",
      "Epoch 00033: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0472 - mae: 0.1686 - val_loss: 0.0470 - val_mae: 0.1694\n",
      "Epoch 34/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1689\n",
      "Epoch 00034: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0472 - mae: 0.1689 - val_loss: 0.0467 - val_mae: 0.1662\n",
      "Epoch 35/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1686\n",
      "Epoch 00035: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0471 - mae: 0.1687 - val_loss: 0.0482 - val_mae: 0.1706\n",
      "Epoch 36/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0471 - mae: 0.1687\n",
      "Epoch 00036: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0471 - mae: 0.1687 - val_loss: 0.0488 - val_mae: 0.1736\n",
      "Epoch 37/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0469 - mae: 0.1683\n",
      "Epoch 00037: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0470 - mae: 0.1684 - val_loss: 0.0469 - val_mae: 0.1687\n",
      "Epoch 38/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0469 - mae: 0.1682\n",
      "Epoch 00038: val_loss did not improve from 0.04593\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0469 - mae: 0.1683 - val_loss: 0.0467 - val_mae: 0.1679\n",
      "Epoch 00038: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0679 - mae: 0.2127\n",
      "Epoch 00001: val_loss improved from inf to 0.06054, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0678 - mae: 0.2125 - val_loss: 0.0605 - val_mae: 0.2009\n",
      "Epoch 2/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0587 - mae: 0.1958\n",
      "Epoch 00002: val_loss improved from 0.06054 to 0.05611, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0587 - mae: 0.1958 - val_loss: 0.0561 - val_mae: 0.1921\n",
      "Epoch 3/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0571 - mae: 0.1922\n",
      "Epoch 00003: val_loss improved from 0.05611 to 0.05568, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 77us/sample - loss: 0.0571 - mae: 0.1921 - val_loss: 0.0557 - val_mae: 0.1907\n",
      "Epoch 4/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0564 - mae: 0.1903\n",
      "Epoch 00004: val_loss improved from 0.05568 to 0.05450, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0563 - mae: 0.1902 - val_loss: 0.0545 - val_mae: 0.1896\n",
      "Epoch 5/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0557 - mae: 0.1890\n",
      "Epoch 00005: val_loss improved from 0.05450 to 0.05423, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0557 - mae: 0.1890 - val_loss: 0.0542 - val_mae: 0.1868\n",
      "Epoch 6/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0551 - mae: 0.1876\n",
      "Epoch 00006: val_loss improved from 0.05423 to 0.05297, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0551 - mae: 0.1876 - val_loss: 0.0530 - val_mae: 0.1872\n",
      "Epoch 7/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0544 - mae: 0.1862\n",
      "Epoch 00007: val_loss improved from 0.05297 to 0.05204, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0544 - mae: 0.1860 - val_loss: 0.0520 - val_mae: 0.1823\n",
      "Epoch 8/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1844\n",
      "Epoch 00008: val_loss improved from 0.05204 to 0.05184, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0536 - mae: 0.1844 - val_loss: 0.0518 - val_mae: 0.1841\n",
      "Epoch 9/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1829\n",
      "Epoch 00009: val_loss improved from 0.05184 to 0.05059, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0529 - mae: 0.1828 - val_loss: 0.0506 - val_mae: 0.1791\n",
      "Epoch 10/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1816\n",
      "Epoch 00010: val_loss did not improve from 0.05059\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0524 - mae: 0.1816 - val_loss: 0.0511 - val_mae: 0.1803\n",
      "Epoch 11/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1804\n",
      "Epoch 00011: val_loss improved from 0.05059 to 0.05022, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0518 - mae: 0.1804 - val_loss: 0.0502 - val_mae: 0.1763\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1791\n",
      "Epoch 00012: val_loss improved from 0.05022 to 0.04954, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0512 - mae: 0.1790 - val_loss: 0.0495 - val_mae: 0.1770\n",
      "Epoch 13/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1780\n",
      "Epoch 00013: val_loss improved from 0.04954 to 0.04883, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0507 - mae: 0.1780 - val_loss: 0.0488 - val_mae: 0.1745\n",
      "Epoch 14/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1774\n",
      "Epoch 00014: val_loss did not improve from 0.04883\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0505 - mae: 0.1773 - val_loss: 0.0490 - val_mae: 0.1764\n",
      "Epoch 15/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1766\n",
      "Epoch 00015: val_loss improved from 0.04883 to 0.04809, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0502 - mae: 0.1766 - val_loss: 0.0481 - val_mae: 0.1733\n",
      "Epoch 16/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1759\n",
      "Epoch 00016: val_loss did not improve from 0.04809\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0500 - mae: 0.1759 - val_loss: 0.0492 - val_mae: 0.1752\n",
      "Epoch 17/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1759\n",
      "Epoch 00017: val_loss improved from 0.04809 to 0.04746, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 6s 138us/sample - loss: 0.0499 - mae: 0.1758 - val_loss: 0.0475 - val_mae: 0.1702\n",
      "Epoch 18/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1748\n",
      "Epoch 00018: val_loss did not improve from 0.04746\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0496 - mae: 0.1748 - val_loss: 0.0510 - val_mae: 0.1796\n",
      "Epoch 19/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1749\n",
      "Epoch 00019: val_loss did not improve from 0.04746\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0496 - mae: 0.1749 - val_loss: 0.0498 - val_mae: 0.1754\n",
      "Epoch 20/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1748\n",
      "Epoch 00020: val_loss improved from 0.04746 to 0.04717, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0496 - mae: 0.1748 - val_loss: 0.0472 - val_mae: 0.1699\n",
      "Epoch 21/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1739\n",
      "Epoch 00021: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0493 - mae: 0.1740 - val_loss: 0.0482 - val_mae: 0.1767\n",
      "Epoch 22/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1739\n",
      "Epoch 00022: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0491 - mae: 0.1738 - val_loss: 0.0475 - val_mae: 0.1709\n",
      "Epoch 23/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1739\n",
      "Epoch 00023: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0492 - mae: 0.1740 - val_loss: 0.0483 - val_mae: 0.1759\n",
      "Epoch 24/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1735\n",
      "Epoch 00024: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0490 - mae: 0.1734 - val_loss: 0.0473 - val_mae: 0.1684\n",
      "Epoch 25/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1731\n",
      "Epoch 00025: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0489 - mae: 0.1732 - val_loss: 0.0506 - val_mae: 0.1750\n",
      "Epoch 26/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1731\n",
      "Epoch 00026: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0489 - mae: 0.1731 - val_loss: 0.0498 - val_mae: 0.1715\n",
      "Epoch 27/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1731\n",
      "Epoch 00027: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0488 - mae: 0.1731 - val_loss: 0.0491 - val_mae: 0.1781\n",
      "Epoch 28/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1727\n",
      "Epoch 00028: val_loss did not improve from 0.04717\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0488 - mae: 0.1727 - val_loss: 0.0494 - val_mae: 0.1744\n",
      "Epoch 29/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1730\n",
      "Epoch 00029: val_loss improved from 0.04717 to 0.04659, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_11.hdf5\n",
      "40000/40000 [==============================] - 5s 128us/sample - loss: 0.0489 - mae: 0.1730 - val_loss: 0.0466 - val_mae: 0.1692\n",
      "Epoch 30/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1728\n",
      "Epoch 00030: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0487 - mae: 0.1729 - val_loss: 0.0471 - val_mae: 0.1729\n",
      "Epoch 31/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1726\n",
      "Epoch 00031: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0486 - mae: 0.1726 - val_loss: 0.0473 - val_mae: 0.1737\n",
      "Epoch 32/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1724\n",
      "Epoch 00032: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0485 - mae: 0.1724 - val_loss: 0.0470 - val_mae: 0.1681\n",
      "Epoch 33/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1722\n",
      "Epoch 00033: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0485 - mae: 0.1722 - val_loss: 0.0475 - val_mae: 0.1703\n",
      "Epoch 34/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1722\n",
      "Epoch 00034: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0484 - mae: 0.1722 - val_loss: 0.0471 - val_mae: 0.1689\n",
      "Epoch 35/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1721\n",
      "Epoch 00035: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0484 - mae: 0.1722 - val_loss: 0.0517 - val_mae: 0.1745\n",
      "Epoch 36/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1717\n",
      "Epoch 00036: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0482 - mae: 0.1717 - val_loss: 0.0476 - val_mae: 0.1693\n",
      "Epoch 37/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1717\n",
      "Epoch 00037: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 6s 139us/sample - loss: 0.0482 - mae: 0.1716 - val_loss: 0.0485 - val_mae: 0.1720\n",
      "Epoch 38/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1718\n",
      "Epoch 00038: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0483 - mae: 0.1717 - val_loss: 0.0470 - val_mae: 0.1712\n",
      "Epoch 39/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1717\n",
      "Epoch 00039: val_loss did not improve from 0.04659\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0482 - mae: 0.1718 - val_loss: 0.0491 - val_mae: 0.1687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00039: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0608 - mae: 0.1993\n",
      "Epoch 00001: val_loss improved from inf to 0.05464, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 5s 126us/sample - loss: 0.0609 - mae: 0.1993 - val_loss: 0.0546 - val_mae: 0.1875\n",
      "Epoch 2/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0554 - mae: 0.1880\n",
      "Epoch 00002: val_loss improved from 0.05464 to 0.05319, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0554 - mae: 0.1879 - val_loss: 0.0532 - val_mae: 0.1834\n",
      "Epoch 3/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1849\n",
      "Epoch 00003: val_loss did not improve from 0.05319\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0540 - mae: 0.1849 - val_loss: 0.0552 - val_mae: 0.1850\n",
      "Epoch 4/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1834\n",
      "Epoch 00004: val_loss improved from 0.05319 to 0.05209, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0532 - mae: 0.1833 - val_loss: 0.0521 - val_mae: 0.1831\n",
      "Epoch 5/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1815\n",
      "Epoch 00005: val_loss improved from 0.05209 to 0.05056, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0523 - mae: 0.1815 - val_loss: 0.0506 - val_mae: 0.1763\n",
      "Epoch 6/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1799\n",
      "Epoch 00006: val_loss did not improve from 0.05056\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0517 - mae: 0.1798 - val_loss: 0.0510 - val_mae: 0.1764\n",
      "Epoch 7/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1788\n",
      "Epoch 00007: val_loss improved from 0.05056 to 0.04896, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0513 - mae: 0.1788 - val_loss: 0.0490 - val_mae: 0.1737\n",
      "Epoch 8/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1770\n",
      "Epoch 00008: val_loss improved from 0.04896 to 0.04883, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0506 - mae: 0.1770 - val_loss: 0.0488 - val_mae: 0.1748\n",
      "Epoch 9/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1760\n",
      "Epoch 00009: val_loss did not improve from 0.04883\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0502 - mae: 0.1761 - val_loss: 0.0537 - val_mae: 0.1801\n",
      "Epoch 10/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1752\n",
      "Epoch 00010: val_loss improved from 0.04883 to 0.04775, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0498 - mae: 0.1752 - val_loss: 0.0477 - val_mae: 0.1743\n",
      "Epoch 11/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1741\n",
      "Epoch 00011: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0494 - mae: 0.1741 - val_loss: 0.0488 - val_mae: 0.1792\n",
      "Epoch 12/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1741\n",
      "Epoch 00012: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 6s 156us/sample - loss: 0.0495 - mae: 0.1742 - val_loss: 0.0503 - val_mae: 0.1753\n",
      "Epoch 13/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1731\n",
      "Epoch 00013: val_loss did not improve from 0.04775\n",
      "40000/40000 [==============================] - 5s 113us/sample - loss: 0.0491 - mae: 0.1732 - val_loss: 0.0508 - val_mae: 0.1768\n",
      "Epoch 14/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1733\n",
      "Epoch 00014: val_loss improved from 0.04775 to 0.04681, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0491 - mae: 0.1732 - val_loss: 0.0468 - val_mae: 0.1671\n",
      "Epoch 15/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1726\n",
      "Epoch 00015: val_loss improved from 0.04681 to 0.04627, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0488 - mae: 0.1726 - val_loss: 0.0463 - val_mae: 0.1685\n",
      "Epoch 16/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1724\n",
      "Epoch 00016: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0487 - mae: 0.1723 - val_loss: 0.0472 - val_mae: 0.1673\n",
      "Epoch 17/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1718\n",
      "Epoch 00017: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 6s 140us/sample - loss: 0.0486 - mae: 0.1719 - val_loss: 0.0472 - val_mae: 0.1738\n",
      "Epoch 18/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1717\n",
      "Epoch 00018: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0483 - mae: 0.1716 - val_loss: 0.0471 - val_mae: 0.1694\n",
      "Epoch 19/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1711\n",
      "Epoch 00019: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 6s 147us/sample - loss: 0.0482 - mae: 0.1711 - val_loss: 0.0464 - val_mae: 0.1697\n",
      "Epoch 20/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1712\n",
      "Epoch 00020: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0481 - mae: 0.1710 - val_loss: 0.0476 - val_mae: 0.1703\n",
      "Epoch 21/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0479 - mae: 0.1705\n",
      "Epoch 00021: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0479 - mae: 0.1705 - val_loss: 0.0491 - val_mae: 0.1738\n",
      "Epoch 22/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0477 - mae: 0.1700\n",
      "Epoch 00022: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0477 - mae: 0.1701 - val_loss: 0.0470 - val_mae: 0.1676\n",
      "Epoch 23/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1698\n",
      "Epoch 00023: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0476 - mae: 0.1698 - val_loss: 0.0479 - val_mae: 0.1696\n",
      "Epoch 24/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0477 - mae: 0.1700\n",
      "Epoch 00024: val_loss did not improve from 0.04627\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0477 - mae: 0.1700 - val_loss: 0.0466 - val_mae: 0.1701\n",
      "Epoch 25/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1693\n",
      "Epoch 00025: val_loss improved from 0.04627 to 0.04608, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0475 - mae: 0.1696 - val_loss: 0.0461 - val_mae: 0.1710\n",
      "Epoch 26/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1693\n",
      "Epoch 00026: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0474 - mae: 0.1693 - val_loss: 0.0513 - val_mae: 0.1769\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1686\n",
      "Epoch 00027: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0471 - mae: 0.1687 - val_loss: 0.0477 - val_mae: 0.1737\n",
      "Epoch 28/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0471 - mae: 0.1686\n",
      "Epoch 00028: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0472 - mae: 0.1686 - val_loss: 0.0464 - val_mae: 0.1686\n",
      "Epoch 29/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0471 - mae: 0.1685\n",
      "Epoch 00029: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0471 - mae: 0.1685 - val_loss: 0.0461 - val_mae: 0.1676\n",
      "Epoch 30/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1685\n",
      "Epoch 00030: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0470 - mae: 0.1684 - val_loss: 0.0462 - val_mae: 0.1682\n",
      "Epoch 31/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0468 - mae: 0.1680\n",
      "Epoch 00031: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0468 - mae: 0.1680 - val_loss: 0.0480 - val_mae: 0.1680\n",
      "Epoch 32/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0467 - mae: 0.1678\n",
      "Epoch 00032: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0467 - mae: 0.1678 - val_loss: 0.0466 - val_mae: 0.1696\n",
      "Epoch 33/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0467 - mae: 0.1679\n",
      "Epoch 00033: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0467 - mae: 0.1679 - val_loss: 0.0477 - val_mae: 0.1715\n",
      "Epoch 34/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0466 - mae: 0.1675\n",
      "Epoch 00034: val_loss did not improve from 0.04608\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0466 - mae: 0.1675 - val_loss: 0.0466 - val_mae: 0.1697\n",
      "Epoch 35/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0464 - mae: 0.1671\n",
      "Epoch 00035: val_loss improved from 0.04608 to 0.04549, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_12.hdf5\n",
      "40000/40000 [==============================] - 5s 116us/sample - loss: 0.0464 - mae: 0.1672 - val_loss: 0.0455 - val_mae: 0.1659\n",
      "Epoch 36/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0464 - mae: 0.1671\n",
      "Epoch 00036: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 5s 119us/sample - loss: 0.0463 - mae: 0.1671 - val_loss: 0.0470 - val_mae: 0.1675\n",
      "Epoch 37/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0463 - mae: 0.1667\n",
      "Epoch 00037: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 5s 116us/sample - loss: 0.0464 - mae: 0.1669 - val_loss: 0.0467 - val_mae: 0.1705\n",
      "Epoch 38/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0463 - mae: 0.1667\n",
      "Epoch 00038: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 5s 123us/sample - loss: 0.0463 - mae: 0.1667 - val_loss: 0.0459 - val_mae: 0.1673\n",
      "Epoch 39/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0462 - mae: 0.1666\n",
      "Epoch 00039: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 6s 160us/sample - loss: 0.0462 - mae: 0.1666 - val_loss: 0.0466 - val_mae: 0.1685\n",
      "Epoch 40/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0461 - mae: 0.1667\n",
      "Epoch 00040: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 6s 148us/sample - loss: 0.0461 - mae: 0.1665 - val_loss: 0.0474 - val_mae: 0.1662\n",
      "Epoch 41/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0463 - mae: 0.1670\n",
      "Epoch 00041: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 5s 133us/sample - loss: 0.0463 - mae: 0.1669 - val_loss: 0.0461 - val_mae: 0.1675\n",
      "Epoch 42/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0459 - mae: 0.1661\n",
      "Epoch 00042: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 5s 136us/sample - loss: 0.0459 - mae: 0.1660 - val_loss: 0.0463 - val_mae: 0.1670\n",
      "Epoch 43/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0459 - mae: 0.1662\n",
      "Epoch 00043: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 5s 125us/sample - loss: 0.0459 - mae: 0.1660 - val_loss: 0.0470 - val_mae: 0.1695\n",
      "Epoch 44/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0459 - mae: 0.1658\n",
      "Epoch 00044: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0459 - mae: 0.1659 - val_loss: 0.0469 - val_mae: 0.1697\n",
      "Epoch 45/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0457 - mae: 0.1657\n",
      "Epoch 00045: val_loss did not improve from 0.04549\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0457 - mae: 0.1657 - val_loss: 0.0472 - val_mae: 0.1689\n",
      "Epoch 00045: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0659 - mae: 0.2093\n",
      "Epoch 00001: val_loss improved from inf to 0.06182, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 7s 163us/sample - loss: 0.0659 - mae: 0.2093 - val_loss: 0.0618 - val_mae: 0.1999\n",
      "Epoch 2/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0597 - mae: 0.1976\n",
      "Epoch 00002: val_loss improved from 0.06182 to 0.05899, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 5s 114us/sample - loss: 0.0597 - mae: 0.1976 - val_loss: 0.0590 - val_mae: 0.1958\n",
      "Epoch 3/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0575 - mae: 0.1927\n",
      "Epoch 00003: val_loss improved from 0.05899 to 0.05561, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0575 - mae: 0.1927 - val_loss: 0.0556 - val_mae: 0.1913\n",
      "Epoch 4/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0563 - mae: 0.1906\n",
      "Epoch 00004: val_loss improved from 0.05561 to 0.05439, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0563 - mae: 0.1906 - val_loss: 0.0544 - val_mae: 0.1891\n",
      "Epoch 5/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0558 - mae: 0.1894\n",
      "Epoch 00005: val_loss did not improve from 0.05439\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0559 - mae: 0.1895 - val_loss: 0.0570 - val_mae: 0.1931\n",
      "Epoch 6/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1880\n",
      "Epoch 00006: val_loss improved from 0.05439 to 0.05275, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 5s 114us/sample - loss: 0.0551 - mae: 0.1879 - val_loss: 0.0527 - val_mae: 0.1851\n",
      "Epoch 7/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1868\n",
      "Epoch 00007: val_loss improved from 0.05275 to 0.05262, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0546 - mae: 0.1868 - val_loss: 0.0526 - val_mae: 0.1852\n",
      "Epoch 8/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0543 - mae: 0.1862\n",
      "Epoch 00008: val_loss improved from 0.05262 to 0.05254, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0543 - mae: 0.1862 - val_loss: 0.0525 - val_mae: 0.1854\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0539 - mae: 0.1855\n",
      "Epoch 00009: val_loss improved from 0.05254 to 0.05243, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0539 - mae: 0.1855 - val_loss: 0.0524 - val_mae: 0.1843\n",
      "Epoch 10/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0534 - mae: 0.1846\n",
      "Epoch 00010: val_loss improved from 0.05243 to 0.05192, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0534 - mae: 0.1846 - val_loss: 0.0519 - val_mae: 0.1811\n",
      "Epoch 11/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1839\n",
      "Epoch 00011: val_loss did not improve from 0.05192\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0531 - mae: 0.1840 - val_loss: 0.0563 - val_mae: 0.1870\n",
      "Epoch 12/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1835\n",
      "Epoch 00012: val_loss did not improve from 0.05192\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0530 - mae: 0.1834 - val_loss: 0.0537 - val_mae: 0.1861\n",
      "Epoch 13/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1827\n",
      "Epoch 00013: val_loss improved from 0.05192 to 0.05060, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0525 - mae: 0.1827 - val_loss: 0.0506 - val_mae: 0.1790\n",
      "Epoch 14/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1822\n",
      "Epoch 00014: val_loss did not improve from 0.05060\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0524 - mae: 0.1822 - val_loss: 0.0508 - val_mae: 0.1799\n",
      "Epoch 15/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1818\n",
      "Epoch 00015: val_loss improved from 0.05060 to 0.05029, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0523 - mae: 0.1818 - val_loss: 0.0503 - val_mae: 0.1803\n",
      "Epoch 16/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1811\n",
      "Epoch 00016: val_loss did not improve from 0.05029\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0520 - mae: 0.1811 - val_loss: 0.0507 - val_mae: 0.1779\n",
      "Epoch 17/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1805\n",
      "Epoch 00017: val_loss did not improve from 0.05029\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0518 - mae: 0.1806 - val_loss: 0.0505 - val_mae: 0.1824\n",
      "Epoch 18/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1802\n",
      "Epoch 00018: val_loss improved from 0.05029 to 0.04964, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0516 - mae: 0.1802 - val_loss: 0.0496 - val_mae: 0.1780\n",
      "Epoch 19/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1802\n",
      "Epoch 00019: val_loss improved from 0.04964 to 0.04958, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0515 - mae: 0.1802 - val_loss: 0.0496 - val_mae: 0.1761\n",
      "Epoch 20/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1800\n",
      "Epoch 00020: val_loss improved from 0.04958 to 0.04953, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 5s 134us/sample - loss: 0.0515 - mae: 0.1800 - val_loss: 0.0495 - val_mae: 0.1785\n",
      "Epoch 21/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1792\n",
      "Epoch 00021: val_loss improved from 0.04953 to 0.04937, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0512 - mae: 0.1792 - val_loss: 0.0494 - val_mae: 0.1750\n",
      "Epoch 22/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1794\n",
      "Epoch 00022: val_loss did not improve from 0.04937\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0513 - mae: 0.1794 - val_loss: 0.0506 - val_mae: 0.1786\n",
      "Epoch 23/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1794\n",
      "Epoch 00023: val_loss improved from 0.04937 to 0.04890, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0513 - mae: 0.1795 - val_loss: 0.0489 - val_mae: 0.1761\n",
      "Epoch 24/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1791\n",
      "Epoch 00024: val_loss did not improve from 0.04890\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0512 - mae: 0.1791 - val_loss: 0.0500 - val_mae: 0.1781\n",
      "Epoch 25/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1788\n",
      "Epoch 00025: val_loss did not improve from 0.04890\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0511 - mae: 0.1787 - val_loss: 0.0507 - val_mae: 0.1787\n",
      "Epoch 26/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1785\n",
      "Epoch 00026: val_loss improved from 0.04890 to 0.04847, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0510 - mae: 0.1787 - val_loss: 0.0485 - val_mae: 0.1744\n",
      "Epoch 27/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1783\n",
      "Epoch 00027: val_loss did not improve from 0.04847\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0509 - mae: 0.1782 - val_loss: 0.0497 - val_mae: 0.1758\n",
      "Epoch 28/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1779\n",
      "Epoch 00028: val_loss did not improve from 0.04847\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0508 - mae: 0.1779 - val_loss: 0.0490 - val_mae: 0.1768\n",
      "Epoch 29/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1782\n",
      "Epoch 00029: val_loss did not improve from 0.04847\n",
      "40000/40000 [==============================] - 5s 113us/sample - loss: 0.0508 - mae: 0.1784 - val_loss: 0.0487 - val_mae: 0.1737\n",
      "Epoch 30/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1777\n",
      "Epoch 00030: val_loss did not improve from 0.04847\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0506 - mae: 0.1777 - val_loss: 0.0488 - val_mae: 0.1756\n",
      "Epoch 31/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1774\n",
      "Epoch 00031: val_loss improved from 0.04847 to 0.04789, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 6s 146us/sample - loss: 0.0505 - mae: 0.1775 - val_loss: 0.0479 - val_mae: 0.1744\n",
      "Epoch 32/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1774\n",
      "Epoch 00032: val_loss did not improve from 0.04789\n",
      "40000/40000 [==============================] - 6s 142us/sample - loss: 0.0504 - mae: 0.1775 - val_loss: 0.0490 - val_mae: 0.1755\n",
      "Epoch 33/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1770\n",
      "Epoch 00033: val_loss did not improve from 0.04789\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0503 - mae: 0.1770 - val_loss: 0.0487 - val_mae: 0.1748\n",
      "Epoch 34/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1769\n",
      "Epoch 00034: val_loss did not improve from 0.04789\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0502 - mae: 0.1767 - val_loss: 0.0501 - val_mae: 0.1751\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1769\n",
      "Epoch 00035: val_loss did not improve from 0.04789\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0503 - mae: 0.1769 - val_loss: 0.0485 - val_mae: 0.1760\n",
      "Epoch 36/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1768\n",
      "Epoch 00036: val_loss improved from 0.04789 to 0.04778, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0502 - mae: 0.1768 - val_loss: 0.0478 - val_mae: 0.1725\n",
      "Epoch 37/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1765\n",
      "Epoch 00037: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 4s 110us/sample - loss: 0.0501 - mae: 0.1765 - val_loss: 0.0488 - val_mae: 0.1757\n",
      "Epoch 38/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1766\n",
      "Epoch 00038: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0501 - mae: 0.1764 - val_loss: 0.0491 - val_mae: 0.1748\n",
      "Epoch 39/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1761\n",
      "Epoch 00039: val_loss did not improve from 0.04778\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0500 - mae: 0.1761 - val_loss: 0.0491 - val_mae: 0.1744\n",
      "Epoch 40/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1760\n",
      "Epoch 00040: val_loss improved from 0.04778 to 0.04737, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_13.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0499 - mae: 0.1759 - val_loss: 0.0474 - val_mae: 0.1721\n",
      "Epoch 41/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1760\n",
      "Epoch 00041: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0499 - mae: 0.1760 - val_loss: 0.0488 - val_mae: 0.1753\n",
      "Epoch 42/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1760\n",
      "Epoch 00042: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0500 - mae: 0.1761 - val_loss: 0.0481 - val_mae: 0.1738\n",
      "Epoch 43/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1758\n",
      "Epoch 00043: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0498 - mae: 0.1759 - val_loss: 0.0481 - val_mae: 0.1740\n",
      "Epoch 44/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1757\n",
      "Epoch 00044: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0499 - mae: 0.1758 - val_loss: 0.0492 - val_mae: 0.1771\n",
      "Epoch 45/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1756\n",
