MMC Classification 2D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bfd832a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "998b2508",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-30 01:01:08.651850: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential,load_model\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D,BatchNormalization, Dense, Dropout, Flatten\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f714c67",
   "metadata": {},
   "source": [
    "## 2. Load npz file\n",
    "The features stored in a *.npz* file and here it will be loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ba5571c",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = np.load(\"Features2D/npz/Features1.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1df6c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec1 = f1['spec'] #(898,1025,1099)\n",
    "mfcc1 = f1['mfcc'] #(898,20,1099)\n",
    "mel1 = f1['mel']#(898,128,1099)\n",
    "y1 = f1['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2863ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "f2 = np.load(\"Features2D/npz/Features2.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89e5c26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec2 = f2['spec'] #(400,1025,1099)\n",
    "mfcc2 = f2['mfcc'] #(400,20,1099)\n",
    "mel2 = f2['mel']#(400,128,1099)\n",
    "y2 = f2['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8121c06a",
   "metadata": {},
   "source": [
    "## 3. Data preprocessing\n",
    "### 3.1 Resize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "352699a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfcc1_resized = np.empty((mfcc1.shape[0], 120, 600))\n",
    "for i in range(0,898):\n",
    "    mfcc1_resized[i] = cv2.resize(mfcc1[i],(600,120))\n",
    "\n",
    "mfcc2_resized = np.empty((mfcc2.shape[0], 120, 600))\n",
    "for i in range(0,400):\n",
    "    mfcc2_resized[i] = cv2.resize(mfcc2[i],(600,120))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f054b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec1_resized = np.empty((spec1.shape[0], 300, 300))\n",
    "for i in range(0,898):\n",
    "    spec1_resized[i] = cv2.resize(spec1[i],(300,300))\n",
    "\n",
    "spec2_resized = np.empty((spec2.shape[0], 300, 300))\n",
    "for i in range(0,400):\n",
    "    spec2_resized[i] = cv2.resize(spec2[i],(300,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3079d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel1_resized = np.empty((mel1.shape[0], 300, 400))\n",
    "for i in range(0,898):\n",
    "    mel1_resized[i] = cv2.resize(mel1[i],(400,300))\n",
    "\n",
    "mel2_resized = np.empty((mel2.shape[0], 300, 400))\n",
    "for i in range(0,400):\n",
    "    mel2_resized[i] = cv2.resize(mel2[i],(400,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c488960",
   "metadata": {},
   "source": [
    "### 3.2 Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6d6aaed",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfcc1_train,mfcc1_test,y1_train,y1_test = train_test_split(mfcc1_resized,y1, \n",
    "                                                   train_size = 0.8, \n",
    "                                                   random_state = 13, \n",
    "                                                   stratify= y1)\n",
    "mfcc2_train,mfcc2_test,y2_train,y2_test = train_test_split(mfcc2_resized,y2, \n",
    "                                                   train_size = 0.9, \n",
    "                                                   random_state = 13, \n",
    "                                                   stratify= y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6dc21bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed(\"Features2D/npz_new/mfcc1_train_test.npz\",\n",
    "                    mfcc_train = mfcc1_train, \n",
    "                    mfcc_test = mfcc1_test, \n",
    "                    y_train = y1_train, \n",
    "                    y_test= y1_test)\n",
    "\n",
    "np.savez_compressed(\"Features2D/npz_new/mfcc2_train_test.npz\",\n",
    "                    mfcc_train = mfcc2_train, \n",
    "                    mfcc_test = mfcc2_test, \n",
    "                    y_train = y2_train, \n",
    "                    y_test = y2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8718d1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec1_train,spec1_test,y1_train,y1_test = train_test_split(spec1_resized,y1, \n",
    "                                                           train_size = 0.8,  \n",
    "                                                           random_state = 13, \n",
    "                                                           stratify = y1)\n",
    "spec2_train,spec2_test,y2_train,y2_test = train_test_split(spec2_resized,y2, \n",
    "                                                           train_size = 0.9, \n",
    "                                                           random_state = 13, \n",
    "                                                           stratify = y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72aea063",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed(\"Features2D/npz_new/spec1_train_test.npz\",\n",
    "                    spec_train = spec1_train, \n",
    "                    spec_test = spec1_test, \n",
    "                    y_train = y1_train, \n",
    "                    y_test= y1_test)\n",
    "\n",
    "np.savez_compressed(\"Features2D/npz_new/spec2_train_test.npz\",\n",
    "                    spec_train = spec2_train, \n",
    "                    spec_test = spec2_test, \n",
    "                    y_train = y2_train, \n",
    "                    y_test = y2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "97fa540f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel1_train,mel1_test,y1_train,y1_test = train_test_split(mel1_resized,y1, \n",
    "                                                           train_size = 0.8,  \n",
    "                                                           random_state = 13, \n",
    "                                                           stratify = y1)\n",
    "mel2_train,mel2_test,y2_train,y2_test = train_test_split(mel2_resized,y2, \n",
    "                                                           train_size = 0.9,  \n",
    "                                                           random_state = 13, \n",
    "                                                           stratify = y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8c6c0f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed(\"Features2D/npz_new/mel1_train_test.npz\",\n",
    "                    mel_train = mel1_train, \n",
    "                    mel_test = mel1_test, \n",
    "                    y_train = y1_train, \n",
    "                    y_test = y1_test)\n",
    "\n",
    "np.savez_compressed(\"Features2D/npz_new/mel2_train_test.npz\",\n",
    "                    mel_train = mel2_train, \n",
    "                    mel_test = mel2_test, \n",
    "                    y_train = y2_train, \n",
    "                    y_test = y2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c240702",
   "metadata": {},
   "source": [
    "## 4. Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38dd2c67",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Train the model\n",
    "def modelBuilder3L(X_train,\n",
    "                   f1,k1,a1,\n",
    "                   mw1,mh1,sw1,sh1,\n",
    "                   f2,k2,a2,\n",
    "                   mw2,mh2,sw2,sh2,\n",
    "                   f3,k3,a3,\n",
    "                   mw3,mh3,sw3,sh3,\n",
    "                   d1,dr1,da1,r1,\n",
    "                   d2,dr2,da2,r2,\n",
    "                   num):\n",
    "    \n",
    "    '''\n",
    "    args:\n",
    "    \n",
    "    X_train: training data\n",
    "    f1,k1,a1: num of filters, filter size and activation func of 1st conv2D layer\n",
    "    mr1,mc1,sr1,sc1: filter size and strides of 1st MaxPooling2D layer\n",
    "    f2,k2,a2: num of filters, filter size and activation func of 2nd conv2D layer\n",
    "    mr2,mc2,sr2,sc2: filter size and strides of 2nd MaxPooling2D layer\n",
    "    f3,k3,a3: num of filters, filter size and activation func of 3rd conv2D layer\n",
    "    mr3,sc3,sr3,sc3: filter size and strides of 3rd MaxPooling2D layer\n",
    "    d1,dr1,da1, r1: num of units, dropout, activation func and regularizer para of 1st fully connected layer\n",
    "    d2,dr2,da2, r2: num of units, dropout, activation func and regularizer para of 2nd fully connected layer\n",
    "    num: integer for distinguishing different model\n",
    "    \n",
    "    return:\n",
    "    model\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    N,row,col,Nc = X_train.shape\n",
    "    \n",
    "    model = Sequential(name = \"CNN2D_\"+str(num))\n",
    "    #L1\n",
    "    model.add(Conv2D(f1,(k1,k1),activation = a1,input_shape = (row,col,Nc), padding = 'same', name = 'Conv2D_1'))\n",
    "    model.add(MaxPooling2D((mw1,mh1), strides = (sw1,sh1), padding = 'same', name = 'MaxPooling2D_1'))\n",
    "    model.add(BatchNormalization(name = \"BN1\"))\n",
    "    \n",
    "    #L2\n",
    "    model.add(Conv2D(f2,(k2,k2), activation = a2, padding = 'same',name = 'Conv2D_2'))\n",
    "    model.add(MaxPooling2D((mw2,mh2),strides = (sw2,sh2), padding = 'same', name = 'MaxPooling2D_2'))\n",
    "    model.add(BatchNormalization(name = \"BN2\"))\n",
    "\n",
    "    #L3\n",
    "    model.add(Conv2D(f3,(k3,k3), activation = a3, padding = 'same',name = 'Conv2D_3'))\n",
    "    model.add(MaxPooling2D((mw3,mh3), strides = (sw3,sh3), padding = 'same', name = 'MaxPooling2D_3'))\n",
    "    model.add(BatchNormalization(name = \"BN3\"))\n",
    "   \n",
    "    #Flatten layer\n",
    "    model.add(Flatten(name = 'Flatten'))\n",
    "    #FC4\n",
    "    model.add(Dense(d1, activation = da1, kernel_regularizer = tf.keras.regularizers.L2(r1), name='Dense1'))\n",
    "    model.add(Dropout(dr1, name = \"Dropout1\"))\n",
    "    #FC5\n",
    "    model.add(Dense(d2, activation = da2, kernel_regularizer = tf.keras.regularizers.L2(r2), name='Dense2'))\n",
    "    model.add(Dropout(dr2, name = \"Dropout2\"))\n",
    "    #Softmax layer\n",
    "    model.add(Dense(4, activation ='softmax', name='Softmax'))\n",
    "\n",
    "    optimizer = Adam(learning_rate=0.0001)\n",
    "    model.compile(optimizer= optimizer, loss= 'sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef79333d",
   "metadata": {},
   "source": [
    "### 4.1 MFCC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b5d3f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load MFCC data set\n",
    "mfcc1_file = np.load(\"Features2D/npz_new/mfcc1_train_test.npz\")\n",
    "\n",
    "mfcc1_train = mfcc1_file['mfcc_train']\n",
    "y1_train = mfcc1_file['y_train']\n",
    "\n",
    "# validation set\n",
    "mfcc1_train,mfcc1_val,y1_train,y1_val = train_test_split(mfcc1_train,y1_train, \n",
    "                                                         train_size = 0.8,  \n",
    "                                                         random_state = 13, \n",
    "                                                         stratify= y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69c85f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mfcc1_train_scaled = (mfcc1_train - np.mean(mfcc1_train))/np.std(mfcc1_train)\n",
    "mfcc1_val_scaled = (mfcc1_val - np.mean(mfcc1_val))/np.std(mfcc1_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0b1eeec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data\n",
    "N,row,col = mfcc1_train_scaled.shape\n",
    "mfcc1_train_reshaped = mfcc1_train_scaled.reshape((N,row,col,1))\n",
    "N,row,col = mfcc1_val_scaled.shape\n",
    "mfcc1_val_reshaped = mfcc1_val_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1f6f874e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CNN2D_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 120, 600, 8)       80        \n",
      "                                                                 \n",
      " MaxPooling2D_1 (MaxPooling2  (None, 60, 300, 8)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN1 (BatchNormalization)    (None, 60, 300, 8)        32        \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 60, 300, 8)        584       \n",
      "                                                                 \n",
      " MaxPooling2D_2 (MaxPooling2  (None, 30, 150, 8)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN2 (BatchNormalization)    (None, 30, 150, 8)        32        \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 30, 150, 16)       1168      \n",
      "                                                                 \n",
      " MaxPooling2D_3 (MaxPooling2  (None, 15, 75, 16)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN3 (BatchNormalization)    (None, 15, 75, 16)        64        \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 18000)             0         \n",
      "                                                                 \n",
      " Dense1 (Dense)              (None, 64)                1152064   \n",
      "                                                                 \n",
      " Dropout1 (Dropout)          (None, 64)                0         \n",
      "                                                                 \n",