      "Epoch 00045: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0498 - mae: 0.1756 - val_loss: 0.0477 - val_mae: 0.1737\n",
      "Epoch 46/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1755\n",
      "Epoch 00046: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0497 - mae: 0.1753 - val_loss: 0.0476 - val_mae: 0.1738\n",
      "Epoch 47/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1752\n",
      "Epoch 00047: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0497 - mae: 0.1753 - val_loss: 0.0476 - val_mae: 0.1719\n",
      "Epoch 48/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1751\n",
      "Epoch 00048: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0497 - mae: 0.1753 - val_loss: 0.0506 - val_mae: 0.1764\n",
      "Epoch 49/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1752\n",
      "Epoch 00049: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0496 - mae: 0.1752 - val_loss: 0.0479 - val_mae: 0.1730\n",
      "Epoch 50/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1754\n",
      "Epoch 00050: val_loss did not improve from 0.04737\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0497 - mae: 0.1753 - val_loss: 0.0480 - val_mae: 0.1734\n",
      "Epoch 00050: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0655 - mae: 0.2077\n",
      "Epoch 00001: val_loss improved from inf to 0.05760, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0654 - mae: 0.2077 - val_loss: 0.0576 - val_mae: 0.1923\n",
      "Epoch 2/100\n",
      "39240/40000 [============================>.] - ETA: 0s - loss: 0.0569 - mae: 0.1917\n",
      "Epoch 00002: val_loss improved from 0.05760 to 0.05656, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0568 - mae: 0.1916 - val_loss: 0.0566 - val_mae: 0.1888\n",
      "Epoch 3/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0558 - mae: 0.1892\n",
      "Epoch 00003: val_loss improved from 0.05656 to 0.05287, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0558 - mae: 0.1891 - val_loss: 0.0529 - val_mae: 0.1847\n",
      "Epoch 4/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0548 - mae: 0.1874\n",
      "Epoch 00004: val_loss did not improve from 0.05287\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0549 - mae: 0.1875 - val_loss: 0.0544 - val_mae: 0.1909\n",
      "Epoch 5/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0544 - mae: 0.1865\n",
      "Epoch 00005: val_loss improved from 0.05287 to 0.05280, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0545 - mae: 0.1866 - val_loss: 0.0528 - val_mae: 0.1841\n",
      "Epoch 6/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1854\n",
      "Epoch 00006: val_loss did not improve from 0.05280\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0539 - mae: 0.1852 - val_loss: 0.0549 - val_mae: 0.1848\n",
      "Epoch 7/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0534 - mae: 0.1840\n",
      "Epoch 00007: val_loss did not improve from 0.05280\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0534 - mae: 0.1840 - val_loss: 0.0545 - val_mae: 0.1904\n",
      "Epoch 8/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1828\n",
      "Epoch 00008: val_loss improved from 0.05280 to 0.05193, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0529 - mae: 0.1828 - val_loss: 0.0519 - val_mae: 0.1821\n",
      "Epoch 9/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0527 - mae: 0.1820\n",
      "Epoch 00009: val_loss improved from 0.05193 to 0.05050, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0527 - mae: 0.1820 - val_loss: 0.0505 - val_mae: 0.1803\n",
      "Epoch 10/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1813\n",
      "Epoch 00010: val_loss improved from 0.05050 to 0.05003, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0527 - mae: 0.1815 - val_loss: 0.0500 - val_mae: 0.1779\n",
      "Epoch 11/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1801\n",
      "Epoch 00011: val_loss did not improve from 0.05003\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0520 - mae: 0.1800 - val_loss: 0.0503 - val_mae: 0.1768\n",
      "Epoch 12/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1792\n",
      "Epoch 00012: val_loss did not improve from 0.05003\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0518 - mae: 0.1793 - val_loss: 0.0518 - val_mae: 0.1854\n",
      "Epoch 13/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1789\n",
      "Epoch 00013: val_loss did not improve from 0.05003\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0515 - mae: 0.1788 - val_loss: 0.0507 - val_mae: 0.1757\n",
      "Epoch 14/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1779\n",
      "Epoch 00014: val_loss improved from 0.05003 to 0.04951, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0512 - mae: 0.1779 - val_loss: 0.0495 - val_mae: 0.1759\n",
      "Epoch 15/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1777\n",
      "Epoch 00015: val_loss did not improve from 0.04951\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0511 - mae: 0.1776 - val_loss: 0.0503 - val_mae: 0.1786\n",
      "Epoch 16/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1773\n",
      "Epoch 00016: val_loss did not improve from 0.04951\n",
      "40000/40000 [==============================] - 4s 108us/sample - loss: 0.0509 - mae: 0.1773 - val_loss: 0.0496 - val_mae: 0.1741\n",
      "Epoch 17/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1769\n",
      "Epoch 00017: val_loss did not improve from 0.04951\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0508 - mae: 0.1768 - val_loss: 0.0501 - val_mae: 0.1737\n",
      "Epoch 18/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1767\n",
      "Epoch 00018: val_loss improved from 0.04951 to 0.04892, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 96us/sample - loss: 0.0507 - mae: 0.1767 - val_loss: 0.0489 - val_mae: 0.1749\n",
      "Epoch 19/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1760\n",
      "Epoch 00019: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0505 - mae: 0.1761 - val_loss: 0.0493 - val_mae: 0.1750\n",
      "Epoch 20/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1752\n",
      "Epoch 00020: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0502 - mae: 0.1752 - val_loss: 0.0494 - val_mae: 0.1732\n",
      "Epoch 21/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1755\n",
      "Epoch 00021: val_loss improved from 0.04892 to 0.04869, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0503 - mae: 0.1755 - val_loss: 0.0487 - val_mae: 0.1751\n",
      "Epoch 22/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1746\n",
      "Epoch 00022: val_loss improved from 0.04869 to 0.04838, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0499 - mae: 0.1746 - val_loss: 0.0484 - val_mae: 0.1752\n",
      "Epoch 23/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1746\n",
      "Epoch 00023: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0498 - mae: 0.1746 - val_loss: 0.0496 - val_mae: 0.1766\n",
      "Epoch 24/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1746\n",
      "Epoch 00024: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0498 - mae: 0.1745 - val_loss: 0.0513 - val_mae: 0.1772\n",
      "Epoch 25/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1745\n",
      "Epoch 00025: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0498 - mae: 0.1745 - val_loss: 0.0487 - val_mae: 0.1741\n",
      "Epoch 26/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1738\n",
      "Epoch 00026: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0495 - mae: 0.1738 - val_loss: 0.0490 - val_mae: 0.1732\n",
      "Epoch 27/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1737\n",
      "Epoch 00027: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0496 - mae: 0.1737 - val_loss: 0.0501 - val_mae: 0.1732\n",
      "Epoch 28/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1741\n",
      "Epoch 00028: val_loss improved from 0.04838 to 0.04748, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_14.hdf5\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0496 - mae: 0.1741 - val_loss: 0.0475 - val_mae: 0.1703\n",
      "Epoch 29/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1739\n",
      "Epoch 00029: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0495 - mae: 0.1738 - val_loss: 0.0496 - val_mae: 0.1782\n",
      "Epoch 30/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1737\n",
      "Epoch 00030: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0495 - mae: 0.1737 - val_loss: 0.0495 - val_mae: 0.1783\n",
      "Epoch 31/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1733\n",
      "Epoch 00031: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0493 - mae: 0.1733 - val_loss: 0.0518 - val_mae: 0.1769\n",
      "Epoch 32/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1728\n",
      "Epoch 00032: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0490 - mae: 0.1728 - val_loss: 0.0481 - val_mae: 0.1745\n",
      "Epoch 33/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1728\n",
      "Epoch 00033: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0489 - mae: 0.1727 - val_loss: 0.0483 - val_mae: 0.1707\n",
      "Epoch 34/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1726\n",
      "Epoch 00034: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 95us/sample - loss: 0.0489 - mae: 0.1726 - val_loss: 0.0532 - val_mae: 0.1832\n",
      "Epoch 35/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1725\n",
      "Epoch 00035: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0489 - mae: 0.1725 - val_loss: 0.0488 - val_mae: 0.1734\n",
      "Epoch 36/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1721\n",
      "Epoch 00036: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0487 - mae: 0.1721 - val_loss: 0.0523 - val_mae: 0.1788\n",
      "Epoch 37/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1721\n",
      "Epoch 00037: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0487 - mae: 0.1721 - val_loss: 0.0484 - val_mae: 0.1738\n",
      "Epoch 38/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1721\n",
      "Epoch 00038: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0487 - mae: 0.1720 - val_loss: 0.0491 - val_mae: 0.1717\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00038: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0651 - mae: 0.2062\n",
      "Epoch 00001: val_loss improved from inf to 0.05872, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 6s 150us/sample - loss: 0.0651 - mae: 0.2061 - val_loss: 0.0587 - val_mae: 0.1950\n",
      "Epoch 2/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0575 - mae: 0.1928\n",
      "Epoch 00002: val_loss improved from 0.05872 to 0.05423, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0575 - mae: 0.1928 - val_loss: 0.0542 - val_mae: 0.1880\n",
      "Epoch 3/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0560 - mae: 0.1894\n",
      "Epoch 00003: val_loss improved from 0.05423 to 0.05262, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0560 - mae: 0.1894 - val_loss: 0.0526 - val_mae: 0.1846\n",
      "Epoch 4/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0550 - mae: 0.1868\n",
      "Epoch 00004: val_loss did not improve from 0.05262\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0549 - mae: 0.1867 - val_loss: 0.0529 - val_mae: 0.1809\n",
      "Epoch 5/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0541 - mae: 0.1847\n",
      "Epoch 00005: val_loss improved from 0.05262 to 0.05147, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0541 - mae: 0.1847 - val_loss: 0.0515 - val_mae: 0.1819\n",
      "Epoch 6/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1837\n",
      "Epoch 00006: val_loss improved from 0.05147 to 0.05118, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0537 - mae: 0.1838 - val_loss: 0.0512 - val_mae: 0.1817\n",
      "Epoch 7/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1824\n",
      "Epoch 00007: val_loss did not improve from 0.05118\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0530 - mae: 0.1823 - val_loss: 0.0544 - val_mae: 0.1872\n",
      "Epoch 8/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1814\n",
      "Epoch 00008: val_loss improved from 0.05118 to 0.05011, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0526 - mae: 0.1814 - val_loss: 0.0501 - val_mae: 0.1772\n",
      "Epoch 9/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1805\n",
      "Epoch 00009: val_loss did not improve from 0.05011\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0523 - mae: 0.1805 - val_loss: 0.0504 - val_mae: 0.1776\n",
      "Epoch 10/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1803\n",
      "Epoch 00010: val_loss did not improve from 0.05011\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0522 - mae: 0.1802 - val_loss: 0.0506 - val_mae: 0.1789\n",
      "Epoch 11/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1794\n",
      "Epoch 00011: val_loss did not improve from 0.05011\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0518 - mae: 0.1792 - val_loss: 0.0505 - val_mae: 0.1777\n",
      "Epoch 12/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1783\n",
      "Epoch 00012: val_loss did not improve from 0.05011\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0514 - mae: 0.1782 - val_loss: 0.0508 - val_mae: 0.1802\n",
      "Epoch 13/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1773\n",
      "Epoch 00013: val_loss improved from 0.05011 to 0.04912, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0509 - mae: 0.1773 - val_loss: 0.0491 - val_mae: 0.1770\n",
      "Epoch 14/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1763\n",
      "Epoch 00014: val_loss did not improve from 0.04912\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0504 - mae: 0.1762 - val_loss: 0.0512 - val_mae: 0.1741\n",
      "Epoch 15/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1759\n",
      "Epoch 00015: val_loss did not improve from 0.04912\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0502 - mae: 0.1757 - val_loss: 0.0493 - val_mae: 0.1759\n",
      "Epoch 16/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1752\n",
      "Epoch 00016: val_loss improved from 0.04912 to 0.04856, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0499 - mae: 0.1751 - val_loss: 0.0486 - val_mae: 0.1746\n",
      "Epoch 17/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1747\n",
      "Epoch 00017: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0498 - mae: 0.1748 - val_loss: 0.0495 - val_mae: 0.1731\n",
      "Epoch 18/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1743\n",
      "Epoch 00018: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0495 - mae: 0.1742 - val_loss: 0.0486 - val_mae: 0.1731\n",
      "Epoch 19/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1740\n",
      "Epoch 00019: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0493 - mae: 0.1739 - val_loss: 0.0514 - val_mae: 0.1769\n",
      "Epoch 20/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1737\n",
      "Epoch 00020: val_loss improved from 0.04856 to 0.04780, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0493 - mae: 0.1736 - val_loss: 0.0478 - val_mae: 0.1700\n",
      "Epoch 21/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1732\n",
      "Epoch 00021: val_loss improved from 0.04780 to 0.04761, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0490 - mae: 0.1731 - val_loss: 0.0476 - val_mae: 0.1708\n",
      "Epoch 22/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1729\n",
      "Epoch 00022: val_loss did not improve from 0.04761\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0489 - mae: 0.1729 - val_loss: 0.0489 - val_mae: 0.1771\n",
      "Epoch 23/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1725\n",
      "Epoch 00023: val_loss did not improve from 0.04761\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0488 - mae: 0.1725 - val_loss: 0.0492 - val_mae: 0.1742\n",
      "Epoch 24/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1724\n",
      "Epoch 00024: val_loss did not improve from 0.04761\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0487 - mae: 0.1724 - val_loss: 0.0485 - val_mae: 0.1722\n",
      "Epoch 25/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1717\n",
      "Epoch 00025: val_loss did not improve from 0.04761\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0485 - mae: 0.1717 - val_loss: 0.0476 - val_mae: 0.1695\n",
      "Epoch 26/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1721\n",
      "Epoch 00026: val_loss did not improve from 0.04761\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0486 - mae: 0.1720 - val_loss: 0.0492 - val_mae: 0.1734\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1714\n",
      "Epoch 00027: val_loss improved from 0.04761 to 0.04761, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0485 - mae: 0.1714 - val_loss: 0.0476 - val_mae: 0.1706\n",
      "Epoch 28/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1714\n",
      "Epoch 00028: val_loss did not improve from 0.04761\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0484 - mae: 0.1713 - val_loss: 0.0482 - val_mae: 0.1713\n",
      "Epoch 29/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1713\n",
      "Epoch 00029: val_loss improved from 0.04761 to 0.04758, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0483 - mae: 0.1713 - val_loss: 0.0476 - val_mae: 0.1707\n",
      "Epoch 30/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1710\n",
      "Epoch 00030: val_loss improved from 0.04758 to 0.04744, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0482 - mae: 0.1710 - val_loss: 0.0474 - val_mae: 0.1678\n",
      "Epoch 31/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1706\n",
      "Epoch 00031: val_loss did not improve from 0.04744\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0481 - mae: 0.1707 - val_loss: 0.0489 - val_mae: 0.1714\n",
      "Epoch 32/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1707\n",
      "Epoch 00032: val_loss improved from 0.04744 to 0.04735, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0480 - mae: 0.1706 - val_loss: 0.0473 - val_mae: 0.1701\n",
      "Epoch 33/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0479 - mae: 0.1702\n",
      "Epoch 00033: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0479 - mae: 0.1702 - val_loss: 0.0481 - val_mae: 0.1685\n",
      "Epoch 34/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1702\n",
      "Epoch 00034: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0479 - mae: 0.1702 - val_loss: 0.0479 - val_mae: 0.1715\n",
      "Epoch 35/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1704\n",
      "Epoch 00035: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0480 - mae: 0.1705 - val_loss: 0.0477 - val_mae: 0.1688\n",
      "Epoch 36/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1697\n",
      "Epoch 00036: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0476 - mae: 0.1697 - val_loss: 0.0504 - val_mae: 0.1794\n",
      "Epoch 37/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0477 - mae: 0.1697\n",
      "Epoch 00037: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0477 - mae: 0.1697 - val_loss: 0.0500 - val_mae: 0.1780\n",
      "Epoch 38/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1694\n",
      "Epoch 00038: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0477 - mae: 0.1696 - val_loss: 0.0474 - val_mae: 0.1723\n",
      "Epoch 39/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1695\n",
      "Epoch 00039: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0476 - mae: 0.1696 - val_loss: 0.0480 - val_mae: 0.1727\n",
      "Epoch 40/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1691\n",
      "Epoch 00040: val_loss improved from 0.04735 to 0.04704, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0474 - mae: 0.1691 - val_loss: 0.0470 - val_mae: 0.1707\n",
      "Epoch 41/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1695\n",
      "Epoch 00041: val_loss did not improve from 0.04704\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0476 - mae: 0.1696 - val_loss: 0.0474 - val_mae: 0.1719\n",
      "Epoch 42/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1697\n",
      "Epoch 00042: val_loss did not improve from 0.04704\n",
      "40000/40000 [==============================] - 4s 91us/sample - loss: 0.0476 - mae: 0.1696 - val_loss: 0.0471 - val_mae: 0.1695\n",
      "Epoch 43/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0473 - mae: 0.1688\n",
      "Epoch 00043: val_loss did not improve from 0.04704\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0473 - mae: 0.1688 - val_loss: 0.0484 - val_mae: 0.1698\n",
      "Epoch 44/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0473 - mae: 0.1691\n",
      "Epoch 00044: val_loss improved from 0.04704 to 0.04692, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0473 - mae: 0.1691 - val_loss: 0.0469 - val_mae: 0.1679\n",
      "Epoch 45/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1693\n",
      "Epoch 00045: val_loss improved from 0.04692 to 0.04639, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_15.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0473 - mae: 0.1691 - val_loss: 0.0464 - val_mae: 0.1680\n",
      "Epoch 46/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1688\n",
      "Epoch 00046: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0472 - mae: 0.1688 - val_loss: 0.0467 - val_mae: 0.1668\n",
      "Epoch 47/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1687\n",
      "Epoch 00047: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0472 - mae: 0.1687 - val_loss: 0.0486 - val_mae: 0.1727\n",
      "Epoch 48/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0471 - mae: 0.1686\n",
      "Epoch 00048: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0471 - mae: 0.1686 - val_loss: 0.0473 - val_mae: 0.1707\n",
      "Epoch 49/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0471 - mae: 0.1687\n",
      "Epoch 00049: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0471 - mae: 0.1688 - val_loss: 0.0492 - val_mae: 0.1740\n",
      "Epoch 50/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1683\n",
      "Epoch 00050: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0470 - mae: 0.1683 - val_loss: 0.0475 - val_mae: 0.1685\n",
      "Epoch 51/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1683\n",
      "Epoch 00051: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 4s 110us/sample - loss: 0.0471 - mae: 0.1684 - val_loss: 0.0483 - val_mae: 0.1727\n",
      "Epoch 52/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0470 - mae: 0.1682\n",
      "Epoch 00052: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0470 - mae: 0.1682 - val_loss: 0.0467 - val_mae: 0.1696\n",
      "Epoch 53/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0469 - mae: 0.1679\n",
      "Epoch 00053: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0469 - mae: 0.1680 - val_loss: 0.0479 - val_mae: 0.1713\n",
      "Epoch 54/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0466 - mae: 0.1675\n",
      "Epoch 00054: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0466 - mae: 0.1675 - val_loss: 0.0478 - val_mae: 0.1691\n",
      "Epoch 55/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0468 - mae: 0.1677\n",
      "Epoch 00055: val_loss did not improve from 0.04639\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0468 - mae: 0.1677 - val_loss: 0.0490 - val_mae: 0.1740\n",
      "Epoch 00055: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0651 - mae: 0.2070\n",
      "Epoch 00001: val_loss improved from inf to 0.05585, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0650 - mae: 0.2069 - val_loss: 0.0559 - val_mae: 0.1911\n",
      "Epoch 2/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0565 - mae: 0.1911\n",
      "Epoch 00002: val_loss did not improve from 0.05585\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0564 - mae: 0.1909 - val_loss: 0.0565 - val_mae: 0.1935\n",
      "Epoch 3/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1882\n",
      "Epoch 00003: val_loss improved from 0.05585 to 0.05257, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0552 - mae: 0.1880 - val_loss: 0.0526 - val_mae: 0.1852\n",
      "Epoch 4/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1865\n",
      "Epoch 00004: val_loss improved from 0.05257 to 0.05234, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0546 - mae: 0.1864 - val_loss: 0.0523 - val_mae: 0.1849\n",
      "Epoch 5/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0538 - mae: 0.1844\n",
      "Epoch 00005: val_loss improved from 0.05234 to 0.05227, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0538 - mae: 0.1844 - val_loss: 0.0523 - val_mae: 0.1830\n",
      "Epoch 6/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0532 - mae: 0.1828\n",
      "Epoch 00006: val_loss did not improve from 0.05227\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0532 - mae: 0.1828 - val_loss: 0.0617 - val_mae: 0.1965\n",
      "Epoch 7/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1819\n",
      "Epoch 00007: val_loss improved from 0.05227 to 0.05052, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0528 - mae: 0.1819 - val_loss: 0.0505 - val_mae: 0.1796\n",
      "Epoch 8/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1808\n",
      "Epoch 00008: val_loss did not improve from 0.05052\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0524 - mae: 0.1807 - val_loss: 0.0521 - val_mae: 0.1809\n",
      "Epoch 9/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1799\n",
      "Epoch 00009: val_loss did not improve from 0.05052\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0520 - mae: 0.1796 - val_loss: 0.0507 - val_mae: 0.1752\n",
      "Epoch 10/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1791\n",
      "Epoch 00010: val_loss improved from 0.05052 to 0.04892, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0519 - mae: 0.1792 - val_loss: 0.0489 - val_mae: 0.1766\n",
      "Epoch 11/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1790\n",
      "Epoch 00011: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0518 - mae: 0.1789 - val_loss: 0.0493 - val_mae: 0.1751\n",
      "Epoch 12/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1785\n",
      "Epoch 00012: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0515 - mae: 0.1783 - val_loss: 0.0498 - val_mae: 0.1744\n",
      "Epoch 13/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1781\n",
      "Epoch 00013: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0514 - mae: 0.1780 - val_loss: 0.0521 - val_mae: 0.1802\n",
      "Epoch 14/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1776\n",
      "Epoch 00014: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0511 - mae: 0.1775 - val_loss: 0.0493 - val_mae: 0.1747\n",
      "Epoch 15/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1770\n",
      "Epoch 00015: val_loss did not improve from 0.04892\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0510 - mae: 0.1770 - val_loss: 0.0498 - val_mae: 0.1735\n",
      "Epoch 16/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1764\n",
      "Epoch 00016: val_loss improved from 0.04892 to 0.04888, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0509 - mae: 0.1765 - val_loss: 0.0489 - val_mae: 0.1767\n",
      "Epoch 17/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1768\n",
      "Epoch 00017: val_loss did not improve from 0.04888\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0509 - mae: 0.1769 - val_loss: 0.0529 - val_mae: 0.1800\n",
      "Epoch 18/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1762\n",
      "Epoch 00018: val_loss improved from 0.04888 to 0.04851, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0506 - mae: 0.1761 - val_loss: 0.0485 - val_mae: 0.1741\n",
      "Epoch 19/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1765\n",
      "Epoch 00019: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0508 - mae: 0.1765 - val_loss: 0.0488 - val_mae: 0.1742\n",
      "Epoch 20/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1756\n",
      "Epoch 00020: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0504 - mae: 0.1755 - val_loss: 0.0492 - val_mae: 0.1780\n",
      "Epoch 21/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1750\n",
      "Epoch 00021: val_loss improved from 0.04851 to 0.04811, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0502 - mae: 0.1750 - val_loss: 0.0481 - val_mae: 0.1708\n",
      "Epoch 22/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1752\n",
      "Epoch 00022: val_loss did not improve from 0.04811\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0503 - mae: 0.1752 - val_loss: 0.0504 - val_mae: 0.1794\n",
      "Epoch 23/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1751\n",
      "Epoch 00023: val_loss did not improve from 0.04811\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0502 - mae: 0.1750 - val_loss: 0.0497 - val_mae: 0.1769\n",
      "Epoch 24/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1752\n",
      "Epoch 00024: val_loss did not improve from 0.04811\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0502 - mae: 0.1752 - val_loss: 0.0483 - val_mae: 0.1738\n",
      "Epoch 25/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1751\n",
      "Epoch 00025: val_loss improved from 0.04811 to 0.04788, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0501 - mae: 0.1751 - val_loss: 0.0479 - val_mae: 0.1721\n",
      "Epoch 26/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1747\n",
      "Epoch 00026: val_loss improved from 0.04788 to 0.04786, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_16.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0500 - mae: 0.1747 - val_loss: 0.0479 - val_mae: 0.1698\n",
      "Epoch 27/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1745\n",
      "Epoch 00027: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0499 - mae: 0.1745 - val_loss: 0.0499 - val_mae: 0.1748\n",
      "Epoch 28/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1737\n",
      "Epoch 00028: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0495 - mae: 0.1737 - val_loss: 0.0504 - val_mae: 0.1794\n",
      "Epoch 29/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1740\n",
      "Epoch 00029: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0496 - mae: 0.1740 - val_loss: 0.0481 - val_mae: 0.1733\n",
      "Epoch 30/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1736\n",
      "Epoch 00030: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0495 - mae: 0.1736 - val_loss: 0.0489 - val_mae: 0.1739\n",
      "Epoch 31/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1736\n",
      "Epoch 00031: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0495 - mae: 0.1737 - val_loss: 0.0492 - val_mae: 0.1758\n",
      "Epoch 32/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1729\n",
      "Epoch 00032: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0492 - mae: 0.1730 - val_loss: 0.0496 - val_mae: 0.1730\n",
      "Epoch 33/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1733\n",
      "Epoch 00033: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0492 - mae: 0.1733 - val_loss: 0.0496 - val_mae: 0.1757\n",
      "Epoch 34/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1732\n",
      "Epoch 00034: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0492 - mae: 0.1732 - val_loss: 0.0484 - val_mae: 0.1727\n",
      "Epoch 35/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1732\n",
      "Epoch 00035: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0492 - mae: 0.1731 - val_loss: 0.0502 - val_mae: 0.1742\n",
      "Epoch 36/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1725\n",
      "Epoch 00036: val_loss did not improve from 0.04786\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0490 - mae: 0.1725 - val_loss: 0.0519 - val_mae: 0.1774\n",
      "Epoch 00036: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0655 - mae: 0.2077\n",
      "Epoch 00001: val_loss improved from inf to 0.06274, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0655 - mae: 0.2078 - val_loss: 0.0627 - val_mae: 0.2042\n",
      "Epoch 2/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0587 - mae: 0.1942\n",
      "Epoch 00002: val_loss improved from 0.06274 to 0.05603, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0587 - mae: 0.1942 - val_loss: 0.0560 - val_mae: 0.1896\n",
      "Epoch 3/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0566 - mae: 0.1898\n",