      " Dense2 (Dense)              (None, 32)                2080      \n",
      "                                                                 \n",
      " Dropout2 (Dropout)          (None, 32)                0         \n",
      "                                                                 \n",
      " Softmax (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,156,236\n",
      "Trainable params: 1,156,172\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "f1,k1,a1 = 8,3,'relu'\n",
    "mw1,mh1,sw1,sh1 = 2,3,2,2\n",
    "f2,k2,a2 = 8,3,'relu'\n",
    "mw2,mh2,sw2,sh2 = 2,3,2,2\n",
    "f3,k3,a3 = 16,3,'relu'\n",
    "mw3,mh3,sw3,sh3 = 4,6,2,2\n",
    "d1,dr1,da1,r1 = 64,0.2,'relu',0.2\n",
    "d2,dr2,da2,r2 = 32,0.2,'relu',0.2\n",
    "num = 1\n",
    "\n",
    "model1 = modelBuilder3L(mfcc1_train_reshaped,\n",
    "                        f1,k1,a1,\n",
    "                        mw1,mh1,sw1,sh1,\n",
    "                        f2,k2,a2,\n",
    "                        mw2,mh2,sw2,sh2,\n",
    "                        f3,k3,a3,\n",
    "                        mw3,mh3,sw3,sh3,\n",
    "                        d1,dr1,da1,r1,\n",
    "                        d2,dr2,da2,r2,\n",
    "                        num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dbf053f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "29/29 [==============================] - 12s 337ms/step - loss: 32.7083 - accuracy: 0.2892 - val_loss: 29.3422 - val_accuracy: 0.3611\n",
      "Epoch 2/80\n",
      "29/29 [==============================] - 9s 319ms/step - loss: 26.5807 - accuracy: 0.3676 - val_loss: 23.8135 - val_accuracy: 0.4375\n",
      "Epoch 3/80\n",
      "29/29 [==============================] - 10s 350ms/step - loss: 21.6396 - accuracy: 0.4634 - val_loss: 19.6903 - val_accuracy: 0.4097\n",
      "Epoch 4/80\n",
      "29/29 [==============================] - 10s 338ms/step - loss: 17.9802 - accuracy: 0.4774 - val_loss: 16.5619 - val_accuracy: 0.4306\n",
      "Epoch 5/80\n",
      "29/29 [==============================] - 9s 293ms/step - loss: 15.2541 - accuracy: 0.5261 - val_loss: 14.4043 - val_accuracy: 0.3056\n",
      "Epoch 6/80\n",
      "29/29 [==============================] - 9s 308ms/step - loss: 13.2714 - accuracy: 0.5383 - val_loss: 12.6711 - val_accuracy: 0.3333\n",
      "Epoch 7/80\n",
      "29/29 [==============================] - 9s 310ms/step - loss: 11.7736 - accuracy: 0.5767 - val_loss: 11.4066 - val_accuracy: 0.3611\n",
      "Epoch 8/80\n",
      "29/29 [==============================] - 9s 311ms/step - loss: 10.5884 - accuracy: 0.6115 - val_loss: 10.7042 - val_accuracy: 0.2500\n",
      "Epoch 9/80\n",
      "29/29 [==============================] - 9s 313ms/step - loss: 9.7220 - accuracy: 0.6063 - val_loss: 9.7978 - val_accuracy: 0.3125\n",
      "Epoch 10/80\n",
      "29/29 [==============================] - 9s 311ms/step - loss: 9.0073 - accuracy: 0.6585 - val_loss: 9.2570 - val_accuracy: 0.2917\n",
      "Epoch 11/80\n",
      "29/29 [==============================] - 9s 311ms/step - loss: 8.4488 - accuracy: 0.6690 - val_loss: 8.6014 - val_accuracy: 0.3542\n",
      "Epoch 12/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 7.9543 - accuracy: 0.6603 - val_loss: 8.2028 - val_accuracy: 0.3681\n",
      "Epoch 13/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 7.5035 - accuracy: 0.7230 - val_loss: 7.6803 - val_accuracy: 0.4444\n",
      "Epoch 14/80\n",
      "29/29 [==============================] - 9s 321ms/step - loss: 7.1955 - accuracy: 0.6655 - val_loss: 7.3839 - val_accuracy: 0.4236\n",
      "Epoch 15/80\n",
      "29/29 [==============================] - 10s 344ms/step - loss: 6.7828 - accuracy: 0.7718 - val_loss: 7.0941 - val_accuracy: 0.4306\n",
      "Epoch 16/80\n",
      "29/29 [==============================] - 10s 344ms/step - loss: 6.5149 - accuracy: 0.7578 - val_loss: 6.8044 - val_accuracy: 0.4792\n",
      "Epoch 17/80\n",
      "29/29 [==============================] - 9s 328ms/step - loss: 6.2561 - accuracy: 0.7509 - val_loss: 6.4759 - val_accuracy: 0.5347\n",
      "Epoch 18/80\n",
      "29/29 [==============================] - 9s 320ms/step - loss: 5.9996 - accuracy: 0.7962 - val_loss: 6.2680 - val_accuracy: 0.5347\n",
      "Epoch 19/80\n",
      "29/29 [==============================] - 10s 329ms/step - loss: 5.7657 - accuracy: 0.8031 - val_loss: 6.0522 - val_accuracy: 0.5486\n",
      "Epoch 20/80\n",
      "29/29 [==============================] - 10s 329ms/step - loss: 5.5344 - accuracy: 0.8101 - val_loss: 5.8572 - val_accuracy: 0.5417\n",
      "Epoch 21/80\n",
      "29/29 [==============================] - 10s 338ms/step - loss: 5.3431 - accuracy: 0.8066 - val_loss: 5.6586 - val_accuracy: 0.5486\n",
      "Epoch 22/80\n",
      "29/29 [==============================] - 9s 326ms/step - loss: 5.1215 - accuracy: 0.8328 - val_loss: 5.4997 - val_accuracy: 0.5694\n",
      "Epoch 23/80\n",
      "29/29 [==============================] - 9s 324ms/step - loss: 4.9458 - accuracy: 0.8606 - val_loss: 5.2812 - val_accuracy: 0.5833\n",
      "Epoch 24/80\n",
      "29/29 [==============================] - 10s 340ms/step - loss: 4.7377 - accuracy: 0.8606 - val_loss: 5.1830 - val_accuracy: 0.5486\n",
      "Epoch 25/80\n",
      "29/29 [==============================] - 9s 321ms/step - loss: 4.5683 - accuracy: 0.8920 - val_loss: 5.0601 - val_accuracy: 0.5278\n",
      "Epoch 26/80\n",
      "29/29 [==============================] - 9s 326ms/step - loss: 4.4054 - accuracy: 0.8937 - val_loss: 4.9014 - val_accuracy: 0.5833\n",
      "Epoch 27/80\n",
      "29/29 [==============================] - 9s 316ms/step - loss: 4.2572 - accuracy: 0.8937 - val_loss: 4.8091 - val_accuracy: 0.5139\n",
      "Epoch 28/80\n",
      "29/29 [==============================] - 9s 312ms/step - loss: 4.1060 - accuracy: 0.9094 - val_loss: 4.6401 - val_accuracy: 0.5764\n",
      "Epoch 29/80\n",
      "29/29 [==============================] - 9s 323ms/step - loss: 3.9451 - accuracy: 0.9216 - val_loss: 4.5203 - val_accuracy: 0.5208\n",
      "Epoch 30/80\n",
      "29/29 [==============================] - 9s 321ms/step - loss: 3.8304 - accuracy: 0.9077 - val_loss: 4.3352 - val_accuracy: 0.5833\n",
      "Epoch 31/80\n",
      "29/29 [==============================] - 9s 325ms/step - loss: 3.6815 - accuracy: 0.9286 - val_loss: 4.2730 - val_accuracy: 0.5833\n",
      "Epoch 32/80\n",
      "29/29 [==============================] - 9s 326ms/step - loss: 3.5449 - accuracy: 0.9408 - val_loss: 4.1793 - val_accuracy: 0.5694\n",
      "Epoch 33/80\n",
      "29/29 [==============================] - 9s 323ms/step - loss: 3.4481 - accuracy: 0.9146 - val_loss: 4.0479 - val_accuracy: 0.5972\n",
      "Epoch 34/80\n",
      "29/29 [==============================] - 10s 333ms/step - loss: 3.3387 - accuracy: 0.9199 - val_loss: 3.9211 - val_accuracy: 0.5903\n",
      "Epoch 35/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 3.2143 - accuracy: 0.9355 - val_loss: 3.8498 - val_accuracy: 0.5486\n",
      "Epoch 36/80\n",
      "29/29 [==============================] - 10s 333ms/step - loss: 3.0860 - accuracy: 0.9425 - val_loss: 3.7726 - val_accuracy: 0.5694\n",
      "Epoch 37/80\n",
      "29/29 [==============================] - 9s 307ms/step - loss: 2.9899 - accuracy: 0.9460 - val_loss: 3.6803 - val_accuracy: 0.6042\n",
      "Epoch 38/80\n",
      "29/29 [==============================] - 9s 325ms/step - loss: 2.9520 - accuracy: 0.9216 - val_loss: 3.5409 - val_accuracy: 0.5764\n",
      "Epoch 39/80\n",
      "29/29 [==============================] - 9s 320ms/step - loss: 2.8236 - accuracy: 0.9408 - val_loss: 3.5302 - val_accuracy: 0.5903\n",
      "Epoch 40/80\n",
      "29/29 [==============================] - 9s 316ms/step - loss: 2.7151 - accuracy: 0.9582 - val_loss: 3.3969 - val_accuracy: 0.5694\n",
      "Epoch 41/80\n",
      "29/29 [==============================] - 9s 318ms/step - loss: 2.6491 - accuracy: 0.9408 - val_loss: 3.2983 - val_accuracy: 0.5833\n",
      "Epoch 42/80\n",
      "29/29 [==============================] - 10s 350ms/step - loss: 2.5504 - accuracy: 0.9495 - val_loss: 3.2640 - val_accuracy: 0.5764\n",
      "Epoch 43/80\n",
      "29/29 [==============================] - 10s 326ms/step - loss: 2.4788 - accuracy: 0.9477 - val_loss: 3.2033 - val_accuracy: 0.5764\n",
      "Epoch 44/80\n",
      "29/29 [==============================] - 9s 320ms/step - loss: 2.4071 - accuracy: 0.9425 - val_loss: 3.1520 - val_accuracy: 0.5417\n",
      "Epoch 45/80\n",
      "29/29 [==============================] - 9s 316ms/step - loss: 2.3442 - accuracy: 0.9495 - val_loss: 3.0180 - val_accuracy: 0.5694\n",
      "Epoch 46/80\n",
      "29/29 [==============================] - 10s 333ms/step - loss: 2.2475 - accuracy: 0.9530 - val_loss: 3.0014 - val_accuracy: 0.5556\n",
      "Epoch 47/80\n",
      "29/29 [==============================] - 10s 332ms/step - loss: 2.1856 - accuracy: 0.9530 - val_loss: 2.8997 - val_accuracy: 0.5417\n",
      "Epoch 48/80\n",
      "29/29 [==============================] - 10s 336ms/step - loss: 2.0930 - accuracy: 0.9669 - val_loss: 2.8706 - val_accuracy: 0.5417\n",
      "Epoch 49/80\n",
      "29/29 [==============================] - 10s 331ms/step - loss: 2.0288 - accuracy: 0.9564 - val_loss: 2.7685 - val_accuracy: 0.5833\n",
      "Epoch 50/80\n",
      "29/29 [==============================] - 9s 320ms/step - loss: 1.9593 - accuracy: 0.9669 - val_loss: 2.7004 - val_accuracy: 0.6042\n",
      "Epoch 51/80\n",
      "29/29 [==============================] - 10s 329ms/step - loss: 1.9104 - accuracy: 0.9617 - val_loss: 2.6536 - val_accuracy: 0.5625\n",
      "Epoch 52/80\n",
      "29/29 [==============================] - 10s 346ms/step - loss: 1.8490 - accuracy: 0.9652 - val_loss: 2.6145 - val_accuracy: 0.5486\n",
      "Epoch 53/80\n",
      "29/29 [==============================] - 10s 346ms/step - loss: 1.7951 - accuracy: 0.9686 - val_loss: 2.5341 - val_accuracy: 0.6042\n",
      "Epoch 54/80\n",
      "29/29 [==============================] - 10s 329ms/step - loss: 1.7244 - accuracy: 0.9774 - val_loss: 2.4728 - val_accuracy: 0.6111\n",
      "Epoch 55/80\n",
      "29/29 [==============================] - 10s 329ms/step - loss: 1.6801 - accuracy: 0.9686 - val_loss: 2.4355 - val_accuracy: 0.5972\n",
      "Epoch 56/80\n",
      "29/29 [==============================] - 9s 322ms/step - loss: 1.6312 - accuracy: 0.9634 - val_loss: 2.3977 - val_accuracy: 0.5694\n",
      "Epoch 57/80\n",
      "29/29 [==============================] - 9s 322ms/step - loss: 1.5498 - accuracy: 0.9808 - val_loss: 2.3364 - val_accuracy: 0.6319\n",
      "Epoch 58/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 1.5213 - accuracy: 0.9739 - val_loss: 2.3586 - val_accuracy: 0.5417\n",
      "Epoch 59/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 1.4713 - accuracy: 0.9826 - val_loss: 2.2570 - val_accuracy: 0.5764\n",
      "Epoch 60/80\n",
      "29/29 [==============================] - 9s 323ms/step - loss: 1.4324 - accuracy: 0.9704 - val_loss: 2.2464 - val_accuracy: 0.5764\n",
      "Epoch 61/80\n",
      "29/29 [==============================] - 9s 327ms/step - loss: 1.4021 - accuracy: 0.9704 - val_loss: 2.2163 - val_accuracy: 0.5903\n",
      "Epoch 62/80\n",
      "29/29 [==============================] - 9s 310ms/step - loss: 1.3476 - accuracy: 0.9669 - val_loss: 2.1741 - val_accuracy: 0.6319\n",
      "Epoch 63/80\n",
      "29/29 [==============================] - 9s 300ms/step - loss: 1.2925 - accuracy: 0.9808 - val_loss: 2.1621 - val_accuracy: 0.6042\n",
      "Epoch 64/80\n",
      "29/29 [==============================] - 9s 300ms/step - loss: 1.2826 - accuracy: 0.9721 - val_loss: 2.0490 - val_accuracy: 0.6181\n",
      "Epoch 65/80\n",
      "29/29 [==============================] - 9s 298ms/step - loss: 1.2525 - accuracy: 0.9739 - val_loss: 2.0534 - val_accuracy: 0.6319\n",
      "Epoch 66/80\n",
      "29/29 [==============================] - 9s 300ms/step - loss: 1.1904 - accuracy: 0.9861 - val_loss: 2.0176 - val_accuracy: 0.6181\n",
      "Epoch 67/80\n",
      "29/29 [==============================] - 9s 303ms/step - loss: 1.1615 - accuracy: 0.9739 - val_loss: 2.0532 - val_accuracy: 0.5903\n",
      "Epoch 68/80\n",
      "29/29 [==============================] - 9s 304ms/step - loss: 1.1273 - accuracy: 0.9861 - val_loss: 1.9371 - val_accuracy: 0.6319\n",
      "Epoch 69/80\n",
      "29/29 [==============================] - 9s 299ms/step - loss: 1.0831 - accuracy: 0.9913 - val_loss: 1.9672 - val_accuracy: 0.5972\n",
      "Epoch 70/80\n",
      "29/29 [==============================] - 9s 298ms/step - loss: 1.0596 - accuracy: 0.9791 - val_loss: 1.9687 - val_accuracy: 0.5764\n",
      "Epoch 71/80\n",
      "29/29 [==============================] - 9s 303ms/step - loss: 1.0416 - accuracy: 0.9791 - val_loss: 1.8767 - val_accuracy: 0.6181\n",
      "Epoch 72/80\n",
      "29/29 [==============================] - 9s 317ms/step - loss: 0.9944 - accuracy: 0.9808 - val_loss: 1.8506 - val_accuracy: 0.6319\n",
      "Epoch 73/80\n",
      "29/29 [==============================] - 9s 320ms/step - loss: 0.9738 - accuracy: 0.9843 - val_loss: 1.8995 - val_accuracy: 0.5625\n",
      "Epoch 74/80\n",
      "29/29 [==============================] - 9s 320ms/step - loss: 0.9580 - accuracy: 0.9774 - val_loss: 1.8218 - val_accuracy: 0.5972\n",
      "Epoch 75/80\n",
      "29/29 [==============================] - 9s 317ms/step - loss: 0.9250 - accuracy: 0.9791 - val_loss: 1.7275 - val_accuracy: 0.6458\n",
      "Epoch 76/80\n",
      "29/29 [==============================] - 9s 309ms/step - loss: 0.9101 - accuracy: 0.9774 - val_loss: 1.7068 - val_accuracy: 0.6319\n",
      "Epoch 77/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 0.9128 - accuracy: 0.9686 - val_loss: 1.7245 - val_accuracy: 0.6319\n",
      "Epoch 78/80\n",
      "29/29 [==============================] - 9s 315ms/step - loss: 0.8522 - accuracy: 0.9895 - val_loss: 1.6939 - val_accuracy: 0.6389\n",
      "Epoch 79/80\n",
      "29/29 [==============================] - 9s 326ms/step - loss: 0.8557 - accuracy: 0.9739 - val_loss: 1.6623 - val_accuracy: 0.6250\n",
      "Epoch 80/80\n",
      "29/29 [==============================] - 10s 344ms/step - loss: 0.8070 - accuracy: 0.9791 - val_loss: 1.6634 - val_accuracy: 0.6389\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fef4592d930>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(mfcc1_train_reshaped,y1_train,validation_data=(mfcc1_val_reshaped,y1_val),batch_size=20,epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "516a2b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfcc2_file = np.load(\"Features2D/npz_new/mfcc2_train_test.npz\")\n",
    "mfcc2_train = mfcc2_file['mfcc_train']\n",
    "y2_train = mfcc2_file['y_train']\n",
    "\n",
    "mfcc2_train,mfcc2_val,y2_train,y2_val = train_test_split(mfcc2_train,y2_train, \n",
    "                                                         train_size = 0.8,  \n",
    "                                                         random_state = 13, \n",
    "                                                         stratify= y2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a7e9c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mfcc2_train_scaled = (mfcc2_train - np.mean(mfcc2_train))/np.std(mfcc2_train)\n",
    "mfcc2_val_scaled = (mfcc2_val - np.mean(mfcc2_val))/np.std(mfcc2_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97aecc0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data\n",
    "N,row,col = mfcc2_train_scaled.shape\n",
    "mfcc2_train_reshaped = mfcc2_train_scaled.reshape((N,row,col,1))\n",
    "N,row,col = mfcc2_val_scaled.shape\n",
    "mfcc2_val_reshaped = mfcc2_val_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb0e8b8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CNN2D_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 120, 600, 8)       80        \n",
      "                                                                 \n",
      " MaxPooling2D_1 (MaxPooling2  (None, 60, 300, 8)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN1 (BatchNormalization)    (None, 60, 300, 8)        32        \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 60, 300, 8)        584       \n",
      "                                                                 \n",
      " MaxPooling2D_2 (MaxPooling2  (None, 30, 150, 8)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN2 (BatchNormalization)    (None, 30, 150, 8)        32        \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 30, 150, 8)        584       \n",
      "                                                                 \n",
      " MaxPooling2D_3 (MaxPooling2  (None, 15, 75, 8)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN3 (BatchNormalization)    (None, 15, 75, 8)         32        \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 9000)              0         \n",
      "                                                                 \n",
      " Dense1 (Dense)              (None, 64)                576064    \n",
      "                                                                 \n",
      " Dropout1 (Dropout)          (None, 64)                0         \n",
      "                                                                 \n",
      " Dense2 (Dense)              (None, 32)                2080      \n",
      "                                                                 \n",
      " Dropout2 (Dropout)          (None, 32)                0         \n",
      "                                                                 \n",
      " Softmax (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 579,620\n",
      "Trainable params: 579,572\n",
      "Non-trainable params: 48\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "f1,k1,a1 = 8,3,'relu'\n",
    "mw1,mh1,sw1,sh1 = 2,3,2,2\n",
    "f2,k2,a2 = 8,3,'relu',\n",
    "mw2,mh2,sw2,sh2 = 2,3,2,2\n",
    "f3,k3,a3 = 8,3,'relu',\n",
    "mw3,mh3,sw3,sh3 = 2,3,2,2\n",
    "d1,dr1,da1,r1 = 64,0.2,'relu',0.2\n",
    "d2,dr2,da2,r2 = 32,0.2,'relu',0.2\n",
    "num = 2\n",
    "model2 = modelBuilder3L(mfcc2_train_reshaped,\n",
    "                        f1,k1,a1,\n",
    "                        mw1,mh1,sw1,sh1,\n",
    "                        f2,k2,a2,\n",
    "                        mw2,mh2,sw2,sh2,\n",
    "                        f3,k3,a3,\n",
    "                        mw3,mh3,sw3,sh3,\n",
    "                        d1,dr1,da1,r1,\n",
    "                        d2,dr2,da2,r2,\n",
    "                        num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "28b782aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "15/15 [==============================] - 8s 393ms/step - loss: 34.2848 - accuracy: 0.3021 - val_loss: 32.6667 - val_accuracy: 0.3056\n",
      "Epoch 2/80\n",
      "15/15 [==============================] - 5s 307ms/step - loss: 31.3979 - accuracy: 0.3958 - val_loss: 30.0932 - val_accuracy: 0.3611\n",
      "Epoch 3/80\n",
      "15/15 [==============================] - 4s 286ms/step - loss: 28.7585 - accuracy: 0.5278 - val_loss: 27.7155 - val_accuracy: 0.4306\n",
      "Epoch 4/80\n",
      "15/15 [==============================] - 4s 288ms/step - loss: 26.3753 - accuracy: 0.6007 - val_loss: 25.5676 - val_accuracy: 0.5417\n",
      "Epoch 5/80\n",
      "15/15 [==============================] - 4s 294ms/step - loss: 24.2542 - accuracy: 0.6771 - val_loss: 23.6331 - val_accuracy: 0.5694\n",
      "Epoch 6/80\n",
      "15/15 [==============================] - 4s 285ms/step - loss: 22.3965 - accuracy: 0.6771 - val_loss: 22.0124 - val_accuracy: 0.4306\n",
      "Epoch 7/80\n",
      "15/15 [==============================] - 5s 329ms/step - loss: 20.7915 - accuracy: 0.6736 - val_loss: 20.4121 - val_accuracy: 0.4306\n",
      "Epoch 8/80\n",
      "15/15 [==============================] - 7s 491ms/step - loss: 19.1529 - accuracy: 0.7708 - val_loss: 19.0748 - val_accuracy: 0.4167\n",
      "Epoch 9/80\n",
      "15/15 [==============================] - 7s 442ms/step - loss: 17.8560 - accuracy: 0.7639 - val_loss: 17.9161 - val_accuracy: 0.3472\n",
      "Epoch 10/80\n",
      "15/15 [==============================] - 6s 398ms/step - loss: 16.6119 - accuracy: 0.7951 - val_loss: 16.8573 - val_accuracy: 0.5000\n",
      "Epoch 11/80\n",
      "15/15 [==============================] - 5s 350ms/step - loss: 15.5573 - accuracy: 0.8229 - val_loss: 15.8523 - val_accuracy: 0.4167\n",
      "Epoch 12/80\n",
      "15/15 [==============================] - 5s 301ms/step - loss: 14.6089 - accuracy: 0.8542 - val_loss: 15.0862 - val_accuracy: 0.3333\n",
      "Epoch 13/80\n",
      "15/15 [==============================] - 5s 306ms/step - loss: 13.7442 - accuracy: 0.8924 - val_loss: 14.1276 - val_accuracy: 0.4722\n",
      "Epoch 14/80\n",
      "15/15 [==============================] - 5s 310ms/step - loss: 12.9452 - accuracy: 0.9028 - val_loss: 13.4529 - val_accuracy: 0.3889\n",
      "Epoch 15/80\n",
      "15/15 [==============================] - 5s 307ms/step - loss: 12.2840 - accuracy: 0.9062 - val_loss: 13.0548 - val_accuracy: 0.3472\n",
      "Epoch 16/80\n",
      "15/15 [==============================] - 5s 308ms/step - loss: 11.6744 - accuracy: 0.9028 - val_loss: 12.3009 - val_accuracy: 0.4167\n",
      "Epoch 17/80\n",
      "15/15 [==============================] - 4s 293ms/step - loss: 11.1103 - accuracy: 0.9201 - val_loss: 12.0059 - val_accuracy: 0.4167\n",
      "Epoch 18/80\n",
      "15/15 [==============================] - 4s 294ms/step - loss: 10.6141 - accuracy: 0.9306 - val_loss: 11.3636 - val_accuracy: 0.4167\n",
      "Epoch 19/80\n",
      "15/15 [==============================] - 5s 305ms/step - loss: 10.1580 - accuracy: 0.9062 - val_loss: 10.6941 - val_accuracy: 0.5278\n",
      "Epoch 20/80\n",
      "15/15 [==============================] - 5s 317ms/step - loss: 9.6884 - accuracy: 0.9549 - val_loss: 10.5793 - val_accuracy: 0.4028\n",
      "Epoch 21/80\n",
      "15/15 [==============================] - 5s 304ms/step - loss: 9.2572 - accuracy: 0.9792 - val_loss: 10.0775 - val_accuracy: 0.4444\n",
      "Epoch 22/80\n",
      "15/15 [==============================] - 5s 303ms/step - loss: 8.9668 - accuracy: 0.9444 - val_loss: 10.0538 - val_accuracy: 0.4028\n",
      "Epoch 23/80\n",
      "15/15 [==============================] - 5s 307ms/step - loss: 8.6664 - accuracy: 0.9410 - val_loss: 9.3670 - val_accuracy: 0.5278\n",
      "Epoch 24/80\n",
      "15/15 [==============================] - 4s 295ms/step - loss: 8.3201 - accuracy: 0.9618 - val_loss: 9.2225 - val_accuracy: 0.4306\n",
      "Epoch 25/80\n",
      "15/15 [==============================] - 6s 430ms/step - loss: 8.0320 - accuracy: 0.9757 - val_loss: 8.8696 - val_accuracy: 0.4444\n",
      "Epoch 26/80\n",
      "15/15 [==============================] - 5s 327ms/step - loss: 7.8129 - accuracy: 0.9549 - val_loss: 8.5042 - val_accuracy: 0.5556\n",
      "Epoch 27/80\n",
      "15/15 [==============================] - 5s 324ms/step - loss: 7.5340 - accuracy: 0.9688 - val_loss: 8.2636 - val_accuracy: 0.5417\n",
      "Epoch 28/80\n",
      "15/15 [==============================] - 5s 301ms/step - loss: 7.3396 - accuracy: 0.9549 - val_loss: 7.9702 - val_accuracy: 0.5972\n",
      "Epoch 29/80\n",
      "15/15 [==============================] - 5s 331ms/step - loss: 7.0893 - accuracy: 0.9757 - val_loss: 7.7780 - val_accuracy: 0.6111\n",
      "Epoch 30/80\n",
      "15/15 [==============================] - 5s 330ms/step - loss: 6.8834 - accuracy: 0.9826 - val_loss: 7.6431 - val_accuracy: 0.6111\n",
      "Epoch 31/80\n",
      "15/15 [==============================] - 5s 299ms/step - loss: 6.7008 - accuracy: 0.9792 - val_loss: 7.3976 - val_accuracy: 0.5972\n",
      "Epoch 32/80\n",
      "15/15 [==============================] - 5s 304ms/step - loss: 6.5590 - accuracy: 0.9549 - val_loss: 7.1359 - val_accuracy: 0.6528\n",
      "Epoch 33/80\n",
      "15/15 [==============================] - 5s 307ms/step - loss: 6.4016 - accuracy: 0.9757 - val_loss: 7.0065 - val_accuracy: 0.6250\n",
      "Epoch 34/80\n",
      "15/15 [==============================] - 4s 299ms/step - loss: 6.1945 - accuracy: 0.9757 - val_loss: 6.7288 - val_accuracy: 0.7222\n",
      "Epoch 35/80\n",
      "15/15 [==============================] - 5s 306ms/step - loss: 6.0497 - accuracy: 0.9826 - val_loss: 6.6304 - val_accuracy: 0.7083\n",
      "Epoch 36/80\n",
      "15/15 [==============================] - 5s 309ms/step - loss: 5.8644 - accuracy: 0.9826 - val_loss: 6.3720 - val_accuracy: 0.7361\n",
      "Epoch 37/80\n",
      "15/15 [==============================] - 5s 301ms/step - loss: 5.7673 - accuracy: 0.9757 - val_loss: 6.3132 - val_accuracy: 0.6944\n",
      "Epoch 38/80\n",
      "15/15 [==============================] - 5s 311ms/step - loss: 5.6176 - accuracy: 0.9861 - val_loss: 6.0740 - val_accuracy: 0.7639\n",
      "Epoch 39/80\n",
      "15/15 [==============================] - 5s 313ms/step - loss: 5.5111 - accuracy: 0.9826 - val_loss: 6.0941 - val_accuracy: 0.6806\n",
      "Epoch 40/80\n",
      "15/15 [==============================] - 5s 317ms/step - loss: 5.3533 - accuracy: 0.9861 - val_loss: 5.8200 - val_accuracy: 0.7361\n",
      "Epoch 41/80\n",
      "15/15 [==============================] - 4s 294ms/step - loss: 5.2382 - accuracy: 0.9861 - val_loss: 5.6647 - val_accuracy: 0.7361\n",
      "Epoch 42/80\n",
      "15/15 [==============================] - 4s 299ms/step - loss: 5.1398 - accuracy: 0.9861 - val_loss: 5.5760 - val_accuracy: 0.7361\n",
      "Epoch 43/80\n",
      "15/15 [==============================] - 4s 283ms/step - loss: 5.0173 - accuracy: 0.9861 - val_loss: 5.4297 - val_accuracy: 0.7639\n",
      "Epoch 44/80\n",
      "15/15 [==============================] - 5s 305ms/step - loss: 4.9079 - accuracy: 0.9792 - val_loss: 5.3558 - val_accuracy: 0.7639\n",
      "Epoch 45/80\n",
      "15/15 [==============================] - 5s 308ms/step - loss: 4.7957 - accuracy: 0.9826 - val_loss: 5.2693 - val_accuracy: 0.7361\n",
      "Epoch 46/80\n",
      "15/15 [==============================] - 4s 298ms/step - loss: 4.7137 - accuracy: 0.9653 - val_loss: 5.1272 - val_accuracy: 0.7500\n",
      "Epoch 47/80\n",
      "15/15 [==============================] - 5s 306ms/step - loss: 4.5962 - accuracy: 0.9896 - val_loss: 5.0299 - val_accuracy: 0.7778\n",
      "Epoch 48/80\n",
      "15/15 [==============================] - 5s 311ms/step - loss: 4.4835 - accuracy: 0.9965 - val_loss: 4.9483 - val_accuracy: 0.7639\n",
      "Epoch 49/80\n",
      "15/15 [==============================] - 5s 300ms/step - loss: 4.4008 - accuracy: 0.9861 - val_loss: 4.8544 - val_accuracy: 0.7639\n",