      "Epoch 00003: val_loss improved from 0.05603 to 0.05389, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0566 - mae: 0.1898 - val_loss: 0.0539 - val_mae: 0.1877\n",
      "Epoch 4/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1869\n",
      "Epoch 00004: val_loss did not improve from 0.05389\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0552 - mae: 0.1868 - val_loss: 0.0582 - val_mae: 0.1895\n",
      "Epoch 5/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0547 - mae: 0.1853\n",
      "Epoch 00005: val_loss did not improve from 0.05389\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0547 - mae: 0.1853 - val_loss: 0.0548 - val_mae: 0.1867\n",
      "Epoch 6/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1833\n",
      "Epoch 00006: val_loss improved from 0.05389 to 0.05172, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0537 - mae: 0.1834 - val_loss: 0.0517 - val_mae: 0.1820\n",
      "Epoch 7/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0534 - mae: 0.1825\n",
      "Epoch 00007: val_loss improved from 0.05172 to 0.05117, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0534 - mae: 0.1825 - val_loss: 0.0512 - val_mae: 0.1808\n",
      "Epoch 8/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1817\n",
      "Epoch 00008: val_loss did not improve from 0.05117\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0531 - mae: 0.1817 - val_loss: 0.0520 - val_mae: 0.1808\n",
      "Epoch 9/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1810\n",
      "Epoch 00009: val_loss did not improve from 0.05117\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0528 - mae: 0.1810 - val_loss: 0.0514 - val_mae: 0.1809\n",
      "Epoch 10/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1805\n",
      "Epoch 00010: val_loss did not improve from 0.05117\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0526 - mae: 0.1806 - val_loss: 0.0519 - val_mae: 0.1846\n",
      "Epoch 11/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0523 - mae: 0.1798\n",
      "Epoch 00011: val_loss improved from 0.05117 to 0.05068, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0522 - mae: 0.1796 - val_loss: 0.0507 - val_mae: 0.1745\n",
      "Epoch 12/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1789\n",
      "Epoch 00012: val_loss improved from 0.05068 to 0.04994, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0519 - mae: 0.1790 - val_loss: 0.0499 - val_mae: 0.1797\n",
      "Epoch 13/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1789\n",
      "Epoch 00013: val_loss did not improve from 0.04994\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0519 - mae: 0.1789 - val_loss: 0.0526 - val_mae: 0.1852\n",
      "Epoch 14/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1786\n",
      "Epoch 00014: val_loss did not improve from 0.04994\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0518 - mae: 0.1787 - val_loss: 0.0510 - val_mae: 0.1819\n",
      "Epoch 15/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1781\n",
      "Epoch 00015: val_loss improved from 0.04994 to 0.04935, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0515 - mae: 0.1781 - val_loss: 0.0494 - val_mae: 0.1750\n",
      "Epoch 16/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1776\n",
      "Epoch 00016: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0512 - mae: 0.1774 - val_loss: 0.0502 - val_mae: 0.1752\n",
      "Epoch 17/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1769\n",
      "Epoch 00017: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0510 - mae: 0.1769 - val_loss: 0.0519 - val_mae: 0.1799\n",
      "Epoch 18/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1767\n",
      "Epoch 00018: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0509 - mae: 0.1767 - val_loss: 0.0510 - val_mae: 0.1788\n",
      "Epoch 19/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1763\n",
      "Epoch 00019: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0507 - mae: 0.1763 - val_loss: 0.0510 - val_mae: 0.1751\n",
      "Epoch 20/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1760\n",
      "Epoch 00020: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0506 - mae: 0.1760 - val_loss: 0.0499 - val_mae: 0.1721\n",
      "Epoch 21/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1756\n",
      "Epoch 00021: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0505 - mae: 0.1756 - val_loss: 0.0497 - val_mae: 0.1756\n",
      "Epoch 22/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1754\n",
      "Epoch 00022: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0504 - mae: 0.1754 - val_loss: 0.0507 - val_mae: 0.1782\n",
      "Epoch 23/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1752\n",
      "Epoch 00023: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0504 - mae: 0.1752 - val_loss: 0.0503 - val_mae: 0.1776\n",
      "Epoch 24/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1749\n",
      "Epoch 00024: val_loss improved from 0.04935 to 0.04920, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0502 - mae: 0.1750 - val_loss: 0.0492 - val_mae: 0.1762\n",
      "Epoch 25/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1745\n",
      "Epoch 00025: val_loss improved from 0.04920 to 0.04911, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0500 - mae: 0.1745 - val_loss: 0.0491 - val_mae: 0.1748\n",
      "Epoch 26/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1745\n",
      "Epoch 00026: val_loss improved from 0.04911 to 0.04824, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_17.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0501 - mae: 0.1745 - val_loss: 0.0482 - val_mae: 0.1744\n",
      "Epoch 27/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1748\n",
      "Epoch 00027: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0501 - mae: 0.1747 - val_loss: 0.0492 - val_mae: 0.1701\n",
      "Epoch 28/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1741\n",
      "Epoch 00028: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0499 - mae: 0.1741 - val_loss: 0.0486 - val_mae: 0.1730\n",
      "Epoch 29/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1737\n",
      "Epoch 00029: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0497 - mae: 0.1738 - val_loss: 0.0487 - val_mae: 0.1738\n",
      "Epoch 30/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1738\n",
      "Epoch 00030: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0498 - mae: 0.1738 - val_loss: 0.0487 - val_mae: 0.1715\n",
      "Epoch 31/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1736\n",
      "Epoch 00031: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0496 - mae: 0.1737 - val_loss: 0.0513 - val_mae: 0.1784\n",
      "Epoch 32/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1734\n",
      "Epoch 00032: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0495 - mae: 0.1733 - val_loss: 0.0483 - val_mae: 0.1739\n",
      "Epoch 33/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1732\n",
      "Epoch 00033: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0496 - mae: 0.1733 - val_loss: 0.0488 - val_mae: 0.1722\n",
      "Epoch 34/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1734\n",
      "Epoch 00034: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0496 - mae: 0.1734 - val_loss: 0.0483 - val_mae: 0.1696\n",
      "Epoch 35/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1733\n",
      "Epoch 00035: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0496 - mae: 0.1733 - val_loss: 0.0490 - val_mae: 0.1711\n",
      "Epoch 36/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1729\n",
      "Epoch 00036: val_loss did not improve from 0.04824\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0492 - mae: 0.1728 - val_loss: 0.0483 - val_mae: 0.1706\n",
      "Epoch 00036: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0630 - mae: 0.2036\n",
      "Epoch 00001: val_loss improved from inf to 0.05668, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0630 - mae: 0.2035 - val_loss: 0.0567 - val_mae: 0.1916\n",
      "Epoch 2/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0569 - mae: 0.1910\n",
      "Epoch 00002: val_loss improved from 0.05668 to 0.05480, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0568 - mae: 0.1909 - val_loss: 0.0548 - val_mae: 0.1884\n",
      "Epoch 3/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1880\n",
      "Epoch 00003: val_loss improved from 0.05480 to 0.05355, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0555 - mae: 0.1880 - val_loss: 0.0536 - val_mae: 0.1881\n",
      "Epoch 4/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0548 - mae: 0.1861\n",
      "Epoch 00004: val_loss improved from 0.05355 to 0.05255, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0548 - mae: 0.1861 - val_loss: 0.0526 - val_mae: 0.1816\n",
      "Epoch 5/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1843\n",
      "Epoch 00005: val_loss did not improve from 0.05255\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0540 - mae: 0.1843 - val_loss: 0.0530 - val_mae: 0.1796\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0535 - mae: 0.1829\n",
      "Epoch 00006: val_loss did not improve from 0.05255\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0535 - mae: 0.1828 - val_loss: 0.0529 - val_mae: 0.1837\n",
      "Epoch 7/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1811\n",
      "Epoch 00007: val_loss improved from 0.05255 to 0.05089, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0527 - mae: 0.1811 - val_loss: 0.0509 - val_mae: 0.1778\n",
      "Epoch 8/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1793\n",
      "Epoch 00008: val_loss improved from 0.05089 to 0.04879, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0518 - mae: 0.1794 - val_loss: 0.0488 - val_mae: 0.1741\n",
      "Epoch 9/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1778\n",
      "Epoch 00009: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0511 - mae: 0.1778 - val_loss: 0.0519 - val_mae: 0.1776\n",
      "Epoch 10/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1763\n",
      "Epoch 00010: val_loss did not improve from 0.04879\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0505 - mae: 0.1763 - val_loss: 0.0509 - val_mae: 0.1811\n",
      "Epoch 11/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1754\n",
      "Epoch 00011: val_loss improved from 0.04879 to 0.04853, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0500 - mae: 0.1755 - val_loss: 0.0485 - val_mae: 0.1744\n",
      "Epoch 12/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1749\n",
      "Epoch 00012: val_loss improved from 0.04853 to 0.04821, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0498 - mae: 0.1749 - val_loss: 0.0482 - val_mae: 0.1744\n",
      "Epoch 13/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1745\n",
      "Epoch 00013: val_loss did not improve from 0.04821\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0498 - mae: 0.1745 - val_loss: 0.0494 - val_mae: 0.1750\n",
      "Epoch 14/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1742\n",
      "Epoch 00014: val_loss improved from 0.04821 to 0.04759, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0494 - mae: 0.1741 - val_loss: 0.0476 - val_mae: 0.1707\n",
      "Epoch 15/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1739\n",
      "Epoch 00015: val_loss improved from 0.04759 to 0.04735, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_18.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0494 - mae: 0.1739 - val_loss: 0.0474 - val_mae: 0.1699\n",
      "Epoch 16/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1729\n",
      "Epoch 00016: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0490 - mae: 0.1730 - val_loss: 0.0511 - val_mae: 0.1764\n",
      "Epoch 17/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1732\n",
      "Epoch 00017: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0491 - mae: 0.1732 - val_loss: 0.0490 - val_mae: 0.1755\n",
      "Epoch 18/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1724\n",
      "Epoch 00018: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0489 - mae: 0.1725 - val_loss: 0.0486 - val_mae: 0.1723\n",
      "Epoch 19/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1726\n",
      "Epoch 00019: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0488 - mae: 0.1726 - val_loss: 0.0476 - val_mae: 0.1711\n",
      "Epoch 20/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1718\n",
      "Epoch 00020: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0486 - mae: 0.1718 - val_loss: 0.0492 - val_mae: 0.1720\n",
      "Epoch 21/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1717\n",
      "Epoch 00021: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0485 - mae: 0.1718 - val_loss: 0.0492 - val_mae: 0.1739\n",
      "Epoch 22/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1714\n",
      "Epoch 00022: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0483 - mae: 0.1714 - val_loss: 0.0479 - val_mae: 0.1721\n",
      "Epoch 23/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1710\n",
      "Epoch 00023: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0482 - mae: 0.1710 - val_loss: 0.0480 - val_mae: 0.1687\n",
      "Epoch 24/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1711\n",
      "Epoch 00024: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0482 - mae: 0.1711 - val_loss: 0.0509 - val_mae: 0.1758\n",
      "Epoch 25/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1708\n",
      "Epoch 00025: val_loss did not improve from 0.04735\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0481 - mae: 0.1709 - val_loss: 0.0474 - val_mae: 0.1688\n",
      "Epoch 00025: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0630 - mae: 0.2025\n",
      "Epoch 00001: val_loss improved from inf to 0.05982, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0630 - mae: 0.2026 - val_loss: 0.0598 - val_mae: 0.1973\n",
      "Epoch 2/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0565 - mae: 0.1900\n",
      "Epoch 00002: val_loss improved from 0.05982 to 0.05403, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0565 - mae: 0.1901 - val_loss: 0.0540 - val_mae: 0.1850\n",
      "Epoch 3/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0551 - mae: 0.1868\n",
      "Epoch 00003: val_loss improved from 0.05403 to 0.05265, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0551 - mae: 0.1869 - val_loss: 0.0526 - val_mae: 0.1858\n",
      "Epoch 4/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1843\n",
      "Epoch 00004: val_loss improved from 0.05265 to 0.05167, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0540 - mae: 0.1843 - val_loss: 0.0517 - val_mae: 0.1840\n",
      "Epoch 5/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0532 - mae: 0.1824\n",
      "Epoch 00005: val_loss did not improve from 0.05167\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0532 - mae: 0.1823 - val_loss: 0.0521 - val_mae: 0.1787\n",
      "Epoch 6/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1807\n",
      "Epoch 00006: val_loss improved from 0.05167 to 0.05053, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0526 - mae: 0.1807 - val_loss: 0.0505 - val_mae: 0.1803\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1795\n",
      "Epoch 00007: val_loss improved from 0.05053 to 0.04918, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0520 - mae: 0.1794 - val_loss: 0.0492 - val_mae: 0.1753\n",
      "Epoch 8/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1779\n",
      "Epoch 00008: val_loss did not improve from 0.04918\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0514 - mae: 0.1780 - val_loss: 0.0524 - val_mae: 0.1846\n",
      "Epoch 9/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1770\n",
      "Epoch 00009: val_loss improved from 0.04918 to 0.04867, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0510 - mae: 0.1771 - val_loss: 0.0487 - val_mae: 0.1761\n",
      "Epoch 10/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1763\n",
      "Epoch 00010: val_loss did not improve from 0.04867\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0507 - mae: 0.1761 - val_loss: 0.0512 - val_mae: 0.1809\n",
      "Epoch 11/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1771\n",
      "Epoch 00011: val_loss improved from 0.04867 to 0.04841, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0509 - mae: 0.1769 - val_loss: 0.0484 - val_mae: 0.1753\n",
      "Epoch 12/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1755\n",
      "Epoch 00012: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0503 - mae: 0.1755 - val_loss: 0.0516 - val_mae: 0.1797\n",
      "Epoch 13/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1754\n",
      "Epoch 00013: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0502 - mae: 0.1754 - val_loss: 0.0487 - val_mae: 0.1723\n",
      "Epoch 14/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1748\n",
      "Epoch 00014: val_loss improved from 0.04841 to 0.04838, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0501 - mae: 0.1747 - val_loss: 0.0484 - val_mae: 0.1718\n",
      "Epoch 15/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1742\n",
      "Epoch 00015: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0498 - mae: 0.1741 - val_loss: 0.0509 - val_mae: 0.1775\n",
      "Epoch 16/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1740\n",
      "Epoch 00016: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0496 - mae: 0.1740 - val_loss: 0.0486 - val_mae: 0.1706\n",
      "Epoch 17/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1737\n",
      "Epoch 00017: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 4s 94us/sample - loss: 0.0495 - mae: 0.1736 - val_loss: 0.0488 - val_mae: 0.1702\n",
      "Epoch 18/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1733\n",
      "Epoch 00018: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0494 - mae: 0.1733 - val_loss: 0.0503 - val_mae: 0.1763\n",
      "Epoch 19/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1735\n",
      "Epoch 00019: val_loss did not improve from 0.04838\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0495 - mae: 0.1734 - val_loss: 0.0509 - val_mae: 0.1781\n",
      "Epoch 20/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1728\n",
      "Epoch 00020: val_loss improved from 0.04838 to 0.04795, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0492 - mae: 0.1728 - val_loss: 0.0479 - val_mae: 0.1715\n",
      "Epoch 21/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1723\n",
      "Epoch 00021: val_loss improved from 0.04795 to 0.04748, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 5s 135us/sample - loss: 0.0489 - mae: 0.1723 - val_loss: 0.0475 - val_mae: 0.1713\n",
      "Epoch 22/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1722\n",
      "Epoch 00022: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0489 - mae: 0.1723 - val_loss: 0.0478 - val_mae: 0.1738\n",
      "Epoch 23/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1720\n",
      "Epoch 00023: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0488 - mae: 0.1720 - val_loss: 0.0477 - val_mae: 0.1714\n",
      "Epoch 24/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1721\n",
      "Epoch 00024: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0489 - mae: 0.1721 - val_loss: 0.0483 - val_mae: 0.1722\n",
      "Epoch 25/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1720\n",
      "Epoch 00025: val_loss did not improve from 0.04748\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0487 - mae: 0.1719 - val_loss: 0.0492 - val_mae: 0.1707\n",
      "Epoch 26/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1715\n",
      "Epoch 00026: val_loss improved from 0.04748 to 0.04701, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_19.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0485 - mae: 0.1715 - val_loss: 0.0470 - val_mae: 0.1695\n",
      "Epoch 27/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1712\n",
      "Epoch 00027: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0485 - mae: 0.1712 - val_loss: 0.0483 - val_mae: 0.1747\n",
      "Epoch 28/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1709\n",
      "Epoch 00028: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0483 - mae: 0.1708 - val_loss: 0.0488 - val_mae: 0.1719\n",
      "Epoch 29/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1709\n",
      "Epoch 00029: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0483 - mae: 0.1710 - val_loss: 0.0486 - val_mae: 0.1751\n",
      "Epoch 30/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1706\n",
      "Epoch 00030: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0482 - mae: 0.1706 - val_loss: 0.0483 - val_mae: 0.1697\n",
      "Epoch 31/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1703\n",
      "Epoch 00031: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0480 - mae: 0.1702 - val_loss: 0.0482 - val_mae: 0.1689\n",
      "Epoch 32/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1704\n",
      "Epoch 00032: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0481 - mae: 0.1704 - val_loss: 0.0472 - val_mae: 0.1709\n",
      "Epoch 33/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1702\n",
      "Epoch 00033: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0481 - mae: 0.1703 - val_loss: 0.0473 - val_mae: 0.1719\n",
      "Epoch 34/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1698\n",
      "Epoch 00034: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0478 - mae: 0.1698 - val_loss: 0.0483 - val_mae: 0.1694\n",
      "Epoch 35/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1698\n",
      "Epoch 00035: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0478 - mae: 0.1697 - val_loss: 0.0496 - val_mae: 0.1726\n",
      "Epoch 36/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0478 - mae: 0.1695\n",
      "Epoch 00036: val_loss did not improve from 0.04701\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0478 - mae: 0.1695 - val_loss: 0.0478 - val_mae: 0.1692\n",
      "Epoch 00036: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0609 - mae: 0.1994\n",
      "Epoch 00001: val_loss improved from inf to 0.05612, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 5s 113us/sample - loss: 0.0609 - mae: 0.1994 - val_loss: 0.0561 - val_mae: 0.1917\n",
      "Epoch 2/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0568 - mae: 0.1904\n",
      "Epoch 00002: val_loss improved from 0.05612 to 0.05397, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0568 - mae: 0.1904 - val_loss: 0.0540 - val_mae: 0.1901\n",
      "Epoch 3/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0552 - mae: 0.1870\n",
      "Epoch 00003: val_loss improved from 0.05397 to 0.05250, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0552 - mae: 0.1871 - val_loss: 0.0525 - val_mae: 0.1838\n",
      "Epoch 4/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0540 - mae: 0.1845\n",
      "Epoch 00004: val_loss did not improve from 0.05250\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0540 - mae: 0.1845 - val_loss: 0.0534 - val_mae: 0.1857\n",
      "Epoch 5/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0528 - mae: 0.1821\n",
      "Epoch 00005: val_loss did not improve from 0.05250\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0528 - mae: 0.1820 - val_loss: 0.0532 - val_mae: 0.1810\n",
      "Epoch 6/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1792\n",
      "Epoch 00006: val_loss improved from 0.05250 to 0.04990, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0516 - mae: 0.1791 - val_loss: 0.0499 - val_mae: 0.1742\n",
      "Epoch 7/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1772\n",
      "Epoch 00007: val_loss improved from 0.04990 to 0.04962, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0507 - mae: 0.1772 - val_loss: 0.0496 - val_mae: 0.1713\n",
      "Epoch 8/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1761\n",
      "Epoch 00008: val_loss did not improve from 0.04962\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0502 - mae: 0.1762 - val_loss: 0.0517 - val_mae: 0.1820\n",
      "Epoch 9/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1743\n",
      "Epoch 00009: val_loss improved from 0.04962 to 0.04664, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0495 - mae: 0.1743 - val_loss: 0.0466 - val_mae: 0.1687\n",
      "Epoch 10/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1736\n",
      "Epoch 00010: val_loss did not improve from 0.04664\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0491 - mae: 0.1736 - val_loss: 0.0499 - val_mae: 0.1760\n",
      "Epoch 11/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1729\n",
      "Epoch 00011: val_loss did not improve from 0.04664\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0489 - mae: 0.1729 - val_loss: 0.0474 - val_mae: 0.1676\n",
      "Epoch 12/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1725\n",
      "Epoch 00012: val_loss did not improve from 0.04664\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0487 - mae: 0.1725 - val_loss: 0.0482 - val_mae: 0.1699\n",
      "Epoch 13/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1721\n",
      "Epoch 00013: val_loss improved from 0.04664 to 0.04591, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_20.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0487 - mae: 0.1722 - val_loss: 0.0459 - val_mae: 0.1672\n",
      "Epoch 14/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1717\n",
      "Epoch 00014: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0483 - mae: 0.1717 - val_loss: 0.0471 - val_mae: 0.1683\n",
      "Epoch 15/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1714\n",
      "Epoch 00015: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0484 - mae: 0.1714 - val_loss: 0.0465 - val_mae: 0.1718\n",
      "Epoch 16/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1710\n",
      "Epoch 00016: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0481 - mae: 0.1710 - val_loss: 0.0468 - val_mae: 0.1694\n",
      "Epoch 17/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1706\n",
      "Epoch 00017: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0480 - mae: 0.1706 - val_loss: 0.0488 - val_mae: 0.1731\n",
      "Epoch 18/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0479 - mae: 0.1705\n",
      "Epoch 00018: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0479 - mae: 0.1705 - val_loss: 0.0482 - val_mae: 0.1731\n",
      "Epoch 19/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1698\n",
      "Epoch 00019: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0476 - mae: 0.1698 - val_loss: 0.0480 - val_mae: 0.1713\n",
      "Epoch 20/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0476 - mae: 0.1698\n",
      "Epoch 00020: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0476 - mae: 0.1698 - val_loss: 0.0479 - val_mae: 0.1685\n",
      "Epoch 21/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0474 - mae: 0.1692\n",
      "Epoch 00021: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0474 - mae: 0.1692 - val_loss: 0.0469 - val_mae: 0.1688\n",
      "Epoch 22/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0473 - mae: 0.1689\n",
      "Epoch 00022: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0473 - mae: 0.1688 - val_loss: 0.0466 - val_mae: 0.1668\n",
      "Epoch 23/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0472 - mae: 0.1689\n",
      "Epoch 00023: val_loss did not improve from 0.04591\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0472 - mae: 0.1689 - val_loss: 0.0469 - val_mae: 0.1685\n",
      "Epoch 00023: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0670 - mae: 0.2104\n",
      "Epoch 00001: val_loss improved from inf to 0.05845, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0670 - mae: 0.2104 - val_loss: 0.0584 - val_mae: 0.1967\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0577 - mae: 0.1932\n",
      "Epoch 00002: val_loss improved from 0.05845 to 0.05455, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0577 - mae: 0.1932 - val_loss: 0.0546 - val_mae: 0.1888\n",
      "Epoch 3/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0559 - mae: 0.1893\n",
      "Epoch 00003: val_loss did not improve from 0.05455\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0559 - mae: 0.1893 - val_loss: 0.0547 - val_mae: 0.1878\n",
      "Epoch 4/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0553 - mae: 0.1876\n",
      "Epoch 00004: val_loss improved from 0.05455 to 0.05361, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0553 - mae: 0.1877 - val_loss: 0.0536 - val_mae: 0.1874\n",
      "Epoch 5/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1861\n",
      "Epoch 00005: val_loss did not improve from 0.05361\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0547 - mae: 0.1861 - val_loss: 0.0546 - val_mae: 0.1868\n",
      "Epoch 6/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0541 - mae: 0.1850\n",
      "Epoch 00006: val_loss did not improve from 0.05361\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0541 - mae: 0.1850 - val_loss: 0.0539 - val_mae: 0.1889\n",
      "Epoch 7/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1843\n",
      "Epoch 00007: val_loss did not improve from 0.05361\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0537 - mae: 0.1843 - val_loss: 0.0543 - val_mae: 0.1873\n",
      "Epoch 8/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0534 - mae: 0.1832\n",
      "Epoch 00008: val_loss improved from 0.05361 to 0.05348, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0533 - mae: 0.1831 - val_loss: 0.0535 - val_mae: 0.1851\n",
      "Epoch 9/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1822\n",
      "Epoch 00009: val_loss improved from 0.05348 to 0.05164, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0530 - mae: 0.1822 - val_loss: 0.0516 - val_mae: 0.1804\n",
      "Epoch 10/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1810\n",
      "Epoch 00010: val_loss improved from 0.05164 to 0.04985, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0525 - mae: 0.1811 - val_loss: 0.0498 - val_mae: 0.1785\n",
      "Epoch 11/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1800\n",
      "Epoch 00011: val_loss did not improve from 0.04985\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0520 - mae: 0.1800 - val_loss: 0.0505 - val_mae: 0.1784\n",
      "Epoch 12/100\n",
      "39264/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1791\n",
      "Epoch 00012: val_loss did not improve from 0.04985\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0515 - mae: 0.1789 - val_loss: 0.0509 - val_mae: 0.1775\n",
      "Epoch 13/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1786\n",
      "Epoch 00013: val_loss improved from 0.04985 to 0.04935, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0513 - mae: 0.1786 - val_loss: 0.0493 - val_mae: 0.1752\n",
      "Epoch 14/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1778\n",
      "Epoch 00014: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0511 - mae: 0.1778 - val_loss: 0.0509 - val_mae: 0.1795\n",
      "Epoch 15/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1768\n",
      "Epoch 00015: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0506 - mae: 0.1768 - val_loss: 0.0500 - val_mae: 0.1797\n",
      "Epoch 16/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1761\n",
      "Epoch 00016: val_loss did not improve from 0.04935\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0505 - mae: 0.1760 - val_loss: 0.0497 - val_mae: 0.1771\n",
      "Epoch 17/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1756\n",
      "Epoch 00017: val_loss improved from 0.04935 to 0.04800, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0502 - mae: 0.1756 - val_loss: 0.0480 - val_mae: 0.1734\n",
      "Epoch 18/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1751\n",
      "Epoch 00018: val_loss did not improve from 0.04800\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0501 - mae: 0.1752 - val_loss: 0.0508 - val_mae: 0.1755\n",
      "Epoch 19/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1746\n",
      "Epoch 00019: val_loss did not improve from 0.04800\n",
      "40000/40000 [==============================] - 4s 100us/sample - loss: 0.0497 - mae: 0.1746 - val_loss: 0.0481 - val_mae: 0.1725\n",
      "Epoch 20/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1743\n",
      "Epoch 00020: val_loss did not improve from 0.04800\n",
      "40000/40000 [==============================] - 4s 102us/sample - loss: 0.0496 - mae: 0.1742 - val_loss: 0.0485 - val_mae: 0.1735\n",
      "Epoch 21/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1743\n",
      "Epoch 00021: val_loss improved from 0.04800 to 0.04747, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_21.hdf5\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0496 - mae: 0.1743 - val_loss: 0.0475 - val_mae: 0.1688\n",
      "Epoch 22/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1741\n",
      "Epoch 00022: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0495 - mae: 0.1740 - val_loss: 0.0480 - val_mae: 0.1691\n",
      "Epoch 23/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1735\n",
      "Epoch 00023: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0493 - mae: 0.1735 - val_loss: 0.0498 - val_mae: 0.1766\n",
      "Epoch 24/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1730\n",
      "Epoch 00024: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 4s 97us/sample - loss: 0.0492 - mae: 0.1731 - val_loss: 0.0481 - val_mae: 0.1747\n",
      "Epoch 25/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1731\n",
      "Epoch 00025: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0491 - mae: 0.1731 - val_loss: 0.0493 - val_mae: 0.1763\n",
      "Epoch 26/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1728\n",
      "Epoch 00026: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0489 - mae: 0.1727 - val_loss: 0.0483 - val_mae: 0.1739\n",
      "Epoch 27/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1729\n",
      "Epoch 00027: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0491 - mae: 0.1728 - val_loss: 0.0478 - val_mae: 0.1689\n",
      "Epoch 28/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1727\n",
      "Epoch 00028: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0490 - mae: 0.1727 - val_loss: 0.0483 - val_mae: 0.1716\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1723\n",
      "Epoch 00029: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0488 - mae: 0.1722 - val_loss: 0.0497 - val_mae: 0.1759\n",
      "Epoch 30/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1726\n",
      "Epoch 00030: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0489 - mae: 0.1725 - val_loss: 0.0479 - val_mae: 0.1697\n",
      "Epoch 31/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1721\n",
      "Epoch 00031: val_loss did not improve from 0.04747\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0488 - mae: 0.1723 - val_loss: 0.0490 - val_mae: 0.1774\n",
      "Epoch 00031: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0680 - mae: 0.2114\n",
      "Epoch 00001: val_loss improved from inf to 0.05688, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 4s 103us/sample - loss: 0.0679 - mae: 0.2111 - val_loss: 0.0569 - val_mae: 0.1927\n",
      "Epoch 2/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0577 - mae: 0.1930\n",
      "Epoch 00002: val_loss improved from 0.05688 to 0.05683, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0576 - mae: 0.1930 - val_loss: 0.0568 - val_mae: 0.1937\n",
      "Epoch 3/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0560 - mae: 0.1892\n",
      "Epoch 00003: val_loss improved from 0.05683 to 0.05539, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0560 - mae: 0.1893 - val_loss: 0.0554 - val_mae: 0.1909\n",
      "Epoch 4/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0550 - mae: 0.1870\n",
      "Epoch 00004: val_loss improved from 0.05539 to 0.05338, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0550 - mae: 0.1870 - val_loss: 0.0534 - val_mae: 0.1872\n",
      "Epoch 5/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0545 - mae: 0.1858\n",
      "Epoch 00005: val_loss improved from 0.05338 to 0.05200, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0544 - mae: 0.1858 - val_loss: 0.0520 - val_mae: 0.1834\n",
      "Epoch 6/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0539 - mae: 0.1846\n",
      "Epoch 00006: val_loss did not improve from 0.05200\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0540 - mae: 0.1848 - val_loss: 0.0522 - val_mae: 0.1832\n",
      "Epoch 7/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1837\n",
      "Epoch 00007: val_loss improved from 0.05200 to 0.05152, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0536 - mae: 0.1836 - val_loss: 0.0515 - val_mae: 0.1833\n",
      "Epoch 8/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0532 - mae: 0.1831\n",
      "Epoch 00008: val_loss improved from 0.05152 to 0.05075, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0532 - mae: 0.1831 - val_loss: 0.0507 - val_mae: 0.1824\n",
      "Epoch 9/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0530 - mae: 0.1824\n",
      "Epoch 00009: val_loss did not improve from 0.05075\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0529 - mae: 0.1822 - val_loss: 0.0512 - val_mae: 0.1808\n",
      "Epoch 10/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1809\n",
      "Epoch 00010: val_loss did not improve from 0.05075\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0524 - mae: 0.1809 - val_loss: 0.0511 - val_mae: 0.1811\n",
      "Epoch 11/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1805\n",
      "Epoch 00011: val_loss improved from 0.05075 to 0.05033, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0523 - mae: 0.1804 - val_loss: 0.0503 - val_mae: 0.1788\n",
      "Epoch 12/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1799\n",
      "Epoch 00012: val_loss did not improve from 0.05033\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0520 - mae: 0.1798 - val_loss: 0.0510 - val_mae: 0.1793\n",
      "Epoch 13/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0520 - mae: 0.1795\n",
      "Epoch 00013: val_loss did not improve from 0.05033\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0520 - mae: 0.1795 - val_loss: 0.0528 - val_mae: 0.1798\n",
      "Epoch 14/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1787\n",
      "Epoch 00014: val_loss did not improve from 0.05033\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0515 - mae: 0.1785 - val_loss: 0.0504 - val_mae: 0.1779\n",
      "Epoch 15/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1783\n",
      "Epoch 00015: val_loss improved from 0.05033 to 0.04920, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0515 - mae: 0.1784 - val_loss: 0.0492 - val_mae: 0.1766\n",
      "Epoch 16/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1775\n",
      "Epoch 00016: val_loss did not improve from 0.04920\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0510 - mae: 0.1775 - val_loss: 0.0494 - val_mae: 0.1772\n",
      "Epoch 17/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1774\n",
      "Epoch 00017: val_loss improved from 0.04920 to 0.04916, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0509 - mae: 0.1773 - val_loss: 0.0492 - val_mae: 0.1751\n",
      "Epoch 18/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1767\n",
      "Epoch 00018: val_loss improved from 0.04916 to 0.04856, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0507 - mae: 0.1767 - val_loss: 0.0486 - val_mae: 0.1726\n",
      "Epoch 19/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1764\n",
      "Epoch 00019: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0507 - mae: 0.1764 - val_loss: 0.0487 - val_mae: 0.1753\n",
      "Epoch 20/100\n",
      "39504/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1760\n",
      "Epoch 00020: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0504 - mae: 0.1759 - val_loss: 0.0487 - val_mae: 0.1763\n",
      "Epoch 21/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1755\n",
      "Epoch 00021: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0502 - mae: 0.1753 - val_loss: 0.0524 - val_mae: 0.1790\n",
      "Epoch 22/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1751\n",
      "Epoch 00022: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0501 - mae: 0.1750 - val_loss: 0.0486 - val_mae: 0.1735\n",
      "Epoch 23/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1748\n",
      "Epoch 00023: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0501 - mae: 0.1748 - val_loss: 0.0487 - val_mae: 0.1765\n",
      "Epoch 24/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1740\n",
      "Epoch 00024: val_loss did not improve from 0.04856\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0497 - mae: 0.1740 - val_loss: 0.0505 - val_mae: 0.1775\n",
      "Epoch 25/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1739\n",
      "Epoch 00025: val_loss improved from 0.04856 to 0.04792, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0495 - mae: 0.1739 - val_loss: 0.0479 - val_mae: 0.1697\n",
      "Epoch 26/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1738\n",
      "Epoch 00026: val_loss did not improve from 0.04792\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0495 - mae: 0.1739 - val_loss: 0.0496 - val_mae: 0.1744\n",
      "Epoch 27/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1734\n",
      "Epoch 00027: val_loss did not improve from 0.04792\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0494 - mae: 0.1734 - val_loss: 0.0491 - val_mae: 0.1744\n",
      "Epoch 28/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1732\n",
      "Epoch 00028: val_loss did not improve from 0.04792\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0493 - mae: 0.1732 - val_loss: 0.0482 - val_mae: 0.1721\n",
      "Epoch 29/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1730\n",
      "Epoch 00029: val_loss did not improve from 0.04792\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0492 - mae: 0.1730 - val_loss: 0.0482 - val_mae: 0.1744\n",
      "Epoch 30/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1727\n",
      "Epoch 00030: val_loss did not improve from 0.04792\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0491 - mae: 0.1727 - val_loss: 0.0526 - val_mae: 0.1816\n",
      "Epoch 31/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1721\n",
      "Epoch 00031: val_loss improved from 0.04792 to 0.04764, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0488 - mae: 0.1721 - val_loss: 0.0476 - val_mae: 0.1683\n",
      "Epoch 32/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1724\n",
      "Epoch 00032: val_loss did not improve from 0.04764\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0489 - mae: 0.1724 - val_loss: 0.0495 - val_mae: 0.1767\n",
      "Epoch 33/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1718\n",
      "Epoch 00033: val_loss did not improve from 0.04764\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0487 - mae: 0.1718 - val_loss: 0.0479 - val_mae: 0.1729\n",
      "Epoch 34/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1718\n",
      "Epoch 00034: val_loss improved from 0.04764 to 0.04697, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_22.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0486 - mae: 0.1717 - val_loss: 0.0470 - val_mae: 0.1696\n",
      "Epoch 35/100\n",
      "39288/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1712\n",
      "Epoch 00035: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0485 - mae: 0.1714 - val_loss: 0.0477 - val_mae: 0.1721\n",
      "Epoch 36/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1717\n",
      "Epoch 00036: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0486 - mae: 0.1717 - val_loss: 0.0475 - val_mae: 0.1709\n",
      "Epoch 37/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1715\n",
      "Epoch 00037: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0483 - mae: 0.1714 - val_loss: 0.0478 - val_mae: 0.1690\n",
      "Epoch 38/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1709\n",
      "Epoch 00038: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0483 - mae: 0.1710 - val_loss: 0.0502 - val_mae: 0.1776\n",
      "Epoch 39/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1712\n",
      "Epoch 00039: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0483 - mae: 0.1710 - val_loss: 0.0476 - val_mae: 0.1689\n",
      "Epoch 40/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1713\n",
      "Epoch 00040: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0483 - mae: 0.1712 - val_loss: 0.0477 - val_mae: 0.1686\n",
      "Epoch 41/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1708\n",
      "Epoch 00041: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0482 - mae: 0.1708 - val_loss: 0.0472 - val_mae: 0.1684\n",
      "Epoch 42/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1708\n",
      "Epoch 00042: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0481 - mae: 0.1708 - val_loss: 0.0473 - val_mae: 0.1711\n",
      "Epoch 43/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1707\n",
      "Epoch 00043: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0480 - mae: 0.1706 - val_loss: 0.0481 - val_mae: 0.1734\n",
      "Epoch 44/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0479 - mae: 0.1702\n",
      "Epoch 00044: val_loss did not improve from 0.04697\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0479 - mae: 0.1703 - val_loss: 0.0485 - val_mae: 0.1719\n",
      "Epoch 00044: early stopping\n",
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39648/40000 [============================>.] - ETA: 0s - loss: 0.0652 - mae: 0.2078\n",
      "Epoch 00001: val_loss improved from inf to 0.05778, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0652 - mae: 0.2077 - val_loss: 0.0578 - val_mae: 0.1924\n",
      "Epoch 2/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0581 - mae: 0.1934\n",
      "Epoch 00002: val_loss improved from 0.05778 to 0.05701, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0581 - mae: 0.1934 - val_loss: 0.0570 - val_mae: 0.1911\n",
      "Epoch 3/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0567 - mae: 0.1903\n",
      "Epoch 00003: val_loss improved from 0.05701 to 0.05346, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0567 - mae: 0.1903 - val_loss: 0.0535 - val_mae: 0.1859\n",
      "Epoch 4/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0555 - mae: 0.1876\n",
      "Epoch 00004: val_loss improved from 0.05346 to 0.05267, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0555 - mae: 0.1877 - val_loss: 0.0527 - val_mae: 0.1857\n",
      "Epoch 5/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0546 - mae: 0.1858\n",
      "Epoch 00005: val_loss did not improve from 0.05267\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0546 - mae: 0.1858 - val_loss: 0.0540 - val_mae: 0.1867\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0537 - mae: 0.1837\n",
      "Epoch 00006: val_loss improved from 0.05267 to 0.05143, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0538 - mae: 0.1838 - val_loss: 0.0514 - val_mae: 0.1825\n",
      "Epoch 7/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0531 - mae: 0.1823\n",
      "Epoch 00007: val_loss did not improve from 0.05143\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0532 - mae: 0.1823 - val_loss: 0.0520 - val_mae: 0.1822\n",
      "Epoch 8/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0527 - mae: 0.1810\n",
      "Epoch 00008: val_loss improved from 0.05143 to 0.05084, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0527 - mae: 0.1810 - val_loss: 0.0508 - val_mae: 0.1754\n",
      "Epoch 9/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1801\n",
      "Epoch 00009: val_loss improved from 0.05084 to 0.05006, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0524 - mae: 0.1800 - val_loss: 0.0501 - val_mae: 0.1755\n",
      "Epoch 10/100\n",
      "39456/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1793\n",
      "Epoch 00010: val_loss did not improve from 0.05006\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0521 - mae: 0.1794 - val_loss: 0.0513 - val_mae: 0.1831\n",
      "Epoch 11/100\n",
      "39624/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1788\n",
      "Epoch 00011: val_loss did not improve from 0.05006\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0519 - mae: 0.1788 - val_loss: 0.0503 - val_mae: 0.1742\n",
      "Epoch 12/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0517 - mae: 0.1785\n",
      "Epoch 00012: val_loss improved from 0.05006 to 0.04964, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0517 - mae: 0.1785 - val_loss: 0.0496 - val_mae: 0.1731\n",
      "Epoch 13/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1782\n",
      "Epoch 00013: val_loss did not improve from 0.04964\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0515 - mae: 0.1781 - val_loss: 0.0503 - val_mae: 0.1768\n",
      "Epoch 14/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1775\n",
      "Epoch 00014: val_loss improved from 0.04964 to 0.04915, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0513 - mae: 0.1776 - val_loss: 0.0492 - val_mae: 0.1750\n",
      "Epoch 15/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0516 - mae: 0.1782\n",
      "Epoch 00015: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0516 - mae: 0.1781 - val_loss: 0.0503 - val_mae: 0.1777\n",
      "Epoch 16/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0511 - mae: 0.1771\n",
      "Epoch 00016: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0511 - mae: 0.1771 - val_loss: 0.0502 - val_mae: 0.1769\n",
      "Epoch 17/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1774\n",
      "Epoch 00017: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0513 - mae: 0.1774 - val_loss: 0.0508 - val_mae: 0.1746\n",
      "Epoch 18/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1764\n",
      "Epoch 00018: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0508 - mae: 0.1763 - val_loss: 0.0517 - val_mae: 0.1782\n",
      "Epoch 19/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1762\n",
      "Epoch 00019: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0507 - mae: 0.1760 - val_loss: 0.0495 - val_mae: 0.1720\n",
      "Epoch 20/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1760\n",
      "Epoch 00020: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0507 - mae: 0.1759 - val_loss: 0.0537 - val_mae: 0.1808\n",
      "Epoch 21/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1756\n",
      "Epoch 00021: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0505 - mae: 0.1757 - val_loss: 0.0494 - val_mae: 0.1765\n",
      "Epoch 22/100\n",
      "39336/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1757\n",
      "Epoch 00022: val_loss did not improve from 0.04915\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0506 - mae: 0.1756 - val_loss: 0.0499 - val_mae: 0.1747\n",
      "Epoch 23/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1753\n",
      "Epoch 00023: val_loss improved from 0.04915 to 0.04903, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0503 - mae: 0.1753 - val_loss: 0.0490 - val_mae: 0.1767\n",
      "Epoch 24/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1751\n",
      "Epoch 00024: val_loss did not improve from 0.04903\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0503 - mae: 0.1753 - val_loss: 0.0505 - val_mae: 0.1777\n",
      "Epoch 25/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1752\n",
      "Epoch 00025: val_loss did not improve from 0.04903\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0502 - mae: 0.1752 - val_loss: 0.0491 - val_mae: 0.1718\n",
      "Epoch 26/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1749\n",
      "Epoch 00026: val_loss improved from 0.04903 to 0.04852, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0502 - mae: 0.1750 - val_loss: 0.0485 - val_mae: 0.1736\n",
      "Epoch 27/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1747\n",
      "Epoch 00027: val_loss did not improve from 0.04852\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0501 - mae: 0.1747 - val_loss: 0.0505 - val_mae: 0.1789\n",
      "Epoch 28/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1740\n",
      "Epoch 00028: val_loss did not improve from 0.04852\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0498 - mae: 0.1742 - val_loss: 0.0497 - val_mae: 0.1750\n",
      "Epoch 29/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1743\n",
      "Epoch 00029: val_loss did not improve from 0.04852\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0498 - mae: 0.1741 - val_loss: 0.0498 - val_mae: 0.1703\n",
      "Epoch 30/100\n",
      "39552/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1741\n",
      "Epoch 00030: val_loss improved from 0.04852 to 0.04835, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0498 - mae: 0.1740 - val_loss: 0.0484 - val_mae: 0.1701\n",
      "Epoch 31/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1736\n",
      "Epoch 00031: val_loss did not improve from 0.04835\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0496 - mae: 0.1737 - val_loss: 0.0489 - val_mae: 0.1711\n",
      "Epoch 32/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1737\n",
      "Epoch 00032: val_loss improved from 0.04835 to 0.04813, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0497 - mae: 0.1737 - val_loss: 0.0481 - val_mae: 0.1735\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1738\n",
      "Epoch 00033: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0497 - mae: 0.1737 - val_loss: 0.0492 - val_mae: 0.1731\n",
      "Epoch 34/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1736\n",
      "Epoch 00034: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0496 - mae: 0.1735 - val_loss: 0.0492 - val_mae: 0.1701\n",
      "Epoch 35/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1733\n",
      "Epoch 00035: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 4s 93us/sample - loss: 0.0495 - mae: 0.1734 - val_loss: 0.0489 - val_mae: 0.1742\n",
      "Epoch 36/100\n",
      "39672/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1735\n",
      "Epoch 00036: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0496 - mae: 0.1735 - val_loss: 0.0483 - val_mae: 0.1715\n",
      "Epoch 37/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1735\n",
      "Epoch 00037: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0495 - mae: 0.1735 - val_loss: 0.0497 - val_mae: 0.1743\n",
      "Epoch 38/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1730\n",
      "Epoch 00038: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0494 - mae: 0.1730 - val_loss: 0.0493 - val_mae: 0.1735\n",
      "Epoch 39/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1730\n",
      "Epoch 00039: val_loss did not improve from 0.04813\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0494 - mae: 0.1730 - val_loss: 0.0488 - val_mae: 0.1750\n",
      "Epoch 40/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1729\n",
      "Epoch 00040: val_loss improved from 0.04813 to 0.04796, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0493 - mae: 0.1729 - val_loss: 0.0480 - val_mae: 0.1710\n",
      "Epoch 41/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1724\n",
      "Epoch 00041: val_loss did not improve from 0.04796\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0490 - mae: 0.1724 - val_loss: 0.0486 - val_mae: 0.1732\n",
      "Epoch 42/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1726\n",
      "Epoch 00042: val_loss did not improve from 0.04796\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0490 - mae: 0.1726 - val_loss: 0.0480 - val_mae: 0.1693\n",
      "Epoch 43/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1723\n",
      "Epoch 00043: val_loss did not improve from 0.04796\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0490 - mae: 0.1723 - val_loss: 0.0482 - val_mae: 0.1693\n",
      "Epoch 44/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1722\n",
      "Epoch 00044: val_loss did not improve from 0.04796\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0490 - mae: 0.1722 - val_loss: 0.0489 - val_mae: 0.1731\n",
      "Epoch 45/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1723\n",
      "Epoch 00045: val_loss did not improve from 0.04796\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0490 - mae: 0.1723 - val_loss: 0.0482 - val_mae: 0.1731\n",
      "Epoch 46/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1725\n",
      "Epoch 00046: val_loss improved from 0.04796 to 0.04781, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0491 - mae: 0.1725 - val_loss: 0.0478 - val_mae: 0.1691\n",
      "Epoch 47/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1722\n",
      "Epoch 00047: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0490 - mae: 0.1722 - val_loss: 0.0487 - val_mae: 0.1698\n",
      "Epoch 48/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0490 - mae: 0.1725\n",
      "Epoch 00048: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0491 - mae: 0.1725 - val_loss: 0.0481 - val_mae: 0.1716\n",
      "Epoch 49/100\n",
      "39432/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1721\n",
      "Epoch 00049: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0489 - mae: 0.1721 - val_loss: 0.0483 - val_mae: 0.1727\n",
      "Epoch 50/100\n",
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0489 - mae: 0.1719\n",
      "Epoch 00050: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0489 - mae: 0.1719 - val_loss: 0.0489 - val_mae: 0.1714\n",
      "Epoch 51/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1717\n",
      "Epoch 00051: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0488 - mae: 0.1717 - val_loss: 0.0499 - val_mae: 0.1739\n",
      "Epoch 52/100\n",
      "39528/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1717\n",
      "Epoch 00052: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0488 - mae: 0.1717 - val_loss: 0.0490 - val_mae: 0.1740\n",
      "Epoch 53/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1714\n",
      "Epoch 00053: val_loss did not improve from 0.04781\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0486 - mae: 0.1714 - val_loss: 0.0480 - val_mae: 0.1722\n",
      "Epoch 54/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1712\n",
      "Epoch 00054: val_loss improved from 0.04781 to 0.04773, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 4s 101us/sample - loss: 0.0485 - mae: 0.1710 - val_loss: 0.0477 - val_mae: 0.1706\n",
      "Epoch 55/100\n",
      "39408/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1710\n",
      "Epoch 00055: val_loss improved from 0.04773 to 0.04751, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_23.hdf5\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0484 - mae: 0.1711 - val_loss: 0.0475 - val_mae: 0.1701\n",
      "Epoch 56/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1708\n",
      "Epoch 00056: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0484 - mae: 0.1709 - val_loss: 0.0524 - val_mae: 0.1762\n",
      "Epoch 57/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1708\n",
      "Epoch 00057: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0484 - mae: 0.1709 - val_loss: 0.0475 - val_mae: 0.1696\n",
      "Epoch 58/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1706\n",
      "Epoch 00058: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0483 - mae: 0.1706 - val_loss: 0.0480 - val_mae: 0.1726\n",
      "Epoch 59/100\n",
      "39576/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1708\n",
      "Epoch 00059: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0483 - mae: 0.1708 - val_loss: 0.0478 - val_mae: 0.1718\n",
      "Epoch 60/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0483 - mae: 0.1708\n",
      "Epoch 00060: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0483 - mae: 0.1707 - val_loss: 0.0477 - val_mae: 0.1684\n",
      "Epoch 61/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39384/40000 [============================>.] - ETA: 0s - loss: 0.0482 - mae: 0.1705\n",
      "Epoch 00061: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0482 - mae: 0.1704 - val_loss: 0.0482 - val_mae: 0.1696\n",
      "Epoch 62/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1704\n",
      "Epoch 00062: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0481 - mae: 0.1704 - val_loss: 0.0484 - val_mae: 0.1719\n",
      "Epoch 63/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1702\n",
      "Epoch 00063: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0481 - mae: 0.1702 - val_loss: 0.0498 - val_mae: 0.1759\n",
      "Epoch 64/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0481 - mae: 0.1700\n",
      "Epoch 00064: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 86us/sample - loss: 0.0480 - mae: 0.1700 - val_loss: 0.0480 - val_mae: 0.1693\n",
      "Epoch 65/100\n",
      "39312/40000 [============================>.] - ETA: 0s - loss: 0.0480 - mae: 0.1701\n",
      "Epoch 00065: val_loss did not improve from 0.04751\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0481 - mae: 0.1702 - val_loss: 0.0487 - val_mae: 0.1720\n",
      "Epoch 00065: early stopping\n"
     ]
    }
   ],
   "source": [
    "#Some layer optimisation again\n",
    "testing_layers = [[10,1],\n",
    "                  [16,1],\n",
    "                  [24,1],\n",
    "                  [48,1],\n",
    "                  [10,10,1],\n",
    "                  [16,16,1],\n",
    "                  [24,24,1],\n",
    "                  [10,24,1],\n",
    "                  [10,48,1],\n",
    "                  [24,48,1],\n",
    "                  [48,48,1],\n",
    "                  [10,96,1],\n",
    "                  [48,96,1],\n",
    "                  [10,10,10,1],\n",
    "                  [24,48,8,1],\n",
    "                  [24,24,24,1],\n",
    "                  [10,48,10,1],\n",
    "                  [10,24,48,1],\n",
    "                  [24,24,48,1],\n",
    "                  [24,48,48,1],\n",
    "                  [48,48,48,1],\n",
    "                  [16,16,16,16,1],\n",
    "                  [10,24,24,10,1],\n",
    "                  [10,24,24,10,1]]\n",
    "\n",
    "base_model_name = os.path.join(path_data, \"nn_2000_queries_trimming_simple\")\n",
    "simple_models = []\n",
    "\n",
    "for i, layers in enumerate(testing_layers):\n",
    "    model_name_loop = f\"{base_model_name}_{i}\"\n",
    "    \n",
    "    # define the keras model\n",
    "    nn_model_simple = Sequential()\n",
    "\n",
    "    #add first layer\n",
    "    nn_model_simple.add(Dense(layers[0], input_dim = X_train.shape[1], activation = 'relu'))\n",
    "    #add other layers\n",
    "    for i in range(1,len(layers)-1): #skip first and last one\n",
    "        nn_model_simple.add(Dense(layers[i], activation = 'relu'))\n",
    "    #add last layer\n",
    "    nn_model_simple.add(Dense(layers[-1], activation = None))\n",
    "\n",
    "    # compile the keras model\n",
    "    nn_model_simple.compile(loss = 'mean_squared_error', optimizer='adam', metrics=['mae'])\n",
    "\n",
    "    # save best model\n",
    "    checkpointer = ModelCheckpoint(filepath= model_name_loop + \".hdf5\", monitor='val_loss', verbose=1, save_best_only=True)\n",
    "    earlystopper = EarlyStopping(monitor='val_loss', patience=10, verbose=1)  # patience - try x more epochs to improve val_loss\n",
    "\n",
    "    # fit the keras model on the dataset\n",
    "    hist_simple = nn_model_simple.fit(X_train, y_train, epochs = 100, batch_size = 24, validation_data=(X_val, y_val),\n",
    "                        callbacks = [checkpointer, earlystopper])\n",
    "    simple_models.append((nn_model_simple, hist_simple, hist_simple.history['loss'][-1], hist_simple.history['val_loss'][-10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 0, Train loss: 0.0513, Val loss: 0.0505\n",
      "Model 1, Train loss: 0.0507, Val loss: 0.0545\n",
      "Model 2, Train loss: 0.0503, Val loss: 0.0493\n",
      "Model 3, Train loss: 0.0495, Val loss: 0.0499\n",
      "Model 4, Train loss: 0.0490, Val loss: 0.0472\n",
      "Model 5, Train loss: 0.0500, Val loss: 0.0483\n",
      "Model 6, Train loss: 0.0482, Val loss: 0.0471\n",
      "Model 7, Train loss: 0.0488, Val loss: 0.0477\n",
      "Model 8, Train loss: 0.0479, Val loss: 0.0479\n",
      "Model 9, Train loss: 0.0480, Val loss: 0.0472\n",
      "Model 10, Train loss: 0.0469, Val loss: 0.0464\n",
      "Model 11, Train loss: 0.0482, Val loss: 0.0471\n",
      "Model 12, Train loss: 0.0457, Val loss: 0.0470\n",
      "Model 13, Train loss: 0.0497, Val loss: 0.0488\n",
      "Model 14, Train loss: 0.0487, Val loss: 0.0496\n",
      "Model 15, Train loss: 0.0468, Val loss: 0.0467\n",
      "Model 16, Train loss: 0.0490, Val loss: 0.0499\n",
      "Model 17, Train loss: 0.0492, Val loss: 0.0492\n",