      "Epoch 50/80\n",
      "15/15 [==============================] - 5s 310ms/step - loss: 4.3214 - accuracy: 0.9861 - val_loss: 4.7781 - val_accuracy: 0.7222\n",
      "Epoch 51/80\n",
      "15/15 [==============================] - 5s 312ms/step - loss: 4.2259 - accuracy: 0.9896 - val_loss: 4.6499 - val_accuracy: 0.7500\n",
      "Epoch 52/80\n",
      "15/15 [==============================] - 4s 295ms/step - loss: 4.1364 - accuracy: 0.9826 - val_loss: 4.6160 - val_accuracy: 0.6944\n",
      "Epoch 53/80\n",
      "15/15 [==============================] - 4s 298ms/step - loss: 4.0833 - accuracy: 0.9826 - val_loss: 4.4712 - val_accuracy: 0.7778\n",
      "Epoch 54/80\n",
      "15/15 [==============================] - 4s 296ms/step - loss: 3.9937 - accuracy: 0.9861 - val_loss: 4.4444 - val_accuracy: 0.6944\n",
      "Epoch 55/80\n",
      "15/15 [==============================] - 4s 297ms/step - loss: 3.8981 - accuracy: 0.9861 - val_loss: 4.3326 - val_accuracy: 0.7639\n",
      "Epoch 56/80\n",
      "15/15 [==============================] - 5s 319ms/step - loss: 3.8170 - accuracy: 0.9861 - val_loss: 4.2764 - val_accuracy: 0.7222\n",
      "Epoch 57/80\n",
      "15/15 [==============================] - 5s 310ms/step - loss: 3.7282 - accuracy: 0.9931 - val_loss: 4.1534 - val_accuracy: 0.7917\n",
      "Epoch 58/80\n",
      "15/15 [==============================] - 5s 303ms/step - loss: 3.6625 - accuracy: 0.9896 - val_loss: 4.1388 - val_accuracy: 0.7222\n",
      "Epoch 59/80\n",
      "15/15 [==============================] - 5s 314ms/step - loss: 3.6004 - accuracy: 0.9826 - val_loss: 4.0490 - val_accuracy: 0.7639\n",
      "Epoch 60/80\n",
      "15/15 [==============================] - 4s 294ms/step - loss: 3.5197 - accuracy: 0.9826 - val_loss: 4.0192 - val_accuracy: 0.7639\n",
      "Epoch 61/80\n",
      "15/15 [==============================] - 4s 286ms/step - loss: 3.4547 - accuracy: 0.9826 - val_loss: 3.8747 - val_accuracy: 0.7778\n",
      "Epoch 62/80\n",
      "15/15 [==============================] - 4s 296ms/step - loss: 3.3710 - accuracy: 0.9965 - val_loss: 3.8523 - val_accuracy: 0.7361\n",
      "Epoch 63/80\n",
      "15/15 [==============================] - 4s 296ms/step - loss: 3.3169 - accuracy: 0.9861 - val_loss: 3.7463 - val_accuracy: 0.7500\n",
      "Epoch 64/80\n",
      "15/15 [==============================] - 4s 288ms/step - loss: 3.2486 - accuracy: 0.9896 - val_loss: 3.7249 - val_accuracy: 0.7639\n",
      "Epoch 65/80\n",
      "15/15 [==============================] - 4s 298ms/step - loss: 3.1885 - accuracy: 0.9965 - val_loss: 3.6512 - val_accuracy: 0.7639\n",
      "Epoch 66/80\n",
      "15/15 [==============================] - 4s 296ms/step - loss: 3.1242 - accuracy: 0.9931 - val_loss: 3.5677 - val_accuracy: 0.7361\n",
      "Epoch 67/80\n",
      "15/15 [==============================] - 4s 286ms/step - loss: 3.0852 - accuracy: 0.9792 - val_loss: 3.5133 - val_accuracy: 0.7778\n",
      "Epoch 68/80\n",
      "15/15 [==============================] - 4s 297ms/step - loss: 3.0009 - accuracy: 0.9965 - val_loss: 3.4396 - val_accuracy: 0.7778\n",
      "Epoch 69/80\n",
      "15/15 [==============================] - 4s 295ms/step - loss: 2.9581 - accuracy: 0.9826 - val_loss: 3.3814 - val_accuracy: 0.7778\n",
      "Epoch 70/80\n",
      "15/15 [==============================] - 4s 289ms/step - loss: 2.8870 - accuracy: 0.9965 - val_loss: 3.3382 - val_accuracy: 0.7778\n",
      "Epoch 71/80\n",
      "15/15 [==============================] - 4s 300ms/step - loss: 2.8282 - accuracy: 0.9896 - val_loss: 3.2975 - val_accuracy: 0.7778\n",
      "Epoch 72/80\n",
      "15/15 [==============================] - 4s 294ms/step - loss: 2.7764 - accuracy: 0.9896 - val_loss: 3.1991 - val_accuracy: 0.8056\n",
      "Epoch 73/80\n",
      "15/15 [==============================] - 4s 289ms/step - loss: 2.7174 - accuracy: 0.9896 - val_loss: 3.1473 - val_accuracy: 0.7778\n",
      "Epoch 74/80\n",
      "15/15 [==============================] - 4s 297ms/step - loss: 2.6878 - accuracy: 0.9861 - val_loss: 3.0882 - val_accuracy: 0.7778\n",
      "Epoch 75/80\n",
      "15/15 [==============================] - 4s 295ms/step - loss: 2.6191 - accuracy: 0.9861 - val_loss: 3.0641 - val_accuracy: 0.7917\n",
      "Epoch 76/80\n",
      "15/15 [==============================] - 5s 318ms/step - loss: 2.5846 - accuracy: 0.9792 - val_loss: 2.9876 - val_accuracy: 0.7639\n",
      "Epoch 77/80\n",
      "15/15 [==============================] - 5s 304ms/step - loss: 2.5293 - accuracy: 0.9965 - val_loss: 2.9457 - val_accuracy: 0.7778\n",
      "Epoch 78/80\n",
      "15/15 [==============================] - 5s 301ms/step - loss: 2.4773 - accuracy: 0.9931 - val_loss: 2.9290 - val_accuracy: 0.7778\n",
      "Epoch 79/80\n",
      "15/15 [==============================] - 4s 293ms/step - loss: 2.4304 - accuracy: 0.9931 - val_loss: 2.8594 - val_accuracy: 0.8194\n",
      "Epoch 80/80\n",
      "15/15 [==============================] - 5s 319ms/step - loss: 2.3843 - accuracy: 0.9896 - val_loss: 2.8340 - val_accuracy: 0.7361\n"
     ]
    }
   ],
   "source": [
    "history2 = model2.fit(mfcc2_train_reshaped,y2_train,validation_data=(mfcc2_val_reshaped,y2_val),batch_size=20,epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fa9049bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'loss')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and Validation accuracy and loss\n",
    "f = plt.figure(figsize = (12,4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('train validation accuracy')\n",
    "plt.plot(history2.history['accuracy'])\n",
    "plt.plot(history2.history['val_accuracy'])\n",
    "plt.legend(['train','validation'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('train validation loss')\n",
    "plt.plot(history2.history['loss'])\n",
    "plt.plot(history2.history['val_loss'])\n",
    "plt.legend(['train','validation'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "91a78282",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfcc2_test = mfcc2_file['mfcc_test']\n",
    "y2_test_mfcc = mfcc2_file['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "96a70ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mfcc2_test_scaled = (mfcc2_test - np.mean(mfcc2_test))/np.std(mfcc2_test)\n",
    "\n",
    "# Reshape the data\n",
    "N,row,col = mfcc2_test_scaled.shape\n",
    "mfcc2_test_reshaped = mfcc2_test_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfc23f2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 82ms/step\n"
     ]
    }
   ],
   "source": [
    "y1_prob = model2.predict(mfcc2_test_reshaped)\n",
    "y1_pred = np.argmax(y1_prob, axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ae2a2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy\n",
    "acc1_2d = round(accuracy_score(y1_pred,y2_test_mfcc),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b22fd43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "cm_mfcc = confusion_matrix(y2_test_mfcc,y1_pred)\n",
    "cm_2d_mfcc = ConfusionMatrixDisplay(confusion_matrix = cm_mfcc, display_labels = ['Angry', 'Happy', 'Relaxed', 'Sad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2017356c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Angry       1.00      1.00      1.00        10\n",
      "       Happy       0.83      1.00      0.91        10\n",
      "     Relaxed       0.89      0.80      0.84        10\n",
      "         Sad       0.89      0.80      0.84        10\n",
      "\n",
      "    accuracy                           0.90        40\n",
      "   macro avg       0.90      0.90      0.90        40\n",
      "weighted avg       0.90      0.90      0.90        40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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hAkJERCRjQsu34Qq+jI6IiIioGFtAiIiIZEwJCUotXkanzbGViQkIERGRjKmEduM4VEKHwegQu2CIiIhI79gCQkREJGMqLQehanNsZWICQkREJGMqSFBpMY5Dm2MrkzzTIiIiIjJqbAEhIiKSMX3PhKovTECIiIhkjGNAqFL0ahAIC8nS0GGYhF03Txs6BJMT5NnM0CEQkUwxASEiIpIxFbR8F4xMB6EyASEiIpIxoeVTMIIJCBEREZWXsb4NV54jU4iIiMiosQWEiIhIxvgUDBEREekdu2CIiIiIdIQtIERERDJmrO+CYQJCREQkY+yCISIiItIRtoAQERHJmLG2gDABISIikjFjTUDYBUNERER6xxYQIiIiGTPWFhAmIERERDImoN2jtEJ3oegUExAiIiIZM9YWEI4BISIiIr1jCwgREZGMGWsLCBMQIiIiGTPWBIRdMERERKR3bAEhIiKSMWNtAWECQkREJGNCSBBaJBHaHFuZ2AVDREREescWECIiIhlTQdJqIjJtjq1MTECIiIhkzFjHgLALhoiIiPSOLSBEREQyZqyDUJmAEBERyZixdsEwASEiIpIxY20B4RgQIiIi0ju2gBAREcmY0LILRq4tIExAiIiIZEwAEEK74+WIXTBERESkd2wBISIikjEVJEicCZWIiIj0iU/BEBEREekIW0CIiIhkTCUkSJyIjIiIiPRJCC2fgpHpYzDsgiEiIiK9YwsIERGRjHEQKpm8noPuYfXR3/D9lV+wOPEi/JvlGjoko3H2iB2mDPRF/+aNEeTZDMk/OGlsFwJYPc8d/Zs1Rs+6TTDp7Xq4ccXKQNEaL37G9Yv1XTaPExBtFjliAkJl0vHNB3hv6k2sXeiOEUENcOU3a8xedwVO1QsNHZpRyMs1Q93Gf2HknD9L3b5pmRv+b1UNjPpvGhYnXoC1rQof/aceCvLk+YulKuJnXL9Y32X3+G242ixyVCUSkEGDBiEkJKRE+b59+yBJEjIzM/Uek6np/d497FxXDT9urIbrF62xZFIt5P8lIaj/fUOHZhRe/H8PMWjSbbR/PavENiGArStroP+Y22jXLRt1A/IwcckfyLhjieSdTqWcjSqCn3H9Yn1TlUhAyLAsLFXwa5KLkwcd1GVCSDh10AEBLdlkWtluX7fC/buWaNEhR11m56hCw+a5OJ9iZ8DIjAc/4/rF+i6fx0/BaLPIkdEkIBkZGejfvz+8vLxga2uLwMBArF+/XmOfTp06YeTIkRg5ciScnJzg6uqK6OhoiH/9dOrUqYOZM2eif//+sLOzg5eXF5YtW6beHh4ejh49emict7CwEG5uboiLi6vcmzQQx2pKmFsAmemaY5Yf3LOAS40iA0VlOu7fLa535xqaTdPONQrV20g7/IzrF+u7fIqTCG3GgBj6DkpnNAlIXl4eWrZsie3bt+PcuXN477338O677+LYsWMa+61evRoWFhY4duwYFi9ejIULF2LlypUa+8yfPx9NmzbFqVOnEBUVhTFjxmD37t0AgIiICOzcuRO3bt1S75+YmIjc3Fz069fvqfHl5+cjOztbYyEiIjJVVebPp8TERNjb22uUKZVK9b+9vLwQGRmpXh81ahR27dqFTZs2oXXr1upyb29vLFq0CJIkwd/fH2fPnsWiRYswdOhQ9T7t27dHVFQUAKBBgwZISkrCokWL0LVrV7Rr1w7+/v746quvMHHiRABAfHw8+vbtWyK+f4uJicH06dO1qwQDyb5vDmUR4PzEXyYurkV4kF5lPkJVVjW34nrPTLdE9Zr//Awy0y1Rr/FfhgrLqPAzrl+s7/LhY7gG1rlzZ5w+fVpj+XfLhVKpxMyZMxEYGIhq1arB3t4eu3btwvXr1zXO89JLL0GS/vlhtG3bFhcvXtRIZtq2batxTNu2bXH+/Hn1ekREBOLj4wEAd+7cwQ8//IDw8PBnxj958mRkZWWpl7S0tPJXgoEUFZrh4i+2aP7yQ3WZJAk0ezkHv6XYGjAy0+BeuwDV3Apx6tA/Ce6jh2b4/ZQtGrV8ZMDIjAc/4/rF+i4foYNFjqpMqmlnZ4f69etrlP355z+PLM6fPx+LFy9GbGwsAgMDYWdnh7Fjx6KgoEDnsQwcOBBRUVE4fPgwkpOT4evriw4dOjzzGIVCAYVCofNY9OW7L1wRGZuGC2dskXrKFr2GpsPaVoUfN1QzdGhG4a9HZrh59Z/Px+00K1w+ZwMH5yK41SpESEQ61i+uCS/ffLjXLsDqeR6oXrMQ7bqVfGqGKoafcf1ifVOVSUCeJykpCcHBwRgwYAAAQKVS4cKFCwgICNDY7+jRoxrrR44cgZ+fH8zNzTXKntynUaNG6vXq1asjJCQE8fHxOHz4MAYPHqzr25Gd/dtc4FRdiYETbsOlRhGu/GqDj0N9kXnP0tChGYULZ2wx8a1/EuzPp3kBALq+fR+Rsdfx9oi7yMs1w+KJ3sjJNkfjFx9h9torsLKW6982VQ8/4/rF+i47Y+2CMZoExM/PD5s3b0ZycjJcXFywcOFC3Llzp0QCcv36dYwfPx7vv/8+Tp48iaVLl2LBggUa+yQlJWHevHkICQnB7t278c0332D79u0a+0RERKBHjx5QKpUICwur9PuTg23xrtgW72roMIxS03Y52HXz9FO3SxIQNvE2wibe1l9QJoifcf1ifZeRtv0oMv07pcqMAXmeTz75BC1atEBQUBA6deoEd3f3UicvGzhwIP766y+0bt0aI0aMwJgxY/Dee+9p7PPhhx/ixIkTaN68OWbNmoWFCxciKChIY58uXbrAw8MDQUFB8PT0rMxbIyIiU6btNOwVaAG5ceMGBgwYgOrVq8PGxgaBgYE4ceKETm+rSrSAJCQklFreqVMnjTk8tm7d+txzWVpaIjY2FsuXL3/qPo6Ojti0adMzz/Po0SM8ePAAQ4YMee41iYiIqooHDx6gffv26Ny5M3744QfUqFEDFy9ehIuLi06vUyUSEDlRqVS4d+8eFixYAGdnZ7z55puGDomIiIyYtrOZlvfYuXPnwtvbW/20JwD4+vpWPICnMJouGH25fv06atasiXXr1mHVqlWwsGAOR0RElUdXb8N9cjLM/Pz8Uq+3bds2tGrVCn379oWbmxuaN2+OL7/8Uuf3ZVLfnvv27XvuPteuXXvm9jp16mh0+xAREVUF3t7eGutTp07FtGnTSux35coVLF++HOPHj8dHH32E48ePY/To0bCystLpQxcmlYAQERFVORUcSKpxPIC0tDQ4Ojqqi582N5VKpUKrVq0wZ84cAEDz5s1x7tw5rFixQqcJCLtgiIiIZExXb8N1dHTUWJ6WgHh4eJSYwqJRo0YlZhbXFhMQIiIiUmvfvj1SU1M1yi5cuAAfHx+dXocJCBERkZzp+WUw48aNw5EjRzBnzhxcunQJ69atwxdffIERI0bo5n7+VqYxINu2bSvzCflYKhERke7oeyr2F198EVu2bMHkyZMxY8YM+Pr6IjY2FqGhoRWOoTRlSkBKm1G0NJIkabxVloiIiKqeHj16oEePHpV6jTIlICqVqlKDICIiomcwwtkftHoMNy8vD9bW1rqKhYiIiJ5grG/DLfcgVKVSiZkzZ8LLywv29va4cuUKACA6OhpxcXE6D5CIiMik6XkQqr6UOwGZPXs2EhISMG/ePFhZWanLX3jhBaxcuVKnwREREZFxKncCsmbNGnzxxRcIDQ2Fubm5urxp06b4/fffdRocERERSTpY5KfcY0Bu3LiB+vXrlyhXqVQoLCzUSVBERET0N227UYylCyYgIAAHDx4sUb5582Y0b95cJ0ERERGRcSt3C8iUKVMQFhaGGzduQKVS4bvvvkNqairWrFmDxMTEyoiRiIjIdLEFpFhwcDC+//577NmzB3Z2dpgyZQrOnz+P77//Hl27dq2MGImIiEzX47fharPIUIXmAenQoQN2796t61iIiIjIRFR4IrITJ07g/PnzAIrHhbRs2VJnQREREVExIYoXbY6Xo3InIH/++Sf69++PpKQkODs7AwAyMzPRrl07bNiwAbVq1dJ1jERERKaLY0CKRUREoLCwEOfPn8f9+/dx//59nD9/HiqVChEREZURIxERERmZcreA7N+/H8nJyfD391eX+fv7Y+nSpejQoYNOgyMiIjJ52g4kNZZBqN7e3qVOOKZUKuHp6amToIiIiKiYJIoXbY6Xo3J3wcyfPx+jRo3CiRMn1GUnTpzAmDFj8Omnn+o0OCIiIpNnpC+jK1MLiIuLCyTpnyacR48eoU2bNrCwKD68qKgIFhYWCA8PR0hISKUESkRERMajTAlIbGxsJYdBREREpTLlMSBhYWGVHQcRERGVxkgfw63wRGQAkJeXh4KCAo0yR0dHrQIiIiIi41fuQaiPHj3CyJEj4ebmBjs7O7i4uGgsREREpENGOgi13AnIxIkT8dNPP2H58uVQKBRYuXIlpk+fDk9PT6xZs6YyYiQiIjJdRpqAlLsL5vvvv8eaNWvQqVMnDB48GB06dED9+vXh4+ODtWvXIjQ0tDLiJCIiIiNS7haQ+/fvo27dugCKx3vcv38fAPDyyy/jwIEDuo2OiIjI1D1+CkabRYbKnYDUrVsXV69eBQA0bNgQmzZtAlDcMvL45XRERESkG49nQtVmkaNyJyCDBw/GmTNnAABRUVFYtmwZrK2tMW7cOEyYMEHnARIREZHxKfcYkHHjxqn/3aVLF/z+++9ISUlB/fr10aRJE50GR0REZPI4D0jpfHx84OPjo4tYiIiIyESUKQFZsmRJmU84evToCgdDREREmiRo+TZcnUWiW2VKQBYtWlSmk0mSxASEiIiInqtMCcjjp16IqrLuL4cYOgST8+Lpy4YOwaSc6l3P0CGYDlU+oK+vRlN+GR0REREZiJEOQi33Y7hERERE2mILCBERkZwZaQsIExAiIiIZ03Y2U6OZCZWIiIhIWxVKQA4ePIgBAwagbdu2uHHjBgDgq6++wqFDh3QaHBERkckTOlhkqNwJyLfffougoCDY2Njg1KlTyM/PBwBkZWVhzpw5Og+QiIjIpDEBKTZr1iysWLECX375JSwtLdXl7du3x8mTJ3UaHBERERmncg9CTU1NxSuvvFKi3MnJCZmZmbqIiYiIiP7GQah/c3d3x6VLl0qUHzp0CHXr1tVJUERERPS3xzOharPIULkTkKFDh2LMmDE4evQoJEnCzZs3sXbtWkRGRmLYsGGVESMREZHpMtIxIOXugomKioJKpcKrr76K3NxcvPLKK1AoFIiMjMSoUaMqI0YiIiIyMuVOQCRJwscff4wJEybg0qVLyMnJQUBAAOzt7SsjPiIiIpNmrGNAKjwTqpWVFQICAnQZCxERET2JU7EX69y5MyTp6QNafvrpJ60CIiIiIuNX7gSkWbNmGuuFhYU4ffo0zp07h7CwMF3FRURERACgZReM0bSALFq0qNTyadOmIScnR+uAiIiI6F+MtAtGZy+jGzBgAFatWqWr0xEREZERq/Ag1CcdPnwY1tbWujodERERAUbbAlLuBKR3794a60II3Lp1CydOnEB0dLTOAiMiIiI+hqvm5OSksW5mZgZ/f3/MmDEDr732ms4CIyIiIuNVrgREqVRi8ODBCAwMhIuLS2XFREREREauXINQzc3N8dprr/Gtt0RERPpipO+CKfdTMC+88AKuXLlSGbEQERHREx6PAdFmkaNyJyCzZs1CZGQkEhMTcevWLWRnZ2ssRERERM9T5jEgM2bMwIcffog33ngDAPDmm29qTMkuhIAkSVAqlbqPkoiIyJTJtBVDG2VOQKZPn44PPvgAP//8c2XGQ0RERP9m6vOACFF8Bx07dqy0YIiIiMg0lOsx3Ge9BZeIiIh0jxORAWjQoMFzk5D79+9rFRARERH9i6l3wQDF40CenAmViIiIqLzKlYC88847cHNzq6xYiIiI6AnG2gVT5nlAOP6DiIjIAAw8E+p///tfSJKEsWPHaneiJ5Q5AXn8FAwRERGZhuPHj+Pzzz9HkyZNdH7uMicgKpWK3S9ERET6ZqAWkJycHISGhuLLL7+slBfQlnsqdiIiItIfXb0L5slXp+Tn5z/zuiNGjED37t3RpUuXSrkvJiBERERypqMWEG9vbzg5OamXmJiYp15yw4YNOHny5DP30Va5noIhIiKiqiktLQ2Ojo7qdYVC8dT9xowZg927d8Pa2rrS4mECQkREJGc6mojM0dFRIwF5mpSUFNy9exctWrRQlymVShw4cAD/+9//kJ+fD3Nzcy0CKsYEhIiISMb0PQ/Iq6++irNnz2qUDR48GA0bNsSkSZN0knwAVTwB2bdvHzp37owHDx7A2dnZYHEkJCRg7NixyMzMNFgM+tBz0D28NewuqtUowpXfbPDZJ15IPW1r6LCMUuOm99DnP5dQ3z8T1V3zMXNyaxw56GHosIyWUAI3VkjI2C6hMAOwqgG4vingMVSAUyBVDn7G5cvBwQEvvPCCRpmdnR2qV69eolwbBh2EOmjQIEiSBEmSYGlpCV9fX0ycOBF5eXmGDItK0fHNB3hv6k2sXeiOEUENcOU3a8xedwVO1QsNHZpRsrZR4uolJyxfqPtn76mkW/ES0r+R4BOlQuB3KtQao8KtBAl31zP7qCz8jJeDgSciqywGbwHp1q0b4uPjUVhYiJSUFISFhUGSJMydO9fQodG/9H7vHnauq4YfN1YDACyZVAutX81GUP/72PS/mgaOzvikHKmJlCOsV33JOSPBuZOA8yvF6wov4P5OIOccwJ9C5eBnvOzkMBX7vn37tD/JEwz+GK5CoYC7uzu8vb0REhKCLl26YPfu3QCKJz+LiYmBr68vbGxs0LRpU2zevPmp58rIyED//v3h5eUFW1tbBAYGYv369ert6enpcHd3x5w5c9RlycnJsLKywt69ewEA+fn5iIyMhJeXF+zs7NCmTZsSFZ+QkIDatWvD1tYWvXr1QkZGhg5rRH4sLFXwa5KLkwcd1GVCSDh10AEBLXMNGBmRbtg3Fcg+KiHvj+L13FTg4SnAub1h4yIyZgZvAfm3c+fOITk5GT4+PgCAmJgYfP3111ixYgX8/Pxw4MABDBgwADVq1EDHjh1LHJ+Xl4eWLVti0qRJcHR0xPbt2/Huu++iXr16aN26NWrUqIFVq1YhJCQEr732Gvz9/fHuu+9i5MiRePXVVwEAI0eOxG+//YYNGzbA09MTW7ZsQbdu3XD27Fn4+fnh6NGjGDJkCGJiYhASEoKdO3di6tSpz723/Px8jUlfsrOzdVRrlc+xmhLmFkBmuubH5cE9C3jXf/ZENkRVgUe4gPIRcDbEDJJ58ZgQr5EC1bvLtO2aTIuOnoKRG4MnIImJibC3t0dRURHy8/NhZmamfsxnzpw52LNnD9q2bQsAqFu3Lg4dOoTPP/+81ATEy8sLkZGR6vVRo0Zh165d2LRpE1q3bg0AeOONNzB06FCEhoaiVatWsLOzU0+0cv36dcTHx+P69evw9PQEAERGRmLnzp2Ij4/HnDlzsHjxYnTr1g0TJ04EADRo0ADJycnYuXPnM+8zJiYG06dP177CiEjn7v8oIWOHhLoxAjb1BHJTJVyfL6kHoxIZFBOQytG5c2csX74cjx49wqJFi2BhYYE+ffrg119/RW5uLrp27aqxf0FBAZo3b17quZRKJebMmYNNmzbhxo0bKCgoQH5+PmxtNZ/U+PTTT/HCCy/gm2++QUpKinoylrNnz0KpVKJBgwYa++fn56N69eoAgPPnz6NXr14a29u2bfvcBGTy5MkYP368ej07Oxve3t7PPEYusu+bQ1kEONco0ih3cS3Cg3SDf4SItJa2SILHYIHq3Yp/U9v6CRTcAm6tkpiAEFUSg3972NnZoX79+gCAVatWoWnTpoiLi1M/6rN9+3Z4eXlpHPO02dvmz5+PxYsXIzY2FoGBgbCzs8PYsWNRUFCgsd/ly5dx8+ZNqFQqXLt2DYGBgQCKX7xjbm6OlJSUEs8529vba3WfCoXiqXHLXVGhGS7+YovmLz/E4Z1OAABJEmj2cg62JVQ3cHRE2lPlAdKTI+LMAKEySDhEGqS/F22OlyODJyD/ZmZmho8++gjjx4/HhQsXoFAocP369VK7W0qTlJSE4OBgDBgwAEDxINYLFy4gICBAvU9BQQEGDBiAfv36wd/fHxERETh79izc3NzQvHlzKJVK3L17Fx06dCj1Go0aNcLRo0c1yo4cOVLBO646vvvCFZGxabhwxhapp2zRa2g6rG1V+HFDNUOHZpSsbYrg6fVIve7ukYu69bPw8KEl0u9w7hVdc35F4OZKCVbuAjb1igeh3vlagmswWz8qCz/j5cAuGP3o27cvJkyYgM8//xyRkZEYN24cVCoVXn75ZWRlZSEpKQmOjo4ICwsrcayfnx82b96M5ORkuLi4YOHChbhz545GAvLxxx8jKysLS5Ysgb29PXbs2IHw8HAkJiaiQYMGCA0NxcCBA7FgwQI0b94c6enp2Lt3L5o0aYLu3btj9OjRaN++PT799FMEBwdj165dz+1+MQb7t7nAqboSAyfchkuNIlz51QYfh/oi856loUMzSn4NM/HfpUnq9aGjzwEA9uzwxqI5LZ52GFWQT5TAjWXAHzFmKLxfPBFZjT4Cnu/L9De3EeBnvOzk8BhuZZBdAmJhYYGRI0di3rx5uHr1KmrUqIGYmBhcuXIFzs7OaNGiBT766KNSj/3kk09w5coVBAUFwdbWFu+99x5CQkKQlZUFoPg55tjYWPz888/q+fC/+uorNG3aFMuXL8ewYcMQHx+PWbNm4cMPP8SNGzfg6uqKl156CT169AAAvPTSS/jyyy8xdepUTJkyBV26dMEnn3yCmTNn6qeCDGhbvCu2xbsaOgyTcPaUK7q/HGzoMEyGuR1Qe6JA7Yky/U1thPgZJ0kIwf9xBpCdnQ0nJyd0QjAsJLYi6INF3TqGDsHkNP/usqFDMCmnetczdAgmo0iVjz1XlyIrK6tML3iriMffE43fnwNzRcXfSqvMz8Ovn39UqbFWhOxaQIiIiOgJRthUYPCZUImIiMj0sAWEiIhIxjgIlYiIiPTPSB/DZRcMERER6R1bQIiIiGSMXTBERESkf+yCISIiItINtoAQERHJGLtgiIiISP+MtAuGCQgREZGcGWkCwjEgREREpHdsASEiIpIxjgEhIiIi/WMXDBEREZFusAWEiIhIxiQhIImKN2Noc2xlYgJCREQkZ+yCISIiItINtoAQERHJGJ+CISIiIv1jFwwRERGRbrAFhIiISMbYBUNERET6Z6RdMExAiIiIZMxYW0A4BoSIiIj0ji0gREREcsYuGCIiIjIEuXajaINdMERERKR3bAEhIiKSMyGKF22OlyEmIERERDLGp2CIiIiIdIQtIERERHLGp2CIiIhI3yRV8aLN8XLELhgiIiLSO7aAEBERyRm7YIiIiEjfjPUpGCYgREREcmak84BwDAgRERHpHVtAiIiIZIxdMERE5XSqdz1Dh2BSmn932dAhmIz8nELsaa+nixnpIFR2wRAREZHesQWEiIhIxtgFQ0RERPrHp2CIiIiIdIMtIERERDLGLhgiIiLSPz4FQ0RERKQbbAEhIiKSMXbBEBERkf6pRPGizfEyxASEiIhIzjgGhIiIiEg32AJCREQkYxK0HAOis0h0iwkIERGRnHEmVCIiIiLdYAJCREQkY48fw9VmKY+YmBi8+OKLcHBwgJubG0JCQpCamqrz+2ICQkREJGdCB0s57N+/HyNGjMCRI0ewe/duFBYW4rXXXsOjR490cz9/4xgQIiIiUtu5c6fGekJCAtzc3JCSkoJXXnlFZ9dhAkJERCRjkhCQtBhI+vjY7OxsjXKFQgGFQvHc47OysgAA1apVq3AMpWEXDBERkZypdLAA8Pb2hpOTk3qJiYl5/qVVKowdOxbt27fHCy+8oNPbYgsIERGRCUhLS4Ojo6N6vSytHyNGjMC5c+dw6NAhncfDBISIiEjGdNUF4+joqJGAPM/IkSORmJiIAwcOoFatWhW+/tMwASEiIpIzPb8LRgiBUaNGYcuWLdi3bx98fX21uPjTMQEhIiKSMz3PhDpixAisW7cO//d//wcHBwfcvn0bAODk5AQbG5uKx/EEDkIlIiIiteXLlyMrKwudOnWCh4eHetm4caNOr8MWECIiIhmryGymTx5fHkJP745hAkJERCRnfBkdERERkW6wBYSIiEjGJFXxos3xcsQEhIiISM7YBUNERESkG2wBISIikjM9T0SmL0xAiIiIZExXU7HLDbtgiIiISO/YAkJERCRnRjoIlQkIERGRnAkA2jxKK8/8gwkIERGRnHEMCBEREZGOsAWEiIhIzgS0HAOis0h0igkIERGRnBnpIFR2wRAREZHesQVEBxISEjB27FhkZmYaOpRK1XPQPbw17C6q1SjCld9s8NknXkg9bWvosIxS46b30Oc/l1DfPxPVXfMxc3JrHDnoYeiwjBrrXH+EErixQkLGdgmFGYBVDcD1TQGPoQKSZOjoZEgFQJt6kenL6EyuBSQ9PR3Dhg1D7dq1oVAo4O7ujqCgICQlJRk6NFnr+OYDvDf1JtYudMeIoAa48ps1Zq+7AqfqhYYOzShZ2yhx9ZITli9sYuhQTAbrXH9uxUtI/0aCT5QKgd+pUGuMCrcSJNxdz+yjNI+fgtFmkSOTawHp06cPCgoKsHr1atStWxd37tzB3r17kZGRYejQZK33e/ewc101/LixGgBgyaRaaP1qNoL638em/9U0cHTGJ+VITaQcYb3qE+tcf3LOSHDuJOD8SvG6wgu4vxPIOQfwJ2A6TKoFJDMzEwcPHsTcuXPRuXNn+Pj4oHXr1pg8eTLefPNNAMDChQsRGBgIOzs7eHt7Y/jw4cjJydE4T0JCAmrXrg1bW1v06tXL6JMXC0sV/Jrk4uRBB3WZEBJOHXRAQMtcA0ZGRFWRfVOB7KMS8v4oXs9NBR6eApzbGzYu2Xo8CFWbRYZMKgGxt7eHvb09tm7divz8/FL3MTMzw5IlS/Drr79i9erV+OmnnzBx4kT19qNHj2LIkCEYOXIkTp8+jc6dO2PWrFn6ugWDcKymhLkFkJmu2WD24J4FXGoUGSgqIqqqPMIFqnUTOBtihhOtzPDrO2aoGSpQvbs8vygNzkgTEJPqgrGwsEBCQgKGDh2KFStWoEWLFujYsSPeeecdNGlS3O87duxY9f516tTBrFmz8MEHH+Czzz4DACxevBjdunVTJyUNGjRAcnIydu7c+cxr5+fnayQ92dnZOr47IqKq4f6PEjJ2SKgbI2BTTyA3VcL1+ZJ6MCqZBpNqAQGKx4DcvHkT27ZtQ7du3bBv3z60aNECCQkJAIA9e/bg1VdfhZeXFxwcHPDuu+8iIyMDubnFXQ3nz59HmzZtNM7Ztm3b5143JiYGTk5O6sXb21vn91ZZsu+bQ1kEOD/R2uHiWoQH6SaVwxKRDqQtkuAxWKB6NwFbP8C1h4D7AIFbqzgItVRG2gJicgkIAFhbW6Nr166Ijo5GcnIyBg0ahKlTp+LatWvo0aMHmjRpgm+//RYpKSlYtmwZAKCgoECra06ePBlZWVnqJS0tTRe3ohdFhWa4+Istmr/8UF0mSQLNXs7Bbyl8DJeIykeVB0hPfvuYAUKmj4sanEoHiwzxz1cAAQEB2Lp1K1JSUqBSqbBgwQKYmRX/79i0aZPGvo0aNcLRo0c1yo4cOfLcaygUCigUCt0FrWfffeGKyNg0XDhji9RTtug1NB3Wtir8uKGaoUMzStY2RfD0eqRed/fIRd36WXj40BLpd5j0VQbWuf44vyJwc6UEK3cBm3rFg1DvfC3BNVief6kbmrG+jM6kEpCMjAz07dsX4eHhaNKkCRwcHHDixAnMmzcPwcHBqF+/PgoLC7F06VL07NkTSUlJWLFihcY5Ro8ejfbt2+PTTz9FcHAwdu3a9dzxH8Zg/zYXOFVXYuCE23CpUYQrv9rg41BfZN6zNHRoRsmvYSb+u/SfuWmGjj4HANizwxuL5rQwVFhGjXWuPz5RAjeWAX/EmKHwfvFEZDX6CHi+L88vSqockhAyTY0qQX5+PqZNm4Yff/wRly9fRmFhIby9vdG3b1989NFHsLGxwaJFizB//nxkZmbilVdeQWhoKAYOHIgHDx7A2dkZALBq1SpMnToVGRkZ6NKlCzp27IiZM2eWaybU7OxsODk5oROCYSHxS1wfLOrWMXQIRJWq+XeXDR2CycjPKcTC9onIysqCo6NjpVzj8fdEF79xsDCveAt6kTIfey4uqtRYK8KkEhA5YQKif0xAyNgxAdEfvSYg9cZqn4BcjpVdAmKSg1CJiIjIsExqDAgREVGVo+2jtDLt6GACQkREJGvazuUhzwSEXTBERESkd2wBISIikjN2wRAREZHeqQS06kZRyTMBYRcMERER6R1bQIiIiORMqLR7UY5MX7LDBISIiEjOOAaEiIiI9I5jQIiIiIh0gy0gREREcsYuGCIiItI7AS0TEJ1FolPsgiEiIiK9YwsIERGRnLELhoiIiPROpQKgxVweKnnOA8IuGCIiItI7toAQERHJGbtgiIiISO+MNAFhFwwRERHpHVtAiIiI5MxIp2JnAkJERCRjQqggtHijrTbHViYmIERERHImhHatGBwDQkRERFSMLSBERERyJrQcAyLTFhAmIERERHKmUgGSFuM4ZDoGhF0wREREpHdsASEiIpIzdsEQERGRvgmVCkKLLhi5PobLLhgiIiLSO7aAEBERyRm7YIiIiEjvVAKQjC8BYRcMERER6R1bQIiIiORMCADazAMizxYQJiBEREQyJlQCQosuGMEEhIiIiMpNqKBdCwgfwyUiIqIqYtmyZahTpw6sra3Rpk0bHDt2TKfnZwJCREQkY0IltF7Ka+PGjRg/fjymTp2KkydPomnTpggKCsLdu3d1dl9MQIiIiORMqLRfymnhwoUYOnQoBg8ejICAAKxYsQK2trZYtWqVzm6LY0AM5PGgoCIUajW/DJWDKt/QERBVqvycQkOHYDLyHxXXtT4GeGr7PVGE4lizs7M1yhUKBRQKRYn9CwoKkJKSgsmTJ6vLzMzM0KVLFxw+fLjigTyBCYiBPHz4EABwCDsMHIkJuWroAIgq1572ho7A9Dx8+BBOTk6Vcm4rKyu4u7vj0G3tvyfs7e3h7e2tUTZ16lRMmzatxL737t2DUqlEzZo1Ncpr1qyJ33//XetYHmMCYiCenp5IS0uDg4MDJEkydDhllp2dDW9vb6SlpcHR0dHQ4Rg91rf+sc71q6rWtxACDx8+hKenZ6Vdw9raGlevXkVBQYHW5xJClPiuKa31Q5+YgBiImZkZatWqZegwKszR0bFK/bKo6ljf+sc616+qWN+V1fLxb9bW1rC2tq706/ybq6srzM3NcefOHY3yO3fuwN3dXWfX4SBUIiIiUrOyskLLli2xd+9edZlKpcLevXvRtm1bnV2HLSBERESkYfz48QgLC0OrVq3QunVrxMbG4tGjRxg8eLDOrsEEhMpFoVBg6tSpBu87NBWsb/1jnesX61ue+vXrh/T0dEyZMgW3b99Gs2bNsHPnzhIDU7UhCblOEk9ERERGi2NAiIiISO+YgBAREZHeMQEhIiIivWMCQkQmYd++fZAkCZmZmQaNIyEhAc7OzgaNoapi3RkXJiAm5PDhwzA3N0f37t0NHYrRGjRoEEJCQkqUy+XLryobNGgQJEmCJEmwtLSEr68vJk6ciLy8PEOHRqVIT0/HsGHDULt2bSgUCri7uyMoKAhJSUmGDo1kgo/hmpC4uDiMGjUKcXFxuHnzZqVOIQwUv9DIysqqUq9BpqVbt26Ij49HYWEhUlJSEBYWBkmSMHfuXEOHRk/o06cPCgoKsHr1atStWxd37tzB3r17kZGRYejQSCbYAmIicnJysHHjRgwbNgzdu3dHQkKCetvjv8737t2LVq1awdbWFu3atUNqaqrGOWbNmgU3Nzc4ODggIiICUVFRaNasmXr747/+Z8+eDU9PT/j7+2PGjBl44YUXSsTTrFkzREdHV9btylpGRgb69+8PLy8v2NraIjAwEOvXr9fYp1OnThg5ciRGjhwJJycnuLq6Ijo6WuPNm3Xq1MHMmTPRv39/2NnZwcvLC8uWLVNvDw8PR48ePTTOW1hYCDc3N8TFxVXuTVaSx39Je3t7IyQkBF26dMHu3bsBFM/UGBMTA19fX9jY2KBp06bYvHnzU8/1vJ9Deno63N3dMWfOHHVZcnIyrKys1DNE5ufnIzIyEl5eXrCzs0ObNm2wb98+jeskJCSgdu3asLW1Ra9evUziCzgzMxMHDx7E3Llz0blzZ/j4+KB169aYPHky3nzzTQDFr3sPDAyEnZ0dvL29MXz4cOTk5GicxxTrzqQIMglxcXGiVatWQgghvv/+e1GvXj2hUqmEEEL8/PPPAoBo06aN2Ldvn/j1119Fhw4dRLt27dTHf/3118La2lqsWrVKpKamiunTpwtHR0fRtGlT9T5hYWHC3t5evPvuu+LcuXPi3LlzIi0tTZiZmYljx46p9zt58qSQJElcvnxZPzevR2FhYSI4OLhE+eM6fvDggfjzzz/F/PnzxalTp8Tly5fFkiVLhLm5uTh69Kh6/44dOwp7e3sxZswY8fvvv4uvv/5a2Nraii+++EK9j4+Pj3BwcBAxMTEiNTVVfZ4ff/xRCCFEUlKSMDc3Fzdv3lQf89133wk7Ozvx8OHDyquESvJk3Z49e1a4u7uLNm3aCCGEmDVrlmjYsKHYuXOnuHz5soiPjxcKhULs27dPCKH5MxBClOnnsH37dmFpaSmOHz8usrOzRd26dcW4cePU2yMiIkS7du3EgQMHxKVLl8T8+fOFQqEQFy5cEEIIceTIEWFmZibmzp0rUlNTxeLFi4Wzs7NwcnKq3MoysMLCQmFvby/Gjh0r8vLySt1n0aJF4qeffhJXr14Ve/fuFf7+/mLYsGHq7aZad6aECYiJaNeunYiNjRVCFP9ycHV1FT///LMQ4p9fzHv27FHvv337dgFA/PXXX0IIIdq0aSNGjBihcc727duXSEBq1qwp8vPzNfZ7/fXXNX6xjBo1SnTq1EmXtycbYWFhwtzcXNjZ2Wks1tbWGl9+T+revbv48MMP1esdO3YUjRo1UieJQggxadIk0ahRI/W6j4+P6Natm8Z5+vXrJ15//XX1ekBAgJg7d656vWfPnmLQoEHa3qZB/LtuFQqFACDMzMzE5s2bRV5enrC1tRXJyckaxwwZMkT0799fCFEyASnNkz8HIYQYPny4aNCggfjPf/4jAgMD1V+of/zxhzA3Nxc3btzQ2P/VV18VkydPFkII0b9/f/HGG29obO/Xr59JfIlu3rxZuLi4CGtra9GuXTsxefJkcebMmafu/80334jq1aur10257kwFu2BMQGpqKo4dO4b+/fsDACwsLNCvX78SzfBNmjRR/9vDwwMAcPfuXfU5WrdurbH/k+sAEBgYWGLcx9ChQ7F+/Xrk5eWhoKAA69atQ3h4uPY3JlOdO3fG6dOnNZaVK1eqtyuVSsycOROBgYGoVq0a7O3tsWvXLly/fl3jPC+99JLG67Pbtm2LixcvQqlUapT9W9u2bXH+/Hn1ekREBOLj4wEUv8nyhx9+qNJ1/7hujx49irCwMAwePBh9+vTBpUuXkJubi65du8Le3l69rFmzBpcvXy71XGX9OXz66acoKirCN998g7Vr16qnDD979iyUSiUaNGigcc39+/err3n+/Hm0adNG43y6fJmXnPXp0wc3b97Etm3b0K1bN+zbtw8tWrRQd//u2bMHr776Kry8vODg4IB3330XGRkZyM3NBWDadWcqOAjVBMTFxaGoqEhj0KkQAgqFAv/73//UZZaWlup/P/7iU6lU5bqWnZ1dibKePXtCoVBgy5YtsLKyQmFhId56663y3kaVYWdnh/r162uU/fnnn+p/z58/H4sXL0ZsbKy6D3zs2LEoKCjQeSwDBw5EVFQUDh8+jOTkZPj6+qJDhw46v46+/LtuV61ahaZNmyIuLk49zmj79u3w8vLSOOZp7xgp68/h8uXLuHnzJlQqFa5du4bAwEAAxeOqzM3NkZKSAnNzc41j7O3tdXK/VZ21tTW6du2Krl27Ijo6GhEREZg6dSo6deqEHj16YNiwYZg9ezaqVauGQ4cOYciQISgoKICtra2hQyc9YAJi5IqKirBmzRosWLAAr732msa2kJAQrF+/Hg0bNnzuefz9/XH8+HEMHDhQXXb8+PEyxWBhYYGwsDDEx8fDysoK77zzDmxsbMp3I0YkKSkJwcHBGDBgAIDiJO/ChQsICAjQ2O/o0aMa60eOHIGfn5/Gl92RI0dK7NOoUSP1evXq1RESEoL4+HgcPnxYp2+yNDQzMzN89NFHGD9+PC5cuACFQoHr16+jY8eOZTq+LD+HgoICDBgwAP369YO/vz8iIiJw9uxZuLm5oXnz5lAqlbh79+5Tk7pGjRqV+nM0VQEBAdi6dStSUlKgUqmwYMECmJkVN8Rv2rRJY1/WnfFjAmLkEhMT8eDBAwwZMgROTk4a2/r06YO4uDjMnz//uecZNWoUhg4dilatWqFdu3bYuHEjfvnlF9StW7dMcURERKi/GE19HgA/Pz9s3rwZycnJcHFxwcKFC3Hnzp0SCcj169cxfvx4vP/++zh58iSWLl2KBQsWaOyTlJSEefPmISQkBLt378Y333yD7du3a+wTERGBHj16QKlUIiwsrNLvT5/69u2LCRMm4PPPP0dkZCTGjRsHlUqFl19+GVlZWUhKSoKjo2Op912Wn8PHH3+MrKwsLFmyBPb29tixYwfCw8ORmJiIBg0aIDQ0FAMHDsSCBQvQvHlzpKenY+/evWjSpAm6d++O0aNHo3379vj0008RHByMXbt2YefOnfqsIoPIyMhA3759ER4ejiZNmsDBwQEnTpzAvHnzEBwcjPr166OwsBBLly5Fz549kZSUhBUrVmicw1TrzqQYehAKVa4ePXqUGMj12NGjRwUAsXjx4hKD806dOiUAiKtXr6rLZsyYIVxdXYW9vb0IDw8Xo0ePFi+99JJ6+9OeAHmsQ4cOonHjxtrekqyV5SmYjIwMERwcLOzt7YWbm5v45JNPxMCBAzWO69ixoxg+fLj44IMPhKOjo3BxcREfffSRxqBUHx8fMX36dNG3b19ha2sr3N3dxeLFi0tcW6VSCR8fn6d+DqqKp9VtTEyMqFGjhsjJyRGxsbHC399fWFpaiho1aoigoCCxf/9+IUTJQajP+zn8/PPPwsLCQhw8eFB9ratXrwpHR0fx2WefCSGEKCgoEFOmTBF16tQRlpaWwsPDQ/Tq1Uv88ssv6mPi4uJErVq1hI2NjejZs6f49NNPjX4gZV5enoiKihItWrQQTk5OwtbWVvj7+4tPPvlE5ObmCiGEWLhwofDw8BA2NjYiKChIrFmzpsTvIVOsO1MiCfGviQWIyqFr165wd3fHV1999dx9hRDw8/PD8OHDMX78eD1EV7V16tQJzZo1Q2xs7FP3qVOnDsaOHYuxY8c+81w5OTnw8vJCfHw8evfurdtAiYgqiF0wVCa5ublYsWIFgoKCYG5ujvXr12PPnj3qSaCeJT09HRs2bMDt27eNagyC3KlUKty7dw8LFiyAs7OzegIoIiI5YAJCZSJJEnbs2IHZs2cjLy8P/v7++Pbbb9GlS5fnHuvm5gZXV1d88cUXcHFx0UO0BBSPIfH19UWtWrWQkJAACwv+dyci+WAXDBEREekdJyIjIiIivWMCQkRERHrHBISIiIj0jgkIERER6R0TECITNmjQIISEhKjXO3Xq9Nx5RSrDvn37IEkSMjMzn7qPJEnYunVrmc85bdo0NGvWTKu4rl27BkmScPr0aa3OQ0QlMQEhkplBgwZBkiRIkgQrKyvUr18fM2bMQFFRUaVf+7vvvsPMmTPLtG9ZkgYioqfhxABEMtStWzfEx8cjPz8fO3bswIgRI2BpaYnJkyeX2LegoABWVlY6uW61atV0ch4ioudhCwiRDCkUCri7u8PHxwfDhg1Dly5dsG3bNgD/dJvMnj0bnp6e8Pf3BwCkpaXh7bffhrOzM6pVq4bg4GBcu3ZNfU6lUonx48fD2dkZ1atXx8SJE/HkNEBPdsHk5+dj0qRJ8Pb2hkKhQP369REXF4dr166hc+fOAAAXFxdIkoRBgwYBKJ6BNSYmBr6+vrCxsUHTpk2xefNmjevs2LEDDRo0gI2NDTp37qwRZ1lNmjQJDRo0gK2tLerWrYvo6GgUFhaW2O/zzz+Ht7c3bG1t8fbbbyMrK0tj+8qVK9GoUSNYW1ujYcOG+Oyzz8odCxGVHxMQoirAxsYGBQUF6vW9e/ciNTUVu3fvRmJiIgoLCxEUFAQHBwccPHgQSUlJsLe3R7du3dTHLViwAAkJCVi1ahUOHTqE+/fvY8uWLc+87sCBA7F+/XosWbIE58+fx+effw57e3t4e3vj22+/BQCkpqbi1q1bWLx4MQAgJiYGa9aswYoVK/Drr79i3LhxGDBgAPbv3w+gOFHq3bs3evbsidOnTyMiIgJRUVHlrhMHBwckJCTgt99+w+LFi/Hll19i0aJFGvtcunQJmzZtwvfff4+dO3fi1KlTGD58uHr72rVrMWXKFMyePRvnz5/HnDlzEB0djdWrV5c7HiIqJ0O+CY+ISvr3W19VKpXYvXu3UCgUIjIyUr29Zs2aIj8/X33MV199Jfz9/TXelpufny9sbGzErl27hBBCeHh4iHnz5qm3FxYWilq1apV4C++YMWOEEEKkpqYKAGL37t2lxvnk22WFKH4Lqq2trUhOTtbYd8iQIaJ///5CCCEmT54sAgICNLZPmjSpxLmeBEBs2bLlqdvnz58vWrZsqV6fOnWqMDc3F3/++ae67IcffhBmZmbi1q1bQggh6tWrJ9atW6dxnpkzZ4q2bdsKIYrffgtAnDp16qnXJaKK4RgQIhlKTEyEvb09CgsLoVKp8J///AfTpk1Tbw8MDNQY93HmzBlcunQJDg4OGufJy8vD5cuXkZWVhVu3bqFNmzbqbRYWFmjVqlWJbpjHTp8+DXNzc3Ts2LHMcV+6dAm5ubno2rWrRnlBQQGaN28OADh//rxGHADQtm3bMl/jsY0bN2LJkiW4fPkycnJyUFRUBEdHR419ateuDS8vL43rqFQqpKamwsHBAZcvX8aQIUMwdOhQ9T5FRUVwcnIqdzxEVD5MQIhkqHPnzli+fDmsrKzg6elZ4kVydnZ2Gus5OTlo2bIl1q5dW+JcNWrUqFAMNjY25T4mJycHALB9+3aNL36geFyLrhw+fBihoaGYPn06goKC4OTkhA0bNmDBggXljvXLL78skRCZm5vrLFYiKh0TECIZsrOzQ/369cu8f4sWLbBx40a4ubmVaAV4zMPDA0ePHsUrr7wCoPgv/ZSUFLRo0aLU/QMDA6FSqbB///5S33r8uAVGqVSqywICAqBQKHD9+vWntpw0atRIPaD2sSNHjjz/Jv8lOTkZPj4++Pjjj9Vlf/zxR4n9rl+/jps3b8LT01N9HTMzM/j7+6NmzZrw9PTElStXEBoaWq7rE5H2OAiVyAiEhobC1dUVwcHBOHjwIK5evYp9+/Zh9OjR+PPPPwEAY8aMwX//+19s3boVv//+O4YPH/7MOTzq1KmDsLAwhIeHY+vWrepzbtq0CQDg4+MDSZKQmJiI9PR05OTkwMHBAZGRkRg3bhxWr16Ny5cv4+TJk1i6dKl6YOcHH3yAixcvYsKECUhNTcW6deuQkJBQrvv18/PD9evXsWHDBly+fBlLliwpdUCttbU1wsLCcObMGRw8eBCjR4/G22+/DXd3dwDA9OnTERMTgyVLluDChQs4e/Ys4uPjsXDhwnLFQ0TlxwSEyAjY2triwIEDqF27Nnr37o1GjRphyJAhyMvLU7eIfPjhh3j33XcRFhaGtm3bwsHBAb169XrmeZcvX4633noLw4cPR8OGDTF06FA8evQIAODl5YXp06cjKioKNWvWxMiRIwEAM2fORHR0NGJiYtCoUSN069YN27dvh6+vL4DicRnffvsttm7diqZNm2LFihWYM2dOue73zTffxLhx4zBy5Eg0a9YMycnJiI6OLrFf/fr10bt3b7zxxht47bXX0KRJE43HbCMiIrBy5UrEx8cjMDAQHTt2REJCgjpWIqo8knjaCDQiIiKiSsIWECIiItI7JiBERESkd0xAiIiISO+YgBAREZHeMQEhIiIivWMCQkRERHrHBISIiIj0jgkIERER6R0TECIiItI7JiBERESkd0xAiIiISO+YgBAREZHe/X+2eXFmJoyPpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y2_test_mfcc, y1_pred, target_names = ['Angry', 'Happy', 'Relaxed', 'Sad']))\n",
    "cm_2d_mfcc.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0fa5d3",
   "metadata": {},
   "source": [
    "### 4.2 Spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e0edf2ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load Spectrogram data set\n",
    "spec1_file = np.load(\"Features2D/npz_new/spec1_train_test.npz\")\n",
    "\n",
    "spec1_train = spec1_file['spec_train']\n",
    "y1_train = spec1_file['y_train']\n",
    "\n",
    "spec1_train,spec1_val,y1_train,y1_val = train_test_split(spec1_train,y1_train, \n",
    "                                                         train_size = 0.8,\n",
    "                                                         random_state = 13, \n",
    "                                                         stratify= y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e88d5179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "spec1_train_scaled = spec1_train / np.amax(spec1_train)\n",
    "spec1_val_scaled = spec1_val / np.amax(spec1_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4f14e0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data\n",
    "N,row,col = spec1_train_scaled.shape\n",
    "spec1_train_reshaped = spec1_train_scaled.reshape((N,row,col,1))\n",
    "\n",
    "N,row,col = spec1_val_scaled.shape\n",
    "spec1_val_reshaped = spec1_val_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "da77716e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CNN2D_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 300, 300, 5)       50        \n",
      "                                                                 \n",
      " MaxPooling2D_1 (MaxPooling2  (None, 150, 150, 5)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN1 (BatchNormalization)    (None, 150, 150, 5)       20        \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 150, 150, 8)       368       \n",
      "                                                                 \n",
      " MaxPooling2D_2 (MaxPooling2  (None, 75, 75, 8)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN2 (BatchNormalization)    (None, 75, 75, 8)         32        \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 75, 75, 16)        1168      \n",
      "                                                                 \n",
      " MaxPooling2D_3 (MaxPooling2  (None, 38, 38, 16)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN3 (BatchNormalization)    (None, 38, 38, 16)        64        \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 23104)             0         \n",
      "                                                                 \n",
      " Dense1 (Dense)              (None, 64)                1478720   \n",
      "                                                                 \n",
      " Dropout1 (Dropout)          (None, 64)                0         \n",
      "                                                                 \n",
      " Dense2 (Dense)              (None, 32)                2080      \n",
      "                                                                 \n",
      " Dropout2 (Dropout)          (None, 32)                0         \n",
      "                                                                 \n",
      " Softmax (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,482,634\n",
      "Trainable params: 1,482,576\n",
      "Non-trainable params: 58\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "f1,k1,a1 = 5,3,'relu'\n",
    "mw1,mh1,sw1,sh1 = 3,3,2,2\n",
    "f2,k2,a2 = 8,3,'relu'\n",
    "mw2,mh2,sw2,sh2 = 3,3,2,2\n",
    "f3,k3,a3 = 16,3,'relu'\n",
    "mw3,mh3,sw3,sh3 = 3,3,2,2\n",
    "d1,dr1,da1,r1 = 64,0.2,'relu',0.2\n",
    "d2,dr2,da2,r2 = 32,0.2,'relu',0.2\n",
    "num = 3\n",
    "model3 = modelBuilder3L(spec1_train_reshaped,\n",
    "                        f1,k1,a1,\n",
    "                        mw1,mh1,sw1,sh1,\n",
    "                        f2,k2,a2,\n",
    "                        mw2,mh2,sw2,sh2,\n",
    "                        f3,k3,a3,\n",
    "                        mw3,mh3,sw3,sh3,\n",
    "                        d1,dr1,da1,r1,\n",
    "                        d2,dr2,da2,r2,\n",
    "                        num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3342b58c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "29/29 [==============================] - 21s 650ms/step - loss: 33.8302 - accuracy: 0.4199 - val_loss: 31.6609 - val_accuracy: 0.2569\n",
      "Epoch 2/80\n",
      "29/29 [==============================] - 14s 494ms/step - loss: 29.4773 - accuracy: 0.5244 - val_loss: 27.6527 - val_accuracy: 0.2917\n",
      "Epoch 3/80\n",
      "29/29 [==============================] - 12s 399ms/step - loss: 25.6020 - accuracy: 0.5436 - val_loss: 24.0639 - val_accuracy: 0.3750\n",
      "Epoch 4/80\n",
      "29/29 [==============================] - 13s 444ms/step - loss: 22.1381 - accuracy: 0.6045 - val_loss: 21.3829 - val_accuracy: 0.2569\n",
      "Epoch 5/80\n",
      "29/29 [==============================] - 14s 470ms/step - loss: 19.3225 - accuracy: 0.6359 - val_loss: 18.6483 - val_accuracy: 0.3472\n",
      "Epoch 6/80\n",
      "29/29 [==============================] - 14s 497ms/step - loss: 16.9368 - accuracy: 0.7021 - val_loss: 16.6603 - val_accuracy: 0.4444\n",
      "Epoch 7/80\n",
      "29/29 [==============================] - 14s 486ms/step - loss: 15.0088 - accuracy: 0.7422 - val_loss: 15.2690 - val_accuracy: 0.2500\n",
      "Epoch 8/80\n",
      "29/29 [==============================] - 16s 546ms/step - loss: 13.4430 - accuracy: 0.8066 - val_loss: 13.6714 - val_accuracy: 0.3958\n",
      "Epoch 9/80\n",
      "29/29 [==============================] - 14s 484ms/step - loss: 12.1179 - accuracy: 0.8171 - val_loss: 12.6152 - val_accuracy: 0.3958\n",
      "Epoch 10/80\n",
      "29/29 [==============================] - 11s 388ms/step - loss: 11.0061 - accuracy: 0.8659 - val_loss: 11.4874 - val_accuracy: 0.4653\n",
      "Epoch 11/80\n",
      "29/29 [==============================] - 11s 392ms/step - loss: 10.0413 - accuracy: 0.8920 - val_loss: 10.6413 - val_accuracy: 0.5556\n",
      "Epoch 12/80\n",
      "29/29 [==============================] - 11s 391ms/step - loss: 9.2838 - accuracy: 0.8920 - val_loss: 10.0761 - val_accuracy: 0.4583\n",
      "Epoch 13/80\n",
      "29/29 [==============================] - 12s 413ms/step - loss: 8.5787 - accuracy: 0.9268 - val_loss: 9.6314 - val_accuracy: 0.4236\n",
      "Epoch 14/80\n",
      "29/29 [==============================] - 12s 414ms/step - loss: 8.0090 - accuracy: 0.9321 - val_loss: 9.0729 - val_accuracy: 0.4306\n",
      "Epoch 15/80\n",
      "29/29 [==============================] - 13s 457ms/step - loss: 7.4976 - accuracy: 0.9408 - val_loss: 8.9462 - val_accuracy: 0.4028\n",
      "Epoch 16/80\n",
      "29/29 [==============================] - 12s 414ms/step - loss: 7.0502 - accuracy: 0.9390 - val_loss: 8.4284 - val_accuracy: 0.4236\n",
      "Epoch 17/80\n",
      "29/29 [==============================] - 15s 502ms/step - loss: 6.6170 - accuracy: 0.9547 - val_loss: 7.8582 - val_accuracy: 0.4375\n",
      "Epoch 18/80\n",
      "29/29 [==============================] - 11s 389ms/step - loss: 6.2965 - accuracy: 0.9460 - val_loss: 7.4042 - val_accuracy: 0.4375\n",
      "Epoch 19/80\n",
      "29/29 [==============================] - 12s 402ms/step - loss: 5.9558 - accuracy: 0.9599 - val_loss: 6.9722 - val_accuracy: 0.4792\n",
      "Epoch 20/80\n",
      "29/29 [==============================] - 12s 418ms/step - loss: 5.6802 - accuracy: 0.9686 - val_loss: 6.6526 - val_accuracy: 0.5347\n",
      "Epoch 21/80\n",
      "29/29 [==============================] - 11s 387ms/step - loss: 5.4095 - accuracy: 0.9652 - val_loss: 6.4286 - val_accuracy: 0.5556\n",
      "Epoch 22/80\n",
      "29/29 [==============================] - 12s 414ms/step - loss: 5.1788 - accuracy: 0.9652 - val_loss: 6.1921 - val_accuracy: 0.5625\n",
      "Epoch 23/80\n",
      "29/29 [==============================] - 13s 439ms/step - loss: 4.9628 - accuracy: 0.9582 - val_loss: 5.8025 - val_accuracy: 0.6250\n",
      "Epoch 24/80\n",
      "29/29 [==============================] - 14s 465ms/step - loss: 4.7752 - accuracy: 0.9547 - val_loss: 5.4654 - val_accuracy: 0.6319\n",
      "Epoch 25/80\n",
      "29/29 [==============================] - 12s 398ms/step - loss: 4.5914 - accuracy: 0.9617 - val_loss: 5.4313 - val_accuracy: 0.6111\n",
      "Epoch 26/80\n",
      "29/29 [==============================] - 11s 395ms/step - loss: 4.3914 - accuracy: 0.9686 - val_loss: 5.1216 - val_accuracy: 0.6389\n",
      "Epoch 27/80\n",
      "29/29 [==============================] - 11s 390ms/step - loss: 4.2036 - accuracy: 0.9808 - val_loss: 5.1139 - val_accuracy: 0.5903\n",
      "Epoch 28/80\n",
      "29/29 [==============================] - 11s 390ms/step - loss: 4.0597 - accuracy: 0.9791 - val_loss: 4.8948 - val_accuracy: 0.6875\n",
      "Epoch 29/80\n",
      "29/29 [==============================] - 13s 433ms/step - loss: 3.9313 - accuracy: 0.9686 - val_loss: 4.7263 - val_accuracy: 0.5833\n",
      "Epoch 30/80\n",
      "29/29 [==============================] - 13s 433ms/step - loss: 3.8187 - accuracy: 0.9634 - val_loss: 4.6032 - val_accuracy: 0.6250\n",
      "Epoch 31/80\n",
      "29/29 [==============================] - 12s 398ms/step - loss: 3.6589 - accuracy: 0.9774 - val_loss: 4.4628 - val_accuracy: 0.6458\n",
      "Epoch 32/80\n",
      "29/29 [==============================] - 11s 390ms/step - loss: 3.5327 - accuracy: 0.9704 - val_loss: 4.3964 - val_accuracy: 0.6597\n",
      "Epoch 33/80\n",
      "29/29 [==============================] - 12s 412ms/step - loss: 3.3882 - accuracy: 0.9895 - val_loss: 4.2018 - val_accuracy: 0.6528\n",
      "Epoch 34/80\n",
      "29/29 [==============================] - 13s 435ms/step - loss: 3.2720 - accuracy: 0.9843 - val_loss: 4.1647 - val_accuracy: 0.6319\n",
      "Epoch 35/80\n",
      "29/29 [==============================] - 12s 404ms/step - loss: 3.1557 - accuracy: 0.9878 - val_loss: 3.9678 - val_accuracy: 0.6181\n",
      "Epoch 36/80\n",
      "29/29 [==============================] - 12s 410ms/step - loss: 3.0218 - accuracy: 0.9878 - val_loss: 3.9178 - val_accuracy: 0.6389\n",
      "Epoch 37/80\n",
      "29/29 [==============================] - 12s 420ms/step - loss: 2.9435 - accuracy: 0.9739 - val_loss: 3.7643 - val_accuracy: 0.6111\n",
      "Epoch 38/80\n",
      "29/29 [==============================] - 11s 395ms/step - loss: 2.8705 - accuracy: 0.9739 - val_loss: 3.6981 - val_accuracy: 0.6319\n",
      "Epoch 39/80\n",
      "29/29 [==============================] - 13s 445ms/step - loss: 2.7871 - accuracy: 0.9634 - val_loss: 3.6673 - val_accuracy: 0.6458\n",
      "Epoch 40/80\n",
      "29/29 [==============================] - 13s 440ms/step - loss: 2.7017 - accuracy: 0.9739 - val_loss: 3.5495 - val_accuracy: 0.6319\n",
      "Epoch 41/80\n",
      "29/29 [==============================] - 12s 412ms/step - loss: 2.5943 - accuracy: 0.9861 - val_loss: 3.5253 - val_accuracy: 0.6458\n",
      "Epoch 42/80\n",
      "29/29 [==============================] - 11s 391ms/step - loss: 2.4944 - accuracy: 0.9791 - val_loss: 3.3384 - val_accuracy: 0.5972\n",
      "Epoch 43/80\n",
      "29/29 [==============================] - 11s 392ms/step - loss: 2.4034 - accuracy: 0.9826 - val_loss: 3.2757 - val_accuracy: 0.6111\n",
      "Epoch 44/80\n",
      "29/29 [==============================] - 11s 393ms/step - loss: 2.3326 - accuracy: 0.9791 - val_loss: 3.2879 - val_accuracy: 0.5764\n",
      "Epoch 45/80\n",
      "29/29 [==============================] - 12s 398ms/step - loss: 2.2687 - accuracy: 0.9826 - val_loss: 3.1377 - val_accuracy: 0.6111\n",
      "Epoch 46/80\n",
      "29/29 [==============================] - 11s 396ms/step - loss: 2.1872 - accuracy: 0.9826 - val_loss: 3.0398 - val_accuracy: 0.6111\n",
      "Epoch 47/80\n",
      "29/29 [==============================] - 12s 400ms/step - loss: 2.0977 - accuracy: 0.9913 - val_loss: 3.0369 - val_accuracy: 0.6042\n",