      "Model 18, Train loss: 0.0481, Val loss: 0.0511\n",
      "Model 19, Train loss: 0.0478, Val loss: 0.0483\n",
      "Model 20, Train loss: 0.0472, Val loss: 0.0471\n",
      "Model 21, Train loss: 0.0488, Val loss: 0.0480\n",
      "Model 22, Train loss: 0.0479, Val loss: 0.0477\n",
      "Model 23, Train loss: 0.0481, Val loss: 0.0524\n",
      "Best model: 10, it has [48, 48, 1] layers\n"
     ]
    }
   ],
   "source": [
    "min_val_loss = (0, 1)\n",
    "for i, layers in enumerate(testing_layers):\n",
    "    simp_model, hist_simp, train_loss, val_loss = simple_models[i]\n",
    "    if val_loss < min_val_loss[1]:\n",
    "        min_val_loss = (i, val_loss)\n",
    "    print(f\"Model {i}, Train loss: {hist_simp.history['loss'][-10]:.4f}, Val loss: {val_loss:.4f}\")\n",
    "\n",
    "print(f\"Best model: {min_val_loss[0]}, it has {testing_layers[min_val_loss[0]]} layers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best simple model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10.hdf5\n",
      "Evaluation on test set\n",
      "30000/30000 [==============================] - 1s 35us/sample - loss: 0.0488 - mae: 0.1706\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04878429824113846, 0.17055081]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the best model: #10\n",
    "base_model_name = os.path.join(path_data, \"nn_2000_queries_trimming_simple\")\n",
    "best_model_name = f\"{base_model_name}_10.hdf5\"\n",
    "print(best_model_name)\n",
    "print(\"Evaluation on test set\")\n",
    "best_model = load_model(best_model_name)\n",
    "best_model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_simple_10_hist.pickle\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28a36b45fc8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GGSXWFxQUxJgxY5g6dSqXXnopa9euZd26daUm2ktPT/caXJekbdu2hQJ9gM2bN/PQQw+xbNky9u/fj8fjAWD79u2lBvvHHXdc/vOYGGeJcHx8PG3atCmxfLdu3QrNMoiJiWH9+vWA8+/m7+9Pnz598s8fe+yxNG7c2Gv7SUlJxMXFMWDAgPxj/v7+9OvXr9BU/sq+v/j4eB5++GEWLlzI3r17cbvdHDp0qFgOiJCQEA4dOuS1Hl/z9cj+ncAUa+1bua8nGGOG4oze31+0sLV2QpFD/zDGnA+MANbk1QF8a619Kvf1U8aYU3OPj65Mu/XBpce35tLjW/u6GyIiIiJSR4UGlh065O0MtScpo8R1+waILrIzVHnqLY8pU6aQk5NDy5Yt849ZawkICODAgQM0btyYkJCQUuswxhRb911SMru8ID7P888/z4svvsjkyZPp0aMHDRo0YMKECWRlOcsTymoXnKn8vXr1YufOnUydOpUzzjiDtm3bei3ftGlTDhw4UGa93voMMGLECFq3bs2bb75JixYt8Hg8dO/ePb/f3gQEBOQ/z5t5kBdIl1U+75q88kU/7zzejldEZd/fuHHj2LdvH5MnT6Zt27YEBQUxYMCAYtclJiYWu4FSm/gsQZ8xJhDoC3xT5NQ3wMBy1uECwoDEAocHlFDn13l1VrZdY0yQMSY875HbroiIiIiI5CptZ6i819WxM1ROTg7Tp0/n+eefZ+3atfmPdevW0bZtW959913AGZEubVu4Zs2aERd3eLeAP//8s1wjt4sWLeL8889nzJgx9OzZk2OOOYY///wz/3zHjh0JCQkpte0ePXrQr18/3nzzTWbOnMk111xTapu9e/dmz549xQL+gIAA3G53mX1OSEhg06ZNPPjgg5xxxhl07dq1QjcPqkqXLl3IyclhzZo1+cf++usvDh486PWaiIgIYmJiWLZsWf6xnJycQvvel+f95a3RL/p5LVq0iNtuu43hw4fTrVs3goKC2L9/f7F+bNiwgd69e1fo/dYkX2bjbwr4AXuLHN8LRBcvXqK7gAbArALHosuos7Lt3g8kFXjsLGcfaw1rLRt2JTH3l92+7oqIiIiI1FPedoaKjgiutp2h5s6dy4EDB7j22mvp3r17ocfFF1/MlClTAJg0aRLvvfcekyZNYtOmTaxfv55//etf+fUMHjyYV155hdWrV7Ny5UpuuummYqPSJTn22GP59ttvWbJkCZs2beLGG29kz549+eeDg4OZOHEi9957L9OnT2fz5s0sW7Ysv195rrvuOp5++mncbjcjR44stc3evXvTrFmzYonx2rVrx4IFC0q8EVBQ48aNiYyM5I033uCvv/5i4cKF3HnnnWW+16rWpUsXzjzzTG644QaWL1/OmjVruOGGGwgJCfGarwDg9ttv5+mnn+aTTz7ht99+4+abby50g6A87y8qKoqQkBDmzZvH3r17SUpKApx/z3feeYdNmzbx888/c8UVV5Q4O2PRokWcddZZVfNBVIPasPVe0fkZpoRjxRhjRgOPAKOstUVTNZanzoq2+xQQUeDRqqw+1ja/7Ezi3JcXc9/s9WTleJ9mIyIiIiJyJIZ1j2HxxMG8d/2J/PuyXrx3/Yksnji42raAnjJlCmeeeSYRERHFzl100UWsXbuW1atXc9ppp/Hhhx8yZ84cevXqxeDBg/n555/zyz7//PO0bt2aU045hcsvv5y7776b0NDQMtt/6KGH6NOnD0OHDuW0004jOjqaCy64oFiZu+66i4cffpiuXbsyatSoYhnnR48ejb+/P5dffnmZ6/H9/Py45ppr8mctFHwP3377La1bty511NnlcvH++++zatUqunfvzh133MGzzz5b5nutDtOnT6d58+accsopjBw5kuuvv56wsLBSP4O77rqLsWPHMm7cOAYMGEBYWFihGyTleX/+/v689NJL/Pe//6VFixacf/75AEydOpUDBw7Qu3dvrrzySm677TaioqIKXbt06VKSkpK4+OKLq/CTqFqmKtZCVKphZzr9IeCSgpnyjTH/BnpZa08t5dpRwLTca78ocm478KK19sUCx+7Aybbf9kjaLdJOOJCUlJREeHjdSHrn8VhOeGoB+1IymX5Nf07pVHvXl4iIiIhIzcvIyGDLli20b9++QsnfpGrs2LGDdu3asWLFikIJ67zZu3cv3bp1Y9WqVaWu769rdu7cSevWrZk/f77XpIa+dskll9C7d2/+8Y9/VHndpf0cJicn593UirDWJpdWj89G9q21WcAqYEiRU0OAJd6uyx3Rfxu4vGign2tpCXWelVdnZdutD1wuw5ldnTtS8zcVXcUgIiIiIiK+kJ2dzfbt25k4cSInnnhiuQJ9gObNmzNlypRiWeLrmoULFzJnzhy2bNnCkiVLuOyyy2jXrh2nnHKKr7tWoszMTHr27Mkdd9zh666UytfT+F8ArjPGXGOM6WqMeRFoA7wOYIx5yhgzPa9wbqA/HWet/jJjTHTuo+B8nX8DZxljJhpjuhhjJgJn4my1V65267MzuzYHYP7GvVWS4VJERERERI7MTz/9RNu2bVm1ahWvv16xkOT888/n5JNPrqae1Yzs7Gz+8Y9/0K1bN0aOHEmzZs34/vvvy5UvwReCgoJ48MEHy7XLgi/5dOs9a+0HxphI4GEgBtgADLfWbsstEoMThOe5EafP/8l95PkfMC63ziXGmMuAx4HHgM046/rzF+OUo91666RjmxIS4MfupAw2xiXTrUXxdU0iIiIiIlJzTjvttKN6IG7o0KEMHTrU192od3wa7ANYa18FXvVyblyR16eVs86PgI8q2259Fhzgx8kdm/LNxr18u3Gvgn0REREREZF6yNfT+MUHzox1pvIv3Zzg456IiIiIiIhIdfD5yL7UvLNim9Pq+hM4vl0TX3dFREREREREqoGC/aNQo9BABnZo6utuiIiIiIiISDXRNH4RERERERGRekbB/lEqK8fDI3N+ZfBz35OSke3r7oiIiIiIiEgVUrB/lAr0d/HjH/v4e38aP/6x39fdEREREZH6xuOGLYtg/UfOV4/b1z2ql0455RRmzpxZZfVt3boVYwxr164F4Pvvv8cYw8GDB71e8/bbb9OoUaMjbruq6qmMdu3aMXny5GpvZ/369bRq1Yq0tLRqb0vB/lEsLyv//E17fdwTEREREalXNs6Byd3hf+fC7Gudr5O7O8er2ZIlS/Dz82PYsGHV3pavzZ07lz179nDZZZdVWxsDBw4kLi6OiIiq3bK7pOB61KhR/PHHH1XaTnWp7I2JHj160L9/f1588cWq71QRCvaPYmd2dYL9hb/Fk+P2+Lg3IiIiIlIvbJwDs8ZC8u7Cx5PjnOPVHPBPnTqVW2+9lcWLF7N9+/ZqbQsgO9t3S2Jfeuklrr76alyu6gvrAgMDiY6OxhhTbW3kCQkJISoqqtrb8bWrr76a1157Dbe7eme7KNg/ivVt25jGoQEkpWezYusBX3dHRERERGqzrDTvj+wMp4zHDfMmAraECnKPzZtYeEq/tzorIS0tjVmzZjF+/HjOPfdc3n777WJl5syZQ79+/QgODqZp06ZceOGF+eeMMXz66aeFyjdq1Ci/nrwp7rNmzeK0004jODiYGTNmkJCQwOjRo2nVqhWhoaH06NGD9957r1A9Ho+HZ555hmOPPZagoCDatGnDE088AcDgwYO55ZZbCpVPSEggKCiIhQsXlvhe9+/fz/z58znvvPPyj40ePbrYKH92djZNmzZl2rRpAMybN49BgwbRqFEjIiMjOffcc9m8ebPXz7Skafxvv/02bdq0ITQ0lJEjR5KQkFDoms2bN3P++efTvHlzGjZsyPHHH8/8+fPzz5922mls27aNO+64A2NM/o2EkkbLX3vtNTp06EBgYCCdO3fmnXfeKXTeGMNbb73FyJEjCQ0NpWPHjsyZU/oNpfj4eEaMGEFISAjt27fn3XffLVbmhRdeoEePHjRo0IDWrVtz8803k5qamv+ZXH311SQlJeX3/5FHHgFgxowZ9OvXj7CwMKKjo7n88suJj48vVPfQoUNJSEjghx9+KLWfR0rB/lHMz2UY3EVT+UVERESkHJ5s4f0x60qnzLYlxUf0C7HO+W1LDh+a3KPkOivhgw8+oHPnznTu3JkxY8Ywbdo0rD184+GLL77gwgsv5JxzzmHNmjUsWLCAfv36VbidiRMnctttt7Fp0yaGDh1KRkYGffv2Ze7cuWzYsIEbbriBK6+8kp9//jn/mvvvv59nnnmGhx56iI0bNzJz5kyaN3f+Fr/uuuuYOXMmmZmZ+eXfffddWrRowemnn15iHxYvXkxoaChdu3bNP3bFFVcwZ86c/KAU4OuvvyYtLY2LLroIcG6I3HnnnaxYsYIFCxbgcrkYOXIkHk/5Zvr+/PPPXHPNNdx8882sXbuW008/nccff7xQmdTUVIYPH878+fNZs2YNQ4cOZcSIEfkzLT7++GNatWrFP//5T+Li4oiLiyuxrU8++YTbb7+du+66iw0bNnDjjTdy9dVX89133xUq9+ijj3LppZfyyy+/MHz4cK644goSExO9vodx48axdetWFi5cyEcffcSrr75aLCB3uVy89NJLbNiwgf/9738sXLiQe++9F3CWNkyePJnw8PD8/t99990AZGVl8dhjj7Fu3To+/fRTtmzZwrhx4wrVHRgYSM+ePVm0aFHZH/iRsNbqUYkHEA7YpKQkW5d9tX63bTtxrj3lXwutx+PxdXdERERExIfS09Ptxo0bbXp6evGTk8K9P2Zc7JT55cPSy+U9fvnwcL3PtC+5TCUMHDjQTp482VprbXZ2tm3atKn99ttv888PGDDAXnHFFV6vB+wnn3xS6FhERISdNm2atdbaLVu2WCC/jdIMHz7c3nXXXdZaa5OTk21QUJB98803SyybkZFhmzRpYj/44IP8Y7169bKPPPKI1/pffPFFe8wxxxQ6lpWVZZs2bWqnT5+ef2z06NH2kksu8VpPfHy8Bez69esLvcc1a9ZYa6397rvvLGAPHDiQX9+wYcMK1TFq1CgbERHhtQ1rrY2NjbUvv/xy/uu2bdvaF198sVCZadOmFapn4MCB9vrrry9U5pJLLrHDhw/Pfw3YBx98MP91amqqNcbYr776qsR+/P777xawy5Ytyz+2adMmCxTrT0GzZs2ykZGRXvvqzfLlyy1gU1JSCh0fOXKkHTduXInXlPZzmJSUZHGmyYTbMmJW/+q9lSC13ckdm9G+aQNO7tiUzBwPwQF+vu6SiIiIiNRG/yhlxN7k/g3ZsHn56ipYbsL6yvepgN9//53ly5fz8ccfA+Dv78+oUaOYOnUqZ555JgBr167l+uuvP+K2is4GcLvdPP3003zwwQfs2rWLzMxMMjMzadCgAQCbNm0iMzOTM844o8T6goKCGDNmDFOnTuXSSy9l7dq1+SPD3qSnpxMcHFzoWEBAAJdccgnvvvsuV155JWlpaXz22WeFsvVv3ryZhx56iGXLlrF///78Ef3t27fTvXv3Mt/7pk2bGDlyZKFjAwYMYN68efmv09LSePTRR5k7dy67d+8mJyeH9PT0CudQ2LRpEzfccEOhYyeddBL//ve/Cx077rjj8p83aNCAsLCwYiP1Bev09/cv9G/YpUuXYssHvvvuO5588kk2btxIcnIyOTk5ZGRkkJaWlv/vWpI1a9bwyCOPsHbtWhITEwt9vrGxsfnlQkJCOHToUOkfwBFSsH+UaxDkz3d3n+brboiIiIhIbRfoPcDJ13YghLdwkvGVuG7fOOfbDqxYveUwZcoUcnJyaNmyZf4xay0BAQEcOHCAxo0bExISUmodxphC0/6h5AR8RYO9559/nhdffJHJkyfnr/OeMGECWVlZAGW2C85U/l69erFz506mTp3KGWecQdu2bb2Wb9q0KQcOFM+7dcUVV3DqqacSHx/Pt99+S3BwMGeffXb++REjRtC6dWvefPNNWrRogcfjoXv37vl9LUvRz6ck99xzD19//TXPPfccxx57LCEhIVx88cXlbqOgookBrbXFjgUEBBS7xtuyhLz+l5ZwcNu2bQwfPpybbrqJxx57jCZNmrB48WKuvfbaUhMypqWlcdZZZ3HWWWcxY8YMmjVrxvbt2xk6dGix956YmEiHDh281lUVtGZfRERERESqhssPhj2T+6JoMJX7etjTTrkqlJOTw/Tp03n++edZu3Zt/mPdunW0bds2PwHbcccdx4IFC7zW06xZs0Lrx//8889yjb4uWrSI888/nzFjxtCzZ0+OOeYY/vzzz/zzHTt2JCQkpNS2e/ToQb9+/XjzzTeZOXMm11xzTalt9u7dmz179hQL+AcOHEjr1q354IMPePfdd7nkkksIDAwEnKR/mzZt4sEHH+SMM86ga9euJd4wKE1sbCzLli0rdKzo60WLFjFu3DhGjhxJjx49iI6OZuvWrYXKBAYGlpmNvmvXrixevLjQsSVLlhTKU1BRXbt2JScnh5UrV+Yf+/333wslIFy5ciU5OTk8//zznHjiiXTq1InduwvPbCmp/7/99hv79+/n6aef5uSTT6ZLly5eZxhs2LCB3r17V/p9lIdG9gWAHLeHVdsO0Kl5GI0bBPq6OyIiIiJSV8WeB5dOd7LuF0zWF97CCfRjz/N+bSXNnTuXAwcOcO211xbbD/7iiy9mypQp3HLLLUyaNIkzzjiDDh06cNlll5GTk8NXX32Vn3ht8ODBvPLKK5x44ol4PB4mTpxYbNS4JMceeyyzZ89myZIlNG7cmBdeeIE9e/bkB6XBwcFMnDiRe++9l8DAQE466ST27dvHr7/+yrXXXptfz3XXXcctt9ySn+W+NL1796ZZs2b89NNPnHvuufnHjTFcfvnlvP766/zxxx+Fktk1btyYyMhI3njjDWJiYti+fTv33Xdf2R9wAbfddhsDBw7kX//6FxdccAHffPNNoSn8eZ/Hxx9/zIgRIzDG8NBDDxUbaW/Xrh0//vgjl112GUFBQTRt2rRYW/fccw+XXnopffr04YwzzuDzzz/n448/LpTZv6I6d+7MsGHDuP7663njjTfw9/dnwoQJhWZfdOjQgZycHF5++WVGjBjBTz/9xOuvv16s/6mpqSxYsICePXsSGhpKmzZtCAwM5OWXX+amm25iw4YNPPbYY8X6sHXrVnbt2pW/vKTalLWoX4/6naAvzxVvLrNtJ8617/28zdddEREREREfKTVBX0W5c6z9+0cnGd/fPzqvq8m5555bKGlbQatWrbKAXbVqlbXW2tmzZ9tevXrZwMBA27RpU3vhhRfml921a5c966yzbIMGDWzHjh3tl19+WWKCvrzkdXkSEhLs+eefbxs2bGijoqLsgw8+aMeOHWvPP//8/DJut9s+/vjjtm3btjYgIMC2adPGPvnkk4XqSUlJsaGhofbmm28u1/u+77777GWXXVbs+K+//moB27Zt22JJuL/99lvbtWtXGxQUZI877jj7/fffF0pMWFaCPmutnTJlim3VqpUNCQmxI0aMsM8991yhZHVbtmyxp59+ug0JCbGtW7e2r7zyij311FPt7bffnl9m6dKl9rjjjrNBQUHWCUtLTnr36quv2mOOOcYGBATYTp06FUo+aG3ZSRVLEhcXZ8855xwbFBRk27RpY6dPn14sYeALL7xgY2JibEhIiB06dKidPn16sc/hpptuspGRkRawkyZNstZaO3PmTNuuXTsbFBRkBwwYYOfMmVPse+bJJ5+0Q4cO9dq/qkrQZ2w51lxIccaYcCApKSmJ8PBwX3fniL284E+e//YPzuwaxVtXHe/r7oiIiIiID2RkZLBlyxbat29fLPmbVL8dO3bQrl07VqxYQZ8+fcosv3fvXrp168aqVatKXd8vtUdmZiYdO3bkvffe46STTiqxTGk/h8nJyXmzVyKstcmltaU1+wLAkG5ORtRFf+7nUFaOj3sjIiIiInL0yM7OZvv27UycOJETTzyxXIE+QPPmzZkyZUqFs9yL72zbto0HHnjAa6BflbRmXwDo3DyMVo1D2HkgncV/7uesbtG+7pKIiIiIyFHhp59+4vTTT6dTp0589NFHFbr2/PPPr6ZeSXXo1KkTnTp1qpG2NLIvgJPI48yuzuj+/E17fdwbEREREZGjx2mnnYa1lt9//50ePXr4ujtSTyjYl3xnxTrB/oJN8bg9yuUgIiIiIiJSVynYl3zHt29CWLA/CWlZrN1x0NfdEREREREfURJvEd+pqp8/rdmXfAF+Lp66sAetG4fSo2VE2ReIiIiISL2St6f8oUOHCu07LiI159ChQ8Dhn8fKUrAvhZx7XAtfd0FEREREfMTPz49GjRoRHx8PQGhoKMYYH/dK5OhgreXQoUPEx8fTqFEj/Pz8jqg+nwf7xpibgXuAGOBXYIK1dpGXsjHA80BfoCPwkrV2QpEyAcD9wFVAS+B3YKK1dl6BMo8Ak4pUv9daqxT0IiIiInJUi452/iTOC/hFpGY1atQo/+fwSPg02DfGjAImAzcDPwE3Al8ZY2KttSVtFhkE7AOeAO7wUu3jwBjgeuA3YCjwiTFmoLV2TYFyvwJnFnjtPoK3Uq+s2pbIByt20KdNYy7r38bX3RERERGRGmSMISYmhqioKLKzs33dHZGjSkBAwBGP6Ofx9cj+ncAUa+1bua8nGGOGAuNxRucLsdZuBW4HMMZc46XOK4EnrLVf5r5+LbfOu3BuAuTJsdbuOfK3UP+s35nErJU72ZpwSMG+iIiIyFHKz8+vyoIOEal5PsvGb4wJxJmO/02RU98AA4+g6iAgo8ixdGBQkWMdjTG7jTFbjDHvG2OOKaO/QcaY8LwHEHYEfazVzujqbMG3cmsiB9KyfNwbERERERERqShfbr3XFPAD9hY5vhc4kgUKXwN3GmM6GmNcxpghwPk4OQHy/AyMxZnif31ue0uMMZGl1Hs/kFTgsfMI+lirtW4SSpfoMDwWvvtda7VERERERETqGl8G+3mKbiJoSjhWEbcDf+Ks188CXgGmUWBNvrX2K2vtbGvtemvtfOCc3FNXlVLvU0BEgUerI+hjrXdWrDO6P39T0XsxIiIiIiIiUtv5MtjfjxOAFx3Fj6L4aH+5WWv3WWsvABoAbYEuQCqwpZRr0oD1OBn+vZXJtNYm5z2AlMr2sS44MzfY/+H3fWTmKHehiIiIiIhIXeKzYN9amwWsAoYUOTUEWFIF9WdYa3fhJCG8CPjMW1ljTBDQFYg70nbri+4tImgeHkRalpulmxN83R0RERERERGpAF9n438BeMcYsxJYCtwAtAFeBzDGPAW0tNaOzbvAGNMr92lDoFnu6yxr7cbc8ycALYG1uV8fwbmp8a8CdTwHfA5sx5lJ8CAQDvyvWt5lHeRyGc7o2pzlWxLJzPH4ujsiIiIiIiJSAT4N9q21H+QmxXsYJ4HeBmC4tXZbbpEYnOC/oDUFnvcFLge2Ae1yjwUDjwPH4Ezf/xK40lp7sMB1rYD3cJIE7gOWAScWaFeAR0Z0I9C/NqR1EBERERERkYow1h5JLryjV+72e0lJSUmEh4f7ujsiIiIiIiJSzyUnJxMREQEQkZtLzisN20qZMrLdbN2f5utuiIiIiIiISDkp2JdSLdm8n97//Jbx7672dVdERERERESknBTsS6m6RIeTmeNmU1wyOw8c8nV3REREREREpBwU7EupmjQIpF+7JgAs2BTv496IiIiIiIhIeSjYlzIN6docgG837vVxT0RERERERKQ8FOxLmc6MdYL9ZX8nkJyR7ePeiIiIiIiISFkU7EuZ2jdtQIdmDcjxWH74fZ+vuyMiIiIiIiJlULAv5TIkNhrQVH4REREREZG6wN/XHZC64byeLYgICWBI7pR+ERERERERqb0U7Eu5xLYIJ7ZFuK+7ISIiIiIiIuWgafwiIiIiIiIi9YyCfSm3jGw3n67ZxYOfrsda6+vuiIiIiIiIiBcK9qXcPNYycfYvzFi2nT/2pvq6OyIiIiIiIuKFgn0pt9BAfwYd2xSAbzfu8XFvRERERERExBsF+1IhZ+Zm4/92U7yPeyIiIiIiIiLeKNiXCjmjSxQA63YcJD45w8e9ERERERERkZIo2JcKiQoPplfrRgDM1+i+iIiIiIhIraRgvz7zuGHLIlj/kfPV466SaofkTuWfv2lvldQnIiIiIiIiVcvf1x2QarJxDsybCMm7Dx8LbwHDnoHY846o6jO7NufZr39n98F0Pluzi6jwYPq3b4Kfyxxhp0VERERERKQqKNivjzbOgVljAVv4eHKcc/zS6UcU8P+9L5WosCB+25PC7R+sBSAmIphJI2IZ1j2m8v0WERERERGRKqFp/PWNx+2M6BcN9OHwsXn3VXpK/7wNcdz87mriUzILHd+TlMH4GauZtyGuUvWKiIiIiIhI1VGwX99sW1J46n4xFpJ3OeUqyO2xPPr5xtJuI/Do5xtxe0oqISIiIiIiIjVFwX59k1rOpHnlLVfA8i2JxCV5327PAnFJGSzfkljhukVERERERKTqKNivbxo2r9pyBcSneA/0K1NOREREREREqofPg31jzM3GmC3GmAxjzCpjzMmllI0xxsw0xvxujPEYYyaXUCbAGPOwMWZzbp3rjDHDjqTdOqXtQCfrPqVkxg9v6ZSroKiw4CotJyIiIiIiItXDp8G+MWYUMBl4AugNLAK+Msa08XJJELAvt/w6L2UeB24EbgVigdeBT4wxvY+g3brD5edsrwd4Dfj7XuWUq6D+7ZsQExFc2m0EgvxddG8ZXuG6RUREREREpOr4emT/TmCKtfYta+0ma+0EYAcwvqTC1tqt1trbrbXTgSQvdV4JPGmt/dJa+7e19jXga+CuyrZb58Se52yvF15kG7yAUOfr2pmQlVbhav1chkkjYgHv8wYyczyMm7ZCSfpERERERER8yGfBvjEmEOgLfFPk1DdAxeeYHxYEFF00ng4MOpJ2jTFBxpjwvAcQdgR9rH6x58GEDXDVXLhoivP1jo0Q3goO7oCtiytV7bDuMbw2pg/REYWn6sdEBHPP0M40Dg1gVL/W+LlKG/8XERERERGR6uTvw7abAn5A0bTwe4HoI6j3a+BOY8yPwGbgDOD83LaOpN37gUlH0K+a5/KD9kVSEVz0JgSEQIveJV9TDsO6xzAkNprlWxKJT8kgKiyY/u2b4OcyXHFCGxqFBuaX9XgsLgX+IiIiIiIiNcrX0/iBYtu2mxKOVcTtwJ/Ab0AW8AowDXAfYbtPAREFHq2OoI++03bgEQX6efxchgEdIjm/V0sGdIjMH8kvGOgnpGZyzsuLWbCp4tv8iYiIiIiISOX5MtjfjxOAFx1Nj6L4qHu5WWv3WWsvABoAbYEuQCqw5UjatdZmWmuT8x5ASmX7WGvs/RWWvV5t1b/x499sikvm+ukreW/59mprR0RERERERArzWbBvrc0CVgFDipwaAiypgvozrLW7cJYqXAR8VhPt1hkHtsIbp8G8+2D7smpp4u6hnbm4bys8Fu7/eD0vfPM71ipxn4iIiIiISHXz9TT+F4DrjDHXGGO6GmNeBNrgbJeHMeYpY8z0ghcYY3oZY3oBDYFmua9jC5w/wRhzoTHmGGPMycA8nPf5r/K2e1Ro3A56XApY+PRmyE6v8iYC/Fw8e/Fx3Db4WABeWvgX9370C9luT5W3JSIiIiIiIof5MkEf1toPjDGRwMNADLABGG6t3ZZbJAYnCC9oTYHnfYHLgW1Au9xjwcDjwDE40/e/BK601h6sQLtHh6FPwOYFkLgZFj7uvK5ixhjuPKsz0REhPPjpej5ctZP4lExevaIPDYJ8+u0nIiIiIiJSbxlNq66c3O33kpKSkggPD/d1dyrvj69h5qWAgWu+hjYnVFtTCzbt5f9mriY6PJjZ4wcS2TCo2toSERERERGpb5KTk4mIiACIyM0l55WC/UqqN8E+wCc3wbr3ILIj3LTI2ZqvmqzdcZDGoQG0jWxQbW2IiIiIiIjURxUJ9n29Zl9qg2FPQcNoSPgTVv2vWpvq1bpRoUB/9qqdrNp2oFrbFBEREREROdpo0bRASGMY8W9I+Av6X19jzS7dnMC9s3/B32V4eXRvzupWdDdEERERERERqQyN7Iuj8zAYeAu4/GqsyZ6tIzi1UzMyczzcNGMV7yxz8iO6PZalmxP4bO0ulm5OwO3RUhMREREREZGK0Jr9SqpXa/aLyk6HrYuh45BqbyrH7eGhzzbw3vIdAAzt1px1O5PYk5SRXyYmIphJI2IZ1j2m2vsjIiIiIiJSW2nNvlRe+kF4fZCToX/Xqmpvzt/PxZMje3DHmZ0A+PrXvYUCfYA9SRmMn7GaeRviqr0/IiIiIiIi9YGCfSkspBG06A3WA5/+H+RkVnuTxhhuGXwsESEBJZ7Pm3vy6OcbNaVfRERERESkHBTsS3Fn/wsaNIN9m+CHf9VIk8u3JJKUnu31vAXikjJYviWxRvojIiIiIiJSlynYl+JCm8A5LzjPF78Iu9dUe5PxKRllF6pAORERERERkaOZgn0pWex50O1CsG749GbIyarW5qLCgqu0nIiIiIiIyNFMwb54N/xZCG0K8Rth8QvV2lT/9k2IiQjGlFImNNCPPm0aVWs/RERERERE6gMF++Jdg6ZwzvPQ4QzoPaZam/JzGSaNiAXwGvAfynJzzf9WcCCtemcZiIiIiIiI1HXGWmU3rwxjTDiQlJSURHh4uK+7U72sBVPamHvVmbchjkc/30hcge33YiKCOa9nC95Zto1DWW7aNAnlrav60al5WI30SUREREREpDZITk4mIiICIMJam1xaWQX7lXRUBfsFHdgGjdtWaxNuj2X5lkTiUzKICgumf/sm+LkMv+1J5vrpK9mRmM6xUQ35esIp+Llq5iaEiIiIiIiIrynYrwFHXbDvzoa5d8C69+GG7yC6h0+6kZiWxV2z1nLnkM70aBXhkz6IiIiIiIj4QkWCfa3Zl/Jx+UPGQfBkw6fjneDfB5o0CGTa1f0LBfo//53Aoawcn/RHRERERESkNlKwL+VjDJzzAoQ0hj3rYdHzsGURrP/I+epx+6Rb63YcZOzU5Vz82lJ2HUz3SR9ERERERERqG39fd0DqkIZRMPw5mH0tfP9U4XPhLWDYMxB7Xo12KcfjoWGQPxvjkjnv5cW8fmVfjm/XpEb7ICIiIiIiUttoZF8qxi+g5OPJcTBrLGycU6Pd6du2CXNuHURsTDgJaVlc/uYy3lu+vUb7ICIiIiIiUtso2Jfy87hh3n1eTuYmepx3X41P6W/ZKISPxg/gnB4xZLst93+8nkmfbSDb7anRfoiIiIiIiNQWCval/LYtgeTdpRSwkLzLKVfDQgP9eeXy3tx9VicA/rd0G7NX7azxfoiIiIiIiNQGWrMv5Ze6t2rLVTFjDLcM7kin5mF8sT6OS/u19kk/REREREREfE3BvpRfw+blKxcYVr39KMNZ3aI5q1t0/uuMbDcrtx5gUMemuD2W5VsSiU/JICosmP7tm+DnMj7srYiIiIiISNVTsC/l13agk3U/OY78NfolmXMrDHsSul/kbNnnQ9Za/vHxej5es4sRx8WwYtsB9iRl5J+PiQhm0ohYhnWP8WEvRUREREREqpbP1+wbY242xmwxxmQYY1YZY04upWyMMWamMeZ3Y4zHGDPZS7kJuWXSjTE7jDEvGmOCC5x/xBhjizz2VMPbq19cfs72egAUDeJzXzdsDml7ne35pp8P+/+syR4W47EQHuLsIPD5L3GFAn2APUkZjJ+xmnkb4nzRPRERERERkWrh02DfGDMKmAw8AfQGFgFfGWPaeLkkCNiXW36dlzqvAJ4GHgW6AtcCo4AiG8PzKxBT4NHjCN7K0SP2PLh0OoQXGQkPbwGXvgMT1sPpD4J/MGz5AV4dAEtf9U1fAT+X4aFzY4kIKXnLwLz5CY9+vhG3p5TZCiIiIiIiInWIr6fx3wlMsda+lft6gjFmKDAeuL9oYWvtVuB2AGPMNV7qHAD8ZK2dmft6qzHmPaB/kXI51lqN5ldG7HnQ5Rwn637qXmc0v+1AZ+Qf4NR7oMfF8NW98Oc30MjbvZuasXxLIknp2V7PWyAuKYPlWxIZ0CGy5jomIiIiIiJSTSo0sm+M6W+M8Svw2hQ5H2SMubScdQUCfYFvipz6BhhYkX4VsRjoa4zpn9vOMcBw4Isi5ToaY3bnLiF4P7dcaf0NMsaE5z0A32ah8zWXH7Q/2Qnq2598ONDP06Q9XD4Lrp7n3BjIs3khHNxRo12NT8kou1AFyomIiIiIiNR2FZ3GvxQoOPSZVCRIbgS8V866mgJ+QNF92vYC0cWLl4+19n3gIWCxMSYb2Ax8Z619ukCxn4GxwFDg+tz2lhhjShvWvR9IKvDQJu5lMQbaDjicpC9tP3x4NfynPyx+EXKyaqQbUWHBZReqQDkREREREZHarqLBvpesbGUeK03RhdKmhGPlZow5DXgAuBnoA1wInGuMeSi/QWu/stbOttaut9bOB/KGnq8qpeqngIgCj1aV7eNRKysNmneH7EMw/xF4fRBsWVTtzfZv34SYiGAM4MLDia6NnOdawomujbjwYIAmoQF4tGZfRERERETqiepYs1/eiGk/4Kb4KH4UxUf7K+Ix4J0CeQDWG2MaAG8YY56w1nqKXmCtTTPGrAc6eqvUWpsJZOa9Nj7eUq5OatwWxs2FXz6Arx+A/b/D/86F40bBWY9DwyinnMftPR9AJfi5DJNGxPLpzNd5OGA6LUxi/rndtgmPZo/lh+wTuXLqz9x1VmfGn9oBl0v/viIiIiIiUnf5LBu/tTYLWAUMKXJqCLDkCKoOBYoG9G6cGQMlRnDGmCCczP3af626GQM9L4NbV0K/awHjBP//6Q/pB2DjHJjc3bkJMPta5+vk7s7xIzDMtYLXAv9NdIFAHyDaJPJ64L+5r+2feCw8+/XvXP32ChLTamaJgYiIiIiISHWoTLAfa4w5zhhzHE7w3KXA624VrOsF4DpjzDXGmK7GmBeBNsDrAMaYp4wx0wteYIzpZYzpBTQEmuW+ji1Q5HNgvDHmMmNMe2PMEJzR/jnWWnduHc8ZY07NPX8C8BEQDvyvgv2XygppDOe+ANcvgJhe0O1CZ0r/rLGQvLtw2eQ453hlA36PG+ZNxGCLfcO7cL6JxyW/zrMXdSPI38UPf+zjnJcWsWpbYgmViYiIiIiI1H7G2vKvUzbGeHCm6Zc0Qp533Fpryz3n2hhzM3Avzl73G4A7rLU/5p57G2hnrT2tQPmSOrzNWtsu97w/zpr9K4GWwD6cGwAPWGsP5pZ5HzgFJ0ngPmAZ8JC1dmMF+h0OJCUlJREeHl7ey6QkHjdkHYJX+xcP9Atq0AwufBM6nH742IbZsO93Jx9AVmru19znOVlw7dfOTYT/nVt2P66ay6bgnvzfu6v5e38a/i7DfWd34bqTS92oQUREREREpEYkJycTEREBEGGtTS6tbEXX7LevdK+8sNa+Crzq5dy4Eo6VupjaWpsDPJr78Fbmsor1UqqVyw/i1pYe6AOk7YN3L4WH9x0+9sss+GOe92vc2c7a//JI3UvX9uHMuXUQ93+8ns/X7WbngfTyXSsiIiIiIlKLVCjYt9ZuK6tM7hT7MsuJFFLegLxhc/B4wJU7Ib/jEIhoDYENILAhBIYWeN4AMM415a0baBjkz0uX9WJIbHOGdjt8rbVWiRlFRERERKROqJJs/MaYCOAK4DqgJ1D51OlydCpvQD7ytcOBPsDx15V9TduBEN7CWfvvbbOI8BZOuVzGGM7r2SL/dY7bw9Vvr2BIbHOuPLGtgn4REREREanVjigbvzFmsDFmBk4W+1uBL4F+VdExOcrkBeQlb5jgHA9vWSggLzeXHwx75nA9JRn6VKnb+81Zt5tFf+7n4c9+5Zb31pCSkV3xfoiIiIiIiNSQCgf7xphWxpgHjTF/A+8BB4AA4CJr7YPW2jVV3Uk5CpQakOe+HvZ0qQF5qWLPg0unQ3hM4eMNmzvHu11Q6uUje7fkoXNj8XcZvvgljvNe+YlNcaXmwxAREREREfGZimbj/xIYBMwF3gXmWWvdxphsoGdFstnXdcrGX002zoF5Ewsn6wtv6QT6secdef0eN2xb4uQIaNjcmSmQdwPBnQ1r34XeYwsvFShg1bYD3DJzNXFJGQT5u3js/O5c0q8VxhjcHsvyLYnEp2QQFRZM//ZN8HNpur+IiIiIiFSNimTjr2iwnwO8BLxmrf2zwHEF+1J1SgvIq4u1MGssbJoD/W+As/8FXtblJ6ZlceestXz/u7MrwG1ndCQ2JoxHP99IXFJGfrmYiGAmjYhlWPeYEusRERERERGpiIoE+xWdxn8yEAasNMb8bIy5xRjTrJL9FCmZyw/anww9Lna+VnegD05g3+VcwMDyN2Dh416LNmkQyNSrjueeoZ0JC/KnUUgA42esLhToA+xJymD8jNXM2xBXzZ0XEREREREprEIj+/kXGRMKXAZcA/THyb5/JzDVWptSpT2spTSyX0+tmAJf3Ok8P/NRGDSh1OL7UzIZ8criYoF+HgNERwSzeOJgTekXEREREZEjUp0j+wBYaw9Za6daawcBPYDngfuAeGPMnMrUKVIrHH8tnPmI83z+JCf4L8Wf8aleA31wNvqLS8pg+ZbEquujiIiIiIhIGY5o6z0Aa+3v1tp7gVY4o/0VnyogUpsMugMG5Y7uf3EX/DLLa9H4FO+BfmXKiYiIiIiIVAX/ihQ2xkwtR7GESvZFpPY442HITIG1M6FhlNdiUWHB5aquvOVERERERESqQoWCfWAcsA1YQ/HN0PNoZF/qPmOcjPwn3AhNO3ot1r99E2IigtmTlOH1G79xaAD92zepnn6KiIiIiIiUoKLT+F8HIoBjgO+Aa621I4s8LqzyXor4gstVONDf9zvsXFmoiJ/LMGlELOD97teBQ9ms3XGgmjopIiIiIiJSXIWCfWvtzUAM8AwwAthhjJlljBlqjJdNyUXqg32/w7SzYcaFsGd9oVPDusfw2pg+REcUnqofExHMqZ2acV7PFvRp07gmeysiIiIiIke5Sm29l3+xMW1xpvaPBQKAWGttatV0rXbT1ntHmaw0eOdC2LEMGjSDq+dB02MLFXF7LMu3JBKfkkFUWDD92zfBz2XIcXvw93Puq6Vl5pCckU1MRIgv3oWIiIiIiNRh1b71XgE292GqoC6R2iuwAVz+AUT3gLR9MP18OLijUBE/l2FAh0jO79WSAR0i8XM5k13yAn2Px3LnrLWMePknVm/XtH4REREREak+FQ7QjTFBxpjRxphvgd+BHsAtQJujZVRfjlIhjWDMJxDZEZJ3OgF/any5L09Kz2ZbwiH2p2Zy2X+XMXvVzurrq4iIiIiIHNUqFOwbY14F4oCJwFyglbX2Emvtl9ZaT3V0UKRWadgMxn4GEW0gcTNMvwAOJZbr0sYNApk9fiBDuzUny+3hrg/X8eSXm3B7tIGFiIiIiIhUrQqt2TfGeIDtOFvveb3waMjIrzX7R7mEzU7Cvsbtnen9IY3A44ZtSyB1LzRsDm0Hgsuv2KUej2Xy/D94aeFfAJzaqRkvX96b8OCAGn4TIiIiIiJSl1RkzX5Fg/23KSXIz2OtvbrcldZRCvaFhM0QFu2s5984B+ZNhOTdh8+Ht4Bhz0DseSVePveX3dz94Toysj0MOCaS9244sYY6LiIiIiIidVG1BftymIJ9ybdxDswaS/H7YLm7UV463WvAv2FXErfMXM0Lo3ppez4RERERESmVgv0aoGBfAGfq/uTuhUf0CzHOCP+E9SVO6QcKbc0HsHV/Gm0jQzHGVEOHRURERESkrqrJrfdEjm7blpQS6ANYSN7llPOiYKD/6+4kzv73IibO/oXMHHcVdlRERERERI4m/r7ugEidlrq3Sstt2JVEZo6bWSt38ve+NF4b05dmYUG4PZblWxKJT8kgKiyY/u2b4OfSyL+IiIiIiJTM5yP7xpibjTFbjDEZxphVxpiTSykbY4yZaYz53RjjMcZM9lJuQm6ZdGPMDmPMi8aY4Mq2K+JVw+ZVWm7U8W2YOu54woL9WbntAOe/spg3ftzMoGcWMvrNZdz+/lpGv7mMQc8sZN6GuCPouIiIiIiI1Gc+DfaNMaOAycATQG9gEfCVMaaNl0uCgH255dd5qfMK4GngUaArcC0wCnjqCNoVKVnbgc6afLyNshsIb+mUK6fTOkfx6f+dxDFNG7A7KYMnv/yNuKSMQmX2JGUwfsZqBfwiIiIiIlIiX4/s3wlMsda+Za3dZK2dAOwAxpdU2Fq71Vp7u7V2OpDkpc4BwE/W2pm55b8B3gP6VbZdAGNMkDEmPO8BhFXwvUp95PJzttcDSg74LQx72mtyPm86NGvIRzcNJNC/5B/RvLSaj36+EbdHSTZFRERERKQwnwX7xphAoC/wTZFT3wDlHwYtbjHQ1xjTP7edY4DhwBdH2O79ODcY8h47j6CPUp/EnudsrxceU/h4aCRc+o7XbffK8vveFLJyPF7PWyAuKYPlWxIrVb+IiIiIiNRfvkzQ1xTwA4pmLtsLRFe2Umvt+8aYZsBi4+xd5g+8Zq19+gjbfQp4ocDrMBTwS57Y86DLOU7W/dS9zhr9tgMrPKJfUHxKRtmFKlBORERERESOHrUhG3/ROcimhGPlZow5DXgAuBn4GTgW+LcxJs5a+1hl27XWZgKZBdqpbBelvnL5QfsS8jwe3AG/fwUn3FCh6qLCgssuBHy4ciftIhvQs3WjCtUvIiIiIiL1ly+D/f2Am+Kj6VEUH3WviMeAd6y1b+W+Xm+MaQC8YYx5ohrbFSkuLQFeHwQZB6FRG+g8rNyX9m/fhJiIYPYkZZR692vxX/tZ/Nd+erdpxLiB7Ti7e4zXtf4iIiIiInJ08FlEYK3NAlYBQ4qcGgIsOYKqQ4GiC53dOCP3phrbFSmuQST0usJ5/tn/QUr57yf5uQyTRsQCxVP/mdzHfWd34cI+LQn0c7Fm+0Hu+egXUjKyq6TrIiIiIiJSd/l6Gv8LwDvGmJXAUuAGoA3wOoAx5imgpbV2bN4FxpheuU8bAs1yX2dZazfmHv8cuNMYs4bD0/gfA+ZYa93laVekSp3xMGz5AfZugM9uhis+gnIuAxnWPYbXxvTh0c83Ftp+LzoimEkjYhnW3UkKeP/ZXXlv+XbSsnKIbBiUX+6VhX9yaqcoerSKqNr3JCIiIiIitZqx1rfbdhljbgbuBWKADcAd1tofc8+9DbSz1p5WoHxJHd5mrW2Xe94fZ83+lUBLYB/ODYAHrLUHy9NuOfsdDiQlJSURHh5e3svkaBW/Cd44DXIy4OxnK7x+3+2xLN+SSHxKBlFhwfRv3wQ/V+k3DH7dncQ5Ly0GoG/bxowb2I5h3aMJ8Cs8oacydYuIiIiISM1LTk4mIiICIMJam1xaWZ8H+3WVgn2psJ/fgK/uAb8guPEHiOparc39vS+Vlxf+xdxfdpPtdn7Oo8ODuXJAWy47vjWRDYOYtyGu2KyBmCKzBkREREREpHZQsF8DFOxLhVkL714Cf30L3S6ES6bVSLPxyRm8+/N23v15O/tTnQ0lAv1dTDijI89+/XuJ21IAvDamjwJ+EREREZFaRMF+DVCwL5WSGg/LXoVTJ0JASI02nZnj5sv1cUz7aStxB9Pxc7nYk5xRYlmDkxdg8cTBmtIvIiIiIlJLKNivAQr2pa6y1jLv1z2Mn7G6zLLvXX8iAzpE1kCvRERERESkLBUJ9rUZt4iveNyw5GVIS6jRZo0xZOUU3Z2yZPEpJY/8i4iIiIhI7aZgX8RX5k6Abx6EObc66/lrUFRYcLnKhQT4VXNPRERERESkOijYF/GV468Hv0D4/QtY9XaNNt2/fRNiIoIpazX+nbPWsvPAoRrpk4iIiIiIVB0F+yK+EnMcnDHJeT7vftj/Z4017ecyTBoRC1As4M973apRCL3bNKZlo5pNJCgiIiIiIkdOwb6IL514MxxzGuSkw+zrICerxpoe1j2G18b0ITqi8JT+6IhgXh/Thx/uPZ2XLuuNMU74n5CaydXTlrNux8Ea66OIiIiIiFSOsvFXkrLxS5VJjoPXBkD6ARh0B5z5SI027/ZYlm9JJD4lg6iwYPq3b1LidnuPzPmVt5dsBWBk75bcM7QzLTTqLyIiIiJSY7T1Xg1QsC9VatPn8MEYCAiF29ZCWHNf96iYuKR0nv36dz5evQuA4AAXN5x8DDee2oEGQf4+7l0d4XHDtiWQuhcaNoe2A8GlJIgiIiIiUj4K9muAgn2pcj8+B52HQ/NYX/ekVL/sPMjjczexfGsiAM3Cgpg4rAsX923l457VchvnwLyJkLz78LHwFjDsGYg9z3f9EhEREZE6Q8F+DVCwL0czay1f/7qHp776jW0Jhxg7oC3/PL97oTLlXR5wVNg4B2aNBYr+vs39PC6droBfRERERMqkYL8GKNiXarVzJSTvgtjzfd2TUmXmuJmxbDsX9GpBZMMgALbsT+OHP+L57w9/E5eUkV82JiKYSSNiGdY9xlfd9Q2PGyZ3LzyiX4hxRvgnrNeUfhEREREpVUWCfS20Faltti+DacMhIASad4fIDr7ukVdB/n5cO6h9/mtrLTe/u4pNcSnFyu5JymD8jNW8NqbP0RXwb1tSSqAPYJ0bO9uWQPuTa6xbIiIiIlK/aes9kdqm1fHQ5kTISoWPbwB3tq97VG5pmW7+3pdW4rm8OUSPfr4Rt+comlGUurdqy4mIiIiIlIOCfZHaxuUHI/8LQRGwayX88C9nKviWRbD+I+erx+3rXpZo/a4kMnM8Xs9bIC4pg+VbEmuuU77WsJw7K5S3nIiIiIhIOWgav0ht1Kg1jHgRProGfnwWVk6FQ/sPn6+lWdzjUzLKLpRbbltCGht2JXNyp6aEBwdUc898KLQpGBdY7zdBCIt2tuETEREREakiCvZFaqvuF8GKqbBtceFAHyA5zsnuXsuyuEeFBZe73Gdrd/PCt3/g7zL0bduYwV2iOL1LFB2jGmJM6Vn73Tk5/Pbz16Qf2EVI45Z0OWEofv618NdZ4t8wY2SBQN9QPCM/YA2k7Ycwje6LiIiISNWohX8diwjgTNVP3OzlpAUMzLsPupxTa7K492/fhJiIYPYkZZQU0mKA6AhnG74/41Po0KwBm/el8fOWRH7ekshTX/1Gy0YhnN6lGXef1ZlGoYHF6ljz9f9osfRRupGQf2zvt5HsHjCJ3kOvqr43V1FJO+F/50NKHDTrCgNvhe8eL5ysr2E0eLIhNQ7eHw3XLYAybnSIiIiIiJSHtt6rJG29J9VuyyL437lll7tqbq3K4j5vQxzjZ6wGCo9h54WwRbPxb084xHe/x7Pwt3iW/p1AVo6HsCB/Vj88hAA/J63I8i2JxEQEs3/Fh/RcchsArgIxcV6+v3UDX6odAX/KXph2tnOzpkkHuPorZ9Te43ay7qfuddbotx0IB7fBzMtgxL+h7QBf91xEREREarGKbL2nYL+SFOxLtVv/Ecy+tuxyF02BHhdXf38qYN6GOB79fCNxSYfX8MdEBDNpRGyp2+6lZ7lZ+vd+9iZnMrp/G8DZzu/UZ79nZ2IqPwXdRnMSCwX6eTwW4k0kzR78w7dT+tMS4O1zYN8miGgD13wFEa1Kv8bjrjWzM0RERESk9qpIsK9p/CK1VR3O4j6sewxDYqNZviWR+JQMosKcqft+JUXpBYQE+jG4S+H3k5qZQ0xEMC0PriTGeM/i7zIQTQK//vw13U46p0reR6VkJkFmCoTFwFVzyg70oXCgv2c9fPckXPgGBIVVXz9rg5JmOuimh4iIiEiVULAvUlu1Hehk3U+Oo8SkbgDhLWttFnc/l2FAh8gjricsOIAPbhzAig8Xw69ll/9p7QbiGvejT9vGNGlQfM1/tWtyjDOan50BTdpX7Fp3Dsy6ypn+/+6lMOYjCGxQPf30tY1zYN7EwjkMaukuEyIiIiJ1kcvXHTDG3GyM2WKMyTDGrDLGeF18bIyJMcbMNMb8bozxGGMml1Dme2OMLeHxRYEyj5Rwfk81vUWRynH5OYEPcHjFexEn33l0jITuWEG/X58sV9GFu1xcN30lfR77lsHPf8+9H61j1ood/BWfSnmWLbk9lqWbE/hs7S6Wbk7A7SnHUqfsdNi+7PDrRm2gWady9bcQP3+4eAoERcD2JfDeaKfu+mbjHGc3iYKBPhzeZWLjHN/0S0RERKQe8Wmwb4wZBUwGngB6A4uAr4wxbbxcEgTsyy2/zkuZC4GYAo/ugBv4sEi5X4uU61HZ9yFSbWLPc7bXCy+yzt2VOylnycuQUg/vU+37A/5acPh1i17QoClZ+OMtXvdY2EMkF7bNpmNTZwvAv/elMWvlTu6d/QtnvvADby/Zml8+I9tNRra7UB3zNsQx6JmFjH5zGbe/v5bRby5j0DMLmbchzntfczLhgyvh7XNh0+eVfMMFtOgNY2ZDYEPY8gN8MMZpo77wuJ0R/RJnq+Qem3efU05EREREKs2nCfqMMT8Dq6214wsc2wR8aq29v4xrvwfWWmsnlFFuAvBPIMZam5Z77BHgAmttryPouxL0Sc0pura5yTHw9nA4sBWiusHVX0BIY1/38shkpsKvn8Cad2DHz84ShQnrD89cSD/Amh/nlJqNf3e7C2i17VNo2Y+koS+xMq0pK7cdYNW2A6zbcZD3bziR3m2cz2n2qp3c9/EvdGsRQd+2jfEz8MaiLcW65W0XAcCZdv/ROCfI9w+BKz+uumUV25bAjIsg+xB0Hg6X/A/8fbAsoarV0V0mRERERGqDOpGgzxgTCPQFni5y6hugKhchXwu8nxfoF9DRGLMbyAR+Bv5hrf27lP4G4cwsyFPPM2dJreLyKx74XPkpTB0K8b/C7Oud9d21SXmSr1kLO5bDmumw4RPIzv0xNX4QfRykH4AGTZ1jIY3pPfQq1gAtlj5KcxLyq4k3kcQNmETvmBDY8x3sWknE9MGcccbDnHHWeHC5yMrxFEoQuCkumWy3Ze2Og6zdcdDr27A4Af+jn29kSGz04To8bvj0JifQ9wuE0TOrNn9C24Ew+n2YeSn8/iUseh5OL/UeaN2Qurdqy4mIiIhIiXw2sm+MaQHsAk6y1i4pcPwfwFXW2s5lXP89ZYzsG2P64wTyJ1hrlxc4fjYQCvwBNAceBLoA3ay1CV7qegSYVPS4RvbFp/b+Ch9e7azzjq5FK1HKm3xt4ePw47OHX0ceC73HQM/REBbttXp3Tg6//fw16Qd2EdK4JV1OGHp4u72kXTDnFti80HndZiBc8B9nNkQB1lp2Hkhn5bZEvvgljvmb4st8Wzec0p4LerWiU1QD/L+cAKunO0sqRs2AzmeXeb3bYyu8QwF/zYelrzrLOYIaltlGraeRfREREZFKq8jIfm0I9gdaa5cWOP4AcKW1tksZ139P2cH+f3PrLzUKMsY0ADYD/7LWvuClTEkj+zsV7IvP1bY92vOSrxVbk22cY0OfhgG5K3d2roT/jYBuI6H3ldDmRDBlBL/lYS2smgZfP+jMFghoABe8Ct0uKLH4Z2t3cfv7a8tbOf8MfIexrnl4cLGi77P0GX4NAX6lp0CZtyGORz/fSFxSRv6xmIhgJo2ILb48oKT3U/BzKfq6LvG4YXL3UnaZMM6NoYJLOEREREQEqFiw78sEfftxEucVHb6LAo54/qYxJhS4DHirrLK5U/zXAx1LKZNprU3OewApR9pHkSpRMCDathS+fgCvWeyqW3mSry385+Hkay37wt1/OIF42wFVF8AaA/2ugZuXQNtB4M6Cpl5/vIkKCy5Xtd1ahBMR5EeI5xAA92TdwHUrW+NXoN+frNnJ5+t2sy0hLT/7/7wNcYyfsbpQoA+wJymD8TNWl54AMO/95PnhXzD3Dt/9Gx+pMneZsDDsaQX6IiIiIkfIZ2v2rbVZxphVwBDgkwKnhgCfVUETl+KMxM8oq2DuqH1XnN0AROqmtP3w7sWQlepMLR/yaM33YduS4tupFZV9yCnX/mQniA2qxvQXjdvBVZ9D3Fpo3u3w8bhfnGUPuUF0//ZNiIkIZk9ShrexZqIjgplzyyAMsHX/SSz+5Tsi0jtyMRZXgan4k+f/ybYE52ZAREgAPVqGs2bHQa+3P0rMB+DN3l/h+6fAepw8AWc/UzdH+PN2mSi61KNhcxj+XOGlHiIiIiJSKT4L9nO9ALxjjFkJLAVuANoArwMYY54CWlprx+ZdYIzplfu0IdAs93WWtXZjkbqvxcnqX2wNvjHmOeBzYDvOTIIHgXDgf1X2zkRqWoOmMPRJ+Pw2+Gmyk51/0ISa7UNtTL7mckHLPodf714Db50Jx54JI/4NYU6QPWlELONnrM5bbJAvL5R++fgE/Kwb/Pw5JiqcY848n0FFmvJ4LKd2asa6nUls2p1MUno2i/8qMQ1IPgvEJWWwfEsiAzpElv5emneD8/8Dn94My//rZOcf8li1B/yVyjXgtbJsWPeek5ehyzllJ3EUERERkUrxabBvrf3AGBMJPIyz1/0GYLi1dltukRic4L+gNQWe9wUuB7YB7fIOGmM6AYOAs7w03Qp4D2gK7AOWAScWaFekbup7FWQchG8fhvmTIKQR9B1Xc+03bF615apD/CYwLvhjHvznBDjneeh+EcO6x/DamD7F1tVHRwTzVuwvdFv8CCR+ARdP9RqQulyGf57fHYCsHA9/7E1h+tKtzFq5s8xuPf3VJoZ1j6Fnqwi6t4ogPDig5IK9LneWJXx+Oyx5GfyC4IyHSk9aeASOKNdASZa/CV/fD+s+gHFekvDFb3ISKvoHFT8nIiIitVd5dmOSGuOzBH11nTEmHEhSgj6pleY/AotfBAxcMs1JgFedUvdBeqKTTb8uJF+L3wSf3ORM7wfoeh6c+yI0aFosaO4akohrzi1OuUF3wBmTKjSSvnRzAqPfXFbhLl41oC2P5t44sNaSke0hJLDAZ7b8TfjybgB2txqO386fC21HuJdIdg+YRO+hV1W47Tx5uQZKSrUI8NqYPhUL+FPj4eW+kJnszKoo6UbU1w/A0v/A8Geh//WV7LmIiIjUuPLuxiRHpCIJ+nw9jV9EqsMZk5w96le9DbOvh/BW0Pr4qm/H43baWPAoRLSBG753fqHPGgveJsTXhuRrUV3huvmw6AX48V+waY5zF7r3GPzWz6JbSXkHTripwoE+lC8fQJMGgVx3cns27Erml10H2ZGYTkyjkPwy2xMPMfj5H+gY1ZDjWkVwXKtG9Gx1KV3PzMB//oO02Pmlk6+vQNea2QSaLbmNNVCpgN/tsTz6+caqyTWQZ/6jTqAf08vZfaEkjds5Lfz4LPS6AgJDK9z3Ok0jIiIiUhd5240pOc45ful0Bfw+oGBfpD4yBs55ATKSnOAh5riqb2P3Gph7J+xe7bxuZCAt3nvytfAWTqBfW37R+wXAaROh8zD4ZDzE/+rkOvCmzcBKrY0vTz6AJ0Z2LzRCnpiWRcH4eePuZNwey297UvhtT0r+soAgV3tWBoTQkPRiXXMZ8FiIWfooi9oOJQcXmTluerdpTPNwZ/eBP/amsGBTPJk5brJyPGTmeHK/utl1IL3Y7gEFVSjXAMDOVbA2N1/q8GfB5VdyLoA+V8FPL0HSdljxFpx0W9l11xcaERERkbqozN2YDMy7z8nVoxvYNUrT+CtJ0/ilTsjJcn6pVuUv1vSDsPBxJxDDQlA4DH4Q+l0LfgXuH9alEcqsQ/BCVyffQYmOfPnBka5935OUwS87D/LLziTW7TzI+l1JdMlYx/uBj5d57WVZD7LMEwvAf6/sy9Buzo6nn63dxe3vr63U+8nTLCyInq0acd3J7TnxGCfot9ZiCt598HjgrTOcG0M9R8PI10v/PLIXwGf/ByFNYMIv1btjQ23hbUQk75aQRkRERKS22rII/ndu2eWu8pKrRypE0/hFxOEfePi5xwM/PAM9RznJzyojcQtMGQJp+5zXPS6Bsx6HsOjiZV1+decX+q5VpQT6ABaSdx3eMrAShnWPYUhsdKWz2kdHBBMdEc1ZuYG6tZYfZv/lpDUtQ6eQNNIaRRDk7yIs+PCv/baRDbi4byuC/F0E+rsI8vfLfx6XlM6MZdvLrHtfSibzN+3lsuNb5x/7asMeHv7sV46NakCHZg0ZljWfk3evxhPYEM54hG+85ALYk5TB+Bmref3yUxkaeSwk/AXLXodT7ynXZ1RnaURERETqstq4G5MACvZFjh4/Pgs/PO1se3bN1xBeiUzqjdpCkw7Otn7Dn4NjTq36fvpCDf0n5ecy5ZvyXg7GGJrFtC1XsD9q8PH886SiGwVCr9aN6NW6UYnXuD2WBZviS801EBUexHMX92RLQhrHtY7IP7c5PpX9qZnsT81k2d+JrDAhPOIfy8K03sz41xpCg/xLzQXwyBd/MOSc+3F9fK2z40D/65zvufpq25LCU/eLOfKbTSIiItWmLuzGdJRSsC9ytOg7zgn0D2yBd0bC1V9CaJPSr8lKgyWvwICbnanULhdc8jaERhaeNVDX1dH/pLqcMJS930bSzCZQ0gQBj4V4E0mXE4ZWuO7y5Bp49LxunNypGSfTrNC11wxqz6mdm7F5Xyp/xaeyOT6ah+O7sD0hlSy3h4ycLK/t5uUC+Dn0FAZExTo3WOI3OctA6iuNiIiISF3W+kRnuWNZuzHV5//LaymXrzsgIjUkrDmM/RTCYmDfJph5KWSmOlOItyyC9R85Xz1usBY2zYVX+sP3T8L3Tx+uJzymfgX64PznE96CQunsCzEQ3rLW/Sfl5+/P7gGTACewL8oAcSc+hJ9/5e7rDusew2tj+hAdEVzoeHREcKnb7jUI8ue4Vo0Y2TOGe4Z24fUr+zL/rtP49bHhPDC8a7naHv/uWv4Vfh/7r11R6z73KldHbzaJiIiQsgemngV98nb+KelvKVs7dmM6CmlkX+Ro0rgdXPkJTDsbdq6AqUPhUCKkFJhC3LC5c0Mgbw/6Rm2gXT2fOuzyqxtbBpag99CrWAO0WPoozUnIP25xNg/obf46ovornWvAWnj3YojsCKf/A0IaEeDnonvLiNKvy3UwPZu3fgvktlGN8o99/eseDHBih0jCgwNKvb7ETP/l3SKwpgWFg3GB9XgpoBERERGphRI2O7NFD25ztta9eBp884/iS9Nc/hDdwzd9PMopG38lKRu/1Gk7V8K04eDO9F7G+MGgO+Dku46evc5L3PqsZe3aMtALd04Ov/38NekHdhHSuCVdwjPwm32Nc/KC16DX5TXboY2fOTdP/ILg/36GJu2dfnosg55ZWGougObhwTx9YQ92HkxnzIltnRsHv83liq9y+Ck+EJeBHq0acVKHSAYd25Q+bRsTHHD4RsyR7nxQliq9kfDXfJg1DrJScg8UvdmUe0zZ+EVEpDaJ+wVmXORsu9y4vTOY1KR98d2Y9v0G7QZBVPlm9knZKpKNX8F+JSnYlzrN44bnOsKhBO9lGjaHOzfVytHsalWXtgwsy3dPwYbZcPkHENmh5trNOgT/6Q9JO+CUe2HwA4VOz8vNxg8l5wIotkTg8wmwaho/N72Q+zOu4u/9aYXqC/J3MbxHDC+O6pVft5cN7EpdflAeVXojYdXbMPdOsG5n9kyvK2DhP4uPiMT0hhu/r3SfRUREqtTWn+C9y5zR/OY9YMxsZ7mo1AgF+zVAwb7UadoP9ejg8UB2Ws3vU//dk842j+Gt4JYVJc4MqVDQvHUxvH0OuALg1lXEuaL46a8Elvy1n8V/7Sc+JZML+7Tk2Yt7MuiZhYXqLMjg5BtYPHFwpUbiq/RGwpKX4ZsHnefHXQbnvezkwih4s8mdDZ/9n3Mz4PIPodNZFe6ziIhIlfrtS/joasjJgLYnwej3ILh8S/TYuQrSD0DHM6u3j/VcRYJ9rdkXORop+/fRweUqHOhvWwLRx0FQw+pr88BWWDzZeT70ca9LQCqUC6DdIDjmNPj7e/jhX8Rc8B8u7tuKi/u2wlrL5n2pgGH5lkSvgT4czvT/j0/W88xFxwGwLyWTp77cRHhIAGHB/rkP53l4cADtIhvQJjIUt8fyyOcbS90y8NHPNzIkNrp8NxKOPdPZDvOE8XDafU6CBcCNi+WeWOLdxxAVFswJJ96Ma+nLzvKSY04F/6Cy6xYREakuGz9zAv3Ow+HiqRAQUr7rNn8HMy50ttK9+Wdo2Kzsa+SIKdgXORop+/fRZ/V0Zzp8l+FwyXTnRkB1+PoBJxdEu5Mh9oJSi/q5DAM6RJav3sEPOcH+uplOLommxwJgjOHYKOeGxq+7k8pVVUp6dv7zfSmZfLxml9eyN5xyDP8Y3pXlWxLZU44bCcu3JHp/Tx734SUhUV3hlpXQMCr/dEmzHTqEn8AXwe8TnPg3rHgLBvxfud5jSepU0kIREamdznsZYnpC/xvArwKhZNuTIKob7F0PcyfAqBn5N7ql+ijYFzka5W01p/1Qjx5NOzuB5qbP4funiq2jrxLJcc4SEeMHw5+t2v/EW/WDTmfDH18520FePLVYkaiw4BIuLG5Erxb5z5s2DOS+s7uQkpFNSkZO7iOb5NznrRo7IxbxKd4D/YLyyu1NzuCM53+geXgQ0RHBdAvazw27H2RV9weg3cl0bh5Gu6aFA/2Slgj8neziXtcoJvTI5Jj8bY0qrrqTFoqISD1lrZP/p9uFzkCBfyAMuLni9fgHwsjX4I3T4Le5zpbPx11S5d2VwhTsixyN6vBWc1JJbU6AcyfDZzfDj/9yRpa7X1i1bYTHwK2rYOuP1ZN19/R/OMH+htkw6E6I7l7odP/2TYiJCC410390RDBnxUbnH4sKD+amU8tOXljeGwl55eKSMkjNzCF1Xw6N96/ipsAXaGJSafnz44xY9DjXndyBB86JzS2bzq3vrfG6RGCOZyArtgazOKABlfmJ9HYjYU9SBuNnrD7ipIUiIlJPuXOcUfg178D2ZXDOc0dWX3QPOHUifPcEfHm3s0wvXP//VCcF+yJHq9jznO28im0116JObDUnldD7CojfCEtfgU9vdrbIadG7atto2Ay6X1S1deaJOc5ZGpC4GXKKbxvp5zJMGhHL+Bmrvd3CYtKI2EpNXS/vjYT+7ZsAEBsTzoK7TiVn3Yccu+Qp/DzZ7AztytvN/slxhxpwbNThvAnzN8WT7S49WW7eEoEWEYGMnjyXrOBmhJeQY+D0Ls3yA/eMbDcLN8Xz4Gcbqi7XgBd1cYlAXeyziEiNyc6A2dc6o/DG5QTqVWHQHfDbFxC31rmRMPp9TeevRsrGX0nKxi/1Rn3aak7K5nHDzEud/d3DWsAN30FYdNnXlSY1HnavrZls8RnJENiw1JwD1TVlvUJbBloLi1+ABf90Xnc5Fy58s8SEhe8t38b9H28os/23zm3MwF8eYNuefZyb9STuEsb5bz6tA/cO6wLA1v1pnPbc9+V6bzOu7Y8FWjYKoUWjEIIDyv87oC4uEaiLfRYRqTEZyfD+5bB1EfgFwkVTqnYQKH4T/PcUcGfB5bOg09Cqq/sooK33aoCCfRGpszKS4K0zYf8fcOYjzl32I/HpzbD2XThpAgx5tCp6eMSqa9S2XEGiOwe+uMNJighw4s1w1uNeb6It3ZzA6DeXldn2h2M70+/zMzHpB9hz0j/ZfuyVJKdnk5J5ON9AnzaN8xMEbt2fxrhpy9macKjMuh86tyuPzd2U/7ppwyBaNg6hVaMQWjYO4eSOTTm5o5M52VqLyR2FqdLtCGtIXeyziEiNSd0H714EcesgMAxGz4T2p1R9O8teB78A6HeNRvYrSFvviYiId8ERzrS5v+Y72XSPxM6VTqAP0HXEkfetvDJTYfl/oe0gJx9BERXK9F8B5doy0OUH2enOtMdhz8AJpX/G5V0i0KdLB0zaQ/DFnUSvep7oAZeXumNGu6YNeOrC48p1I6FBoD/HRjVk14F00rPd7E/NZH9qJut2HAQgyN+VH+xvTzzEuS8vpkVEMFsTDtWpJQJuj+XRqtxC0Qe0/EBEqo3HDe9cAHs3QGhTGDMbWvSqnrZOvKl66pVCNLJfSRrZF5F6xdqK31n3eOCtM2D3auh1BVzwavX0rSTz/gHL/uNs5TPui9o3KpCTCTtXOMmHyqHcSwQ8bieT8Z5foPeVcP4rpdbr9lgGPbOwzBsJiycOxs9lsNZy4FA2uw6ks+tg7uNAOoO7RDGoY1MAlvy1n8vf+rlc7wvgtE5N6d2mCS0bh9CyUQitGocQHRFMgF/Z2z/O2xDHY3PW0zp1HVEcJJ5G7GjYk4fO61Hu0ff1O5P4akMcOw+k89ueZP7Ym1rmNU9c0J1Rx7fGvxx9rElafiAiVaK0JZyb5sI3D8AVs/O3ua12mSkQ9wu0O6lm2qvjNI2/BijYF5F6IyMZPrkR+o6r2Lq51dNhzq3ONL9bV0GY91HmKpe0C17qDe5MuPIT6DC45tou6Y+kvRucz+PsZ0vNJ1Cacgdy25fB1KGAgesXQMu+ZdZb7lwD5ZCZ42ZH4iE+WrWT13/4u9zXFeQy0Dw8mFsHd+TyE9oAkJSezertB/KXDvz4xz4+nfk6DwdMp4VJzL92t23CP7PHcsI542jftAE7D6SzM/fmxM4Dh9h5IJ0XLu2ZPxPho1U7ufvDdRXuY4NAP/q0bczx7ZpwfLsm9G7TqNy5DKpj9F3LD0SkSmycUzw5c1gLOPuZw+vyczLBP6hm+nNwO7x9DhxKhPFLoHHbmmm3DlOwXwMU7ItIvfH9M87e9YFhcN235ds2L/0gvNwXDu2Hs56AgbdUezeL+eo++Pk1J9i9bkHNjO6X9EdSaKSzrMCdCUMeg5Nuq3T15Q4SP74Rfnnfee/Xzi/zBkN1jAiXN9fAxX1a4ecyh2cKHEwnK8cDwFMX9mB0fyfYX7J5P5e/eXjGwDC/5bzqPxlwbg7k8eT+2TI+ewJfe/qX2GbBen/bk8zMn7fTqnEIh7LcTJ7/Z5l9DglwkZ7tKXQswM/w1e0nc2xUmNMPj8VVwr9NdXzWeTM0CtZZUNEZGpWlJQJSbykZsWPjnNxtl73M9bp0es3vxuRxO8H+9qXQ7mQYO6fSN82PFgr2a4CCfRGpN9zZMP0C2LYYGreD67+D0CalX5MXaDftDON/cpLs1LTUePh3T8g+BJe9B12GV297pf6RBDTvBld/5eREqG4pe+Dlfs72iVd8VK5ZFVUdyFV0iUAej8eyPy2TXQfSadk4hKiwYAB++ms/j83dyK4D6aRlZrE46DaiSaSkLnos7CGS0SH/pWNMY1o1DinwCKVd0wY0DCqelqi8ff7hntPZvC+VFVsTWb4lkRVbE0nJyOGXSWflT+2//+NfWL3tIMe3d0b/+7dvwrodB49o9N3jsazafoD45EziUzKIT8lkX0omf+xN4ZedSV6vyzO6f2tO7tiMNk1Cad0klIiQ8v9c1uUlAtV5k0I3QOqBkm7ShrdwcqocTdsMe9wwuXvhz6Go8JYwYX3N3whJ2AyvD3L+Pz/72TJz3RztFOzXAAX7IlKvpCXAm6fDwW3OnfUrPyk9gP/9K5h3H5z7Ys1OoS9q/iOw+EVo3h1uXFR9owG18Y+kPRucWRg+HJ2q6iUCeRZ+NZvBP19TZrnFJ73NoCEjK1R3ZfpsrWVfamb+jQmAwc9/z9/70gqV8zPgLuXPqkYhAdx46jHsT80iPiWT+OQMusaE88h53fLb6fTgV2SXVkk59W/fhFk3Dsh//crCP2nSIIg2TUJp0ySUmEaH8ybUxBIBn+6QUQvrlhri9SZt7veeL0ayfWXLIvjfuWWXu2outD+5+vtT1M9vwFf3QEAo3LQYIjvUfB/qiDoV7BtjbgbuAWKAX4EJ1tpFXsrGAM8DfYGOwEvW2glFynwPnFrC5V9aa8+pTLte+qJgX0Tql70bYcoQyEqFftfCuS+UXt6d7ZsR/YIOJTqj+5nJcPE06H5h9bRT2/9I8qHqCIj+mD+NTosnlF1u0GQ6nXl1heuvij7vS8lk5dZEVmw9wIqtiWzYleRtzkep+rZtzOzxA/NfX/r6UjzWEhUeRFRYMM3CgkhOz+a/P5adH+GkDpGkZ7vZnpjOaZ2b8dwlPQEnz0KXh+ZR8E8+P5ehRaNgWjUKYcPuZFIyckqssyqWCFRX0FydNymUI6EeKPMmrXFG+H0xku0L6z+C2deWXe6iKdDj4urvT1EeD0w/D7YugjYDnOS7R8O/SyXUma33jDGjgMnAzcBPwI3AV8aYWGvt9hIuCQL2AU8A3jaGvhAILPA6ElgHfHgE7YqI1H/NY+Git+C90bByijMt/fgifxgUzNrv60AfnOUGA2+Ffb9B9HHV107q3qotV5Wy02HxZGd0qnm3Gm++XNsRVlCHYzrA4nKWq4Sq6HOzsCDO7hHD2T2cgG/Wyh3c+9EvZV7Xr21j+rRtTFRYEM3CnJH2gmbdNKDYNW6PZc663WUuP5h+7Qn578HtOVwyI9vD2BPbsuNAOtsTD7E98RBZOR52JKazIzG91P5aIC4pg3FTl9O1RTh92jQqFOTuT82kcWig18/OW9C8JymD8TNWVzpors5tFOv6Fo1aepBr25LSZ2NhIXmXU+5ouElbylatlSpX1VwuOP8/8NpAZ/3+irfghBt905d6xKfBPnAnMMVa+1bu6wnGmKHAeOD+ooWttVuB2wGMMSXO77PWJhZ8bYy5DDhEgWC/ou2KiBw1Op8NZzzs/Cebl+U9L7HRwe3w47Nwwk1w/HXg5+v/QnKdck/1Jufb+hP8+W35yvrij6Sv/wErp8K2n+Cqz32yDaGfyzCgQ2TV1dfuJNJDogk6tKfENfsAWQHhBB7BNk1V3efWjUPLLgTcdVbnCrfr5zJMGhHL+BmrMZS8/GDSiNhCAV3B5xEhATx6fvf81x6Psyxhe+IhPl2zi3d/LnucY9Ff+1n0134u7tsqPzjPyHbT7/H5uAw0aRBE04aBNAsLolnDIJqGBdGrVQSPfbHJa9AM8NBnv+bnF2iV+xnmuD0s+ms/OW5LttuT+3Ce57g9tGoSSrC/n9eEhXn1xyVl8MicX3nsgsPv/db31pCe5cZjLW6PxWMtOW6L21pic5dULN+SWK66l29JrPT3UF1c1lDnlPfm6+9fOjdKy8pVU9e1HejMZChrpkPbgV7O14DGbeGsx+Gv+dCtYku0pGQ++0vNGBOIMx3/6SKnvgGq8rvsWuB9a23akbRrjAnCmVmQJ6wK+ygiUnsMusPZhi+0ScmJjb7+h5OB/rhLfNbFQqoruN2yCH54xplSCNCgGaTtx2sWY1/9kTToDlg70+nnrx9D94tqvg9VKScTMlMJGfEsdtZYPFgKZmLw4AS4gee/dHiK5+bvnMSILfv4oMOO/u2bEBMRXOboe//2lQsohnWP4bUxfYoFctGVCORcLkPz8GCahweT47blCvZH929Ng0B/erZulH8sIS0LY5yEiftTM9mfmslve1Lyz5/aqWmpQTM4yyHOeWkxF/VpxfOXOssOstwerp62wus1w3tEM7RbdJl9Bli5rdAYEPM37iU9213qNfEppfe5pHJrdxykdeMQIhuWvV1ZTS9rONJZFAXVmVkDHjfsXlO+sstehWWvQcxxcNLtFf8dWlcy/bv8nKSEpeUwGPa07/ved5zz8MGN6/rIl8MyTQE/oOhtt71A+X6Dl8EY0x/ojhPwH2m79wOTqqJfIiK1mjGHA/2S/iiwbvj4emcP3tqU2ChxixOctxkAfa+qXB3WwpYfnXq2/eQccwVAnyshuifMnQDexlZ99UdSozYw6E5n+8RvHoJOwyCwQc33oyp4PPDpzbBrFYyZjbl0erGbTSa8JWbY04X3g55zGyRtd/5IP+NhZ1eJGlaZ0feKqo4lE+W9SfH4BT2KtdOyUQh/Pn42iWlZ7EvNZH9qFvtSnKB/f0om2W4PP7C/zD6EBfnTMOjwz06An4vuLcMJ8HMR4HIR4G/wd7kI8HMR6G/o06ZxoWSJLjz0d/1GFAeJpxHLPV3w5N4iOiu28J92j5wXi8eCnzG4XAZ/l/PVzxgiGzqrQAvWXZq8cm6PZdR/l5KZ46FpwyC6xoTRJTqMztHhdIkO49iohgQHOO+vOpc1PDLn12pdelBnZg3sWAFf3g1xa8suG9gAwlpCwh8Qtw5ysg6f2/cH/PIBtD8FWveHgJDi19e1TP+x5zlJCUvs89O1o89Fg/yEzUrWdwRqwxzMkm4tVVXWwGuBDdba5VXQ7lNAwWxVYcDOI+ueiEgt5XE7fwyU9mtx3n3Q5RzfjwLk+eNrWPeeE6wfNwoCyvcHe76DO5ybGNuXOq/9AqHPWGfkPKKVcyy0Se38I+mk22DtDGepxaLnnYC3Llr4GGz4CFz+znuJPQ/T5ZxCo2am6KhZVhq0OwnW7YANs50/vvvfAKfcXePTcqty9N2bKl8ycYQ3Kfz9XESFBxMVXvznbenmBP63dFuZfXhjbL9C7ynAz8XcW0tfQ+32WGIigumZ8iMPB0ynhTk8gr/bNuGf2WNZF3YKt53RsdB1o45vU2Z/ynMDpHl4UP4sjYTUTGIigtmWeIj9qZks+jOTRX8evskxtFtz/ntlPycgr0AugKT0bJZvSSQpPTv/kZz7SErP5tyeMYzs7fxumrVyO3uSM72+p4JLD2Jjwrn7o3U0CQ2kScNAIhsE0rjA8xaNQmhaZIZC3k0Kg4cTC9xYWZHUpcpmDRyx1Hhnh5a17zqvg8Kh6whYOzP38z38yVuM8/19wevO7+6UPbB1sRPY5/nza1j0nPPwC3IC/vanOmVa9nF2pSnphnhynHO8NmX6T0uAzQudGXmx5zn/d9f22QjZGfDZzbDpc7jhByevkFSYL4P9/YCb4qPpURQfda8wY0wocBlQ9C+eSrVrrc0E8n+LGk0tEZH6rC4mNuo7Dpa85PRr1TQ4cXzFrm8Y5QSYfkHOzICTJkBEy8JlausfSQEhzg2H9y+HJS9Dryvq3kjIymmwOPee+oiXoMPpznOXX+nfY6FNYOTrzr/3tw/D39/Dsv/Amhlw8p1OgqeSRuSqSXWMvle36rpJUZ1LG/xchlf77KTnksnFzkWTyKsBk1nX55hKfe7luQHyyHnd8uuOCg/m+3tOJy0zhz/2pvDbnhR+35PCprhkftuTQudoZ9em5VsS2VOBXAA7Dxzi+ukrvZY/Nqoh9HaeZ2R5yvXe4lMyaBYWxLcbvf+pPXZAW/6Zm+PhQFoWV075mT/iUznLtZxJXm6sPPp58BEnLDyiJQKHEuGVfpCR5LzuNQbOnAQNo1gTcgItlj5KcxLyi++lCXEDJtE7LxgPiy6egb55N+fG8d8/QOoeZ6nU1kXwHeAfCkENKPmGeO6tm9pyQ9zjgU9ucNbBJ/wJp/+j7N+rtYF/kHMz150Fn46H6+bXjsTAdYzPgn1rbZYxZhUwBPikwKkhwGdV0MSlOGvsZ9RwuyIidV9tzj7vTUAwnHovfH47/PgcRB7r/OFXUkBurZN0b917cOGbTrJB/yBnN4LG7SG8lOCmtv6R1Hk4dDgDNi9wgt7L3vV1j8rvj2/gi7uc56feB72vqHgdMT1h7GfwV+7737sB5k+CVsc7I/81qKpH32tCddykqNalDR43vX99GmsO15XHZZyR296/PgNDxlQq2KrMDZAGQf70btOY3m0a5x+z1pLtdt55RXMBRDYIomfrRkSEBBAREkB4sH/+84iQALq1iMi/plN0+VJJRYUF06RBII9f0J0DaVkkpGWRWOTRvMAsjYS0TDbsTmaoazmvBUwuVl/ejZXxKbDozx4s2BRPmyahtG4SSttI52vDoLLDjSNeIhDaBLqMcH7uhz8HrY/Pr3f8d00x/LvQUo8Vni54vnPxWss47/V3GOw8rIWEv2DLD87MsS2LIDMF0vaV0qEjvyFeZfkRfnrRCfT9g6FrLZlpUB7GwIh/w39OcJZkLH7R+T9eKsRYW9IdqRpq3NkC7x3gJmApcANwPdDNWrvNGPMU0NJaO7bANb1yn74F/A48C2RZazcWqXsRsMtae1lF2y1n38OBpKSkJMLDw8v/pkVE6oK6uq+8Oxte6Fr8j7C8NZRdR8Af85w1+XnJm0a+AT1H1Xxfq8P+P52p8EP+6ZN165Wyey1MGw7ZadDzcrjg1SNPzORxwy+zYMcy54/FPAe2Odme88pU1wyNupKwq6hq6ne1rPWuod9RVZmQbunmBEa/uazMcu9df2KFbxa5PZZBzyxkT1IGpoQcBhYX0RHBLJ44uEL9T83M4eX5m7hq+XlEk1ji7hgeC3uIZM5pX/H0138VOx/ZIJDWTUK57PjWXNbfWUaR7fawLyWT6PBgvtm4p8Q8BnlNlbhEIGknzH8UTr8fmhzjHMtMdWbx5H7P5n0m3pJE5s0sqehngscDP7/mJKstSyX3rK+yn5mti+F/I8B64LxXnPwzVG+ixSqv+5cP4ePrnOVd186HrNS697u1iiUnJxMREQEQYa1NLq2sT9fsW2s/MMZE4ky1jwE2AMMLBNwxQNHFVQVTa/YFLge2Ae3yDhpjOgGDgLMq2a6IyNEtf4ueOGpd9vnS/P5VyaMtyXEw60po1BYO5v6qDwiF4689PF28Pmja0VknWldYC18/4AT67U91AvOqWCbn8oNeo51HnpQ98OoAaHOCMwNi2X+qJ6lWXUvYlaca+10tSxtqaPZRVc7SqO5lDZNGxPLpzNe95jC4YMRN5f/MrQVjaBjkz3mNtheqryiXgRYk0MO9kZtP68r2xEPsSDzE9sRDHDiUTULuDIKzuh3emvTvfWkMnfwjAS6Dh1Inw/PwZ7+yfEsiOR5LTmYGA+PfY0jCDIJsBiv/3MHus6dyXs8WENSQX3cncd3/VpKZ4+FQZg4ZOd6XN+Qtm/hg+XbO792SBuWYgeC8YRdEH1e+spXYjrXKkjimxsNH1ziBfs/R0HtMfv3VlWixWurucTFs/BR+mwtTzgRPzuFzdeF3q4/5dGS/LtPIvojUe/nZ+KHECbi1KfkQOKOSk7uXkWsAZ63lCdfDgFuhYbOa6ZuvpOyFsIr/sVmj0hJg/sMw9Eln+7zq8usnMPt68GR7KVAF39fedrCorT8zeepiv8s7sn/89c5Ml8DQ6u9TOeQFclDysoYjSnS3cQ521lhsidtVGmd3i4L/jjtXQdIO53dm8q7cx25I2gVN2sO4uc71v3yI6+Prymzec+FbuI67xNkhw99J8Jeckc32BCf479jc2ZkAYNGf+7h62gpyPOWPQ053reFh/+m0dzk3cFZ4OjEpexzDh5zFLYOdRIwbdycz/KVF5a6zoBYRwXSIakiHZg3p0KwBJx4TScfmXpZH5P5/Y5PjCiX+y2Nxdg5hwvoKjTxnZLs55V/fEZ/iPdliVFgQ399zGqGBpdyc8LjhnZHO0oNmXeD6hRDYwOuNhKr4/qvOulk9A+b8X7HD+YkWa+PvqGpUkZF9BfuVpGBfRI4KJY72tfR99vmSlPeP/1Ezoes51d8fX8o6BF/c6fz73bL88G4CR7v9f8LrJ0NOupcCuTNWLpoKNsdZ4+ofdPirX5CTG6KkmxJl3mzKrbuCf/yX2E5VTrWvqX5XtcSt8HJvZ9SyLA2awYBbnJk8QeVb216dqmX0szw3OwMawP07Dv87vhDrBPgladTG+TeHii2ZaDMAnu3g3Cxoe1LuYwCENC5WPMft4Z2l23h0rrMS19sWim3MXl5q9AG90p0lEGmBTVnT5S52tz6XoAA/usaE0yk3KE/PcrN5XypB/i427k7m9g/Wltnt8GB/kjNyih2//+wu3Hiqk+h06/40Xl74Fx2iGuTeDGjIwVUf0WfZ7U7fS5gwsSfmDJpd9xEH07NJTMtif2oWCWmZ9G/XJH/3igWb9vLfH/5mf1omCalZJKV7uxlZXMMgf5o0CKRxA2cXhetPPiZ/FsqBDd/S6KNL8PgHEz/qK8Lb9CDI38XJ//qu6pc1UI1LJqDAjZXdxfJzQG7AXwW/o6pzaUNVqzPT+EVEpJarrdnnS1Le6bpeA716JCAEDmx1psd/8yBc8rave3SYtfDZLU4Crb7jarbtlD1l/PvnJtX65IbDyz2KCm8JdxZIEzT9fNj7q/O8PAm7Vk6FTsOcnyX/wIr1v6qm2mcdgpQ45/H393Vv5430A/DeqAKBvpf0f33HOQkrD253kjX+NBlu+N7n+SyqZVlDmTuo4Pw+KPjv2LKPcyMwvIXzfR3e0nmedyxP7rIu76PYucFW24EQvxEyDjo5UXavgaWvAAaad3fOd7sgf/mXv5+LLjHOgNlQL5n+H80eS0ezywn0Xf5w4s00OPVeBnm5aRMS6Ef3ls7NuGOaNeTpeb+VuWxi8cTBJKdn8/f+VDbHp7F5Xyqb96VyXKtG+WU3xiUze3XRHbebMdQ1wek3h/udZEOJMIeIjlvAXQ/dz2zPKYWuenNsP4bEOsF+amYOy7d6XyJRmtTMHFIzc9ieeAiAS/sdvqm7yN2Nj7LuJSwrnS+m7AZ2E+AyZJcykyJvWcNVU5fTpEEgbmvxeCzjBrbjhGOcmwirtiXywrd/kOO2eKzF7bG4LSSnZ3sN9AvW/dSXm+jfvgmRDYNoFxlKZJEtHkuU+73t7afDVMHvqOpc2uBrCvZFRKR0tTX7fFHlXRtZiTWUdY4xcPa/4I1Tnenr/a4pvH+0L333JKydAb+8D+1OrtktAst7Qyi4ETQNgpwMZ0pyTgbkZDlf/YoE6IcSygjyi/jybucBEBoJYTHOtl8RrQonE0zY7MwmaNjc2W7K21T7gnt6dx7uvMe8QD5lj/Pz2++aw+X/cwLs+638/c1TW3beyMmE969w3kNYDJw6EX78Vwk3QHJnH7mznWSNi56H4HAnb0ced7bPtvKq8h0bKpPDYNQM7+UKcvnBsGcws8bmTpsuYb/6YU875aJ7wB2/OoHXtp9g60/Odm971zuP0MjDuV4OJXJC2kJuaLiK+7JfK9ZsNIm8FjCZhwPuxNP9ClwnTYBmncrXZyq2G0TjBoH0bdCEvm1LzpfQMaohdw3pxOZ9qfy1L5U/9qSS5fbwtac/32b2KzYjYbTfQk5x/cJnHue9RoQEENkwkKYNgggJOHyzvF+7JrxyeW8iGwTRtGEgf+9L5cbcZR6leWtsP45p1oDE3HwIB9Ky6FHg5kRIgB8HYk5hc1oWwWmZZGR7Sg30C1r81/5Cr8/s2pwTcp8npmXz018JxS8qp7cWb+GtxVsA+MfwLtxwivP7f1NcMhNn/0LThkFENggksqHzeTRtGESn+D+ILUfdnh+fxZUWD20HVWj5WpXlSKilNI2/kjSNX0SklsmfxlpGUsHaNh25On1xF6x4C6Ji4cYffb9H8erpMOdW5/l5L0OfsaWXr2pHmsHdWic5VMHP8cA2Jzv09mXO0omyNGgG6QeL5w4Ii4G7CgThU8+G7Uuc5yGRkJlUODFVUcZV8pT2Rm1hwi+HX785GHatchJUhsU4X/euL7vftWXnje+edHbTCAyDa+ZBdPfyLW3wuJ3zeSPWGclOwsbuFzpT/Gt7bouy1MTuBJVd1pWy1/le3voT9LrcmVEAsPGz/LwwufkAi/FYyAyNJuSejZX+vV0do7afrtnFhDKXCFgeO787l/VvQ0Dee3O5Sr2i4K4KZc1GKDYTxJ3j/PsMuMVZRlHAoawc5m/cy23vl9VnGHNCG9o3a4ifcW6YDDy2KR2aObkW4pLSWb4lEZcx+LkMLmPwdxn+3JvCM1//Xmbdfdo0wmOdLR3vGdrFSawIfPdbPFe/vaLEa050beT9wMfLrLuQyGNh3Jck/T979x0eVZX/cfx9MmmEFCCUhN6RgCCgKNhdEUTButhQUde6iq5rWV0V9eeuumvBCq699wYWsCvSuyIgxdBDC5CEkjZzfn/cSZgkk2QmmWRSPq/nmWcy95577rlJxHxP+R5XC+at20XTGBfxMZHERUcSHxNJ0xgXTaMjsVC8/MDfMpKq7mJR0zSNX0REGh/v6JPzx2M54zhFo0+NxYn/hGUfOVNr5/3PySAdruUYa76BqTc5Xx93a+0H+lD9XSaMKdthUrSVX6tDYMYjgXU2YZyp6DkZsHerMwJfOlA3BiKinE6BAwGMpBVdHxEJ8SnObIHE1JIj2QBjXoeYeIhJdO5RaScZTqdAXdl54+gbnd/nI/7iBPoQ2OyjCFfJqenLPoTsTTDrSee/jUHjYOh4SGpX8rr6so1ibeygUtVlXQltoM9ZzqtEkyKgRTfYtbbcTTgiDDQ5sLVaU7RrYtlEG++a+4oZurdOICrCOB2vBfvhjGcq/H4FMxuhjO8fcDp3f58G4xeXWCYUFx3Jaf3a8uCXlS9ruO+MvuV+b1KTmnDGYe3KHD/xkNa8Nmd9pXW/f81Qv3Uf2j6JFy45nJ1788jcl++8e3McrN/eny15LSrc+nEP8XziPoYjI1aQFrEBs38XNG3F6g17uPK1Bdwe+TYtyGGOpzdzPb3ZQksAzjuiAxlZuRUuI5meNZh56btCOxOnFmlkv4o0si8iUkfVp6SCtWHBy/DZTZTpAKnNLYsyfoGXT3VGwPudD2dNDs0We1VRk7tMhLpuj8fpFFj0Gnx7b+XlT3sMBl1W6ehhGeW22ys+Fa78pmElerQWVn/tLAHY5B1RdEXDYRfBMTc56/rryzaK81+EnsNh86L6tYMKwK8fwIdXVF6uivvV15SgRuC3LnVm1Fg39D0Hznqu0llWQc9GWDUd3hrjfH3uy86MlXLqrandIGqq7tlrM3nlxSeZFDURKJkMsWhlwrUFN5E44GxaNI1mRLdYBsTvgnaDWLpxD/d8uoznMy+ltT24PGGjpxVzbW/y2w1h2cbtPBD5coV1jxxzld9OjnBRNv5aoGBfRKQOqy+jcbXht0/g/Uv9nKilAGD/Lpg01BnF7nwsjP0o+MR0oVaTHUI1UXe4pmjHt3GWDuzPhKQOcMmntZtjociKqbBtORx/W+g7iax1tif78b+w/mfnmCsGTn8cPv0rdXo7Qmvh63uc2Qkte8FV38Oab+tXZ2dt/G7XkKCC2xVT4f3LnJk6vUc5O35U8u9gwNnh92yE5451OgYHXwUj/1tpu2sqGV1N1F3UsdI/5yfuKTP6nsz9BRezNOG48qfaezzOzLJ1M2D9TOyWJRjrLj5dYF24cJc7a2AryawfO4chPVpXqf01QcF+LVCwLyIidV5d2FbNWic52rIP4bIvoUmzmrlPsGqyQ6jGtser4XwU/tqdvcXZcWDXWufYxZ9Am0DSZYXIhrnw2mgnOeLZz0O/MTV3r/Wz4Kf/QpMWzhrzurwdobsApoyHpW85n4fd7yxxgPrV2VnPc60EFdz+Pg3euxjc+dDzVBjzqpOEszoK8+GVkc7slLYD4PLpAdVZk9vM1UTdRR0rEXg4wmdd/Xzv9oxBzRrIy4GNc2Hdz9iVX2J2Vp6w1H3JVFxd60iSWxTs1woF+yIiUufVpVGzglxnj3qpmppcflCZvdvh9bNg2zIYMNZZd1wbdq6GF4c5I5Y9T3Wyx7tqId3Umu/hjTMrLxeu0eb8/fD+OFg9HYzLSXY54KLab0eohPN3OwSCCm7XfOPsJlGYC93+BOe/6WyVWlXT/+lsbxib5CRhDfO2kjWpRmYk1NNlJErQJyIiIoFvxzXrSWe0qdPQ6v3hWcRamPucExjGOFmcFehXU9poJ+jxu4a8hqdox7eGcZ/BjMfgpLtq7j6+9m6HN85xAv12g+DcF2sn0IfAEiJCeLYj3L8L3joPNs2DyCbw51eg14jab0cohfN3OwSC2kax+8lw4Xvw9vnwxw/OiHxVt0UtyHVmcACcOalBB/pQM4kWG8OWvRrZryKN7IuISJ0X6Mh+EVcMnP8W9Dg5uPuUnjac/pOT+KzdILj8q9oL0hqDujJF2+NxRvpT+4W+7vx98MppsGUxNO8CV3wN8a1Cf5/y1KUZMaW9Pw5++xhimzlBY8cjK7ui/qgrv9u1Yd1M5znLSaQXsMI8J9Fk7yD+nZeD6ukyEo3si4iISGDbcTVp7owM/vEjZG8uuR570evOH9/dToKuJ/gPuPwldisy4GIF+qEWyDZzNc1amH4nzH8ezv6fk2E8lHV/cLkT6Mclw9gPazfQhwD+u/Fa8lbt/yyGP+hs1Xj649C6d+3eu6bVhd/t2tL56JKfszZBTIIzHb8y1h5MVBkZo0C/OhrBlr36P7CIiEhDFcgfMqOecKbJWgu7/ii5F/lvH8Pabw8mAUvpB93/5AT/HY50tnt67xLKDYji6ue+xFIJ64H9O51M/R9c4YzED7yk8usCYQwccroz8nnBu+HJ/l/pfzfWeW/Vs3bas28nNHX2BScx1Ul0Ga6tKyX0src4M1matICLP3I6YCvy5W1Ox8AJd6ozNRTq+TKSymgafxVpGr+IiNQbVd0Obt1MJwnY2u9g668lz8U2d9b359ThjOVSczxu+PxmWPiK83n4v2HIX0NX//5dENcidPVVRUX/3SR3g+TuBzOfb5gDuVnOfvehtPprZ+r+6ROh359DW7fUDVuXwauj4MAup0P14k+gaTkdpcs+dGa+gJN5v+NRtdbMBq8eLSNRNv5aoGBfRETqler+IbN3u5NQas23TvCf2BYyllR+XR3cH1tCxHefd4AT7oDjb6/aqPPvX0K7w2t/yn5lAvnvxl0Ak4+BHSuhx3AY8WBoZiT88h58cq0zg6LnCLjgHY3oN1TbljtbTO7bAa37wCWfOp1dvr97TVvDCydB/l449u/wp3vC3WoJEwX7tUDBvoiINFrWwsJX4bMbKy9bx7YskhCzFmY8At894Hw++kZnz/dgrPkW3hoDSe29yfhah76dNangAPzwEMx+BjwF4IqGoTc4AVl006rVOfsZJy8CQN9znWzrkdGha7PUPTt+h1dHw96tkNDWWS6zd+vB8xGRTsdPp2OczgBN4W+0ggn2I2qnSSIiItJgGBP4yGU93rJIAmAMHHers8bduCD1sOCuz/jFWRvvKYT2R0BcyxppZo2KagLD7oPrZjt7p7vzYcaj8PQRTt6LYAbWrIWvJxwM9I+8Fs5+XoF+Y9CqF1z2hbN2P2dLyUAfnP9GAPqNUaAvAVOwLyIiIsErylhOedOKjbO+udPQ2myVhMtR18D184PbSmzPBnjzz8605M7HwhnPQEQ9/tO0ZQ9n94Dz34JmHZ3dLd4f5yx/CYTHA59eDzMnOp//NMFZElCfvycSnOadwRVVcZkfH3aWl4gEQP96iIiISPCKMpYDZQP+hrFlkQTJd7ZH1maYMt6Z4u7Pgd3wxrnO6GXrNDjvjYPJ7uozY+CQ0+Cv85xs6T1OcbatLFI0yu9xQ/oM+PUD593jdoL6pi3BRMDop+DYm7VGv7EpWqNfkezNTjmRAGgOiIiIiFRNA9+ySKrI44G3z4etv0DmWrjgbWftelEg06QF/PRf2Pm7szb5ovehSbNwtzq0oprACbeX3BP9wB54eaQz2+X3z/38N/MwnHwv9DkT2g4IQ6Ml7CoL9IMtJ42egn0RERGpurTRzkhmPdmySGpBRASc+jC8dR6s/xmeOw4KD0COzxpk44LIJjD2AycxX0PlOzI/9znY/pvzKi07w8ldMOY1dZI1ZoHmOFEuFAmQpvGLiIhI9US4nO31Dj3XeVegL52GwqVTIDoedqeXDPTByTReeMAZ+W8sho6HmPJ2cPJO75/2D63HbsyUC0VCTMG+iIiIiIReSj+IiivnpAVM4wpuNy+AvIp2ybJaj93YKReKhJiCfREREREJvfWzYN/2Cgo0suBW67ElEEW5UBJTSx5PbKtlHhK0sAf7xpjrjDHpxphcY8xCY8yxFZRNNca8ZYz53RjjMcZMLKdcM2PMM8aYDG+9K4wxI33O32uMsaVeW/3VJSIiIiJVoOC2JK3HlkCljYablsGln8E5LzrvN/2qQF+CFtYEfcaY84CJwHXATOBq4EtjTJq1doOfS2KAHcC/gL+VU2c08DWwHTgX2AR0AHJKFf0NONnncyOZQyYiIiJSCxTcllS0Hjs7g+I1+iUY57zWYwsczIUiUg3hHtm/GXjRWvuCtXaFtfYmYCNwrb/C1tp11tobrbWvAVnl1Hk50AI401o701q73lr7s7V2aalyhdbarT6vHSF6JhERERFRsrGStB5bRGpZ2IJ97wj8IOCrUqe+Aqrzr/5oYDbwjDFmmzFmmTHmTmNM6X85exhjtniXELxjjOlaSXtjjDGJRS8goRptFBEREWnYFNyWpfXYIlKLwjmNvyXgAkov1NoGpFSj3q7AScCbwEigB/AMzrPe7y0zF7gEWAW0Ae4CZhlj+lhrM8up9w5gQjXaJSIiItK4FAW3026H7C0Hjye2dQL9xhjcpo2GQ05zEhPu3eYsY+g0tHF1eohIrQjrmn2v0ouWjJ9jwYjAWa9/lbXWDSw0xrQFbsUb7Ftrv/Qp/6sxZjawFrgUeKyceh8sdS4BJx+AiIiIiJRHwW1ZWo8tIrUgnMH+TpykeKVH8VtTdrQ/GBlAgTfQL7ICSDHGRFtr80tfYK3dZ4z5FWcWgF/W2jwgr+izMeWtPxMRERGREhTciojUurCt2fcG3QuBYaVODQOqs+HqTKC7Mcb32XoCGf4CfXDW4wO9cToKREREREREROq1cGfjfwz4izHmcmNMb2PM40BHYDKAMeZBY8xrvhcYYw4zxhwGxAOtvJ/TfIpMApKBJ4wxPY0xpwF34qzbL6rjEWPM8caYLsaYI4EPgETg1Zp7VBEREREREZHaEdY1+9bad40xycA9QCqwDBhprV3vLZKKE/z7Wuzz9SDgQmA90Nlb50ZjzCnA48AvwGbgCeBhn+vaA2/jJAncAcwBjvK5r4iIiIiIiEi9ZaytTi68xsu7/V5WVlYWiYmJ4W6OiIiIiIiINHDZ2dkkJSUBJFlrsysqWxey8ddr2dkVfn9FREREREREQiKY+FMj+1VkjGmHtt4TERERERGR2tfeWru5ogIK9qvIOHvvtQVywt2WSiTgdEq0p+63VSqmn2XDoJ9jw6CfY8Ogn2PDoZ9lw6CfY8Ogn2PNSwC22EqCeU3jryLvN7bCnpS6wOmTACCnsjUdUrfpZ9kw6OfYMOjn2DDo59hw6GfZMOjn2DDo51grAvq+hnvrPREREREREREJMQX7IiIiIiIiIg2Mgv2GLw+4z/su9Zt+lg2Dfo4Ng36ODYN+jg2HfpYNg36ODYN+jnWEEvSJiIiIiIiINDAa2RcRERERERFpYBTsi4iIiIiIiDQwCvZFREREREREGhgF+yIiIiIiIiINjIJ9ERERERERkQZGwb6IiIiIiIhIA6NgX0RERERERKSBUbAvIiIiIiIi0sAo2BcRERERERFpYBTsi4iIiIiIiDQwCvZFREREREREGhgF+yIiIiIiIiINjIJ9ERERERERkQZGwb6IiIiIiIhIA6NgX0RERERERKSBUbAvIiIiIiIi0sAo2BcRERERERFpYBTsi4iIiIiIiDQwCvZFREREREREGhgF+yIiIiIiIiINjIJ9ERERERERkQZGwb6IiIiIiIhIA6NgX0RERERERKSBUbAvIiIiIiIi0sAo2BcRERERERFpYBTsi4iIiIiIiDQwCvZFREREREREGhgF+yIiIiIiIiINjIJ9ERGRRsIYM84YY72vE/ycN8aYNd7zP5Q6l2yMedAYs9wYs88Yk2WMWWmMed0Y06+ce/h7lblvqfu8YoxZF4rnFRERacwiw90AERERqXU5wBXAD6WOHw90854vZoyJB+YA8cB/gaVAE6AncDZwGPBLqbouA1b6uffyarVcREREAqJgX0REpPF5F7jIGPNXa222z/ErgNlAYqnyfwa6AydZa78vde4xY4y/mYLLrLULQtZiERERCYqm8YuIiDQ+b3vfLyg6YIxJAs4BXvJTPtn7nuGvMmutJ6StK8UYE+tdQpBujMk3xmw2xjxjjGlWqtxJxpgfjDGZxpgDxpgNxpgPjTFxPmWuNcYsNcbsNcbkeJci/Lsm2y8iIhIOCvZFREQan2zgA+Byn2MXAB6cUf/SZnvfXzPGnGmMSfZTpjSXMSay1MsVbEONMQb4BLgFeB04DXgMuBT4zhgT4y3XGfgcyPc+1wjgH8A+INpb5nzgWeBH4CzgTOBxoGmw7RIREanrNI1fRESkcXoJ+N4Y08da+xtOgPy+tTbHia8PstbONMbcA9wFfAxgjEkHpgOTrLWl1+uDs8a/NDfB/+1xCjAcuM1a+1/vsa+NMRtxOiYuAZ4HBgGxwK3W2qU+17/l8/XRwB5r7XifY98G2R4REZF6QSP7IiIijdOPwFrgcmPMocAR+J/CD4C19v+AjjidAs8Be4FrgIXGmAv8XHKJt07f15FVaOdJ3vdXSh1/H2fU/k/ez0twRvX/Z4y51BjT1U9d84Bmxpi3jTFnGGNaVqE9IiIi9YKCfRERkUbIWmuBl4GxOEH7KmvtjEqu2Watfdlae421th9O9v584Ak/xVdYaxeUei2sQlOTgUJr7Q4/7d/qPY+1di1wMrAdeAZYa4xZa4y50eea13E6KzoBHwLbjTFzjTHDqtAuERGROk3BvoiISOP1CtASJ9h/OdiLrbU/AV8BrYwxrUPbtGKZQKQxppXvQe9a/hRgp097ZlhrRwFJwFE4uQYmetfqF5V52Vo71FvmNMAAnxljOtVQ+0VERMJCwb6IiEgjZa3dDPwXmAq8Wl45Y0wbf9vreRPu9QD2A3tqqJlFa+rHljp+Dk5ivTJr7q21bmvtXOCv3kMD/ZTZZ639EvgXTgK/PiFrsYiISB2gBH0iIiKNmLX2HwEUuxi42hjzFjAfyALaA3/BCZLvt9bml7qmrzHG398Za0tPya/E1ziJAB82xiQCM4F+wH3AYpwM/RhjrsFZ3/85sAEnWV/RbgPfeMs8Dxzw1pGBMzPgDu/zzA+iTSIiInWegn0RERGpzOc4gfFI4FqgOZAD/AJcbK19w8815S0LuBJ4IdAbW2utMeZM4F7gMuCfOFP3XwfutNbmeYsuwcncf5+3rXuBZcBoa+1X3jIzgHHAGO8z7AR+Bi4JsgNCRESkzjNOfhsRERERERERaSi0Zl9ERERERESkgVGwLyIiIiIiItLAKNgXERERERERaWAU7IuIiIiIiIg0MAr2RURERERERBoYBfsiIiIiIiIiDUxkuBtQXxljDNAWZ59hERERERERkdqQAGyx1tqKCinYr7q2wKZwN0JEREREREQanfbA5ooKKNivuhyAjRs3kpiYGO62iIiIiIiISAOXnZ1Nhw4dIIAZ5gr2qykxMVHBvoiIiIiIiNQpStAnIiIiIiIi0sAo2BcRERERERFpYBTsi4iIiIiIiDQwWrMvIiIiIiISBLfbTUFBQbibIQ1QVFQULpcrJHUp2BcREREREQmAtZatW7eyZ8+ecDdFGrBmzZqRkpKCMaZa9SjYb8DcHsu89F1sz8mldUIsg7u0wBVRvV8YEREREZHGqijQb926NXFxcdUOxkR8WWvZv38/27dvByA1NbVa9SnYb6CmLcvgvqnLycjKLT6WmhTLhFFpjOhbvV8aEREREZHGxu12Fwf6ycnJ4W6ONFBNmjQBYPv27bRu3bpaU/rDnqDPGHOdMSbdGJNrjFlojDm2kvLHe8vlGmP+MMZc46dMM2PMM8aYDG+5FcaYkT7n7zDGzDfG5BhjthtjPjHG9KqJ5wuHacsyuPaNRSUCfYCtWblc+8Yipi3LCFPLRERERETqp6I1+nFxcWFuiTR0Rb9j1c0LEdZg3xhzHjAR+BcwAJgBfGmM6VhO+S7AF95yA4B/A08aY87xKRMNfA10Bs4FegFXApt9qjoeeAY4ChiGM8PhK2NM09A9XXi4PZb7pi7H+jlXdOy+qctxe/yVEBERERGRimjqvtS0UP2OhXsa/83Ai9baF7yfbzLGDAeuBe7wU/4aYIO19ibv5xXGmMOBW4APvccuB1oAQ621RV0h630rsdaO8P1sjLkM2A4MAn6q1hOF2bz0XWVG9H1ZICMrl3npuxjSTdOPREREREREGqKwjex7R+AHAV+VOvUVMLScy4b4KT8dONwYE+X9PBqYDTxjjNlmjFlmjLnTGFPRYock7/uuCtobY4xJLHoBCRXUFzbbc8oP9KtSTkRERERExFfnzp2ZOHFiwOV/+OEHjDFh2cXglVdeoVmzZrV+37ognNP4WwIuYFup49uAlHKuSSmnfKS3PoCuONP3XcBI4AHg78A//VVonDkSjwE/W2uXVdDeO4Asn9emCsqGTeuE2JCWExERERGR0HJ7LLPXZvLpks3MXptZ40tsTzjhBG666aaQ1Td//nyuuuqqgMsPHTqUjIwMkpKSKi9cBwTbmVFXhXsaP1Bmebnxc6yy8r7HI3Cm5F9lrXUDC40xbYFbgfv91Pc00A84ppJ2PojTKVAkgToY8A/u0oLUpFi2ZuX6/SYaICXJ2YZPRERERERqV13dNctai9vtJjKy8hCxVatWQdUdHR1NSkp547lSU8I5sr8TcFN2FL81ZUfvi2wtp3whkOn9nAGs8gb6RVYAKd6lA8WMMU/hTPs/0VpbYeBurc2z1mYXvYCcisqHiyvCMGFUGnCwF6S0CaPScEUosYiIiIiISG0Kx65Z48aN48cff+SJJ57AGIMxhnXr1hVPrZ8+fTqHH344MTExzJgxg7Vr13LGGWfQpk0b4uPjOeKII/jmm29K1Fl65NsYwwsvvMBZZ51FXFwcPXr0YMqUKcXnS0/jL5paP336dHr37k18fDwjRowgI+Pg8xcWFjJ+/HiaNWtGcnIyt99+O5deeilnnnlmhc/7yiuv0LFjR+Li4jjrrLPIzMwscb6y5zvhhBNYv349f/vb34q/XwCZmZlccMEFtG/fnri4OA499FDefvvtYH4UtS5swb61Nh9YiJMN39cwYFY5l832U/4UYIFPMr6ZQHdjjO+z9QQyvPfEOJ4GzgZOstamV/1J6p4RfVOZNHYgKUklp+pHuQyTxg4Ma4+hiIiIiEhDsz+/sNxXboEzBhnIrln3lto1q7w6g/HEE08wZMgQrrzySjIyMsjIyKBDhw7F52+77TYefPBBVqxYQb9+/di7dy8jR47km2++YfHixQwfPpxRo0axYcOGCu9z3333MWbMGH755RdGjhzJRRddxK5d5aZEY//+/TzyyCO8/vrr/PTTT2zYsIFbbrml+PzDDz/Mm2++ycsvv8zMmTPJzs7mk08+qbANc+fO5fLLL+e6665jyZIlnHjiiTzwwAMlylT2fB999BHt27fn/vvvL/5+AeTm5jJo0CA+++wzli1bxlVXXcXFF1/M3LlzK2xTOIV7Gv9jwOvGmAU4gfxVQEdgMoAx5kGgnbX2Em/5ycD1xpjHgOdxEvZdAVzgU+ck4AbgCe/IfQ/gTuBJnzLPABcCZwA5xpii2QJZ1toDIX/KMBjRN5VhaSnMS9/Fmh053P3JbxS4LYO7KAO/iIiIiEgopd0zvdxzJ/ZqxcuXDQ5o16ytpXbNOubh79m1L79M2XUPnRZw25KSkoiOjiYuLs7vVPr777+fYcMOjqcmJyfTv3//4s8PPPAAH3/8MVOmTOH6668v9z7jxo3jggucsOzf//43Tz31FPPmzWPEiBF+yxcUFDB58mS6desGwPXXX8/99x9cdf3UU09xxx13cNZZZwHw9NNP88UXX1T4rE888QTDhw/nH//4BwA9e/Zk1qxZTJs2rbhM//79K3y+Fi1a4HK5SEhIKPH9ateuXYnOiBtuuIFp06bx/vvvc+SRR1bYrnAJ5zR+rLXvAjcB9wBLgOOAkdbaoq3yUnGC/6Ly6ThJ907wlr8bGG+t/dCnzEac0f4jgF9wgvwngId8bn0tTgb+H3Cm/Re9zgvl84WbK8IwpFsyFx/VmV5tnM0Dlm7cE95GiYiIiIg0QnV116zDDz+8xOd9+/Zx2223kZaWRrNmzYiPj2flypWVjuz369ev+OumTZuSkJDA9u3byy0fFxdXHOgDpKamFpfPyspi27ZtDB48uPi8y+Vi0KBBFbZhxYoVDBkypMSx0p+r+nxut5t//etf9OvXj+TkZOLj4/nqq68qvS6cwj2yj7X2WeDZcs6N83PsR2BgJXXOBo6q4HyjW7D+6Jj+pCTF0jI+JtxNERERERFpUJbfP7zccxHeNd9V2TXr59tPrF7DAtC0adMSn2+99VamT5/OI488Qvfu3WnSpAnnnnsu+fllZxj4ioqKKvHZGIPH4wmqvLW2zDFfpc+XVtl5qPrzPfroozz++ONMnDiRQw89lKZNm3LTTTdVel04hT3Yl9rRt1392OZCRERERKS+iYuuPKyqyq5ZgdQbiOjoaNxud+UFgRkzZjBu3Lji6fN79+5l3bp1IWlHoJKSkmjTpg3z5s3j2GOPBZyR9cWLF3PYYYeVe11aWhpz5swpcaz050Cez9/3a8aMGZxxxhmMHTsWAI/Hw+rVq+ndu3dVHrFWhHUav4iIiIiISGNQ0a5ZRZ9rateszp07M3fuXNatW8fOnTsrHHHv3r07H330EUuWLGHp0qVceOGFFZavKTfccAMPPvggn376Kb///js33ngju3fvLjPa72v8+PFMmzaN//znP6xatYqnn366xHp9COz5OnfuzE8//cTmzZvZuXNn8XVff/01s2bNYsWKFVx99dVs3bo19A8eQgr2G5FPl2zmwufn8N6CjeFuioiIiIhIo1PerlkpSbE1umvWLbfcgsvlIi0tjVatWlW4zvzxxx+nefPmDB06lFGjRjF8+HAGDqxwFXWNuP3227ngggu45JJLGDJkCPHx8QwfPpzY2PKXQxx11FG88MILPPXUUxx22GF89dVX3HXXXSXKBPJ8999/P+vWraNbt260atUKgLvvvpuBAwcyfPhwTjjhBFJSUirdBjDcTCDrGqQsY0wikJWVlUViYmK4mxOQp75dzaNfr+LUvilMGltxcgsRERERETkoNzeX9PR0unTpUmHAGQi3xzIvfRfbc3JpneBM3a+JEf2GxOPx0Lt3b8aMGcP//d//hbs5Naqi37Xs7GySkpIAkqy12RXVozX7jcjRPVry6NermLU2E7fH6h8UEREREZEwKNo1S8q3fv16vvrqK44//njy8vJ4+umnSU9P58ILLwx30+oNTeNvRPq1SyIhNpKsAwUs25wV7uaIiIiIiIj4FRERwSuvvMIRRxzB0Ucfza+//so333xTpxPi1TUa2W9EIl0RDOmazFfLt/Hzmp3079As3E0SEREREREpo0OHDsycOTPczajXNLLfyBzboyUAM1bvCHNLREREREREpKYo2G9kjunhZJNctH4P+/MLw9waERERERERqQmaxt/IdE6Oo0freNo3b8Lu/QXERetXQEREREREpKFRpNfIGGP46m/HYYwy8YuIiIiIiDRUmsbfCCnQFxERERERadgU7DdiW/YcYG+e1u2LiIiIiIg0NAr2G6mrX1/A0Ie+49sV28LdFBERERERqeM6d+7MxIkTiz8bY/jkk0/KLb9u3TqMMSxZsqRa9w1VPVUxbtw4zjzzzFq/b6go2G+kOiU3BeDn1TvD3BIRERERkUbG44b0GfDrB867xx3uFgUtIyODU089NaR1+guuO3ToQEZGBn379g3pvWpCODsm/FGCvkbqmO4t+d9Pf/Dzmp1Ya7WOX0RERESkNiyfAtNuh+wtB48ltoURD0Pa6PC1K0gpKSm1ch+Xy1Vr92poNLLfSA3u0oLoyAgysnL5Y+e+cDdHRERERKThWz4F3rukZKAPkJ3hHF8+JeS3fO6552jXrh0ej6fE8dGjR3PppZcCsHbtWs444wzatGlDfHw8RxxxBN98802F9Zaexj9v3jwGDBhAbGwshx9+OIsXLy5R3u12c8UVV9ClSxeaNGlCr169eOKJJ4rP33vvvbz66qt8+umnGGMwxvDDDz/4HS3/8ccfGTx4MDExMaSmpvKPf/yDwsKDuchOOOEExo8fz2233UaLFi1ISUnh3nvvrfB53G43N998M82aNSM5OZnbbrsNa22JMtOmTeOYY44pLnP66aezdu3a4vNdunQBYMCAARhjOOGEEwCYP38+w4YNo2XLliQlJXH88cezaNGiCtsTCgr2G6nYKBeHd2oOaCq/iIiIiEi15O8r/1WQ65TxuJ0RfayfCrzHpt1eckp/eXUG4c9//jM7d+7k+++/Lz62e/dupk+fzkUXXQTA3r17GTlyJN988w2LFy9m+PDhjBo1ig0bNgR0j3379nH66afTq1cvFi5cyL333sstt9xSoozH46F9+/a89957LF++nHvuuYc777yT9957D4BbbrmFMWPGMGLECDIyMsjIyGDo0KFl7rV582ZGjhzJEUccwdKlS5k0aRIvvvgiDzzwQIlyr776Kk2bNmXu3Ln85z//4f777+frr78u9xkeffRRXnrpJV588UV+/vlndu3axccff1zmOW+++Wbmz5/Pt99+S0REBGeddVZxR8q8efMA+Oabb8jIyOCjjz4CICcnh0svvZQZM2YwZ84cevTowciRI8nJyQno+1tVmsbfiB3ToyWz1mYyY/VOLh3aOdzNERERERGpn/7dtvxzPU6Bi96H9bPKjuiXYJ3z62dBl2OdQxMPhf2ZZYvemxVw01q0aMGIESN46623+NOf/gTA+++/T4sWLYo/9+/fn/79+xdf88ADD/Dxxx8zZcoUrr/++krv8eabb+J2u3nppZeIi4ujT58+bNq0iWuvvba4TFRUFPfdd1/x5y5dujBr1izee+89xowZQ3x8PE2aNCEvL6/CafvPPvssHTp04Omnn8YYwyGHHMKWLVu4/fbbueeee4iIcMaz+/Xrx4QJEwDo0aMHTz/9NN9++y3Dhg3zW+/EiRO54447OOeccwCYPHky06dPL1Gm6FyRF198kdatW7N8+XL69u1Lq1atAEhOTi7xDCeddFKJ65577jmaN2/Ojz/+yOmnn17us1aXRvYbsWO7O7+Mc/7IpNDtqaS0iIiIiIhU2d4Ad8EKtFwQLrroIj788EPy8vIAJzg///zzcblcgDNifdttt5GWlkazZs2Ij49n5cqVAY/sr1ixgv79+xMXF1d8bMiQIWXKTZ48mcMPP5xWrVoRHx/P888/H/A9fO81ZMiQEjnHjj76aPbu3cumTZuKj/Xr16/EdampqWzfvt1vnVlZWWRkZJRoc2RkJIcffniJcmvXruXCCy+ka9euJCYmFk/br+wZtm/fzjXXXEPPnj1JSkoiKSmJvXv3Bv3swQr7yL4x5jrgViAV+A24yVo7o4LyxwOPAX2ALcB/rLWTS5VpBvwLOBtoDqQDf7fWflHV+zZEaW0TGTe0M0d0boHH32wiERERERGp3J0VjNgbJ6Amvk1gdfmWu+nXqrfJx6hRo/B4PHz++eccccQRzJgxg8cee6z4/K233sr06dN55JFH6N69O02aNOHcc88lPz8/oPpLr23357333uNvf/sbjz76KEOGDCEhIYH//ve/zJ07N6hn8ZdcvOj+vsejoqJKlDHGlMlbEKxRo0bRoUMHnn/+edq2bYvH46Fv376Vfp/GjRvHjh07mDhxIp06dSImJoYhQ4YE/P2tqrAG+8aY84CJwHXATOBq4EtjTJq1tkw3hzGmC/AF8DwwFjgaeNYYs8Na+6G3TDTwNbAdOBfYBHQAcnzqCeq+DZUrwnDv6D7hboaIiIiISP0W3bTyMp2GOln3szPwv27fOOc7+axTD6TeADRp0oSzzz6bN998kzVr1tCzZ08GDRpUfH7GjBmMGzeOs846C3DW8K9bty7g+tPS0nj99dc5cOAATZo0AWDOnDklysyYMYOhQ4dy3XXXFR/zTW4HEB0djdtd8TaEaWlpfPjhhyWC/lmzZpGQkEC7du0CbrOvpKQkUlNTmTNnDscddxwAhYWFLFy4kIEDBwKQmZnJihUreO655zj2WGeZxc8//1ym/UCZZ5gxYwbPPvssI0eOBGDjxo3s3FnzedPCPY3/ZuBFa+0L1toV1tqbgI3AteWUvwbYYK29yVv+BeAlwDf7w+VAC+BMa+1Ma+16a+3P1tql1biviIiIiIhI1UW4nO31ACi97bX384iHnHI14KKLLuLzzz/npZdeYuzYsSXOde/enY8++oglS5awdOlSLrzwwqBGwS+88EIiIiK44oorWL58OV988QWPPPJImXssWLCA6dOns2rVKu6++27mz59fokznzp355Zdf+P3339m5cycFBQVl7nXdddexceNGbrjhBlauXMmnn37KhAkTuPnmm4vX61fFjTfeyEMPPcTHH3/MypUrue6669izZ0/x+ebNm5OcnMz//vc/1qxZw3fffcfNN99coo7WrVvTpEkTpk2bxrZt28jKyip+9tdff50VK1Ywd+5cLrroouJOkZoUtmDfOwI/CPiq1KmvgLJpFx1D/JSfDhxujCmapzEamA08Y4zZZoxZZoy50xhn/kwV74sxJsYYk1j0AhIqfsL6wVrLog27eerb1ezNK6z8AhERERERqZq00TDmNUhMLXk8sa1zPG10jd36pJNOokWLFvz+++9ceOGFJc49/vjjNG/enKFDhzJq1CiGDx9ePKIdiPj4eKZOncry5csZMGAA//znP3n44YdLlLnmmms4++yzOe+88zjyyCPJzMwsMcoPcOWVV9KrV6/idf0zZ84sc6927drxxRdfMG/ePPr3788111zDFVdcwV133RXEd6Osv//971xyySWMGzeueJlB0UwHgIiICN555x0WLlxI3759+dvf/sZ///vfEnVERkby5JNP8txzz9G2bVvOOOMMAF566SV2797NgAEDuPjiixk/fjytW7euVnsDYQJZX1EjNzamLbAZONpaO8vn+J3ApdbaXn6uWQW8Yq39t8+xoThT8dtaazOMMSuBzsCbwLNAD+AZ4Alr7f1Vua/3/L3AhNLHs7KySExMDPbx65Tj/vM9G3bt56Vxh3PSIQGuJRIRERERaURyc3NJT0+nS5cuxMbGVq8yj9vJur93m7NGv9PQGhvRl/qnot+17OxskpKSAJKstdkV1RPuafxQdsGK8XOssvK+xyNw1utfZa1daK19BydZX+kp+sHe90EgyefVvoKy9crR3VsCMGN1za8bERERERFp9CJczvZ6h57rvCvQlxoQzmB/J+AGSm+i2Boob7+JreWULwSKNqDMAFZZa32zIqwAUrxT+KtyX6y1edba7KIXPgn/6rtjezjB/sw1CvZFREREREQagrAF+9bafGAhMKzUqWHArLJXAM5a/NLlTwEWWGuLsjfMBLobY3yfrSeQYa3Nr+J9G7QhXZMxBlZt28u27NxwN0dERERERESqKdzT+B8D/mKMudwY09sY8zjQEZgMYIx50Bjzmk/5yUAnY8xj3vKXA1cAvqkeJwHJwBPGmJ7GmNOAO3HW7Qd038amedNoDm2XBMDPmsovIiIiIiJS70WG8+bW2neNMcnAPUAqsAwYaa1d7y2SihOEF5VPN8aMBB4H/gpsAcZbaz/0KbPRGHOKt8wvOMn4ngAe9ilT2X0bnaO7t+SXTVnMXLOTcwY1mHQEIiIiIiIhFa4E59J4hOp3LKzBPoC19lmcrPn+zo3zc+xHoMJ9IKy1s4GjqnrfxujY7i2Z9MNalmzcE+6miIiIiIjUOVFRzk7f+/fvr5U90qXx2r9/P3Dwd66qwh7sS90wsFNzPrhmCP07NAt3U0RERERE6hyXy0WzZs3Yvn07AHFxcRhjKrlKJHDWWvbv38/27dtp1qwZLlf1dmlQsC8AxEa5OLxzi3A3Q0RERESkzkpJcTb0Kgr4RWpCs2bNin/XqkPBvoiIiIiISACMMaSmptK6dWsKCgoqv0AkSFFRUdUe0S+iYF+K7csr5N9frGDRhj18+tejiY4M92YNIiIiIiJ1j8vlCllAJlJTFM1JsSZRLqYt28qKjGwl6hMREREREanHFOxLsYgIw9DuLQH4efWOMLdGREREREREqkrBvpRwrDfYn7FmZ5hbIiIiIiIiIlWlYF9KOLqHE+wv3biH7FwlHREREREREamPFOxLCe2aNaFry6Z4LMxemxnu5oiIiIiIiEgVKNiXMo72TuWfqan8IiIiIiIi9ZKCfSnjmB4t6ZwcR6v4mHA3RURERERERKrAWGvD3YZ6yRiTCGRlZWWRmJgY7uaElLUWY0y4myEiIiIiIiI+srOzSUpKAkiy1mZXVFYj+1KGAn0REREREZH6TcG+lKvA7SF9575wN0NERERERESCpGBf/Fq1LYcB93/NuZNm4fFoqYeIiIiIiEh9omBf/Oqc3BSPtWTuy2fl1pxwN0dERERERESCoGBf/IqOjODILi0A+HnNjjC3RkRERERERIKhYF/KdXT3lgD8vCYzzC0RERERERGRYCjYl3Id26MVAPPSM8ktcIe5NSIiIiIiIhIoBfsNmccN6TPg1w+cd09wAXvPNvG0Soght8DDovW7a6iRIiIiIiIiEmphD/aNMdcZY9KNMbnGmIXGmGMrKX+8t1yuMeYPY8w1pc6PM8ZYP69YnzKRxpgHvPc94K3nHmNM2L8fIbN8CkzsC6+eDh9e4bxP7OscD5AxhmOKp/LvrKmWioiIiIiISIhFhvPmxpjzgInAdcBM4GrgS2NMmrV2g5/yXYAvgOeBscDRwLPGmB3W2g99imYDvXyvtdbm+ny8HbgGuBT4DTgceBnIAp4IycOF0/Ip8N4lQKkt87IznONjXoO00QFVdeaAdnRt2ZST09qEvp0iIiIiIiJSI4y14dtD3RgzF1hkrb3W59gK4BNr7R1+yj8MjLbW9vY5Nhnob60d4v08DphorW1WwX0/A7ZZa6/wOfYhsN9ae3GAbU8EsrKyskhMTAzkktrhcTsj+NlbyilgILEt3PQrRLhqtWkiIiIiIiJSddnZ2SQlJQEkWWuzKyobtmnrxphoYBDwValTXwFDy7lsiJ/y04HDjTFRPsfijTHrjTGbjDGfGWMGlLrmZ+BPxpie3rb0B47BmTVQXntjjDGJRS8goaLnC5v1syoI9AEsZG92yomIiIiIiEiDFM5p/C0BF7Ct1PFtQEo516SUUz7SW18GsBIYB/wKJAI3AjONMf2ttau91zwMJAErjTFubzv+aa19u4L23gFMqPyxwmxv6W9PNcsBWQcKmLF6BwVuD2cNaF/FhomIiIiIiEhtCeuafa/S6wiMn2OVlS8+bq2dA8wpPmnMTGARcAMw3nv4PJw1/xfirNk/DJhojNlirX21nPs+CDzm8zkB2FRBO8MjPsC19YGWAxas28X1by2mU3Kcgn0REREREZF6IJzB/k7ATdlR/NaUHb0vsrWc8oVApr8LrLUeY8x8oIfP4f8CD1lr3/F+/tUY0wln9N5vsG+tzQPyij4bY/wVC79OQ501+dkZ+O8z8a7Z71TeSomyjuyaTGSEYX3mfjbu2k+HFnEha66IiIiIiIiEXtjW7Ftr84GFwLBSp4YB5S0on+2n/CnAAmttgb8LjBOVH4Yzxb9IHOApVdRNHdiKsNoiXDDiYe+HcjokRjwUVHK++JhIBnRsBsCM1dqCT0REREREpK4Ld3D7GPAXY8zlxpjexpjHgY7AZABjzIPGmNd8yk8GOhljHvOWvxy4AnikqIAxZoIxZrgxpqsx5jDgRZxgf7JPPVOBfxpjTjPGdDbGnAXcDHxcc49ai9JGO9vrJaaWPXf28wFvu+fr6O4tAZi5RsG+iIiIiIhIXRfWNfvW2neNMcnAPUAqsAwYaa1d7y2SihP8F5VPN8aMBB4H/gpsAcZbaz/0qbYZ8D+c6f5ZwGLgOGvtPJ8yNwD/BzyLswxgC/AccH+onzFs0kbDIac5WfdztsL0f8K+bRAZU6Xqju3RkonfrGbm2p24PRZXRB1dxiAiIiIiIiIYayvKhSfl8W6/l5WVlUViYmK4m1O56f+E2U/DYRfBmc8GfXmB28OA+79mb14hU64/mn7tm4W+jSIiIiIiIlKu7OxskpKSAJKstdkVlQ33NH6pLQMvhQvehdMeq7ysH1GuCI7qmgzA0o17QtgwERERERERCTWN7FdRvRvZD4E123OIi46kbbMm4W6KiIiIiIhIoxPMyH5Y1+xL/dK9dUK4myAiIiIiIiIB0DT+xiQ3C765F14aAR53uFsjIiIiIiIiNUTBfmMS2QQWvAQbZsPGeZWX92PG6h1c9vI8Jn6zKsSNExERERERkVBRsN+YREZDz1Odr1dMrVIVu/bl8/3vO/h6+bYQNkxERERERERCScF+Y9N7lPO+YipUITnj0G4tAfhtSzaZe/NC2TIREREREREJEQX7jU23k5zp/FkbYOsvQV/eKiGGQ1KcRH2z1maGunUiIiIiIiISAgr2G5voOOhxsvN1FafyH9PdGd3/efXOULVKREREREREQkjBfmPUe7TzXtVgv4c32F+zE1uFpQAiIiIiIiJSsxTsN0Y9ToGEttD+CCgMft394C4tiIowbN5zgBd/Tmf22kzcHgX9IiIiIiIidUVkuBsgYdCkGdy8HIyp0uU/rdqBiTDgsTzw+QoAUpNimTAqjRF9U0PYUBEREREREakKjew3VlUM9Kcty+DaNxaRX+gpcXxrVi7XvrGIacsyQtE6ERERERERqQYF+42ZxwObFsC+wBLtuT2W+6Yux9+E/aJj901drin9IiIiIiIiYaZgvzF7/xJ44U/w6wcBFZ+XvouMrNxyz1sgIyuXeem7QtRAERERERERqQoF+41Zx6HOe4BZ+bfnlB/oV6WciIiIiIiI1AwF+41Z79Od9w2zYO+OSou3TogNqNpAy4mIiIiIiEjNULDfmDXrCKmHgfXA719UWnxwlxakJsVSUWq/xNhIBndpEbImioiIiIiISPAU7Dd2vUc57wFM5XdFGCaMSgMoN+DPzi3kiW9XY62S9ImIiIiIiISLgv3Grvdo5/2PHyA3q9LiI/qmMmnsQFKSSk7VT02K5YzD2gLw5Ler+eLXraFuqYiIiIiIiAQoMtwNMMZcB9wKpAK/ATdZa2dUUP544DGgD7AF+I+1drLP+XHAy34ubWKtzfUp1w54GDgVaAKsAq6w1i6s7jPVK616QsuesHMVrP4aDj230ktG9E1lWFoK89J3sT0nl9YJsQzu0gJXhGFQp+YsWr+bEX1TaqHxIiIiIiIi4k9Yg31jzHnAROA6YCZwNfClMSbNWrvBT/kuwBfA88BY4GjgWWPMDmvthz5Fs4FevteWCvSbe+/3PU6wvx3oBuwJ1bPVK6c8ANHx0PGogC9xRRiGdEsuc/ySIZ25+KhOGONM9C9we/BYS0ykK2TNFRERERERkYqZcK6tNsbMBRZZa6/1ObYC+MRae4ef8g8Do621vX2OTQb6W2uHeD+PAyZaa5tVcN+HgKOttcdWo+2JQFZWVhaJiYlVraZB83gsf3tvCbv25fPcxYOIiw77RBIREREREZF6Kzs7m6SkJIAka212RWXDtmbfGBMNDAK+KnXqK2BoOZcN8VN+OnC4MSbK51i8MWa9MWaTMeYzY8yAUteMBhYYY943xmw3xiw2xlxZSXtjjDGJRS8goaLyAmt27OWr37YxY/VOLnlxHtm5BeFukoiIiIiISKMQzgR9LQEXsK3U8W1AeQu+U8opH+mtD2AlMA4noL8AyAVmGmN6+FzTFbgWWA0MByYDTxpjLqmgvXcAWT6vTRWUrX92rILP/w5f3R2yKnu2SeCNvxxJYmwkC9bv5oL/zWHn3ryQ1S8iIiIiIiL+1YVs/KXXERg/xyorX3zcWjvHWvuGtXapN9HfGJzkezf4XBOBs3zgTmvtYmvtczh5AK6lfA8CST6v9hWUrX/27YD5L8Ci18AduhH4QZ2a885VQ2gZH81vW7IZ89xsMrIOhKx+ERERERERKSvoYN8YM8IYc4zP578aY5YYY97yJr4L1E7ATdlR/NaUHb0vsrWc8oVApr8LrLUeYD7gO7KfASwvVXQF0LG8xlpr86y12UUvIKe8svVSx6MgriXk7oF1P4e06rS2ibx39RDaJsXyx459nDtpNut27gvpPUREREREROSgqozs/xdIBDDGHAo8ipMhvyvOlngBsdbmAwuBYaVODQNmlXPZbD/lTwEWWGv9DkcbJy38YTgBfpGZlMrWD/QE1lfa8IYqwgWHnOZ8vWJqyKvv2iqe968dSteWTdmRk8eWPRrdFxERERERqSlVCfa7cHBU/BzgM2vtnTjb550aZF2PAX8xxlxujOltjHkcZ3R9MoAx5kFjzGs+5ScDnYwxj3nLXw5cATxSVMAYM8EYM9wY09UYcxjwIk6wP9mnnseBo4wxdxpjuhtjLgSuAp4Jsv0NS+/RzvvKz8HjCXn17Zo14d2rh/DiuMMZ2r1l5ReIiIiIiIhIlVRlL7R8IM779clAUTC+C++If6Cste8aY5KBe4BUYBkw0lpbNMKeis/UemttujFmJE6w/ldgCzDeWvuhT7XNgP/hTPfPAhYDx1lr5/nUM98YcxbOOvx7gHTgJmvtm8G0v8HpchzEJMLerbB5AXQYHPJbtEqIoVVCq+LPa7bnsD0nj6HdFPyLiIiIiIiEirG2olx4fi4wZgoQjTMV/m6gi7V2szHmFOBpa23P0Dez7vFuv5eVlZVFYmJQfRx124d/gV/fh6E3wCkP1Oittuw5wFnPzmT3/gKeuXAgw9La1Oj9RERERERE6rPs7GySkpIAkry55MpVlWn81+MkxDsXuNZau9l7/FRgWhXqk7qk9yhI6ugk66thLZpG0699M/ILPVzzxkI+Wez8Krk9ltlrM/l0yWZmr83E7QmuQ0pERERERKSxC3pkXxwNdmTf4wYTAcZUXjYECt0ebvvgFz5avBlj4LwjOvDj7zvIyMotLpOaFMuEUWmM6JtaK20SERERERGpi2p0ZN8YM9Cbhb/o8xnGmE+MMf82xkQH31ypUyJctRboA0S6Injkz/25ZEgnrIV35m0sEegDbM3K5do3FjFtWUY5tYiIiIiIiIivqkzjfw5nmzqMMV2Bd4D9wJ+B/4SuaRJW7gJYP7tWbhURYbjn9DSaxrj8ni+ae3Lf1OWa0i8iIiIiIhKAqgT7PYEl3q//DPxkrb0QGIezFZ/Ud4V58FhveHkEZK6tlVvOX7ebfXnucs9bICMrl3npu2qlPSIiIiIiIvVZVYJ943PdycAX3q83Ato/rSGIjIE2fZ2vV0ytlVtuz8mtvFAQ5URERERERBqzqgT7C4C7jDEXA8cDn3uPdwG2haphEma9RznvtRTst06IDWk5ERERERGRxqwqwf5NwEDgaeBf1to13uPnArNC1C4Jt0NOAwxsXgBZmystXl2Du7QgNSmWilIDtoqPYXCXFjXeFhERERERkfou6GDfWvuLtfZQa22StfY+n1O3ApeGrmkSVgkp0OFI5+uVn1dcNgRcEYYJo9IAyg34c/IKmPtHZo23RUREREREpL6rysg+AMaYQcaYscaYi4wxA621udbaglA2TsKseCr/lFq53Yi+qUwaO5CUpJJT9dskxtCtVVNyCzxc+vI8Ply4qVbaIyIiIiIiUl8Za4PbyswY0xp4F2e9/h6cgdgk4HvgfGvtjhC3sU4yxiQCWVlZWSQmJoa7OTVj9zp4oj+YCLhlDTRNrpXbuj2Weem72J6TS+uEWAZ3aUGB28OtH/zC1KVbALh5WE/G/6lHrbRHRERERESkLsjOziYpKQkgyVqbXVHZyCrU/xSQAPSx1q4AMMakAa8CTwIXVKFOqYuad4ZT/wMdh0Bc7a2Vd0UYhnRLLnXMxRPnHUaH5k149oe1NI+LqrX2iIiIiIiI1DdVGdnPAk621s4vdXww8JW1tlnomld3NYqR/Tpq0YbdDOzYPNzNEBERERERqVXBjOxXZc1+BOBvbX5BFesTCYpvoL9nfz5XvraAjbv2h7FFIiIiIiIidUtVgvPvgCeMMW2LDhhj2gGPA9+GqmFSh/zxA3x0Va1k5Q/WPZ/+xtfLt3HWszNZsnFPuJsjIiIiIiJSJ1Ql2L8eZ83+OmPMWmPMGiDde+yGUDZO6oi138Mv78Kv74e7JWXcObI3aamJ7Nybz/n/m820ZVvD3SQREREREZGwCzrYt9ZutNYOBE4DJuIk5RtprR1krdWeaA1R79HO++qvoSA3vG0pJSUplveuGcKJvVqRW+Dh2jcX8sKMPwg2F4WIiIiIiEhDUuU19tbar621T1lrn7TWfhPKRkkd03YAJLaD/L3OlP46Jj4mkucvOZyLjuyItfDA5yu4d8pvuD0K+EVEREREpHEKaOs9Y8z4QCu01j5Z9eZInRQRAYecDvOegxVTodeIcLeojEhXBA+c2ZdOyXH8+4uVfLtyOzee3JMWTaPD3TQREREREZFaF9DWe8aY9ADrs9bartVrUv3Q6LbeS58Br54OTZrDLWvAFVA/UVhMW5ZB99bxdG+dEO6miIiIiIiIhEzIt96z1nYJ8BV0oG+Muc4Yk26MyTXGLDTGHFtJ+eO95XKNMX8YY64pdX6cMcb6ecWWU98d3vMTg217o9JxCDRpAQd2w/qZ4W5NhUb0TS0R6H/+Swa/b80BwO2xzF6byadLNjN7baam+ouIiIiISIMU1uFZY8x5OEn+rgNmAlcDXxpj0qy1G/yU7wJ8ATwPjAWOBp41xuyw1n7oUzQb6OV7rbW2TGY5Y8wRwFXALyF5oIbMFQmHjITNi6EwL9ytCdi89F3c9O5iYiNdXH5MF95bsJGMrIO/CqlJsUwYlcaIvqlhbKWIiIiIiEhoBTSNv8ZubsxcYJG19lqfYyuAT6y1d/gp/zAw2lrb2+fYZKC/tXaI9/M4YKK1tlkl944HFuF0NNwFLLHW3hRE2xvXNH6AwnyIrF9r4Pfsz+eq1xYyb90uv+eN933S2IEK+EVEREREpE4L+TT+mmCMiQYGAV+VOvUVMLScy4b4KT8dONwYE+VzLN4Ys94Ys8kY85kxZoCfup4BPg90JwFjTIwxJrHoBTS+BeH1LNAHaBYXzSuXH0FslPOrHoGHoyKWMzpiFkdFLMfgAeC+qcs1pV9ERERERBqMcE7jbwm4gG2ljm8DUsq5JqWc8pHe+jKAlcA44FcgEbgRmGmM6W+tXQ1gjDkfp6Ph8CDaewcwIYjyDVf+PtizAVr3rrxsHbB0Yxa5BR6GR8xjQtRrtDUHR/m32BbcV3AJ07MGMy99F0O6JYexpSIiIiIiIqERtpF9H6WHU42fY5WVLz5urZ1jrX3DWrvUWjsDGAOsAm4AMMZ0AJ4ALvK3jr8CDwJJPq/2QVzbcGyYA//pBu9cCGFcAhKM7Tm5DI+Yx6SoiaRQcjp/CruYFDWR4RHz2J4TzK+DiIiIiIhI3RVwsG+Muc0Y08Tn83HGmBifzwnGmGeDuPdOwE3ZUfzWlB29L7K1nPKFQKa/C6y1HmA+0MN7aJD3moXGmEJjTCFwPDDe+9lVTj151trsoheQU+HTNVRt+oL1wK4/YPvycLcmIK2bRjEh6jUAIkzJc0WfJ0S9TuumUYiIiIiIiDQEwYzsP0jJdeqfAe18PsfhZNMPiLU2H1gIDCt1ahgwq5zLZvspfwqwwFpb4O8CY4wBDsOZ4g/wLXCo91jRawHwJnCYtdYd6DM0SjHx0O0k5+sVn4W3LQEa7FpJW7OrTKBfJMJAW5NJz1xtyiAiIiIiIg1DMGv2S4dK5YROQXkMeN0YswAnkL8K6AhMBjDGPAi0s9Ze4i0/GbjeGPMYzvZ7Q4ArgAuKG2XMBGAOsBpnzf54nID+rwDW2hxgWYkHMWYfkGmtLXFcytF7FKz6Epa+DcndIL4NdBoKEX4nRYSHtbBlESx5G9fmhQFd8uhHMxgW1Y8TD2ldw40TERERERGpWeFM0Ie19l1jTDJwD5CKE4SPtNau9xZJxQn+i8qnG2NGAo/jBO9bgPHW2g99qm0G/A9nun8WsBg4zlo7r4Yfp/Ew3n6e3enw4RXO14ltYcTDkDY6fO0CyN4Cv7wLS96Gnb8HdekfefFc9sp8Lj+6C7ef2