      "Epoch 48/80\n",
      "29/29 [==============================] - 12s 404ms/step - loss: 2.0258 - accuracy: 0.9913 - val_loss: 2.8798 - val_accuracy: 0.6250\n",
      "Epoch 49/80\n",
      "29/29 [==============================] - 12s 399ms/step - loss: 1.9743 - accuracy: 0.9843 - val_loss: 2.9096 - val_accuracy: 0.5972\n",
      "Epoch 50/80\n",
      "29/29 [==============================] - 12s 403ms/step - loss: 1.9068 - accuracy: 0.9808 - val_loss: 2.9974 - val_accuracy: 0.5556\n",
      "Epoch 51/80\n",
      "29/29 [==============================] - 12s 397ms/step - loss: 1.8547 - accuracy: 0.9878 - val_loss: 2.6514 - val_accuracy: 0.6042\n",
      "Epoch 52/80\n",
      "29/29 [==============================] - 12s 400ms/step - loss: 1.7740 - accuracy: 0.9861 - val_loss: 2.7437 - val_accuracy: 0.5625\n",
      "Epoch 53/80\n",
      "29/29 [==============================] - 12s 397ms/step - loss: 1.7152 - accuracy: 0.9895 - val_loss: 2.5791 - val_accuracy: 0.6042\n",
      "Epoch 54/80\n",
      "29/29 [==============================] - 12s 411ms/step - loss: 1.6619 - accuracy: 0.9913 - val_loss: 2.7084 - val_accuracy: 0.5694\n",
      "Epoch 55/80\n",
      "29/29 [==============================] - 12s 398ms/step - loss: 1.6122 - accuracy: 0.9913 - val_loss: 2.4883 - val_accuracy: 0.6111\n",
      "Epoch 56/80\n",
      "29/29 [==============================] - 12s 418ms/step - loss: 1.5455 - accuracy: 0.9878 - val_loss: 2.4155 - val_accuracy: 0.6319\n",
      "Epoch 57/80\n",
      "29/29 [==============================] - 12s 414ms/step - loss: 1.5082 - accuracy: 0.9826 - val_loss: 2.3938 - val_accuracy: 0.6111\n",
      "Epoch 58/80\n",
      "29/29 [==============================] - 12s 407ms/step - loss: 1.4729 - accuracy: 0.9878 - val_loss: 2.2997 - val_accuracy: 0.6319\n",
      "Epoch 59/80\n",
      "29/29 [==============================] - 12s 400ms/step - loss: 1.4266 - accuracy: 0.9791 - val_loss: 2.2689 - val_accuracy: 0.5903\n",
      "Epoch 60/80\n",
      "29/29 [==============================] - 12s 397ms/step - loss: 1.3790 - accuracy: 0.9895 - val_loss: 2.3141 - val_accuracy: 0.5903\n",
      "Epoch 61/80\n",
      "29/29 [==============================] - 12s 397ms/step - loss: 1.3319 - accuracy: 0.9930 - val_loss: 2.2111 - val_accuracy: 0.5972\n",
      "Epoch 62/80\n",
      "29/29 [==============================] - 12s 399ms/step - loss: 1.2757 - accuracy: 0.9913 - val_loss: 2.1355 - val_accuracy: 0.6111\n",
      "Epoch 63/80\n",
      "29/29 [==============================] - 12s 401ms/step - loss: 1.2650 - accuracy: 0.9878 - val_loss: 2.1609 - val_accuracy: 0.6389\n",
      "Epoch 64/80\n",
      "29/29 [==============================] - 11s 393ms/step - loss: 1.2050 - accuracy: 0.9965 - val_loss: 2.1514 - val_accuracy: 0.6042\n",
      "Epoch 65/80\n",
      "29/29 [==============================] - 11s 389ms/step - loss: 1.1717 - accuracy: 0.9878 - val_loss: 2.1018 - val_accuracy: 0.5903\n",
      "Epoch 66/80\n",
      "29/29 [==============================] - 11s 381ms/step - loss: 1.1486 - accuracy: 0.9843 - val_loss: 2.0831 - val_accuracy: 0.6111\n",
      "Epoch 67/80\n",
      "29/29 [==============================] - 12s 413ms/step - loss: 1.0976 - accuracy: 0.9948 - val_loss: 2.0713 - val_accuracy: 0.5972\n",
      "Epoch 68/80\n",
      "29/29 [==============================] - 12s 400ms/step - loss: 1.0728 - accuracy: 0.9913 - val_loss: 1.9378 - val_accuracy: 0.6042\n",
      "Epoch 69/80\n",
      "29/29 [==============================] - 12s 398ms/step - loss: 1.0536 - accuracy: 0.9774 - val_loss: 2.0953 - val_accuracy: 0.5903\n",
      "Epoch 70/80\n",
      "29/29 [==============================] - 11s 393ms/step - loss: 1.0359 - accuracy: 0.9861 - val_loss: 1.9800 - val_accuracy: 0.6111\n",
      "Epoch 71/80\n",
      "29/29 [==============================] - 12s 398ms/step - loss: 1.0102 - accuracy: 0.9808 - val_loss: 1.9632 - val_accuracy: 0.6181\n",
      "Epoch 72/80\n",
      "29/29 [==============================] - 11s 394ms/step - loss: 0.9805 - accuracy: 0.9826 - val_loss: 1.9600 - val_accuracy: 0.5903\n",
      "Epoch 73/80\n",
      "29/29 [==============================] - 11s 397ms/step - loss: 0.9515 - accuracy: 0.9843 - val_loss: 1.8874 - val_accuracy: 0.6111\n",
      "Epoch 74/80\n",
      "29/29 [==============================] - 11s 394ms/step - loss: 0.8919 - accuracy: 0.9983 - val_loss: 1.7712 - val_accuracy: 0.6250\n",
      "Epoch 75/80\n",
      "29/29 [==============================] - 11s 396ms/step - loss: 0.8406 - accuracy: 0.9965 - val_loss: 1.7880 - val_accuracy: 0.5903\n",
      "Epoch 76/80\n",
      "29/29 [==============================] - 11s 396ms/step - loss: 0.8475 - accuracy: 0.9895 - val_loss: 1.6448 - val_accuracy: 0.5972\n",
      "Epoch 77/80\n",
      "29/29 [==============================] - 11s 396ms/step - loss: 0.8094 - accuracy: 0.9948 - val_loss: 1.6856 - val_accuracy: 0.6111\n",
      "Epoch 78/80\n",
      "29/29 [==============================] - 11s 393ms/step - loss: 0.7909 - accuracy: 0.9861 - val_loss: 1.8007 - val_accuracy: 0.5833\n",
      "Epoch 79/80\n",
      "29/29 [==============================] - 11s 397ms/step - loss: 0.7985 - accuracy: 0.9878 - val_loss: 1.7255 - val_accuracy: 0.6181\n",
      "Epoch 80/80\n",
      "29/29 [==============================] - 11s 395ms/step - loss: 0.7737 - accuracy: 0.9808 - val_loss: 1.7379 - val_accuracy: 0.6250\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7feeaddd1270>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.fit(spec1_train_reshaped,y1_train,validation_data=(spec1_val_reshaped,y1_val),batch_size=20,epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f1b97604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Spectrogram data set\n",
    "spec2_file = np.load(\"Features2D/npz_new/spec2_train_test.npz\")\n",
    "\n",
    "spec2_train = spec2_file['spec_train']\n",
    "y2_train = spec2_file['y_train']\n",
    "spec2_train,spec2_val,y2_train,y2_val = train_test_split(spec2_train,y2_train, \n",
    "                                                         train_size = 0.8, \n",
    "                                                         random_state = 13, \n",
    "                                                         stratify = y2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "17ff95e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "spec2_train_scaled = spec2_train / np.amax(spec2_train)\n",
    "spec2_val_scaled = spec2_val / np.amax(spec2_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "938f6a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data\n",
    "N,row,col = spec2_train_scaled.shape\n",
    "spec2_train_reshaped = spec2_train_scaled.reshape((N,row,col,1))\n",
    "N,row,col = spec2_val_scaled.shape\n",
    "spec2_val_reshaped = spec2_val_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a0ad9e5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CNN2D_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 300, 300, 5)       50        \n",
      "                                                                 \n",
      " MaxPooling2D_1 (MaxPooling2  (None, 150, 150, 5)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN1 (BatchNormalization)    (None, 150, 150, 5)       20        \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 150, 150, 8)       368       \n",
      "                                                                 \n",
      " MaxPooling2D_2 (MaxPooling2  (None, 75, 75, 8)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN2 (BatchNormalization)    (None, 75, 75, 8)         32        \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 75, 75, 16)        1168      \n",
      "                                                                 \n",
      " MaxPooling2D_3 (MaxPooling2  (None, 38, 38, 16)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN3 (BatchNormalization)    (None, 38, 38, 16)        64        \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 23104)             0         \n",
      "                                                                 \n",
      " Dense1 (Dense)              (None, 64)                1478720   \n",
      "                                                                 \n",
      " Dropout1 (Dropout)          (None, 64)                0         \n",
      "                                                                 \n",
      " Dense2 (Dense)              (None, 32)                2080      \n",
      "                                                                 \n",
      " Dropout2 (Dropout)          (None, 32)                0         \n",
      "                                                                 \n",
      " Softmax (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,482,634\n",
      "Trainable params: 1,482,576\n",
      "Non-trainable params: 58\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "f1,k1,a1 = 5,3,'relu'\n",
    "mw1,mh1,sw1,sh1 = 3,3,2,2\n",
    "f2,k2,a2 = 8,3,'relu'\n",
    "mw2,mh2,sw2,sh2 = 3,3,2,2\n",
    "f3,k3,a3 = 16,3,'relu'\n",
    "mw3,mh3,sw3,sh3 = 3,3,2,2\n",
    "d1,dr1,da1,r1 = 64,0.2,'relu',0.2\n",
    "d2,dr2,da2,r2 = 32,0.2,'relu',0.2\n",
    "num = 4\n",
    "model4 = modelBuilder3L(spec2_train_reshaped,\n",
    "                        f1,k1,a1,\n",
    "                        mw1,mh1,sw1,sh1,\n",
    "                        f2,k2,a2,\n",
    "                        mw2,mh2,sw2,sh2,\n",
    "                        f3,k3,a3,\n",
    "                        mw3,mh3,sw3,sh3,\n",
    "                        d1,dr1,da1,r1,\n",
    "                        d2,dr2,da2,r2,\n",
    "                        num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b2d456b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "15/15 [==============================] - 10s 514ms/step - loss: 34.4192 - accuracy: 0.4444 - val_loss: 33.4561 - val_accuracy: 0.3889\n",
      "Epoch 2/80\n",
      "15/15 [==============================] - 7s 462ms/step - loss: 32.2662 - accuracy: 0.5729 - val_loss: 31.5020 - val_accuracy: 0.3056\n",
      "Epoch 3/80\n",
      "15/15 [==============================] - 7s 436ms/step - loss: 29.9825 - accuracy: 0.7014 - val_loss: 29.5901 - val_accuracy: 0.2500\n",
      "Epoch 4/80\n",
      "15/15 [==============================] - 7s 448ms/step - loss: 27.9290 - accuracy: 0.7708 - val_loss: 27.7398 - val_accuracy: 0.2500\n",
      "Epoch 5/80\n",
      "15/15 [==============================] - 7s 435ms/step - loss: 25.9103 - accuracy: 0.8576 - val_loss: 26.0233 - val_accuracy: 0.2500\n",
      "Epoch 6/80\n",
      "15/15 [==============================] - 6s 431ms/step - loss: 24.0775 - accuracy: 0.8681 - val_loss: 24.4574 - val_accuracy: 0.2500\n",
      "Epoch 7/80\n",
      "15/15 [==============================] - 7s 430ms/step - loss: 22.3337 - accuracy: 0.8785 - val_loss: 22.9351 - val_accuracy: 0.2500\n",
      "Epoch 8/80\n",
      "15/15 [==============================] - 7s 439ms/step - loss: 20.7254 - accuracy: 0.8958 - val_loss: 21.6846 - val_accuracy: 0.2500\n",
      "Epoch 9/80\n",
      "15/15 [==============================] - 6s 429ms/step - loss: 19.2522 - accuracy: 0.9271 - val_loss: 20.5136 - val_accuracy: 0.2500\n",
      "Epoch 10/80\n",
      "15/15 [==============================] - 7s 428ms/step - loss: 17.8848 - accuracy: 0.9340 - val_loss: 19.0651 - val_accuracy: 0.2500\n",
      "Epoch 11/80\n",
      "15/15 [==============================] - 7s 442ms/step - loss: 16.6605 - accuracy: 0.9375 - val_loss: 17.9185 - val_accuracy: 0.2500\n",
      "Epoch 12/80\n",
      "15/15 [==============================] - 6s 426ms/step - loss: 15.5765 - accuracy: 0.9375 - val_loss: 16.8270 - val_accuracy: 0.2500\n",
      "Epoch 13/80\n",
      "15/15 [==============================] - 7s 435ms/step - loss: 14.6132 - accuracy: 0.9167 - val_loss: 16.3850 - val_accuracy: 0.2500\n",
      "Epoch 14/80\n",
      "15/15 [==============================] - 7s 443ms/step - loss: 13.6276 - accuracy: 0.9583 - val_loss: 15.2053 - val_accuracy: 0.2500\n",
      "Epoch 15/80\n",
      "15/15 [==============================] - 7s 447ms/step - loss: 12.8061 - accuracy: 0.9514 - val_loss: 14.3939 - val_accuracy: 0.2500\n",
      "Epoch 16/80\n",
      "15/15 [==============================] - 7s 447ms/step - loss: 11.9975 - accuracy: 0.9826 - val_loss: 13.8258 - val_accuracy: 0.2500\n",
      "Epoch 17/80\n",
      "15/15 [==============================] - 7s 443ms/step - loss: 11.3252 - accuracy: 0.9792 - val_loss: 13.6884 - val_accuracy: 0.2500\n",
      "Epoch 18/80\n",
      "15/15 [==============================] - 7s 437ms/step - loss: 10.7220 - accuracy: 0.9583 - val_loss: 13.0768 - val_accuracy: 0.2500\n",
      "Epoch 19/80\n",
      "15/15 [==============================] - 7s 439ms/step - loss: 10.1197 - accuracy: 0.9722 - val_loss: 12.2068 - val_accuracy: 0.2500\n",
      "Epoch 20/80\n",
      "15/15 [==============================] - 7s 442ms/step - loss: 9.6025 - accuracy: 0.9722 - val_loss: 11.6375 - val_accuracy: 0.2500\n",
      "Epoch 21/80\n",
      "15/15 [==============================] - 7s 434ms/step - loss: 9.1040 - accuracy: 0.9792 - val_loss: 11.3449 - val_accuracy: 0.2500\n",
      "Epoch 22/80\n",
      "15/15 [==============================] - 7s 446ms/step - loss: 8.6820 - accuracy: 0.9792 - val_loss: 10.4966 - val_accuracy: 0.2500\n",