ouYyDrUeSEiIiIiIhIEYwNMsmaM8QAp1trt3s85OPvb/+H93AbYYq1tFBGSd/u9rKysLBITE8PdnNqzfAq8dwnl5kkc81p4Av70n+Dnx+GPH5ycAgCRsXDI6dBvDHx2E2Rn4D/3o8EmtuX+bu/w8uyNAKSlJvLkBQPo3jq+lh5ARERERESkYtnZ2SQlJQEkeXPJlSvYkf2/GGP2+lw7zhiz0/u58e0739h43DDtdvwHzBYwMO0fcMhp1ZvS73HD+lmwd1v5SwSsBXcBREY7n7O3wNrvnK87DoH+F0CfMyE2yTk24mFvJ4X/zR7MiIeYkNaPY3q24dYPfmF5RjajnvqZ5y4exHE9W1X9WURERERERMIgmJH9dVS8JR4A1tou1WxTvdAoR/bTZ8Crp1debswb3oC/Cjs7Lp/idChkbzl4zHeJwO71sPQdJ1/A4ZfB0Tc6ZfL3waynnFH8Fl0DrxvgiCvhtEeKP27LzuXm95awattept14LMnxMYiIiIiIiIRbMCP7AQf7UlKjDPZ//eDgGv3KuKIhsR0ktXdex98OLbz9QLlZgIHYUt+3CpcIWGh1COxYefBwu8Phym+DewbfWQPrfoYdv8OJd0KXY0sW81g27T5Ax+S44mPpO/fRpWXT4O4nIiIiIiISIjU5jV8as/g2gZd15zsJ/HanO5+PveXguTmT4IcHISYJkto5nQKJbeG3jyh/iQAHA/0ux8NhF0HvAGYZlBbhOhjY9zm73NkHERGmRKD/6ZLN3PzeUm76Uw+uO7E7rvL28RMREREREakDAg72jTFHAi2stV/6HLsEuA9oCnwC3GCtzQt1I6WO6DTUCcorSHRHYlu4fiHs2w7ZmyFrk/NKanew2D5vmoe8LNieBduXB96Gc1+GvmdX5ykOCmKZwfx1u3B7LI9+vYqf1+xk4vmHkZrUJDTtEBERERERCbFg1ux/CfxgrX3Y+/lQYBHwCrACuBV4zlp7b420tI5plNP4wWeqPZQM+IPMxp+319sZsBGyNsOar2HF1MqvO+dFOPTcYFtdsX2ZMP8FOGQkpBxabrGPFm3i7k+WsS/fTbO4KB4+px/D+6SEti0iIiIiIiLlCGYafzAZ1A4DfBdInw/MtdZeaa19DBgPjAmyrVLfpI12AvrE1JLHE9sGt+1eTDy06gXdT4ZBl8LgqwO7LpilBIGafif88G8nwV8Fzh7Yns/HH0u/9kns2V/A1a8v5K5PfiW3wA2A22OZvTaTT5dsZvbaTNwe5cMQEREREZHwCGZkPxfoYa3d6P38MzDNWvuA93Nn4FdrbaPYgq/RjuwXCWR7vGDrm9i38iUCN/1avfv4s2Ux/O8EiIiEG5c6CQUrkF/o4dGvf+e5H/8A4K0rjyT7QAH3TV1ORlZucbnUpFgmjEpjRN/U8qoSEREREREJWE2N7G8DugAYY6KBgcBsn/MJQEFwTZV6qyjR3aHnOu/VDcAjXM72ekDxkoBi3s8jHgp9oA/QdgB0PhY8hTB3cqXFoyMjuOPU3rx+xWBuHd6L7AMFXPvGohKBPsDWrFyufWMR05ZlhL7NIiIiIiIiFQgm2J8GPGSMORZ4ENgPzPA53w9YG8K2SWMTqiUCVTH0Bud9wSverQErd2yPVlxzfDfum7q8oj0EuG/qck3pFxERERGRWhXM1nt3AR8BPwJ7gUuttfk+5y8Hvgph26QxShsNh5wW2iUCgeg+DFr2gp2/w6LXDgb/lZiXvqvMiL4vC2Rk5TIvfRdDuiWHqLEiIiIiIiIVCzjYt9buAI41xiQBe6217lJF/ozTCSBSPUVLBGr1nhEw9HqYcgPMmQRHXgOuqEov255TfqBflXIiIiIiIiKhEMw0fgCstVl+An2stbtKjfSL1C+HjoGkDtDtRMjLCeiS1gmxIS0nIiIiIiISCgGP7BtjXgqknLX28qo3RySMomLhhoUQGRPwJYO7tCA1KZatWbl+1+0DuCIMqUkK9kVEREREpPYEM7I/DjgRaAY0r+AlUn8FEeiDE8hPGJUGlN1DoEjbpFhSFOyLiIiIiEgtCiZB32TgfKAr8BLwhrV2V420SiTcMn6BP76Ho2+stOiIvqlMGjuQ+6YuL5GsLzUplpuH9WRIt2Rio5wEg26PpcDtKf4sIiIiIiJSE4y1gW8JZoyJAc7Gybw/FPgceBH4ygZTUQNgjEkEsrKyskhMTAx3cySUcrbCY73BeuDa2dAmLaDL3B7LvPRdbM/JpXVCLIO7tMAVUXK8f+I3q/hmxTYmXTSIDi3iaqL1IiIiIiLSQGVnZ5OUlASQZK3NrqhsUAn6rLV51tq3rbXDgDTgN+BZYL0xJr6qDRapUxJSoPco5+vZzwR8mSvCMKRbMmcc1o4h3ZLLBPrZuQW8MWc9yzZnc9qTM/hm+bZQtlpERERERKRY0Nn4fVjvy1SzHpG6Z8gNzvsv7zoj/SGQGBvFlOuPYUDHZmTnFvKX1xbw0JcrKXR7QlK/iIiIiIhIkaCCdGNMjDHmAmPM18DvwKHA9UBHa+3emmigSFh0OAI6HAWeApj7XMiqbdusCe9eNYTLju4MwOQf13LRC3PZnpNb8YUiIiIiIiJBCDjYN8Y8C2QAtwOfAe2ttX+21n5hra3y0KQx5jpjTLoxJtcYs9AYc2wl5Y/3lss1xvxhjLmm1Plxxhjr5xXrU+YOY8x8Y0yOMWa7MeYTY0yvqj6DNFBDvaP7C16EvND1ZUVHRjBhVB+euXAgTaNdzE3fxTmTZpFfqBF+EREREREJjWCy8V8DbADSgeOB440pu9mYtfbsQCs0xpwHTASuA2YCVwNfGmPSrLUb/JTvAnwBPA+MBY4GnjXG7LDWfuhTNBsoEbxba32HTo8HngHm43wP/gV85b3vvkDbLw1cr1OhRTfYtRaWvAlHXh3S6k/rl8ohqQlc+8ZC/nJMV6IjtRpGRERERERCI5hg/zWcNfqhdDPworX2Be/nm4wxw4FrgTv8lL8G2GCtvcn7eYUx5nDgFsA32LfW2nIXWltrR/h+NsZcBmwHBgE/VeVBpAGKcMGQ6+DH/0JkbOXlq6Bbq3im3nAMMZEHt+JbtS2HNgmxJMVF1cg9RURERESk4Qs42LfWjgvljY0x0TjB9UOlTn2Fs62fP0O8531NB64wxkRZawu8x+KNMesBF7AEuNtau7iC5iR533dV0N4YIMbnUEIF9UlDMeBi5xUZU3nZKvIN9Hfvy+eyl+cTEQGTLhpE33bOr2Yg2/qJiIiIiIgUCWZkP9Ra4gTjpfcf2waklHNNSjnlI731ZQArgXHAr0AicCMw0xjT31q7unSFxlmL8Bjws7V2WQXtvQOYUMF5aYhqMMj3J3NfHhERsHHXAc6eNIt7R/WheVwU93+2nIysgytRUpNimTAqjRF9U2u1fSIiIiIiUj/UhUXCpZcGGD/HKitffNxaO8da+4a1dqm1dgYwBlgF3FBOfU8D/YALKmnngzgzAIpe7SspLw2Jxw2/fQJbltTobbq3TuCz64/l5N6tyS/0cOfHv3Ltm4tKBPoAW7NyufaNRUxbllGj7RERERERkfopnMH+TsBN2VH81pQdvS+ytZzyhUCmvwu8OwXMB3qUPmeMeQoYDZxord1UUWOttXnW2uyiF5BTUXlpYL69H96/FH78T43fKikuiv9dfDi3jSh/g4iiHq/7pi7H7Ql1Kg0REREREanvwhbsW2vzgYXAsFKnhgGzyrlstp/ypwALfNbrl+Cdpn8YzhT/4mPGmKeBs4GTrLXpQT+ANC4Dxjrvv38BO8usBgm5iAjDgA7NKyxjgYysXOall5tqQkREREREGqlwT+N/DPiLMeZyY0xvY8zjQEdgMoAx5kFjzGs+5ScDnYwxj3nLXw5cATxSVMAYM8EYM9wY09UYcxjwIk6wP9mnnmdwtu67EMgxxqR4X01q7lGlXmvZA3qNBCzMfqZWbrk9J7fyQkGUExERERGRxiOswb619l3gJuAenKz5xwEjrbXrvUVScYL/ovLpwEjgBG/5u4Hx1lrfbfeaAf8DVuBk7m8HHGetnedT5lqcdfc/4Iz4F73OC9nDScMz5HrnfenbsHdHjd+udUJg2/2t27mfQrenhlsjIiIiIiL1ibFW632rwhiTCGRlZWWRmJgY7uZIbbAWnj8JtiyC4/8BJ95Ro7dzeyzHPPwdW7NyK8xYCdCuWRMuOqoj5x/RkRZNo2u0XSIiIiIiEh7Z2dkkJSUBJHlzyZUr3NP4ReoPY2Cod1OH+c9DwYEavZ0rwjBhVJpz69JN8b4P79OGFk2j2bznAP+Z9jtHPfgtz3y/pkbbJSIiIiIidZ+CfZFg9B4NzTpCi66Qs7XGbzeibyqTxg4kJanklP6UpFgmjx3Icxcfzqx/nMQjf+7Poe2SyC/0kJJ4sGxugZv8Qk3xFxERERFpbDSNv4o0jb8R27cTmras1Vu6PZZ56bvYnpNL64RYBndpgSui5Hi/tZZFG/bQp20isVEuAF6Y8QfP/fQHFw7uyEVHdqR1Ytk8AIHULSIiIiIi4RfMNH4F+1WkYF/qgzGTZzNvnbM1X2SE4dRDUxk3tBMDOzbHGMO0ZRncN3U5GVkHM/qnJsUyYVQaI/qmhqvZIiIiIiLih4L9WqBgXziwG37/Eg67MNwtKVd+oYfpv23l1VnrWLB+d/Hxvu0SObxTc16dtb5M8r+iMf1JYwcq4Jea5XHD+lmwdxvEt4FOQyHCFe5WiYiIiNRZCvZrgYL9Ri5/Pzye5gT8f/kW2h8e7hZVatnmLF6bvY5Pl2whr9BDbFQEuQX+1/MbnLwAP99+kqb0S81YPgWm3Q7ZWw4eS2wLIx6GtNHha5eIiIhIHaZs/CI1LToOeo10vp71VHjbEqC+7ZL4z7n9mXPHn7hgcIdyA30AC2Rk5TIvfVftNVAaj+VT4L1LSgb6ANkZzvHlU8LTLhEREZEGRMG+SFUNud55XzEFdqWHty1BaN40mqO6JgdUdntObuWFRILhcTsj+mUWkHDw2LR/OOVEREREpMoU7ItUVZs06H4yWA/MmRTu1gSldULZrPz+xEbqnwgJsfWzyo7ol2Ahe7NTTkRERESqTH/Ji1RH0ej+4tdhf/2Z8j64SwtSk2KpbDX+Te8u4Znv19RKm6SR2LsttOVERERExC8F+yLV0fUEaHMoFOyHBS+FuzUBc0UYJoxKAygT8Bd97tgijgMFHvblFdZq26SBi28T2nIiIiIi4peCfZHqMAaG3gARkbBvJ6TPgF8/cN7r+JrjEX1TmTR2IClJJaf0pyTFMnnsQH689QQmjx3IVcd1LT63cP0uXvw5ndyCuv1sUod1Gupk3S93XomBxHZOORERERGpMm29V0Xaek+KuQtg8Rvw03/q5TZibo9lXvoutufk0johlsFdWvjdbs9ay5jnZjN/3W5Sk2IZ/6cenDuoPVEu9RlKkJZPgfcu9nPC+3s35rU6/9+NiIiISDgEs/Wegv0qUrAvxYq2ESuTXbxhBS4ej+Wd+Rt58tvVbM12svR3So7jbyf3ZFT/tn47CKQUj9tJPLd3mzNNvdNQiHCFu1XhMeNx+P5f4Ck4eCyxHYx4EBJSocPg8LVNREREpI5SsF8LFOwL4ARvE/tWkF3cOCP8N/3aYIK63AI3b87dwLPfryFzXz4APdvEc9dpaRzXs1VxuUBnDDQay6c4W87Vw9kfNcZdCH98D7lZTudH+8HOiP/q6XDJFOh6fLhbKCIiIlKnBBPsR9ZOk0QaqGC2EetybK01qybFRrm44pgunH9EB16ZtY7nflzLqm172Z6TV1xm2rIM7pu6nIys3OJjqUmxTBiVxoi+qeFodniVN/sjO8M53kBmfwTNFQk9hpU81qyD8z7lBrhuNkQ3rf12iYiIiDQAWmwrUh2NeBuxpjGR/PXE7sy47STuHHkIZw1oBziB/jVvLCoR6ANszcrl2jcWMW1ZRjiaGz4etzOiX2aZBwePTftHnU/oGDKFeTBnEhzY4//8yfdCUgfYsx6+vb82WyYiIiLSoCjYF6mOQLcHi2m4Sz2S4qK46rhuuCIMbo/l3im/+S1XFOreN3U5bk8jWj4UzOyPxmDZR07nxgsng79lZDEJMOoJ5+u5z8H62bXbPhEREZEGQsG+SHVUuo2Y10+P1Epzwm1e+i62ZueVe94CGVm5zEvfVXuNCrdGPPujDGth9jPO14dd6Gxd6U/3P8GAsYCFKddDwYFaa6KIiIhIQ6FgX6Q6IlxOgjWgbMDv/dy0FQy5tjZbFTbbc3IrL+RTLrfAzS5vkr8GK9DZH4GWq8/WzYBtv0JUHAwaV3HZU/4F8SmQuQa+/3etNE9ERESkIQl7sG+Muc4Yk26MyTXGLDTGVJjFzBhzvLdcrjHmD2PMNaXOjzPGWD+v2OrcV6RcaaOdBGuJpRLPJbaFMa/D336DtDMPHl/wMnx4JWRtrtVm1obWCbGVF/Ip98Pv2xn4f19z6hMz+L/PlvPN8m1k5xZUcrWT6X/22kw+XbKZ2Wsz6/aygOLZH+UxzpZznYbWWpPCZvazznv/CyCuRcVlmzSD0x+HFl2h54gab5qIiIhIQxPWbPzGmPOAicB1wEzgauBLY0yatXaDn/JdgC+A54GxwNHAs8aYHdbaD32KZgO9fK+11ub61BPUfUUqlTYaDjmt8j3UC/OcvcX37YCVn8HRN8HQGyA6LizNDrXBXVqQmhTL1qxcv+noDJCS5GzDB7B6214AVmRksyIjmxd/TifCwKHtmzGkazKXDu1EalKTEnXUu0z/RbM//GXjLzLioQazNWO5dq6BVdOcr48KcKbLISOh+8kQGV1z7RIRERFpoIz1lyCptm5uzFxgkbX2Wp9jK4BPrLV3+Cn/MDDaWtvb59hkoL+1doj38zhgorW2Waju6z0fA8T4HEoANmVlZZGY2HCTr0kN2LIYvvwHbJzjfE5sD8Pug77nlL+GuR6ZtiyDa99YBJQMbYuebNLYgSWC8p1785jzRyaz1mYye20m6Tv3FZ+bcduJdGjhdIQs2rCbn1fv5PGvV5UJmcuru05ZPsXJyu+brC+xnRPoN4Zt9z7/O8x/AXoMh4veq1odudkQq39vRUREpPHKzs4mKSkJIMlam11R2bAF+8aYaGA/8Gdr7cc+x58ADrPWHu/nmp+AxdbaG32OnQW8B8RZawu8wf4LwGbABSwB7rbWLq7qfb3n7wUmlD6uYF+qxFr47SP4egJkbXSOdTgKTn8M2vQJb9tCoDqj7xlZB5i9NpPftmRz9+lpxceveGU+367cXu51RbMGfr79JFwRVe80cXss89J3sT0nl9YJziyE6tRHYT5EREJEhLO9nr/ZHwW5EBXYEoh6a8p4WPImjP0Iuvr9Z7Z81sKcZ+GHh+CyLyDl0Jppo4iIiEgdF0ywH85p/C1xgvHSKai3ASnlXJNSTvlIb30ZwEpgHPArkAjcCMw0xvS31q6u4n0BHgQe8/mcAGyqoLxI+YxxRvJ7jYRZT8HPj8PGueAuJ1ldeUFiHTWibyrD0lKqFDSnJjXh7IHtOXtgyeOVTXrwzfRf4PbQJNpFp+Q4WsXHYAKcMVEjSwTmPAtL34FTHoAeJ0OXUulBfv0Apt0B570BHY+s2j3qg9FPwgl3QEJF/8xWYMNsyMuGT66DK78DV1Ro2yciIiLSwIR1zb6Xvxm5FU03KG8GrwWw1s4B5hSfNGYmsAi4ARhf1ftaa/OA4j3FAg0eRCoU1QSOv83ZZmzNN9B2wMFz6T9B+8Gw+is/07/bOuvA6/D0b1eEYUi35JDVN6p/W75ZUf7IfpHtObk89vUq1mfuB6BptItOyU3p3DKOTslNOSQlgTMOa1fmuqLlB6X/Edialcu1byyq2hKBvL0w60nYnwn7ymn72u+dc1PHw9U/QWSM/3INQekkloEyBkY+CukzYOsvzvf02L+Htm0iIiIiDUw4g/2dgJuyo+mtKTvqXmRrOeULgUx/F1hrPcaY+UCPatxXpGYltoWBlxz8nLkWXj8bYpvB/h1ly2dnOAnfxrxWpwP+UAo8038MXVo2xe2xbNlzgH35bpZnZLM8w5nl1L99Uolg/y+vzifCwMw1mX57+yxOT+B9U5czLC0luCn98/7nBPotusGhY/yXOeX/nMR1O1bCzCeczp+GZNNCiEmAVj2rV09CGzj1Yfj4amc6/yGnQ6telV8nIiIi0kiFLdi31uYbYxYCw4CPfU4NAz4t57LZwKhSx04BFlhr/e7XZZwh+MNwpvVX9b4itSt7C8S1hL0Z5RTwhqDT/uHsAlCHp/SHSuCZ/pMZ0q0lAHmFbjbtPsC6nftYl7mf9Zn7SmT3L3R7+HHVDgrcFecuKVoiMHvtTo7p0SqwBuflOCPQ4ATwrkj/+QDiWjhB7IdXwE//dbZprG5gXFdYC1/cAlsWwVn/g/7nVa++fufBsg+d2S6f/hUun94ofvdFREREqiLc2fjPA14HrsEJ5K8CrgT6WGvXG2MeBNpZay/xlu8CLAOew9l+bwgwGbigaOs9Y8wEnGn8q3HW7I8HLgaOttbOC+S+AbY9EchSgj6pMau/hjfPrbzcpZ+VXQfeQAWb6b8yhW4PM9bs5LOlW/hw0eZKy8dGRdCpRVPS2iaSlppIn7aJ9E5NpHlTP1vD/fQIfPd/kNwdrpvLtBU7ys8H0CcF3vwzrPkaOg6FcZ87Cf3quw1z4KXh4IqBv/0G8QF2lFQkazM8e5Szfn/4v2HIX6tfp4iIiEg9UV8S9GGtfdcYkwzcA6TiBPIjfQLuVKCjT/l0Y8xI4HHgr8AWYHxRoO/VDPgfzjT9LGAxcFxRoB/gfUXCLzcrsHJ7G8/qkxF9U5k0diD/N+VXOuxdSmv2sJ1mbIzvz92jDw16TX2kK4ITe7UmNtIVULCfW+Dh9205/L4th48XHyzfNimWC4/syPUnOauFbG4WzHrK6YQ4/namrdhReT6A0x+DZ46CDbNg0atw+GVBPUvIdxEIhdlPO+/9zwtNoA+Q1M5Jdvj5zVCYW3l5ERERkUYq7An6rLXPAs+Wc26cn2M/AgPLli4+/zfgb9W5r0idEN8msHJL34bIWOg5vFFkKB8RMZ/hsbdj8g8mLLSxbTERDwNVy18Q6BKBD64Zysqt2Szf4uQA+G1LNht27WdLVi75PksBcha8R2LuHja7OvB8ei8+XvJr5fkAbj8J10l3wfQ7IHNNUO2vkV0EfFSpI2H3Olj5ufP1UddVuw0lDLwEOh8Dyd1CW6+IiIhIAxLWafz1mabxS43zuGFiXycZX3khaFwy7N/pfIxvA4ddCAMubrhB0PIpTmLC8jblqEbCwqouEcjOLWBlRg6tE2Lo3LIpAN+t2Mprr7+AGxczPP0Cuv/9Z/ThhO7JtN67jNguQ4Jud3nblFRpF4FS9VepI2HaHc62g91Ogos/Lr+ciIiIiAQsmGn8CvarSMG+1Iri4Bb8hqCnPwa70mHJWweDfoDOx8KgcU7G8qjAstjXecWdH1vKKWCcXQ1u+rXKSdtCNUKeX+hhzfa9LM/IZurSLfy4ys+OChVoEuWiZUI0yU1juOb4rsX33pGTx5w/MmkZH0PL+Giax0Vz+lM/szXb/3T2ohkJP99+UpWm9Fe5IyE3Gx5Lg/wcuOhD6HFy0PcOWMYv8OXtcNZkaN6p5u4jIiIiUgco2K8FCval1iyfAtNuLxnkJraDEQ8dHMUuzIdVX8Ki12DNtxR3DFz2JXQaWnH9Hjesn+Ws/Y9v45SvixnO02fAq6dXXq6aCQurtfY9LwesB2KTig/NXpvJBc/PqfTSlvHR7M0rJLfAA0B7s4O/RX6A67RHOPOoQwD4/vftXPby/KCf6aRDWtG2WRMiIyKIjDC4XIZT0towqFMLADKyDjB16RbnvMvgijC4jOHBL1eSdcDvRicVdyRsmAvvXODsKPHXuWD8f/9Ckmfg1VGQ/hN0PQEu/qTce4mIiIg0BPUmQZ+IBCBttLO9XkUBeWQ0pJ3hvPZsgMVvwsa50NFnOvjsZyEmHvqc7bxDOR0JbWHEw1WeDl9jAk1EWM2Eha4Iw5BuyVW7eNbTMHeSk0BuoDMjI9B8AD/ffhIRBvbnu9mZk0urt04mbtcK9mZMBiYCEBvpYnCXFmTuzWPn3vxyA/HSvltZdmZB26QmxcH++sz9/PuLlUE9atF2hDe9u5ijuibTqUVTOraIo22zWCI7Hulk39+zsdzgO2R5Bk57HCYfDX/8AItfL/6+i4iIiDR2GtmvIo3sS71ScAAe6QV5WRAdD33PgRZd4Jv7qIn17zWilkb2q+zAbpjYz9kS7s+vQJ+zik9VKR/AHz/Aa2c4pa74CjoMLnPLGat2cPFL88ocL23MoPakNmtCocdDocfidluG903hiM5OsL96Ww6TfljrnPNYCtweNu85wG9bKuws9utvJ/fkxpOdXQm2Z+fy0eLNdGoRR4cWcXRKjiMhNir0eQZmPglf3w0xic5MgsS2QbdbREREpD7QNP5aoGBf6pW8vbDgRVj4KuxaG8AF1V//HnIeNzzWu4KR+zC3+bt/wU//gdZ94JqfISKixOkqjWR/ch0seRNa9Yarf3JmcPhweyzHPPxdQLMGgp0eH+jyg+F92lDgtqzP3MfG3Qd48YR8jj1pFERE+F120KxJJPvy3RS4/f+/p0pt9rjhxWGweSH0HAEXvFN/p/PXl2U1IiIiEhYK9muBgn2pl6yF9TPhp0fgj+8rLx+uUfLyLJ8C713s50SYZyPs3+WM6ufneNtwht9iQa9R378Lnj7CSb544l1w/K1lilR1F4HKVKUjwbNxAREv/gna9IWrfmTx5hxenbWO9bv2s3HXfnbuzQ/4/q9dfgTH9WwdeIO3r4DnjgN3Ppz9PPQbE/i1dUV9WlYjIiLijzqta5yC/VqgYF/qtV8/gA+vqLzcOS/CoefWfHsqs28nNG3pfB1IwsLa9u3/wYxHnCD36hllRvWrpehn5YqGa2dByx5lioRs/bufeoPqSPjgclj2IfS/EM6aVKa+vXmFvD57HQ9P+73Se0e5DEd1TWZIt2SGdmtJ37aJRLoq+b7+9F/47gHoNRIueLvSe1RVSBILllaD20qKiIjUCnVa1woF+7VAwb7Ua3V9/buvn/4LcybDpVOgTR/nWHm9xmu+hd8+htFP1d407v27YOKhkL8XznsDeo8Kbf3Wwpvnwppvyg2ioYYCUILoSNizEZ7oD9btLGNIOdRvfYEuDyjtvauHMLiLk2Ngb14hcVEuIko/n7vA6Rzpdx5uj4eVc6dzYPdmmjRvxyFHDscVWf2ctDXSsVIL20qKiIjUKHVa1xpl4xeRinUa6gQP2RmU/UfZqyiIDqcZjzkjteBsr1YU7Ee4ynZC7N0O71wEhQcguRsc87faaWP6j1Cw3wluDwmgAyVYxsDpj8PiN+CYm8stVq1dBCowom8qw9JSKu9ImPc/J9Dvcly5gT4EvjvBS+OOYO4fmcz+I5NfNmVxWIdmxWUe/nIln/2yhaO6JjO0WzJDurWkW6umGFcUHHYBi6e/StvZ99GHzOJrtn2dzJYhExgw/NIqfy/KSyy4NSuXa99YVOUlE6yfVUGgD2Ahe7NTLtydbyIiIqV53M6Ivt//s1vAwLR/OLtLqdO6VinYF2mMIlzOlKr3LsEJr/z845yXA+t+hq7H13brHDOfhG/vc74+6S446tqKy8e3hhEPwmc3wbf3Q9sBzt7rNa3PWdDmUGeng5qaTdCsI5x4Z83UHYBKOxLy9jrJHwGO+muldU0Ylca1bywq85tX9N2bMCqN3qmJ9E5NZNzRXbDWYny+t4s37mb3/gK+XLaVL5dtBaB1QgxDuyVzZO5Mzkv/Z8kKgVY2k1azxrMYqhTwuz2W+6Yur+jPGO6bupxhaSnBz6iopW0lpYHQelgRqWvUaV1nKdgXaazSRjtTqkqvrUpIheg4yFwLb5wNIx+Bwy+r3bbNfsbZSg3ghDvhuLKJ6fwaNA42LYAlbzjrx6/+CZLa11gzi7XsXvP3KOIuhNVfwSEja++elVnyltPZkdwdepxSafERfVOZNHZgmenwKeVMhzelOlE+vu5oftm0h9lrM5m1NpMF63ezPSePKUs2cXvMowCUjrcjDHgspMy+j9cSjgHjIjWpCcPS2hSXeWHGH+QVerDW4rHgsRaPx/k6r9Bdoq2lWSAjK5d56buCn2ER36byMsGUK0dNLfWQWqT1sCJSF2WuDqycOq1rndbsV5HW7EuD4W+UyF0An/4Vln3glDnqr3DK/9XO6NGcSc5UL4Djbw9+RLvgALw0HDKWQrtBcNmXEBkT+nbuy3Sy5LfqFfq6y+MudJ5t8wJne7lep9bevSvy7lhYMdXpGBp8ZcCXhSr4zC1ws2jDbhb9OIXrN1S+fOP8/LuY40njuJ6teO3ywcXH+9wzjX35br/XdG3ZlD927qu07i4tmzK8Twr/OPWQ4mNuj634uTxueLQXdt8O/JWygElsV601+zWVxFFqkdbDikhds2mBs8PT6ulgPZWXrwu5oBoArdkXkcD5W/8e4YJzXnAC2e//BXOehT5nQofBfqsIGXch/PaJ8/Wxt8AJdwRfR1QTGPM6/O94Z9/1af9w1ryH2syJMOspp40n3B76+v1xRTqdMZsXwOd/h87HQExC7dy7ImNeh3UznM6VIIQqz0BslIuh3VoSvaIQNlRe/vDkPJJTUumdWvJ7d+aAdhS4PUQYgzEGVwREGEOEMXg8NqBgP33nPn7bklXi2DEPf0dslIsuLZvStWVTuraKp2urpnRt1ZRW8TGY3CzyCj3E4ORjLLMaxMKKzhfTuxqBfo3kGpDao/WwIlIXWAuFeRAV63zOzYJVXzpfR0SBp6CcC72JZsOdC6oRUrAvIv4ZA8ff5kzN3rej5gN9cILZsR8427cNvLTqa+Cbd4KzX3Cy2LvznT+UQ/kH8N4dMP8FwELbw0JXbyBOuANWTIHd65wt/0b+p3bv748xTmK+MGvSvF1A5U49agB9jh5Y5vi/zio/saDbY/l6xbYKEwsmx0dz12lpJMVFFR/Pzi0oHk1P37mP70pd96deybzgepiYvEy2exLxGBcp7C4+n2cjiTGFJP3yEoUnX0FkYuuAntG33TWWa0Bqj9bDikg4ZW2Cpe84r57DYfi/nONdT3CWW/Y5E3b87p19BGU7Jq3z94s6I2udpvFXkabxS6O1e52T+T6Uwf/WXyvM4F5lGUshtX/o653+T5j9tDOS/Zdva2+bvyJrv4PXzwIM/OUbaH947d6/SM5WiG5aN2YXAO7CQnY+0JNWNrPMmn1wBiR2mua0uGtNlbbhKxohB/+JBf2NkFtr2Zadxx879rJ25z7+2LGXP3bs44+de9m0+wD/6LOHq9bewAFPJGfn38cq257BEStpzR6204xVnnZ8HD2BThHbWRl9KIfc9h1ERpNb4OaoB78lMsLgijBERkQQ6Sr62nBcj1bcdXpawFsdvn3lUdWaZVEf8wHUqzb/+gF8eEXl5c55EQ49t+bbIyL1WyCJPvP3OcuHlr7t7IhU9H++5l1g/GL/f/v4yytSNOLf6Ri45FNnYEeqRdP4RaRm5GbBW+fBrnQ44xno9+fq17nwVZg6Hk66G467pfr1+fIN9D1uZz1/THz16szZBvNfdL4+4Y7aD/QBup0E/c6HX96BKePh6h/BFVX5daH29QT4/Us4/bE6EWC4IiPZMmQCrWaNx2NLJukr6tfe2e9KWlUh0IfgEwuCk1wwJSmWlKRYhnZvWeJcboGbvAIPM2fG8cb3S1hpOwIwx5NWotwVBbfwcfQEDsn/Fb74O4x6kkKPZc/+8qZLQo82TgfM9pzykwr68i33/oKNtGvehG6t4mmdEFMmQWJpNZ0PoCaC8nqXw6CWkjiKSCMQSKLP6f+EBS9Dgc/ytc7HQv/zIe2M8v/2SRvtLCfy7UiIawkvngzrf4Zv74VTHqixR5OyFOyLSOCMC1p0hR0r4aO/wM5V3mlZEVWrb/EbMPVG5+v9meUsWA6B/bvgoysBAxe+V/X2Asx8AgoPQLvDofvJIWti0Ib/G9Z8Ddt/c9oU6o6SymRnOAkcPYXO70QdMWD4pSwG2s6+jzZkFh/faZqz/bAb6HPm36tV/4i+qQxLSwlJ8Bkb5SI2yoWr63FM+za63HJrbHtuKLiel6MfwSx6DVr3ocngq/nm5uNxeywFbg9uj6XQY513t4fmTZ36WifEBtSWonJ78wq59YNfio83jXbRtVW8k2+gVVMGd25RotOipvMB1ERQXi9zGHQa6vwxXu5Ufq2HFZEAlJfoMzvDOV6U6NNT6AT6LbpC/wug33nOEslA+MsFdeazTv2znnL+fupzZiieRgKgafxVpGn80mh53PDNvTDrSedzn7PgjGed7fqCseQt+OQ6wMLgq+HUh2tulHzrr/DCyVCY602o94+q1ZOzFZ7o79Qz9sPwBvsAS9+F+c/DqCedZIq1uff2t/fDjEeh4xC4fFrN3aeK3IWFrJw7nQO7N9OkeTsOOXJ4yan7mWudP2LCMTPjwG746GpndKNVT9weyzEPf1dhPoCUpFhmHvcbEV/fDUOuP7heshKB1N0mKZaZt5+EK8KwNSuXOz76hfSd+9iwaz+eUhddMLgDD57dD4C9uYUM/L+vyXf7z8Bc1O6fvXUHq7ygvKJlE76sdTpAolxO516B28PiDXu45o2F7NqXXyNtrlEzHsV+e3+ZHRs8gMFglI1fRCriccPEvhXn/yja+WXPeu+SzSND9//Jr+52/naMjocrv6vd3YwamHo1jd8Ycx1wK5AK/AbcZK2dUUH544HHgD7AFuA/1trJ5ZQ9H3gb+NRae6bP8UjgXuAiIAXIAF4BHrA2kH0jRBqxCJezDV+rXjD1JvjtY9i9Hi54GxJSAqtj6bsHA/0j/lKzgT44+QBOnwifXAM/POSste8xLPh6tq9wsv2n9INufwp5M4PWbwwc+mdY+Rm8eU7t7b2dvx8WvOR8fdR1oa8/BFyRkfQ5+jT/J3//Et6/DI4eH/zWjtXlccOHVzqzMrI3w9UzcEVEMGFUGte+sQiD/3wAE0alEdHnJGg30NmFIUCuCFNp3feOSisObFOSYnn5MicfR36hhw279rF2xz4nz8COvQztdnBU/7NftpQb6OO9V0ZWLvPSd9G+eRNGTPypeDZDTFQETbxfx0ZFMKJvKhcf5Ywa7c0r5NGvfuf9BRsrTSw4qFML/v7+UvbnFbI/383+/EL25bs5kO9mX34h5x/RkQfPdvKB7M9zM+a52RV+v3zbHB8TSY828cRGBd9pVhNLD2bZQ+nmaUaC2U+cOdhZsdUmc3/BxZyT34cq/KsmIo1FpYk+KZnoM9Sz9v40AbYsdnbveXesE/DXkZw/DVlYg31jzHnAROA6YCZwNfClMSbNWltmAyVjTBfgC+B5YCxwNPCsMWaHtfbDUmU7AY8A/joObgeuAS7F6WA4HHgZyAKeCMnDiTR0A8ZC887OP9hbFjlbwZ3/ZuXX/fK+E3RjYdBlcOp/a2d09bALYNM8J0j98C/OOvfmnYOro9uJcOMvzu4E4RgRLs0YZ3/7QKbkhdLSt53R6WadnLV59c3ebc5SjB8fhqT2MPCSyq8JlR8edAL9yFhnWqN3SUnA+QB8A/3CfGeWSWzFs8uqkmsAIDoygu6tE+je2v8fY54AZwZuz8mlZXw0+/Ld7Mt3+y3TO+XgM2QdKODlmesqrLMoKF+yYTc/rdpRbrkD+YXFX8fFuGjRNIpd+8rPdVBk7Y693PXJMqJchl4pCfRr34z+7ZM4tF0zeraJJ9JV/lKgmlh64PZY/j7TxZ78x4iikLSIDcVJHNd52vCvqJeI+/RH3Id+h6uCtolII7Z3W2jLBcsVCee+BM8d78xALMyHmJq5lRwU7pH9m4EXrbUveD/fZIwZDlwL+Ntg+xpgg7X2Ju/nFcaYw4FbgOJg3xjjAt4EJgDHAs1K1TMEZ7T/c+/ndcaYC3CCfhEJVOdjnGz0n/8dTnu05LnyMr3u3wnW4wRYpz1WvfXzwRrxkJOhf/NCePdiuOIrZ6Q+GLGJlQZXtSYce297PDBnkvP1UdfWz210Bo2DPRthxiPO7JSE1KrN9AjWis/gp/86X496ssxOEUHlA9iXCe9d7CRmvOjDSrMbhzLXQJEuLQNLdtk6IZZOyU358dYTyC3wcKDATW7xy0NeoZsuLZsWl4+LcvGn3q35dsX2SuvOySvk0T/3Jy7aRVxMJE2jXTSJdtE0OpK4GBcJMQcTV0a5InjmwkEB7U5ggRZNo9m1L59lm7NZtjmbt+Y652KjIrh5WE+uOq4bAB7vWoeICBPSfAAej2XPgQJ2Ze/lx7V7vJ0HsRygZBLHbmYzx0QsI8YWsObb5+l+ytUB1e9PvdqhwEd9bbdIraoLiT7jW8NlX0BSB2XlryVh+y4bY6KBQcBDpU59BZSXYWaI97yv6cAVxpgoa21Rd/09wA5r7YvGGH8bzv4MXGOM6WmtXWWM6Q8cA9xUQXtjKNn/pHknIgDJ3eCST0oe++kRWPCi/2nlR10LrQ6BLsfXbqAPEBnjjHQ/dxxs/cUJhEcFMJknewtsWQK9Tq0bI/pFwrH39talsGstxCQ6szvqq5PucvYN/uUdeO9S54+PtofV3P12/A4fX+N8fdR10P88v8VcESawLfByMpzpkAX7YfqdMPI/lV4ScN0BGtylBalJsWzNysXgKbFl4DzPIVgiSEk6GHh1Sm5aeaVA86bR/OWYrgEF+6lJTYJ6Jt82V5Qf4cLBHRl7ZEc27T7AL5uy+GXzHn7ZmMWyzVnk5BXSPO5gQsXFG/cw7uV5HNoukaWbsipcenDvlN/onNyU3fsL2LUvn1378sjcl8+ufflk7s3nluG9ijs+Jv+0lknTFvNJ9N1scf8JwwgsZf/NXGvbMbHwHG6Peoc2s+7jwT09SWnXme6t4+nROoE2iZXvqAD1cIcCr/q4G4RIWHQaCnHJTkJkv2op0WeLLiU/79sJTVv6LyvVFs4ulZaACyg9V2Qbzjp6f1LKKR/prS/DGHM0cAVwWAX3fhhIAlYaY9zedvzTWvt2BdfcgTNTQEQqMu0OmPNs2eM1Oa08GEntnWlkn/3NWUYQiBmPOYnwDr8cTn+8ZtsXjECn2m2cG7pgv+0AuGERbF9ev9faGQOjn3KC5vQf4a0xcMXXgWcbDkZuFrxzEeTnOFsXDbu/+nWm9IWz/+cso5n3HLQ+xPn9rEVF+QA+eWsy90S9Rluzq/jcFtuC+wsu4cxR11QpMAo0KB/cpUWV2lxZfoSiNndoEUeHFnGc1s8JGj0eS3rmPpKbHgz2f920h5zcQmat3UVFLLA1O48RT5SblojzjuhQHOwnN43mnsjX6BaRwWURX/OW+yQO4H93hf+5T2OEax79I/7g8F/v58pFfy9+ooSYSF69YjADOzYHYHt2LvluD22TmhDhfc7a2KGgprZQrG+7QdQGdVCIXxEuJ3/Rexf7Oen9/RjxUO3N2CvMh6/+Cb994iytTGxbO/dtZOrC/Al/iXYrWghYXmJea4xJAN4ArrTW7qygjvNw1vxfiLNm/zBgojFmi7X21XKueRAnMWCRBGBTBfcQaXw8blhS3rr9GppWXhVdT4C/zgtsb/qsTbDI+89Cn7NqtFlBC3SqnctnW7fd66HggJNgsaqzFFp0KdszXx9FRsN5r8NLpzpbGM6dDCMeDP193AXQtJUzCn/uy4H93gWi9yhnhsJ3D8AXt0Jyj9B16gRoRMR8hkc/gS31v+YUs4tJ0U9gIgYBwXfuBRuUB9XmKuYwAGeqfrdWJZcvXHRUJ47o0oLXZq/n3fkbK71/XLSLlKRYkptGk9w0hhbx0SQ3jaZF0+gSyxnObvoLUZE/AYbUca/Q7O0D5JbT+eHBxb+jrudtz20Mcy3i7tRlvHngKNZn7icnr5DUpIOdBK/NXs/T368hLtpF99bxdGvZlG9WbK80GeKwtJQqB4w1lcfgvqnLa6zd9XKLRupvB4XUoIIDkLcX4ls5gy1jXneWAJaZfflQ7Q7GeAqdmYf7tsP74+DSz5z/L0tIhW3rPe80/v3An621H/scfwI4zFp7vJ9rfgIWW2tv9Dl2FvAeEIeToX8x4JsBqGjOmwfoZa1da4zZCDxkrX3Gp567gLHW2kMCbL+23hMpLX0GvHp65eUu/azWg5IKbV4EzTr6n0b22c3OkoROx8Bln5c9H07F2+hkUG4faXQC3LLq4NaI0/8Js592nrfnCOg53Hm2qAr2Yy/Kv5C51lm2UdPb+tW2rM1O4sYT7qi5NYTuAqfjKNSdJNbCh1fAsg+hSXMnu3GoMyiXp9JtnLxTQm/6tcq/LzUZuIR69HP22syA8gG8feVRlS892JcJzx7l/BE8dDyc8n/FwSf47/yYNHYgIzLfgO8fgNhm8Nd55DVpSfrOffRqk1A8lf++qb/xxpz1FLiD+/vvtENT6JWSSGJsJIlNokiMjeKYHi2LdyvIK3QTFRFRPFugSHW3UNybV8ie/fnsz3ezz7vrwr68Qn7ZtIenv19babuvOa4rvdsmEhPpYki3ZJKaOJ1tO3Ly2L0/n2hXBDFREd53F9GuCCIMHPuf70v83pVue13corG63+tAaNZAPVOQC+9c6Gyll4Ts3wAALMNJREFUd+nUg6Pn5eVVqm27/oDnToC8LGcb5gCWpElwW++FLdgHMMbMBRZaa6/zObYcJ3lemQR9xpiHgVHW2jSfY5NwOgeGGGNige6lLnsAZxT+RmCVtTbfGJMJ3GWtneRTzx3AZdbangG2XcG+SGm/fuAEHpU550U49Nyab08gln0EH1/t7Bd/8ccl/2e3ZyM8OQA8BTDu86C2PKs1y6d4s/GD3xCg9LKJz/4Gi98Ed97BY1Fx0PVE6HkKHHZRyZHn5VPKjgAkpMKp/2m4e3pb67yqm1MiZ2vg21FWR8EBePlUZw1/+8FO4snayC1RS5179SW4cHssxzz8XaVLDyoNEK2F9y+F5Z9Cq95w1Q/FnXGVdn64C+D5kyAvx5m1knKo31sUuj2s37Wf1dv2MnXpZj7/dWuVnnnR3cNo4V3ScPcny3hj7noSYiJJiI0isUkUCTEulm7KIq+w/C0aYyIjOKxDEgcKPMXB/Ofjjy1R7+tz1lepfaV9Mf5Y0to6f7M99e1qHv16VbXqC6jjphyh/r0u+v0rr4MCnN+V6nRQaNZAPVOY7yz1Wj3d+f/8xZ9AxyPD3aqyfv8S3j7f+frs551thaVCwQT74Z7G/xjwujFmATAbuAroCEwGMMY8CLSz1hb9JTsZuN4Y8xjO9ntDcNbnXwBgrc0FlvnewBizx3vO9/hU4J/GmA040/gH4OwM8FLoH1GkEakLmV6D1bo3REQ567a/e8CZFl3U2/3r+06g3/nYuhnog3dK3muBT8k7/XE45QH440dYNQ1Wf+WsW//9c2cLxf9v777jpKrOP45/zmyHbXQWadKkWFDsoNgQe7DEmlhixSRqjP0Xa9QYYyxJbBET7GKCBXvshaaiIEUEleoCS3FhgWXLzPn9cWZ3Z5dpuzuzU/i+X6997e6dO3fOzJ27O885z3nOXuc07Pv5BHj9KrbLGqhYnRz1F+LBW+Mq9Lfr4F6nllr3HTx2qCtiOOa22KXuB5OVB6c/55aUPO7etisi2UbLOMW6sGC8xGzqwbzJLtD3ZMKJjzTKuom4qkJGllsCtV3nhmyeIDIzPPTvkk//LvkU5WVFFewfv3sJ+bmZbNpWy6bKGjZtq6Ugt+Fj5KZtNViLu31bLT+WV0Y8JkBVrY+ZS35qtG1LVW19sN8uJ4OcTI9bcSE7k/Y57nut18e80rCfcQHYo1cR7bMzqar1NWpvdqaHTu2zqar1UV3ro9obukMilGsnz2HkgM6MHz2A3p1Cv95NxSJo3lbj5Ye1W1hcVkG77EzyczLDBvrglqs8a8IMnr/ogPptk2etpGP7bEqKcykpyqMwNzNoQcdUndaww/LWwH/Pc4F+Zh6cOSk5A31wxY8PvtqtVjPlMug61NWlkZhI6Mg+gDHmUuAaoAQXqP/OWvux/7aJQF9r7SEB+48G7sOl7JcCf7bWPhLm+BOBYmvtuIBtBcAfgROBrv7jPAfcZq2tjrLdGtkXaSpiWnnr03rjYt5k+K+/uFleR6hsUmjr0D/A6Kvbvl3N0dKUPGth9VxY9LYLFEZd4bbXVsMd3dwyiUEl6blsre/fh6f8tRmO/gvsd1Hzj1FVAY8dDuu+dRkjZ09Jr3mI3lqo2QKrvk7NaTt14pTG2upAbsbD8L8/wMHXwCHXtro9kcQqI6Gq1sumylo2bathU2UNFdtqeX/hGiZOizwqf+6BfRg9qCvtsjNon5PJwG755GS6c2GtDRp8xiyTws/ns1R7fVTV+pj2/br6KRPR+PCqQ+jrr7fw3GfL+fDbMoaWFDGkpIAhJYX07JBX/xxakmpvrWXKnFIWralg8ZrNLC7bzLL1W/Cv+sh+O3fkzP16c/nzsyO2da/exbx46UjAZXgM+sOb9ccByM/JpKQol5LiPEb278TFo/tHzBpI1mkNOyxvLUz+les0zMiBM5+H/oclulXh+bzwzCnuf3DH/nDpjPT6vxljqTSyj7X2ISBI6W6w1p4bZNtHwF7NOH6wY1Tgltm7ItrjiEgUPBlueb0Xzmb7WpsJqPQarV1Phq8nuYC3aaAP8MEdrqBdMo9iezJaFlAZAyW7u69A818KE+hDXJb1Swb9D4PDb4L3boM3r4HCElcIL1o+n1tib923brrDz59IzAeWJR9DxRrY/eexO2ZVBXz1NEx/CAYcDsf+1XX4hKsZUdA9/ss4tUSw6Sl1y4O28jqPOPoeyf7jXRHRTk1nJTaTzwef/dMtMzou6McsIHYZCTmZGXQpyKBLQcMqxVkZnqiC/bHDSkJmb4RaNjDWRRw9HkOuJ4PcrAyOHNo94moQnQtyuOnYoSwuq6B3x4ZR/anfrePt+Wt4e35DRktBbiZDSgoZ3L2At+evDllUEOC6F+cyZ2U52RkZ/G7MoPrX4I7Xv6GsoqrRfYryshjULZ/hvYvpWhCm7kqAc0f2rf95a42XwwZ3o7S8klUbK/lpaw2bq2pZXOY6FIr99Q0+W7IhbNaAxWUNfLZkQ9Jm4aTKdKBW83nhpYv8gX62y/RJ9kAf3GeYkx+HJ46HQ29QoB9DCQ/2RSTNNDetPBn4vLBqbvh9kmEVgbYU7fNsZYp2Uhp1pavXMOvfLjX+nFeh177R3ffTe2Hha+5D1qlPQUECpqz8OMtlJxgPFPeC3vu37nibSmHmo/DFv10RJYDF77jrJmTnnt+uJyffNVNf56JJe2O4PGiLph5Y2zAFo+uQVj0+AOsWwds3gPXCwCNh2LiQu7ZmhYJw4rWEYrzbHU1Hwh9/Nizo8c8ftTPDexXzzaoKvlm1icVlFVRsq+WzJRuYtWwDkWYLlG+t4eEPf6B7YW59sA9w/B492FrtZVC3fAZ1K2Bg13y6FOTUd4Z4fTaq1/rY3RqWNyvMzWLCOXvX/15Z7aV0YyWryrdRurGSHkV5AJRVhJ8eUKduP2stC1dXMKBrPlkZ0dc+iVdA/ta8Vfxxylx6bZ5DV8opo5gV+Xtw4wm7JfXUgxa9Hls3uPotniz3t2zgmLZpbCy06wgXf9L6ejnSSMLT+FOV0vhFIkiWSq/RSNVVBOJpR39NvLUw6SxX1yCvI1zwrluJIJzF77o0RCwc/zcYcU74/ePF53MF3r6Z4pb8u/B9t/pCc61ZANP+3lC7Atxo8wG/hj3OcLUCIPgoeX43GDAGxj24/XETqQ1WEGiRn5bC5Avh2HugZI/YHfe9P8In97g5/L/+DNqH74CIR7AV1SoCSVohPhZz66trfXy/djMLSjfx0aK1TJkT6r3X4MD+nThyaDfOObBvyMyGUO2Nx2vd3JUmVv60lVF//oDsTA9DSgrZbadCdtupiF13KmJQt4KgHQDxKv731rxVvPzsI9yU9SQ9TEPmXqntyG01ZzPuzEuS8v3XqtdjUymsmZ9agX4w5StcBmFrO6zTUMpU409lCvZF0kgqriIQb6lafyGWqrfAxGPdKEnnQTB+euil+SrL4YE9YFs5jDgPjr+/DRsaRPUW+NdYV4+h227wq7cgJz/y/QK9fwd87F8GqfeBcOBv3XKNwUZdInXuVayBWRPhoN/Hb3nDaCRjJ5bP59q0bCrsPBrOmRK7Y9dWwaOjYe03sOspcMrjsTt2M6RyFfdYBnIxXZ4xhHi81oH1EQw+9vUsrB8h/8w3GIuHbkW5TPXP2Z/5w3oueOILKqpqtztWdqaHa48azPmj3DKktV4fb81fzW+f/SrmSwZ6fZb/u/NO7qxxf8cCT1tdnYIbsq7hjhtuSKoVCppd18Hng1VfwU4jon6MeE5riMmx18x3Kf3WwsUfuyw1qadgvw0o2BdJI8kYACSD5i7rl442l8FTJ8GYW9089XAWTHGp/2c8D5k54fdtC+Ur3DJsW8pg8HFwykRYMSN4QF5bDfNfdBkAdXPsN5fBW9fD/pdCz+g/RG7H53NLA66YAf0PdwFnXodWP70WScaOvekPunT7rPYwfip03Dm2x/9xFkw4wtXgOO0ZGBLF37o42GHmTIcR66KC4R4nHiPNzRkh9/ksyzZsZe6PG5n340bmrtzIvNKNVGyr5W9n7MkJe7jpBJ8sWssv//VZ2MeuWzJw0ZoKPlq0lppaHzVeH9VeS43XV/911n592HWnIgCmfb+Ou9+Yz0PrzqU7Gwj29H0WVtOJ2Sd/TK31UJyXRXG7LIrzsilql0VBTiaeMK9bS4otRtLsYojWwhtXwRf/ghMfjWrZunh2vsXs2DXbXIf1qtnQY084761GK5Ps6BTstwEF+yJpRKPYoQUtZLZT8tZfiAefr/FodrhR7MB518lgxWcuO8FbDdn5UL254bbCHnD4ze55zHgEKkph54NdjYJYm/8SvHwp1Gx1lZbPeB66DIp8v1hLto69td/CIweBtwqOux/2Pi8+j/PuLfDpfdC+K/x6ppsbKwnRFtMa4mLBFOwLZ2OxBOb2+ACDwUTR+evzWZZv2ErH/GwKc13xvz+8PJenZyyP+PDPXbg/yzds4drJoevrPHzWXhy9m3vtpswp5dlJz/B8duQlVJ8c9CA3fV3XAWmpOxse44og3njcUE7aqycA35Vt5ukZyyjMy2Ti1KVs2laLJ0S2Q9eCHK49ajC1/pUe6pZ4rPF/P2xwV/bs7R538ZoKHvzgO1Zv3MaMJUEKBTfxuyMGMmpAJwZ+dSeFcya4No97GIafEfZ+8eigiNuxf1oG/xwNlT/BiHPh+AfUaeiXUtX4RUQSLlVXEWgLQ09whQlTpf5CPAQG+jP/Ce/f5irT18nvBsfc416rZAr0wRUWHHEefPZo40AfXAfOSxc3/J7fzVWBb9q5EQvDTnRB/vNnwobvYcLhbvR80JGxfZxgrIVv33TrTR8TxQoCOUVts4KAt9a9/t4qGHCE+zAbL6Ovg4VvwPrvYOmnO05HXRKKV1HBuPJ54a1rMVia/oWr/0sRRRFbj8fUL1FYZ58+HaMK9ssqtrFz53xOGdGTrAwP2RmGrAwPmQE/D+jaMFVpz17FMCQbvo/89HpmbmT/fv0p31rDhPIL2WRz+cbXk299vfh2W2/aV3UCuxMYw/drNzNx2tL6+471fMbNQbIdbq05m7cr9uXK/8wJ+bid2mfXB/sbtlTz8uzI9Rzq3PfuInI/fJYRma8DcKO9mE/fLaHLjOmcsV8vTtzTdU5srqrl86Ub6JKfQ6f22dwyZUHI1SAMcOurCxgztHuzA2ivz3LrqzE+doc+cPIEePoUmDWRuQzkonlD4jYdKF07EjSy30Ia2RdJQxrFlnDmPN84OG7EJOe0hogF6QBPphtV3v3U+E8/2LzWdaotnwYYOOJmGHlF/DpJyha6IOSHD9zvZ0xyWQ4hp6dY+PmTMOxn/vaWuSKH8WjfR3e7ZT1zi9ya0oU9It+nNUpn+5fajGEBQGmxlAos4pgRE886Bt4fPibjychLp3rPfpWMfge7EeQ/9w2+U24x7H4qC/e6kSmzS5m9opwuS17hviy3rGWwegDja65gaZfD2alDHlkZhuzMDLIzPGRnGrIzPBy1a8OSk6s3buO1r0v5sbySf09dGqHFljuLXubMqv8AcEPN+TzrbZhm9odjh3DBQf0AmL2inHEPTo34GgTao1cRhblZ1HotXmvx+iy1PstZ+/bm1H3c3PnFayq48MkvqPVZfD7L1mov5ZU1EY992OAuDOpWSFH9lIksBnTNZ2C3AvfM/HFpo8KUH/0FPridKpvFKdU30d5U1WdRfO4bjA9PqzNiUq2uiEb2RURaQqPYEorPC+/dGn6fZFyecdm08IE+gK8WOvRtmzoD+V3g7Ffgzatdwb7Zz8K+F0F2+4h3bZbKn+DDu+Czx9zScxnZrsBg31GuUGE0y4NWb4V/H+Nem589GNtlFH0+d26gIdsg3noMj/9jSNRatDxjokS7xGoLlmKNy/KMSz6BNfPI2PciKvO6k7N1dcg5+1XtupPXd6TbkFMEl33lViIp83+tWeAyYraVg8/L4O6FDD6qkJkLlrLvShfoN+0L9Bh37JuznmLZcb/mgIFdIza5e1EuFxzUD6/P8ta81WFfj/9r/0p9oG+Pvpvr9/gVF1RUsbaiirWbqxhS0jAI6bOWYT0KKauoYl1FVah8pkbmrNgYdPthuzQ8D6+1LF2/NYqjNfb+wrW8v3Bto23njezLzccPA2BtRRUH3vU+RXlZFLXLcp0COaM42+7FoeZL/pN9G7mmoVOhLovilik5HDGkG5nNWOqxTqjpB6s3bmP8018m79SaKCnYFxEJ5MnYsYrwSXQiBs3WLRG0bFpyvX/i+CG9xTKzXSZByR6u+nwsA32f13UivH87VPrTagcfB0fe3rjwXTQdez/OgvLlsH4xPHwAnPB3d59Y8HjgFy+6qQWDjorNMZtj9VxY9DYcfFXbP7akDm+tuybyo+zoina/ABkew83HD2X801+GmkTHzccPjS7rwVsDH9zp6lMYAzuNIO/4v2BfOBtfsFoDxpB3/F8arnuPBzr2c1+BhSxrtsG6RZDVrn7T3ls+CJvw4zHQg/V0y1gItoubThTF9KjA1yMDH/sE1AJwo9iGMX2z3PSEsXdi9ruYAqAgN4t+XbZfcWWv3h14/TL3P2nqd+s4a8LMiG24ZHR/dumeT4bHQ4YxZHgMmR5Dvy4Nf6t7d2zH5PEH4DGGTI+H+aUbue7F0PUU6vx8RE8K87Io31rDxspqNlbWNGp3eWUNtT7L+i3VrN9SXb+92LMfh2R9STaNswe6s4GHs+5n/GbY5cYqOrbPoWO7bDq0z+KoYd05d6T7u1/j9fHa16UUt8umY7tsOrbPprhdFnlZGbGffpBkFOyLiIhEkoxBczTi+CG9VYyBvX/VeNusJ6DrUOi1T8uP6/PCjIddoN9lCBz1J+h/aPB9I3Xs7XwQXPwRTL4Q1sx19Qb2OhvG/qn5yxgGfXwP7HJ064/TXJtWwWOHu1oBJXuk/lrc8RZpWcl05PPCvBfhwz+5a2jAEZFrXRSUtLjWRUzqGKz/HiZfAKWu+CHDz/L/PdnXFQ9sksljCnfCRDtFLysXSnZvtCkjtyCq55axpcx1FEw4Arrv7jJsSoa77x37B+0AOGrXEl48dB09pt9KN9bXb19DJ0oPuJk+Rz4IS06DfqOjakOd/ft1iiqL4uqxu0QMbNtlZzKiT0O2xdAehTzw3uKIx77r5N3DHrtf5/bMuP5wyiur2bi1hvLKGj5auIrffD0JC9tlaARmUbxTtbfLbqioAmiU4fDTlmp+N2n7+gkZHvD6/McKUmjRh4dVG7fx2ZINqZOJ04SCfRERkUiSNWiOpM+BET6k+1eaaIuCdOEs/RReu8LVDzj+ARh+ZvT3LV/uAo2MLJc1cMzdsO4715mQ0cqPOV2HwIXvuUyBaX+HL590bT3pMei5d/OPt3wGzJsMR9wS+6kL0Sosca/NzIfh1cvh0umuboBsL2gdlx6uoGuy1eeIBWvhm1fd6Pjab9y2GQ/BoLGRi9gefXdDJ8iHd8GQE6Db0Kgf+qhdSxgztHvz6xhY66YDvXmNK0KaWwTH/w2GjWvYZ+gJmCaZPKa1nTbN+Z9Q+hVUbYJln7qvOtkFrhNh1JUw8IiG7QumsOf0y7FN/mZ3ZQPdpl8OvTq06P0X0yyKOB07M8ND96Jcuhc1LLPXs3wWPeaGXqGgLotiZq+H8PUZyYa83izudBh9OjV0ytb6LKMGdGbDlmrKt7qsgapaX32gH7bQom9fyiqCL4WYClSgr4VUoE9EZAeSysszLpgSpiAdyVFYsGqzK3648DX3+wG/gSNudcF6qJHV6i0uXXfq31yhvwN+Hd82LvkYXrrETdcYcAT8YnLz7l+9BR4eCT8tgZGXw5jb4tPO5rZlr3PghL8lri3Jqv66CbGQWDJcN7FiLSx+Bz64HVb5Rz9zi+DAy2C/iyHHP4odTRHbRW/Ds6e6jrv9L4XR18YmEyaUV34NXz3tfu4zCk56FIp6xu/x6jTnf4K1sHahWzO+dLZ7jVfPhdpKt+sZzzdk+Sx6Gyb9whUSDar1/2viWYwuHsf2ff0fPC9eEP0d8jrCtUsafv/gT66zpdMA6DwQOg+C/G5U1vh475s1vDrpUR7Ouh8IXWjx3PMvS6qR/eYU6FOw30IK9kVEdjCpEDSHkgorTfh88NFd8NGf3e/9D4PdTnVLHTYdWR3yM1jwClT4tw8+Dk5/Jv5trPwJ3rkZDrmu+UX1Xv89fD4BCnvCpdMSP5q+9FOY6K9B8MuX3OstTsRVLJK4c68lAgPm7HwXpB/wa8gr3n7fSNMaNq6EN69t6Lgr7AlH3+Wu0XisavHFv+GNq+DQG9zKHm15PlrzP8Fb69L7V82GgWOhvT+QfOkSmPNc5MduwcoHjR4+jqtBxPzY0a4GsecvXcHZjOzGHZgPDHcdm4GyC6DzAHwlw1k7awpd7PqQRRzLTCe6/GERGZnJkxCvYL8NKNgXEdkBpULQHEqqzD2e/zK8PB5qoqj0XNwHxt4Rv0AiGm/d4NL99/xF6DZ8/z48daL7+Zcvh64j0NZevwo+fwyKerl0/pzo5iEnnVi+tyvLYfYz8PYNkfdtZcCVUNY2vF8XTIEXL4J9L3QBc/sYjGAuetsF4eXL3e8Dx7opNh36tu643hrXoVBXcNNaVy2/88DWHbelYv0/YcbDbmWXSE5+HHY7pfnHT0X+zje7aRUmSBaFxWDCdb59+SSs/RbWLXYdLOXLwPrz9zsPctsiSbJrXcF+G1CwLyKyg0qVoDmV/fgVTDjcLZsXSk4hXLkQchI09x0aj44PPs7NFW7fqfF7JLs9vHaly0LY50I49p7Etbepqs1upYHy5W5awcjLE92i5mvNvPqK1fDTMui9X8O2f+wT3Yd/SN6AK9zfqNKv4P07XHG3A3/rtlkLW9ZCfuQl4pqleit88leY+gD4aqDzLnDpjKiq0ge14QdXMHPzGrjk0+CZB4kQy/8J0Y5iJ1nwGXf+LApXIb8hdrUYl0fRnMy62irYsMSttLJ8Jkz/e+T7JNm13pxgP3nyEURERFKBlmeMv+rN4QN9cHMwS79M7LnofYCrLfD+7S5teeXnrmL/7Ge2TwHP7wZjbk1MO0PJyYcT/gFr5ru52anWkRVqXv2mVW57XQDg87k03lVzYPXXsOprN2d6S5lLW79uRUMA2n13N7q/pSzy41dvifUzar1QnR8H/Mad27r0+tVzYb9LXGFLY2If6ANkt4PDb4TdT4PXr3RTA+pe58DMgkisdantb1zt/jbkFLk58L33j32bWyKW/xNSpahqWxt6Apz6JGa7VRV6ND+LIjMHug52X7nF0QX7yVZ8txk0st9CGtkXERGJk7n/hcnnR94vWUZbVs1xI47rvg2/36lPJe90j3hXno91R0Jz5tX/51z4ZkqQXTzQaSCc+1pDsOutdUFopOJrecWuhsPAsTD6mpatzhBrIYsKBjIu+B59DXTq31Yt2z64/+JfsGw6HHk7FPgDqWDvkaoK11Ewz18Qs/eBcNI/obhX27W9raVyfZh4i9vfkdQqvqs0/jagYF9ERCROUjGVtWoz/HVQmNHe5PzQCMS/8nw8OhK+fROeOz3yfue8Bks+cqs2dBvqRu5Ldofue0C3YW70OVSbwwVcQ09wy9TVzf3tfxgcfA30OaBlz6e1InZ+AJl5cMF70H1Y27UrmOqtcN8wqNzgRukPvxHad4G3r2/c/nad3fet68BkwKHXu2Xqku36iYdUrg+TalKwc0XBfhtQsC8iIhInqTjakoodFBD/yvMt7UioK8T20xL4aan7OuC3kN/F3f7c6S7gj+Tkx2HQUS51NyOr+W0PF3Ct/97NSZ/zfMO0k74HuVHzvge1bdHIb9+C506LvF+yvP9+/NKN2Jd+FXnf9l3c8nTJkD3RllJtWk0qS7HOFc3ZFxERkdTlyXCjvi+cjQsKg4y2HHVXcn3w3bwmtvu1lWXTwo8GY2HTj/DZY7D/JW7T2m/hhw/d8oG5xe57XnHD71l5LtD1ed0H6KAdNq7UFm9dB4OPdedywRT44nFXPGvjyu3rNgwc2xDsdxwQ3fPL79bydd6HnuDaFirg6tQfxj0EB18Nn94Hs5+FpZ+4jopfvdWyx4yWtVC2ABb/Dxa/C8unRXe/ZHn/7bSXyzL44nF44xrCTj3wZEGPPdusaUlD9WHaTqRrPYUlPNg3xlwKXA2UAPOBK6y1n4TZfzRwLzAMKAXuttY+EmLf04HngFesteOa3LYT8GfgaCAPWAScb62d1drnJCIiIq3kL8gUPP07CUdboi3glGyFnqIN/ipWN/y8fAa8eU3ofU/5N+x6UvQdCcumuaBm63rXiVAnM9ct1Vb31S5gSbgjboH5k+NfyCyagKvjzm5d74Ovhqn3N14KcusGWPkFDBzTeKS/paO2Pq8bEV/8jnvtmiuZ3n+eDOgyhPA1BnArWdS9R0TiJU07VxIa7BtjTgPuBy4FpgIXA28aY4Zaa5cH2X9n4A3gMeAXwEjgIWPMWmvt5Cb79gHuAbbrODDGdPA/3ge4YL8M6A+Ux+q5iYiISCul0mhLqlbRjjb422lEw89FPWHoONi2EbaVu++V/u/W60b4ofnZDv1Gw7hHGoL7/G6hl2nLyEy+7I/iXnDsXxtvm/kIfPRnKBnuOgN2OcZVxI+mhoG1UPaNG8GvK0TpyXCp75t+dHPwdz4IBoyB/ofCkyek3vsvVTNiRFJEokf2rwQet9ZO8P9+hTFmLDAeuD7I/pcAy621V/h//8YYszdwFVAf7BtjMoBngJuBg4DiJse5FlhhrT0vYNvSVj0TERERib1UGW1JxakHEH0nxeBjGjYNONx9NWWtWxotI8f93txsh4793Fe0UiH7w5MJWe1g1WyYdBYU9YKNK7bfr265wJMehaz28N07Lj1/00qXxj5oLOQUuH0P/T9XsK7vSDdlok4qvv9SNSNGJEUkrECfMSYb2Ar83Fr7UsD2B4Dh1trRQe7zMfCVtfbygG0nAi8A7ay1Nf5ttwK7W2tPNMZMBIoD0/iNMQuAt4GewGjgR+Aha+1jYdqbA+QEbCoAVqpAn4iIiNRLsUJPQPyqUbdVocVkL2S2ZR1MfxBm/hNqNjfvvpm50HeUyxjo0Dfy/qn2/kvFYpwiCZYqBfo6AxlA07ycNUD3EPfpHmL/TP/xVhljRgLnA8PDPHY/XPbAvcCdwL7A34wxVdbaJ0Pc53pcpoCIiIhIcKk09aBOvEbI2yrbIdmzP9p3hiNuhp77wPNnRN4/vzsM/Zmb5993VOPR+0hS7f2XqhkxIiki0Wn8EHwtlnDpBiHWbsEaYwqAp4ELrbXrwhzDA3xhrb3B//tXxphhuA6AUMH+n3CdA3UKgJVhHkNERER2RMkefAYTryAxFVLt20rN1uj2G3tHwxz9lki195/eIyJxk8hgfx3gZftR/K5sP3pfZ3WI/WuB9bgK/X2BV01DxVMPgDGmFtjFWvs9sApY0OQ43wAnh2qstbYKqKr73bTl2qkiIiIi8RavIDHVRpvjRfPTQ9N7RCQuEhbsW2urjTGzgDHASwE3jQFeCXG36cDxTbYdiRulrzHGLAR2a3L77bhR+MuBuoooU4Fdmuw3CFjWrCchIiIiIpGl2mhzPKTqig1tRe8RkZgLsZ5Jm7kXuMAY8ytjzBBjzH1Ab+ARAGPMn4wxgWn1jwB9jDH3+vf/FW5+/j0A1tpt1tp5gV+45fQq/L9X+49zH7C/MeYGY8wAY8yZwEXAg23xpEVERERkB1M3Px1omIVK4981P11EYiihwb61dhJwBXATMBs4GDjGWls3wl6CC/7r9l8CHAMc4t//RuAya239sntRPu7nwInAGcA8/3GusNY+0+InIyIiIiISTt389MKSxtsLe7R81QMRkRAStvReqjPGFAIbtfSeiIiIiDRLsi8XKCJJK1WW3hMRERER2fFofrqItIFEz9kXERERERERkRhTsC8iIiIiIiKSZhTsi4iIiIiIiKQZBfsiIiIiIiIiaUbBvoiIiIiIiEiaUTX+Vtq0KexqByIiIiIiIiIx0Zz401hr49iU9GWM2QlYmeh2iIiIiIiIyA6np7X2x3A7KNhvIWOMAXoAFYluSwQFuE6JniR/WyU8ncv0oPOYHnQe04POY/rQuUwPOo/pQecx/gqAUhshmFcafwv5X9iwPSnJwPVJAFBhrdWcgxSmc5kedB7Tg85jetB5TB86l+lB5zE96Dy2iaheVxXoExEREREREUkzCvZFRERERERE0oyC/fRXBdzq/y6pTecyPeg8pgedx/Sg85g+dC7Tg85jetB5TBIq0CciIiIiIiKSZjSyLyIiIiIiIpJmFOyLiIiIiIiIpBkF+yIiIiIiIiJpRsG+iIiIiIiISJpRsJ/mjDGXGmOWGGO2GWNmGWMOSnSbJHrGmFuMMbbJ1+pEt0siM8YcbIx51RhT6j9v45rcbvznt9QYU2mM+dAYMyxBzZUQojiPE4NcozMS1FwJwRhzvTHmc2NMhTGmzBjzsjFmlyb76JpMclGeR12TSc4YM94Y87UxZpP/a7ox5uiA23UtpoAozqOuxSSgYD+NGWNOA+4H7gD2BD4B3jTG9E5ku6TZ5gMlAV+7JbY5EqX2wBzgNyFuvwa40n/7PsBq4B1jTEHbNE+iFOk8ArxF42v0mDZolzTPaOBBYH9gDJAJ/M8Y0z5gH12TyS+a8wi6JpPdSuA6YG//1/vAKwEBva7F1BDpPIKuxYTT0ntpzBgzE/jSWjs+YNs3wMvW2usT1zKJljHmFmCctXZ4gpsirWCMscCJ1tqX/b8boBS431r7Z/+2HGANcK219tFEtVVCa3oe/dsmAsXW2nEJapa0gDGmC1AGjLbWfqxrMjU1PY/+bRPRNZlyjDEbgKuBf6FrMWXVnUdr7eO6FpODRvbTlDEmGxgB/K/JTf8DDmz7FkkrDPSnsi0xxjxvjOmX6AZJq+0MdCfg+rTWVgEfoeszFR3iTyleZIx5zBjTNdENkoiK/N83+L/rmkxNTc9jHV2TKcIYk2GMOR2XRTUdXYspKch5rKNrMcEU7KevzkAGric00BrcH1FJDTOBs4GxwIW4czfNGNMpoa2S1qq7BnV9pr43gbOAw4Df41JO3/ePREkS8o/i3wt8aq2d59+sazLFhDiPoGsyJRhjdjPGbAaqgEdwWVML0LWYUsKcR9C1mBQyE90Aibum8zRMkG2SpKy1bwb8OtcYMx34HjgH9yFHUpuuzxRnrZ0U8Os8Y8wXwDLgWODFxLRKIvgHsDswKshtuiZTR9DzqGsyZXwLDAeKgZOBJ4wxowNu17WYGoKeR2vtAl2LyUEj++lrHeBl+17QrmzfWyopwlq7BZgLDEx0W6RV6lZU0PWZZqy1q3AfZnSNJiFjzN+BE4BDrbUrA27SNZlCwpzH7eiaTE7W2mpr7XfW2i/8daTmAJejazGlhDmPwfbVtZgACvbTlLW2GpiFq1YbaAwwre1bJLHgT30aAqxKdFukVZbgPtDUX5/+Ohuj0fWZ0vxTbHqhazSp+Jfy+gdwEnCYtXZJk110TaaAKM5jsPvomkwNBshB12KqqzuP29+gazEhlMaf3u4FnvKnzUwHLgJ64+bUSAowxtwDvAosx/Vq/wEoBJ5IZLskMmNMPjAgYNPOxpjhwAZr7XJjzP3ADcaYxcBi4AZgK/BsW7dVQgt3Hv1ftwCTcR9e+gJ34jKrXmrLdkpEDwJnAj8DKowxdaOGG621ldZaq2syJYQ9j/7r9RZ0TSY1Y8yduPncK4AC4HTgEOAoXYupI9x51LWYPBTspzFr7SR/L9pNuLUt5wHHWGuXJbZl0gw9gedwBRfXAjOA/XUOU8LewAcBv9fVWHgCOBe4G8gDHgI64IoxHmmtrWjDNkpk4c7jeGA3XBHNYtwHmg+A03Qek07dErQfNtl+HjDR/7OuyeQX6Tx60TWZCroBT+E+m24EvsYF+u/4b9e1mBpCnkdjTB66FpOCsVa1LkRERERERETSiebsi4iIiIiIiKQZBfsiIiIiIiIiaUbBvoiIiIiIiEiaUbAvIiIiIiIikmYU7IuIiIiIiIikGQX7IiIiIiIiImlGwb6IiIiIiIhImlGwLyIiIiIiIpJmFOyLiIhIUjLGHGKMscaY4kS3RUREJNUo2BcRERERERFJMwr2RURERERERNKMgn0REREJyjjXGGN+MMZUGmPmGGNO8d9Wl2J/rH/7NmPMTGPMbk2OcbIxZr4xpsoYs9QY8/smt+cYY+42xqzw77PYGHN+k6aMMMZ8YYzZaoyZZozZJeD+exhjPjDGVBhjNhljZhlj9o7biyIiIpIiFOyLiIhIKLcD5wHjgWHAfcDTxpjRAfv8BbgK2AcoA6YYY7IAjDEjgBeA54HdgFuAPxpjzg24/5PA6cBlwBDgEmBzk3bcAfwe2BuoBf4VcNszwEr/448A7gJqWv6URURE0oOx1ia6DSIiIpJkjDHtgXXAYdba6QHbJwDtgH8CHwCnW2sn+W/riAu8z7XWvmCMeQboYq09MuD+dwPHWmuHGWMGAd8CY6y17wZpwyH+xzjCWvuef9sxwOtAnrV2mzFmE/Bba+0TMX8RREREUphG9kVERCSYoUAu8I4xZnPdF3A20D9gv/qOAGvtBlzwPsS/aQgwtclxpwIDjTEZwHDAC3wUoS1fB/y8yv+9q//7vcAEY8y7xpjrjDH9EREREQX7IiIiElTdZ4RjcUF53ddQ4JQI961LGzQBPxOwrU5llG0JTMuvO54HwFp7C26KwevAYcACY8yJUR5XREQkbSnYFxERkWAWAFVAb2vtd02+VgTst3/dD8aYDsAgYGHAMUY1Oe6BwCJrrReYi/ssMppWsNYustbe558u8CKuzoCIiMgOLTPRDRAREZHkY62tMMbcA9xnjPEAnwKFuGB9M7DMv+tNxpj1wBpcIb11wMv+2/4KfG6MuRGYBBwA/Aa41P8YS40xTwD/MsZcBswB+gBdrbUvRGqjMSYPVyDwv8ASoCeuUN/k1j17ERGR1KdgX0REREK5EVdh/3qgH1AOfAncSUN24HXAA8BAXLB+grW2GsBa+6Ux5lTgNv+xVgE3WWsnBjzGeP/xHgI6Acv9v0fD67/Pk0A3XEfDi8DNzX6mIiIiaUbV+EVERKTZAirld7DWlie0MSIiIrIdzdkXERERERERSTMK9kVERERERETSjNL4RURERERERNKMRvZFRERERERE0oyCfREREREREZE0o2BfREREREREJM0o2BcRERERERFJMwr2RURERERERNKMgn0RERERERGRNKNgX0RERERERCTNKNgXERERERERSTP/DyjEbr9/HDgfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# save best model hist\n",
    "base_model_name = os.path.join(path_data, \"nn_2000_queries_trimming_simple\")\n",
    "hist_save = f\"{base_model_name}_10_hist.pickle\"\n",
    "print(hist_save)\n",
    "if not os.path.isfile(hist_save):\n",
    "    with open(hist_save, 'wb') as outf:\n",
    "        pickle.dump(simple_models[10][1].history, outf)\n",
    "else:\n",
    "    with open(hist_save, 'rb') as inf:\n",
    "        best_model_hist = pickle.load(inf)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,8), dpi=100)\n",
    "\n",
    "ax1.plot(best_model_hist['mae'], \"o--\", label='Acuracy (training data)')\n",
    "ax1.plot(best_model_hist['val_mae'], \"o--\", label='Acuracy (validation data)')\n",
    "ax1.set_title('MAE loss')\n",
    "ax1.set_ylabel(\"MAE\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(best_model_hist['loss'], \"o--\", label='training data')\n",
    "ax2.plot(best_model_hist['val_loss'], \"o--\", label='validation data')\n",
    "ax2.set_title('MSE loss')\n",
    "ax2.set_ylabel(\"MSE loss\")\n",
    "ax2.set_xlabel(\"epochs\")\n",
    "ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L1 regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39660/40000 [============================>.] - ETA: 0s - loss: 0.0682 - mae: 0.1986\n",
      "Epoch 00001: val_loss improved from inf to 0.05995, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 5s 133us/sample - loss: 0.0681 - mae: 0.1985 - val_loss: 0.0599 - val_mae: 0.1865\n",
      "Epoch 2/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0597 - mae: 0.1873\n",
      "Epoch 00002: val_loss improved from 0.05995 to 0.05803, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 4s 99us/sample - loss: 0.0597 - mae: 0.1873 - val_loss: 0.0580 - val_mae: 0.1851\n",
      "Epoch 3/100\n",
      "39540/40000 [============================>.] - ETA: 0s - loss: 0.0575 - mae: 0.1836\n",
      "Epoch 00003: val_loss did not improve from 0.05803\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0575 - mae: 0.1835 - val_loss: 0.0643 - val_mae: 0.1954\n",
      "Epoch 4/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0561 - mae: 0.1809\n",
      "Epoch 00004: val_loss improved from 0.05803 to 0.05374, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0561 - mae: 0.1809 - val_loss: 0.0537 - val_mae: 0.1779\n",
      "Epoch 5/100\n",
      "39630/40000 [============================>.] - ETA: 0s - loss: 0.0554 - mae: 0.1799\n",
      "Epoch 00005: val_loss improved from 0.05374 to 0.05244, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0554 - mae: 0.1800 - val_loss: 0.0524 - val_mae: 0.1750\n",
      "Epoch 6/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0549 - mae: 0.1791\n",
      "Epoch 00006: val_loss improved from 0.05244 to 0.05156, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0549 - mae: 0.1791 - val_loss: 0.0516 - val_mae: 0.1745\n",
      "Epoch 7/100\n",
      "39750/40000 [============================>.] - ETA: 0s - loss: 0.0542 - mae: 0.1778\n",
      "Epoch 00007: val_loss did not improve from 0.05156\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0542 - mae: 0.1777 - val_loss: 0.0530 - val_mae: 0.1766\n",
      "Epoch 8/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0539 - mae: 0.1773\n",
      "Epoch 00008: val_loss did not improve from 0.05156\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0539 - mae: 0.1773 - val_loss: 0.0516 - val_mae: 0.1738\n",
      "Epoch 9/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1763\n",
      "Epoch 00009: val_loss did not improve from 0.05156\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0533 - mae: 0.1764 - val_loss: 0.0560 - val_mae: 0.1802\n",
      "Epoch 10/100\n",
      "39870/40000 [============================>.] - ETA: 0s - loss: 0.0529 - mae: 0.1757\n",
      "Epoch 00010: val_loss did not improve from 0.05156\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0529 - mae: 0.1757 - val_loss: 0.0521 - val_mae: 0.1708\n",
      "Epoch 11/100\n",
      "39540/40000 [============================>.] - ETA: 0s - loss: 0.0522 - mae: 0.1744\n",
      "Epoch 00011: val_loss did not improve from 0.05156\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0522 - mae: 0.1744 - val_loss: 0.0527 - val_mae: 0.1719\n",
      "Epoch 12/100\n",
      "39810/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1739\n",
      "Epoch 00012: val_loss improved from 0.05156 to 0.05001, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0518 - mae: 0.1739 - val_loss: 0.0500 - val_mae: 0.1738\n",
      "Epoch 13/100\n",
      "39780/40000 [============================>.] - ETA: 0s - loss: 0.0515 - mae: 0.1732\n",
      "Epoch 00013: val_loss did not improve from 0.05001\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0515 - mae: 0.1732 - val_loss: 0.0519 - val_mae: 0.1714\n",
      "Epoch 14/100\n",
      "39930/40000 [============================>.] - ETA: 0s - loss: 0.0513 - mae: 0.1728\n",
      "Epoch 00014: val_loss did not improve from 0.05001\n",
      "40000/40000 [==============================] - 3s 79us/sample - loss: 0.0513 - mae: 0.1728 - val_loss: 0.0526 - val_mae: 0.1738\n",
      "Epoch 15/100\n",
      "39870/40000 [============================>.] - ETA: 0s - loss: 0.0510 - mae: 0.1724\n",
      "Epoch 00015: val_loss did not improve from 0.05001\n",
      "40000/40000 [==============================] - 3s 84us/sample - loss: 0.0510 - mae: 0.1725 - val_loss: 0.0539 - val_mae: 0.1775\n",
      "Epoch 16/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1719\n",
      "Epoch 00016: val_loss did not improve from 0.05001\n",
      "40000/40000 [==============================] - 3s 85us/sample - loss: 0.0508 - mae: 0.1719 - val_loss: 0.0507 - val_mae: 0.1710\n",
      "Epoch 17/100\n",
      "39870/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1712\n",
      "Epoch 00017: val_loss did not improve from 0.05001\n",
      "40000/40000 [==============================] - 3s 82us/sample - loss: 0.0506 - mae: 0.1712 - val_loss: 0.0535 - val_mae: 0.1757\n",
      "Epoch 18/100\n",
      "39390/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1711\n",
      "Epoch 00018: val_loss did not improve from 0.05001\n",
      "40000/40000 [==============================] - 3s 80us/sample - loss: 0.0505 - mae: 0.1711 - val_loss: 0.0531 - val_mae: 0.1765\n",
      "Epoch 19/100\n",
      "39660/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1704\n",
      "Epoch 00019: val_loss improved from 0.05001 to 0.04829, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0502 - mae: 0.1704 - val_loss: 0.0483 - val_mae: 0.1669\n",
      "Epoch 20/100\n",
      "39540/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1705\n",
      "Epoch 00020: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0502 - mae: 0.1705 - val_loss: 0.0503 - val_mae: 0.1710\n",
      "Epoch 21/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1701\n",
      "Epoch 00021: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0499 - mae: 0.1701 - val_loss: 0.0488 - val_mae: 0.1665\n",
      "Epoch 22/100\n",
      "39270/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1700\n",
      "Epoch 00022: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 4s 90us/sample - loss: 0.0500 - mae: 0.1700 - val_loss: 0.0490 - val_mae: 0.1675\n",
      "Epoch 23/100\n",
      "39450/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1701\n",
      "Epoch 00023: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 4s 105us/sample - loss: 0.0499 - mae: 0.1701 - val_loss: 0.0507 - val_mae: 0.1713\n",
      "Epoch 24/100\n",
      "39750/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1692\n",
      "Epoch 00024: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 5s 123us/sample - loss: 0.0496 - mae: 0.1692 - val_loss: 0.0488 - val_mae: 0.1698\n",
      "Epoch 25/100\n",
      "39540/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1689\n",
      "Epoch 00025: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 5s 115us/sample - loss: 0.0495 - mae: 0.1690 - val_loss: 0.0498 - val_mae: 0.1702\n",
      "Epoch 26/100\n",
      "39510/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1690\n",
      "Epoch 00026: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0495 - mae: 0.1690 - val_loss: 0.0512 - val_mae: 0.1716\n",
      "Epoch 27/100\n",
      "39750/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1693\n",
      "Epoch 00027: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 3s 87us/sample - loss: 0.0495 - mae: 0.1693 - val_loss: 0.0499 - val_mae: 0.1741\n",
      "Epoch 28/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39690/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1684\n",
      "Epoch 00028: val_loss did not improve from 0.04829\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0493 - mae: 0.1684 - val_loss: 0.0495 - val_mae: 0.1713\n",
      "Epoch 29/100\n",
      "39480/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1685\n",
      "Epoch 00029: val_loss improved from 0.04829 to 0.04826, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_l1.hdf5\n",
      "40000/40000 [==============================] - 3s 78us/sample - loss: 0.0492 - mae: 0.1683 - val_loss: 0.0483 - val_mae: 0.1647\n",
      "Epoch 30/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1684\n",
      "Epoch 00030: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 3s 81us/sample - loss: 0.0492 - mae: 0.1684 - val_loss: 0.0484 - val_mae: 0.1672\n",
      "Epoch 31/100\n",
      "39630/40000 [============================>.] - ETA: 0s - loss: 0.0491 - mae: 0.1682\n",
      "Epoch 00031: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 89us/sample - loss: 0.0490 - mae: 0.1680 - val_loss: 0.0501 - val_mae: 0.1679\n",
      "Epoch 32/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1677\n",
      "Epoch 00032: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 3s 83us/sample - loss: 0.0488 - mae: 0.1677 - val_loss: 0.0488 - val_mae: 0.1690\n",
      "Epoch 33/100\n",
      "39450/40000 [============================>.] - ETA: 0s - loss: 0.0487 - mae: 0.1674\n",
      "Epoch 00033: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 92us/sample - loss: 0.0486 - mae: 0.1673 - val_loss: 0.0493 - val_mae: 0.1642\n",
      "Epoch 34/100\n",
      "39780/40000 [============================>.] - ETA: 0s - loss: 0.0486 - mae: 0.1671\n",
      "Epoch 00034: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 107us/sample - loss: 0.0486 - mae: 0.1671 - val_loss: 0.0515 - val_mae: 0.1690\n",
      "Epoch 35/100\n",
      "39600/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1675\n",
      "Epoch 00035: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 106us/sample - loss: 0.0487 - mae: 0.1674 - val_loss: 0.0496 - val_mae: 0.1713\n",
      "Epoch 36/100\n",
      "39510/40000 [============================>.] - ETA: 0s - loss: 0.0484 - mae: 0.1667\n",
      "Epoch 00036: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 104us/sample - loss: 0.0484 - mae: 0.1668 - val_loss: 0.0485 - val_mae: 0.1685\n",
      "Epoch 37/100\n",
      "39750/40000 [============================>.] - ETA: 0s - loss: 0.0488 - mae: 0.1675\n",
      "Epoch 00037: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0488 - mae: 0.1675 - val_loss: 0.0495 - val_mae: 0.1672\n",
      "Epoch 38/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1669\n",
      "Epoch 00038: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 98us/sample - loss: 0.0485 - mae: 0.1669 - val_loss: 0.0487 - val_mae: 0.1681\n",
      "Epoch 39/100\n",
      "39360/40000 [============================>.] - ETA: 0s - loss: 0.0485 - mae: 0.1666\n",
      "Epoch 00039: val_loss did not improve from 0.04826\n",
      "40000/40000 [==============================] - 4s 88us/sample - loss: 0.0484 - mae: 0.1666 - val_loss: 0.0505 - val_mae: 0.1654\n",
      "Epoch 00039: early stopping\n"
     ]
    }
   ],
   "source": [
    "layersl1 = [48,48,48,48,48,1]\n",
    "model_namel1 = os.path.join(path_data, \"nn_2000_queries_trimming_l1\")\n",
    "l1_val = 0.001\n",
    "\n",
    "# define the keras model\n",
    "nn_modell1 = Sequential()\n",
    "\n",
    "#add first layer\n",
    "nn_modell1.add(Dense(layersl1[0], input_dim = X_train.shape[1], activation = 'relu', activity_regularizer=l1(l1_val)))\n",
    "#add other layers\n",
    "for i in range(1,len(layersl1)-1): #skip first and last one\n",
    "    nn_modell1.add(Dense(layersl1[i], activation = 'relu', activity_regularizer=l1(l1_val)))\n",
    "#add last layer\n",
    "nn_modell1.add(Dense(layersl1[-1], activation = None,activity_regularizer=l1(l1_val)))\n",
    "\n",
    "# compile the keras model\n",
    "nn_modell1.compile(loss = 'mean_squared_error', optimizer='adam', metrics=['mae'])\n",
    "\n",
    "# save best model\n",
    "checkpointer = ModelCheckpoint(filepath= model_namel1 + \".hdf5\", monitor='val_loss', verbose=1, save_best_only=True)\n",
    "earlystopper = EarlyStopping(monitor='val_loss', patience=10, verbose=1)  # patience - try x more epochs to improve val_loss\n",
    "\n",
    "# fit the keras model on the dataset\n",
    "histl1 = nn_modell1.fit(X_train, y_train, epochs = 100, batch_size = 30, validation_data=(X_val, y_val),\n",
    "                    callbacks = [checkpointer, earlystopper])\n",
    "historyl1 = histl1.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28a34f6b348>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,8), dpi=100)\n",
    "\n",
    "ax1.plot(histl1.history['mae'], \"o--\", label='MAE (training data)')\n",
    "ax1.plot(histl1.history['val_mae'], \"o--\", label='MAE (validation data)')\n",
    "ax1.set_title('MAE loss')\n",
    "ax1.set_ylabel(\"MAE\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(histl1.history['loss'], \"o--\", label='training data')\n",
    "ax2.plot(histl1.history['val_loss'], \"o--\", label='validation data')\n",
    "ax2.set_title('MSE loss')\n",
    "ax2.set_ylabel(\"MSE loss\")\n",
    "ax2.set_xlabel(\"epochs\")\n",
    "ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0640 - mae: 0.2034\n",
      "Epoch 00001: val_loss improved from inf to 0.05901, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 11s 283us/sample - loss: 0.0640 - mae: 0.2034 - val_loss: 0.0590 - val_mae: 0.1948\n",
      "Epoch 2/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0594 - mae: 0.1952\n",
      "Epoch 00002: val_loss improved from 0.05901 to 0.05632, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 227us/sample - loss: 0.0594 - mae: 0.1951 - val_loss: 0.0563 - val_mae: 0.1914\n",
      "Epoch 3/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0577 - mae: 0.1922\n",
      "Epoch 00003: val_loss improved from 0.05632 to 0.05588, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 236us/sample - loss: 0.0576 - mae: 0.1922 - val_loss: 0.0559 - val_mae: 0.1915\n",
      "Epoch 4/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0563 - mae: 0.1895\n",
      "Epoch 00004: val_loss did not improve from 0.05588\n",
      "40000/40000 [==============================] - 9s 225us/sample - loss: 0.0562 - mae: 0.1894 - val_loss: 0.0573 - val_mae: 0.1881\n",
      "Epoch 5/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0554 - mae: 0.1877\n",
      "Epoch 00005: val_loss improved from 0.05588 to 0.05186, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 218us/sample - loss: 0.0554 - mae: 0.1877 - val_loss: 0.0519 - val_mae: 0.1813\n",
      "Epoch 6/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0549 - mae: 0.1865\n",
      "Epoch 00006: val_loss did not improve from 0.05186\n",
      "40000/40000 [==============================] - 8s 210us/sample - loss: 0.0549 - mae: 0.1865 - val_loss: 0.0526 - val_mae: 0.1867\n",
      "Epoch 7/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0544 - mae: 0.1852\n",
      "Epoch 00007: val_loss did not improve from 0.05186\n",
      "40000/40000 [==============================] - 8s 210us/sample - loss: 0.0544 - mae: 0.1853 - val_loss: 0.0534 - val_mae: 0.1870\n",
      "Epoch 8/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0536 - mae: 0.1837\n",
      "Epoch 00008: val_loss did not improve from 0.05186\n",
      "40000/40000 [==============================] - 9s 229us/sample - loss: 0.0536 - mae: 0.1837 - val_loss: 0.0536 - val_mae: 0.1872\n",
      "Epoch 9/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0533 - mae: 0.1831\n",
      "Epoch 00009: val_loss did not improve from 0.05186\n",
      "40000/40000 [==============================] - 8s 211us/sample - loss: 0.0532 - mae: 0.1830 - val_loss: 0.0531 - val_mae: 0.1785\n",
      "Epoch 10/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0532 - mae: 0.1826\n",
      "Epoch 00010: val_loss did not improve from 0.05186\n",
      "40000/40000 [==============================] - 8s 208us/sample - loss: 0.0532 - mae: 0.1826 - val_loss: 0.0523 - val_mae: 0.1835\n",
      "Epoch 11/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0527 - mae: 0.1818\n",
      "Epoch 00011: val_loss did not improve from 0.05186\n",
      "40000/40000 [==============================] - 9s 232us/sample - loss: 0.0527 - mae: 0.1818 - val_loss: 0.0525 - val_mae: 0.1830\n",
      "Epoch 12/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0526 - mae: 0.1816\n",
      "Epoch 00012: val_loss improved from 0.05186 to 0.05128, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 231us/sample - loss: 0.0526 - mae: 0.1816 - val_loss: 0.0513 - val_mae: 0.1774\n",
      "Epoch 13/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0525 - mae: 0.1814\n",
      "Epoch 00013: val_loss did not improve from 0.05128\n",
      "40000/40000 [==============================] - 9s 222us/sample - loss: 0.0525 - mae: 0.1814 - val_loss: 0.0529 - val_mae: 0.1818\n",
      "Epoch 14/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0524 - mae: 0.1813\n",
      "Epoch 00014: val_loss improved from 0.05128 to 0.05076, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 218us/sample - loss: 0.0524 - mae: 0.1813 - val_loss: 0.0508 - val_mae: 0.1776\n",
      "Epoch 15/100\n",
      "39696/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1801\n",
      "Epoch 00015: val_loss improved from 0.05076 to 0.05042, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 230us/sample - loss: 0.0519 - mae: 0.1801 - val_loss: 0.0504 - val_mae: 0.1797\n",
      "Epoch 16/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1804\n",
      "Epoch 00016: val_loss improved from 0.05042 to 0.05024, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 213us/sample - loss: 0.0521 - mae: 0.1804 - val_loss: 0.0502 - val_mae: 0.1778\n",
      "Epoch 17/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0521 - mae: 0.1801\n",
      "Epoch 00017: val_loss did not improve from 0.05024\n",
      "40000/40000 [==============================] - 8s 211us/sample - loss: 0.0521 - mae: 0.1801 - val_loss: 0.0509 - val_mae: 0.1772\n",
      "Epoch 18/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0518 - mae: 0.1799\n",
      "Epoch 00018: val_loss did not improve from 0.05024\n",
      "40000/40000 [==============================] - 9s 217us/sample - loss: 0.0518 - mae: 0.1798 - val_loss: 0.0505 - val_mae: 0.1780\n",
      "Epoch 19/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0519 - mae: 0.1797\n",
      "Epoch 00019: val_loss did not improve from 0.05024\n",
      "40000/40000 [==============================] - 9s 220us/sample - loss: 0.0518 - mae: 0.1797 - val_loss: 0.0505 - val_mae: 0.1770\n",
      "Epoch 20/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1791\n",
      "Epoch 00020: val_loss improved from 0.05024 to 0.04972, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 234us/sample - loss: 0.0514 - mae: 0.1791 - val_loss: 0.0497 - val_mae: 0.1760\n",
      "Epoch 21/100\n",
      "39816/40000 [============================>.] - ETA: 0s - loss: 0.0514 - mae: 0.1790\n",
      "Epoch 00021: val_loss did not improve from 0.04972\n",
      "40000/40000 [==============================] - 8s 210us/sample - loss: 0.0514 - mae: 0.1789 - val_loss: 0.0503 - val_mae: 0.1790\n",
      "Epoch 22/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1786\n",
      "Epoch 00022: val_loss improved from 0.04972 to 0.04949, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 216us/sample - loss: 0.0513 - mae: 0.1786 - val_loss: 0.0495 - val_mae: 0.1743\n",
      "Epoch 23/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1787\n",
      "Epoch 00023: val_loss did not improve from 0.04949\n",
      "40000/40000 [==============================] - 9s 225us/sample - loss: 0.0512 - mae: 0.1787 - val_loss: 0.0509 - val_mae: 0.1796\n",
      "Epoch 24/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1779\n",
      "Epoch 00024: val_loss did not improve from 0.04949\n",
      "40000/40000 [==============================] - 8s 211us/sample - loss: 0.0509 - mae: 0.1779 - val_loss: 0.0506 - val_mae: 0.1750\n",
      "Epoch 25/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0512 - mae: 0.1784\n",
      "Epoch 00025: val_loss did not improve from 0.04949\n",
      "40000/40000 [==============================] - 8s 209us/sample - loss: 0.0512 - mae: 0.1784 - val_loss: 0.0500 - val_mae: 0.1798\n",
      "Epoch 26/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1776\n",
      "Epoch 00026: val_loss did not improve from 0.04949\n",
      "40000/40000 [==============================] - 9s 224us/sample - loss: 0.0508 - mae: 0.1776 - val_loss: 0.0526 - val_mae: 0.1832\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0508 - mae: 0.1776\n",
      "Epoch 00027: val_loss improved from 0.04949 to 0.04886, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 222us/sample - loss: 0.0508 - mae: 0.1775 - val_loss: 0.0489 - val_mae: 0.1719\n",
      "Epoch 28/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0507 - mae: 0.1773\n",
      "Epoch 00028: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 8s 209us/sample - loss: 0.0506 - mae: 0.1772 - val_loss: 0.0496 - val_mae: 0.1738\n",
      "Epoch 29/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1767\n",
      "Epoch 00029: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 8s 210us/sample - loss: 0.0505 - mae: 0.1767 - val_loss: 0.0493 - val_mae: 0.1765\n",
      "Epoch 30/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0509 - mae: 0.1777\n",
      "Epoch 00030: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 9s 214us/sample - loss: 0.0509 - mae: 0.1777 - val_loss: 0.0501 - val_mae: 0.1800\n",
      "Epoch 31/100\n",
      "39840/40000 [============================>.] - ETA: 0s - loss: 0.0506 - mae: 0.1770\n",
      "Epoch 00031: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 8s 210us/sample - loss: 0.0506 - mae: 0.1770 - val_loss: 0.0490 - val_mae: 0.1749\n",
      "Epoch 32/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1766\n",
      "Epoch 00032: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 9s 219us/sample - loss: 0.0503 - mae: 0.1767 - val_loss: 0.0493 - val_mae: 0.1767\n",
      "Epoch 33/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0505 - mae: 0.1766\n",
      "Epoch 00033: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 8s 206us/sample - loss: 0.0505 - mae: 0.1766 - val_loss: 0.0489 - val_mae: 0.1749\n",
      "Epoch 34/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0503 - mae: 0.1764\n",
      "Epoch 00034: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 8s 205us/sample - loss: 0.0503 - mae: 0.1764 - val_loss: 0.0494 - val_mae: 0.1764\n",
      "Epoch 35/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1761\n",
      "Epoch 00035: val_loss did not improve from 0.04886\n",
      "40000/40000 [==============================] - 8s 206us/sample - loss: 0.0502 - mae: 0.1761 - val_loss: 0.0494 - val_mae: 0.1750\n",
      "Epoch 36/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0504 - mae: 0.1766\n",
      "Epoch 00036: val_loss improved from 0.04886 to 0.04851, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 8s 209us/sample - loss: 0.0504 - mae: 0.1766 - val_loss: 0.0485 - val_mae: 0.1749\n",
      "Epoch 37/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0502 - mae: 0.1760\n",
      "Epoch 00037: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 10s 238us/sample - loss: 0.0501 - mae: 0.1759 - val_loss: 0.0515 - val_mae: 0.1776\n",
      "Epoch 38/100\n",
      "39768/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1762\n",
      "Epoch 00038: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 10s 242us/sample - loss: 0.0501 - mae: 0.1762 - val_loss: 0.0503 - val_mae: 0.1787\n",
      "Epoch 39/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1759\n",
      "Epoch 00039: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 9s 230us/sample - loss: 0.0501 - mae: 0.1759 - val_loss: 0.0496 - val_mae: 0.1743\n",
      "Epoch 40/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0498 - mae: 0.1755\n",
      "Epoch 00040: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 9s 219us/sample - loss: 0.0498 - mae: 0.1755 - val_loss: 0.0497 - val_mae: 0.1736\n",
      "Epoch 41/100\n",
      "39792/40000 [============================>.] - ETA: 0s - loss: 0.0500 - mae: 0.1755\n",
      "Epoch 00041: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 9s 226us/sample - loss: 0.0500 - mae: 0.1754 - val_loss: 0.0499 - val_mae: 0.1761\n",
      "Epoch 42/100\n",
      "39864/40000 [============================>.] - ETA: 0s - loss: 0.0501 - mae: 0.1756\n",
      "Epoch 00042: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 9s 226us/sample - loss: 0.0501 - mae: 0.1756 - val_loss: 0.0499 - val_mae: 0.1736\n",
      "Epoch 43/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1750\n",
      "Epoch 00043: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 9s 231us/sample - loss: 0.0496 - mae: 0.1750 - val_loss: 0.0490 - val_mae: 0.1755\n",
      "Epoch 44/100\n",
      "39984/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1750\n",
      "Epoch 00044: val_loss did not improve from 0.04851\n",
      "40000/40000 [==============================] - 9s 224us/sample - loss: 0.0496 - mae: 0.1750 - val_loss: 0.0493 - val_mae: 0.1740\n",
      "Epoch 45/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1752\n",
      "Epoch 00045: val_loss improved from 0.04851 to 0.04841, saving model to C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_trimming_big_dropout.hdf5\n",
      "40000/40000 [==============================] - 9s 229us/sample - loss: 0.0498 - mae: 0.1753 - val_loss: 0.0484 - val_mae: 0.1726\n",
      "Epoch 46/100\n",
      "39960/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1752\n",
      "Epoch 00046: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 222us/sample - loss: 0.0499 - mae: 0.1752 - val_loss: 0.0507 - val_mae: 0.1771\n",
      "Epoch 47/100\n",
      "39720/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1754\n",
      "Epoch 00047: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 225us/sample - loss: 0.0499 - mae: 0.1754 - val_loss: 0.0504 - val_mae: 0.1747\n",
      "Epoch 48/100\n",
      "39744/40000 [============================>.] - ETA: 0s - loss: 0.0496 - mae: 0.1748\n",
      "Epoch 00048: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 214us/sample - loss: 0.0497 - mae: 0.1749 - val_loss: 0.0496 - val_mae: 0.1718\n",
      "Epoch 49/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0499 - mae: 0.1754\n",
      "Epoch 00049: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 8s 208us/sample - loss: 0.0499 - mae: 0.1754 - val_loss: 0.0496 - val_mae: 0.1745\n",
      "Epoch 50/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0497 - mae: 0.1751\n",
      "Epoch 00050: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 8s 207us/sample - loss: 0.0497 - mae: 0.1751 - val_loss: 0.0490 - val_mae: 0.1752\n",
      "Epoch 51/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0495 - mae: 0.1743\n",
      "Epoch 00051: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 232us/sample - loss: 0.0495 - mae: 0.1743 - val_loss: 0.0489 - val_mae: 0.1760\n",
      "Epoch 52/100\n",
      "39912/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1745\n",
      "Epoch 00052: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 232us/sample - loss: 0.0495 - mae: 0.1745 - val_loss: 0.0493 - val_mae: 0.1749\n",
      "Epoch 53/100\n",
      "39936/40000 [============================>.] - ETA: 0s - loss: 0.0493 - mae: 0.1741\n",
      "Epoch 00053: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 230us/sample - loss: 0.0493 - mae: 0.1741 - val_loss: 0.0495 - val_mae: 0.1740\n",
      "Epoch 54/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0494 - mae: 0.1742\n",
      "Epoch 00054: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 229us/sample - loss: 0.0494 - mae: 0.1742 - val_loss: 0.0496 - val_mae: 0.1765\n",
      "Epoch 55/100\n",
      "39888/40000 [============================>.] - ETA: 0s - loss: 0.0492 - mae: 0.1738\n",
      "Epoch 00055: val_loss did not improve from 0.04841\n",
      "40000/40000 [==============================] - 9s 229us/sample - loss: 0.0493 - mae: 0.1739 - val_loss: 0.0488 - val_mae: 0.1746\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00055: early stopping\n"
     ]
    }
   ],
   "source": [
    "layers2 = [500,500,1]\n",
    "model_name2 = os.path.join(path_data, \"nn_2000_queries_trimming_big_dropout\")\n",
    "drop_rate = 0.25\n",
    "\n",
    "# define the keras model\n",
    "#nn_model2 = Sequential()\n",
    "nn_input = Input(shape=X_train.shape[1])\n",
    "\n",
    "#add first layer\n",
    "nn_layers = Dense(layers2[0], activation = 'relu')(nn_input)\n",
    "nn_layers = Dropout(drop_rate)(nn_layers, training = True)  # 'hack': let dropout layer believe its always training mode\n",
    "#add other layers\n",
    "for i in range(1,len(layers2)-1): #skip first and last one\n",
    "    nn_layers = Dense(layers2[i], activation = 'relu')(nn_layers)\n",
    "    nn_layers = Dropout(drop_rate)(nn_layers, training = True)\n",
    "#add last layer\n",
    "nn_layers = Dense(layers2[-1], activation = None)(nn_layers)\n",
    "\n",
    "# compile the keras model\n",
    "nn_model2 = Model(inputs=[nn_input], outputs=[nn_layers])\n",
    "nn_model2.compile(loss = 'mean_squared_error', optimizer='adam', metrics=['mae'])\n",
    "\n",
    "# save best model\n",
    "checkpointer = ModelCheckpoint(filepath= model_name2 + \".hdf5\", monitor='val_loss', verbose=1, save_best_only=True)\n",
    "earlystopper = EarlyStopping(monitor='val_loss', patience=10, verbose=1)  # patience - try x more epochs to improve val_loss\n",
    "\n",
    "# fit the keras model on the dataset\n",
    "hist2 = nn_model2.fit(X_train, y_train, epochs = 100, batch_size = 24, validation_data=(X_val, y_val),\n",
    "                    callbacks = [checkpointer, earlystopper])\n",
    "history2 = hist2.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28a2eae9bc8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,8), dpi=100)\n",
    "\n",
    "ax1.plot(hist2.history['mae'], \"o--\", label='MAE (training data)')\n",
    "ax1.plot(hist2.history['val_mae'], \"o--\", label='MAE (validation data)')\n",
    "ax1.set_title('MAE loss')\n",
    "ax1.set_ylabel(\"MAE\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(hist2.history['loss'], \"o--\", label='training data')\n",
    "ax2.plot(hist2.history['val_loss'], \"o--\", label='validation data')\n",
    "ax2.set_title('MSE loss')\n",
    "ax2.set_ylabel(\"MSE loss\")\n",
    "ax2.set_xlabel(\"epochs\")\n",
    "ax2.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}