      "Epoch 23/80\n",
      "15/15 [==============================] - 9s 578ms/step - loss: 8.2765 - accuracy: 0.9826 - val_loss: 9.8172 - val_accuracy: 0.2639\n",
      "Epoch 24/80\n",
      "15/15 [==============================] - 7s 462ms/step - loss: 7.9092 - accuracy: 0.9757 - val_loss: 9.6080 - val_accuracy: 0.2639\n",
      "Epoch 25/80\n",
      "15/15 [==============================] - 7s 441ms/step - loss: 7.5826 - accuracy: 0.9826 - val_loss: 9.2426 - val_accuracy: 0.2639\n",
      "Epoch 26/80\n",
      "15/15 [==============================] - 7s 470ms/step - loss: 7.3000 - accuracy: 0.9722 - val_loss: 9.0676 - val_accuracy: 0.2639\n",
      "Epoch 27/80\n",
      "15/15 [==============================] - 7s 501ms/step - loss: 6.9857 - accuracy: 0.9826 - val_loss: 8.6679 - val_accuracy: 0.3056\n",
      "Epoch 28/80\n",
      "15/15 [==============================] - 7s 487ms/step - loss: 6.7293 - accuracy: 0.9826 - val_loss: 8.0359 - val_accuracy: 0.3611\n",
      "Epoch 29/80\n",
      "15/15 [==============================] - 7s 442ms/step - loss: 6.5431 - accuracy: 0.9757 - val_loss: 7.6193 - val_accuracy: 0.4306\n",
      "Epoch 30/80\n",
      "15/15 [==============================] - 7s 454ms/step - loss: 6.3021 - accuracy: 0.9792 - val_loss: 7.4213 - val_accuracy: 0.4722\n",
      "Epoch 31/80\n",
      "15/15 [==============================] - 7s 502ms/step - loss: 6.1015 - accuracy: 0.9861 - val_loss: 7.1569 - val_accuracy: 0.4861\n",
      "Epoch 32/80\n",
      "15/15 [==============================] - 7s 466ms/step - loss: 5.8986 - accuracy: 0.9792 - val_loss: 6.6740 - val_accuracy: 0.6389\n",
      "Epoch 33/80\n",
      "15/15 [==============================] - 7s 462ms/step - loss: 5.7369 - accuracy: 0.9688 - val_loss: 6.6296 - val_accuracy: 0.5278\n",
      "Epoch 34/80\n",
      "15/15 [==============================] - 7s 441ms/step - loss: 5.5595 - accuracy: 0.9861 - val_loss: 6.3054 - val_accuracy: 0.6667\n",
      "Epoch 35/80\n",
      "15/15 [==============================] - 7s 450ms/step - loss: 5.4012 - accuracy: 0.9896 - val_loss: 6.0717 - val_accuracy: 0.6667\n",
      "Epoch 36/80\n",
      "15/15 [==============================] - 6s 423ms/step - loss: 5.3003 - accuracy: 0.9757 - val_loss: 5.9341 - val_accuracy: 0.6667\n",
      "Epoch 37/80\n",
      "15/15 [==============================] - 7s 450ms/step - loss: 5.1297 - accuracy: 0.9931 - val_loss: 5.9081 - val_accuracy: 0.6111\n",
      "Epoch 38/80\n",
      "15/15 [==============================] - 7s 444ms/step - loss: 5.0105 - accuracy: 0.9861 - val_loss: 5.6054 - val_accuracy: 0.7222\n",
      "Epoch 39/80\n",
      "15/15 [==============================] - 6s 422ms/step - loss: 4.9029 - accuracy: 0.9722 - val_loss: 5.5258 - val_accuracy: 0.6944\n",
      "Epoch 40/80\n",
      "15/15 [==============================] - 7s 440ms/step - loss: 4.7592 - accuracy: 0.9931 - val_loss: 5.2665 - val_accuracy: 0.7778\n",
      "Epoch 41/80\n",
      "15/15 [==============================] - 6s 433ms/step - loss: 4.6641 - accuracy: 0.9826 - val_loss: 5.2579 - val_accuracy: 0.6944\n",
      "Epoch 42/80\n",
      "15/15 [==============================] - 6s 422ms/step - loss: 4.5504 - accuracy: 0.9861 - val_loss: 5.1442 - val_accuracy: 0.7500\n",
      "Epoch 43/80\n",
      "15/15 [==============================] - 6s 433ms/step - loss: 4.4712 - accuracy: 0.9861 - val_loss: 4.9373 - val_accuracy: 0.7639\n",
      "Epoch 44/80\n",
      "15/15 [==============================] - 7s 438ms/step - loss: 4.3776 - accuracy: 0.9826 - val_loss: 4.8814 - val_accuracy: 0.7917\n",
      "Epoch 45/80\n",
      "15/15 [==============================] - 7s 435ms/step - loss: 4.2553 - accuracy: 0.9965 - val_loss: 4.7645 - val_accuracy: 0.7500\n",
      "Epoch 46/80\n",
      "15/15 [==============================] - 6s 431ms/step - loss: 4.1778 - accuracy: 0.9826 - val_loss: 4.6666 - val_accuracy: 0.7778\n",
      "Epoch 47/80\n",
      "15/15 [==============================] - 7s 489ms/step - loss: 4.0906 - accuracy: 0.9861 - val_loss: 4.5493 - val_accuracy: 0.7778\n",
      "Epoch 48/80\n",
      "15/15 [==============================] - 7s 442ms/step - loss: 4.0093 - accuracy: 0.9861 - val_loss: 4.5023 - val_accuracy: 0.8194\n",
      "Epoch 49/80\n",
      "15/15 [==============================] - 7s 457ms/step - loss: 3.9188 - accuracy: 0.9896 - val_loss: 4.3903 - val_accuracy: 0.7917\n",
      "Epoch 50/80\n",
      "15/15 [==============================] - 7s 447ms/step - loss: 3.8418 - accuracy: 0.9896 - val_loss: 4.3352 - val_accuracy: 0.7639\n",
      "Epoch 51/80\n",
      "15/15 [==============================] - 7s 452ms/step - loss: 3.7802 - accuracy: 0.9861 - val_loss: 4.2988 - val_accuracy: 0.7778\n",
      "Epoch 52/80\n",
      "15/15 [==============================] - 7s 494ms/step - loss: 3.6927 - accuracy: 0.9931 - val_loss: 4.1161 - val_accuracy: 0.7917\n",
      "Epoch 53/80\n",
      "15/15 [==============================] - 8s 518ms/step - loss: 3.6260 - accuracy: 0.9861 - val_loss: 4.1424 - val_accuracy: 0.7361\n",
      "Epoch 54/80\n",
      "15/15 [==============================] - 7s 451ms/step - loss: 3.5662 - accuracy: 0.9861 - val_loss: 3.9986 - val_accuracy: 0.7778\n",
      "Epoch 55/80\n",
      "15/15 [==============================] - 7s 447ms/step - loss: 3.5390 - accuracy: 0.9688 - val_loss: 3.9900 - val_accuracy: 0.7917\n",
      "Epoch 56/80\n",
      "15/15 [==============================] - 7s 461ms/step - loss: 3.4491 - accuracy: 0.9896 - val_loss: 3.8548 - val_accuracy: 0.8056\n",
      "Epoch 57/80\n",
      "15/15 [==============================] - 7s 481ms/step - loss: 3.3849 - accuracy: 0.9861 - val_loss: 3.8472 - val_accuracy: 0.7778\n",
      "Epoch 58/80\n",
      "15/15 [==============================] - 7s 463ms/step - loss: 3.3059 - accuracy: 0.9861 - val_loss: 3.6713 - val_accuracy: 0.8194\n",
      "Epoch 59/80\n",
      "15/15 [==============================] - 7s 472ms/step - loss: 3.2709 - accuracy: 0.9792 - val_loss: 3.6799 - val_accuracy: 0.8333\n",
      "Epoch 60/80\n",
      "15/15 [==============================] - 7s 480ms/step - loss: 3.1959 - accuracy: 0.9792 - val_loss: 3.6487 - val_accuracy: 0.8056\n",
      "Epoch 61/80\n",
      "15/15 [==============================] - 7s 462ms/step - loss: 3.1208 - accuracy: 0.9965 - val_loss: 3.6651 - val_accuracy: 0.7639\n",
      "Epoch 62/80\n",
      "15/15 [==============================] - 7s 448ms/step - loss: 3.0628 - accuracy: 0.9965 - val_loss: 3.5347 - val_accuracy: 0.7917\n",
      "Epoch 63/80\n",
      "15/15 [==============================] - 7s 461ms/step - loss: 3.0078 - accuracy: 0.9965 - val_loss: 3.4325 - val_accuracy: 0.7917\n",
      "Epoch 64/80\n",
      "15/15 [==============================] - 7s 483ms/step - loss: 2.9327 - accuracy: 0.9965 - val_loss: 3.3899 - val_accuracy: 0.7500\n",
      "Epoch 65/80\n",
      "15/15 [==============================] - 7s 469ms/step - loss: 2.8901 - accuracy: 0.9861 - val_loss: 3.3058 - val_accuracy: 0.7639\n",
      "Epoch 66/80\n",
      "15/15 [==============================] - 7s 449ms/step - loss: 2.8339 - accuracy: 0.9931 - val_loss: 3.2839 - val_accuracy: 0.8056\n",
      "Epoch 67/80\n",
      "15/15 [==============================] - 7s 457ms/step - loss: 2.7953 - accuracy: 0.9792 - val_loss: 3.2389 - val_accuracy: 0.7639\n",
      "Epoch 68/80\n",
      "15/15 [==============================] - 7s 445ms/step - loss: 2.7237 - accuracy: 0.9931 - val_loss: 3.2621 - val_accuracy: 0.7778\n",
      "Epoch 69/80\n",
      "15/15 [==============================] - 7s 441ms/step - loss: 2.6857 - accuracy: 0.9861 - val_loss: 3.3150 - val_accuracy: 0.7778\n",
      "Epoch 70/80\n",
      "15/15 [==============================] - 7s 463ms/step - loss: 2.6474 - accuracy: 0.9792 - val_loss: 3.2709 - val_accuracy: 0.7222\n",
      "Epoch 71/80\n",
      "15/15 [==============================] - 7s 451ms/step - loss: 2.6059 - accuracy: 0.9896 - val_loss: 3.0545 - val_accuracy: 0.7778\n",
      "Epoch 72/80\n",
      "15/15 [==============================] - 8s 502ms/step - loss: 2.5798 - accuracy: 0.9722 - val_loss: 2.9962 - val_accuracy: 0.8194\n",
      "Epoch 73/80\n",
      "15/15 [==============================] - 6s 424ms/step - loss: 2.5274 - accuracy: 0.9896 - val_loss: 3.0088 - val_accuracy: 0.8194\n",
      "Epoch 74/80\n",
      "15/15 [==============================] - 7s 451ms/step - loss: 2.4696 - accuracy: 0.9965 - val_loss: 2.9152 - val_accuracy: 0.8056\n",
      "Epoch 75/80\n",
      "15/15 [==============================] - 7s 457ms/step - loss: 2.4262 - accuracy: 0.9931 - val_loss: 2.8709 - val_accuracy: 0.7639\n",
      "Epoch 76/80\n",
      "15/15 [==============================] - 7s 448ms/step - loss: 2.3742 - accuracy: 0.9896 - val_loss: 2.7972 - val_accuracy: 0.8056\n",
      "Epoch 77/80\n",
      "15/15 [==============================] - 7s 484ms/step - loss: 2.3231 - accuracy: 0.9931 - val_loss: 2.7739 - val_accuracy: 0.7917\n",
      "Epoch 78/80\n",
      "15/15 [==============================] - 7s 442ms/step - loss: 2.2520 - accuracy: 1.0000 - val_loss: 2.7285 - val_accuracy: 0.7917\n",
      "Epoch 79/80\n",
      "15/15 [==============================] - 7s 448ms/step - loss: 2.2241 - accuracy: 0.9896 - val_loss: 2.6726 - val_accuracy: 0.8056\n",
      "Epoch 80/80\n",
      "15/15 [==============================] - 7s 450ms/step - loss: 2.1702 - accuracy: 0.9965 - val_loss: 2.5930 - val_accuracy: 0.8056\n"
     ]
    }
   ],
   "source": [
    "history4 = model4.fit(spec2_train_reshaped,y2_train,validation_data=(spec2_val_reshaped,y2_val),batch_size=20,epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ab81b54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'loss')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GJVRq165t8n47d+5MtWrVeOaZZ5gyZQoajYY//vgj12Fi7dq1Y/ny5UybNo0OHTrg4OBgcjU/uy+//JIBAwbg7+/Pc889Z1xCxdnZmQ8//LBI7+lehfnMvvvuO7p27Urbtm2ZMGECdevW5erVq2zcuJGTJ08C8Nlnn7F161Z69OjBhAkTaNKkCWFhYaxcuZK9e/fi4uJCv3798PX15bnnnuONN95Ap9OxcOFC3N3dc1wAyEu/fv2wsrJi8ODBvPDCCyQkJPDLL7/g4eFBWFiYsZ2TkxNz5szh+eefp0OHDowaNYpq1apx6tQpkpKSTC4MWFpa8sQTTzB37lx0Oh1PPvlk8T5cIYSohOT7w/1Vhe8PuRk8eDC9evXi3Xff5erVq7Rq1YqtW7fy119/8eqrrxov0s+cOZPdu3czaNAgateuTWRkJD/88AO1atWia9eugPp33svLiy5duuDp6cmFCxeYO3cugwYNyjHnXYj7KqMq8kKUiI8//lipWbOmotVqTZYEAZRJkyblus+CBQuUhg0bKtbW1krjxo2VRYsWFWg5EsMSKvcuzbVjx45CLXP1yy+/KIDi6OiYY0ktRVGU8+fPK3369FEcHBwUNzc3Zfz48cqpU6dyLE9SkJgVRVFOnz6t9OjRQ7GxsVFq1qypfPzxx8qCBQtyLKGyb98+5YEHHlBsbW2VGjVqKG+++aayZcuWHO8tISFBGTVqlOLi4qIAxqVLcltCRVEU5d9//1W6dOmi2NraKk5OTsrgwYOV8+fPm7QxvJd7lzLJbUmz3BT0M1MURTl79qwydOhQxcXFRbGxsVH8/PyU999/36TNtWvXlDFjxiju7u6KtbW1Uq9ePWXSpElKamqqsc2xY8eUTp06KVZWVoqvr68ye/bsPJdgGzRoUK5xr1+/XmnZsqViY2Oj1KlTR/n888+VhQsX5vqe169fr3Tu3Nn4OXbs2FH5888/cxzTsCxPv3798v3MhBCiKpPvD/L9QVFyLsGmKIoSHx+vTJ06ValRo4ZiaWmpNGzYUPnyyy9Nls/bvn27MmTIEKVGjRqKlZWVUqNGDeXJJ59ULl26ZGzz008/Kd27d1eqV6+uWFtbK/Xr11feeOMNJTY2Nt+YhMiNRlFKqcKCEEKIUnfq1Clat27N77//ztNPP23ucIQQQgghRDHJnHQhhKjAfvnlFxwcHBg2bJi5QxFCCCGEECVA5qQLIUQFtGHDBs6fP8/PP//M5MmTS63yrRBCCCGEKFsy3F0IISqgOnXqEBERQf/+/fnjjz+kKI0QQgghRCUhSboQQgghhBBCCFFOyJx0IYQQQgghhBCinJAkXQghhBBCCCGEKCeqXOE4vV7PzZs3cXR0RKPRmDscIYQQAkVRiI+Pp0aNGmi1cv28JMjfeyGEEOVJYf7WV7kk/ebNm/j4+Jg7DCGEECKH0NBQatWqZe4wKgX5ey+EEKI8Ksjf+iqXpBsqIIeGhuLk5GTmaIQQQgiIi4vDx8dHqvSXIPl7L4QQojwpzN/6KpekG4a8OTk5yR9tIYQQ5YoMyy458vdeCCFEeVSQv/Uy8U0IIYQQQgghhCgnJEkXQgghhBBCCCHKCUnShRBCCFFm5s+fT8uWLY3D0P39/fnnn3+Mr/fs2RONRmNye/HFF80YsRBCCFG2qtycdCGEEEKYT61atfjf//5Hw4YNURSF3377jSFDhnDixAmaNWsGwPjx45k5c6ZxHzs7O3OFK4QQpSozM5P09HRzhyFKiKWlJTqdrtjHkSRdCCGEEGVm8ODBJs8//fRT5s+fz8GDB41Jup2dHV5eXuYITwghykxCQgLXr19HURRzhyJKiEajoVatWjg4OBTrOJKkCyGEEMIsMjMzWblyJYmJifj7+xu3L1myhMWLF+Pl5cXgwYN5//3379ubnpqaSmpqqvF5XFxcqcUthBDFlZmZyfXr17Gzs8Pd3V1W96gEFEUhKiqK69ev07Bhw2L1qEuSLoQQQogydebMGfz9/UlJScHBwYG1a9fStGlTAEaNGkXt2rWpUaMGp0+f5q233iIgIIA1a9bke8xZs2bx0UcflUX4QghRbOnp6SiKgru7O7a2tuYOR5QQd3d3rl69Snp6erGSdLMWjtu9ezeDBw+mRo0aaDQa1q1bd999du7cSdu2bbG2tqZBgwb8+uuvpR6nEEIIIUqOn58fJ0+e5NChQ0ycOJFnnnmG8+fPAzBhwgT69+9PixYtGD16NL///jtr164lKCgo32NOnz6d2NhY4y00NLQs3ooQQhSL9KBXLiX18zRrkp6YmEirVq2YN29egdoHBwczaNAgevXqxcmTJ3n11Vd5/vnn2bJlSylHKoQQQoiSYmVlRYMGDWjXrh2zZs2iVatWfPvtt7m27dSpEwCBgYH5HtPa2tpYMd5wE0IIISoisw53HzBgAAMGDChw+x9//JG6devy9ddfA9CkSRP27t3LnDlz6N+/f2mFKYQQogq5Gp1IWqaeRp6O5g6lytDr9SbzybM7efIkAN7e3mUYUZbE1AwOX70FQC8/D7PEIIQQomqpUHPSDxw4QJ8+fUy29e/fn1dffTXPfaSQjBAVj6IoMvyrCDIy9eiLWCDWUqep8p956K0k5vx7ibUnbqAB3nyoMS90r5fn53I7MY2k9ExqushcwsKYPn06AwYMwNfXl/j4eJYuXcrOnTvZsmULQUFBLF26lIEDB1K9enVOnz7N1KlT6d69Oy1btjRLvP+cDef1lado5eMiSboQQpSgOnXq8Oqrr+aby1VVFSpJDw8Px9PT02Sbp6cncXFxJCcn51p0QQrJCFGxLD0UwldbA5jYoz7ju9czdzgVxtz/LvPt9sukZxYtS29e04nvnmhDPffiLRlS1iLjU5j7XyBrj9+ghostXRu60bWBGx3rumJvXbA/cTEJqczdEciSgyGkZeoBUID//XORMzdi+fKxlthZZR0rKS2DRfuu8uPOIDrVq87/PdO+NN5apRUZGcmYMWMICwvD2dmZli1bsmXLFvr27UtoaCj//vsv33zzDYmJifj4+DB8+HDee+89s8XbuX51AM5cv0NcSjpONpZmi0UIIcytZ8+etG7dmm+++abYxzpy5Aj29vbFD6oSqlBJelFMnz6dadOmGZ/HxcXh4+NjxoiEML/rt5OYvfUSHeq6MqJdLSx0BS9P8ffpmxwIiuGVBxvi4WRTonGtPXGdd9aeAeDTTRewttQyxr9OiZ6jIBRFYUdAJKuP3aCHnzvD2tQs1GdU1raeC+errZeKdYyzN+IYMncf3zzRmgebeOZ4/XDwLZYcusagFt70a5b3+tUHr8Sw5FAIQ9vUoJefR6n1zselpPPL7iv8355gktMzAQiIiCcgIp4Fe4Ox0Gpo61uNrg3d6NLAjVa1nE1+hrHJ6Ry8EsOey1GsPX6DxDT1GF0aVOfN/o05fSOWj9afY+PpMIIiE/j56fZ4u9iw7Ego322/TFS8OkLrxp1kElIzcCjgBQEBCxYsyPM1Hx8fdu3aVYbR3F8NF1vqutkTHJ3IoSu36Ns0578PIYQQKkVRyMzMxMLi/n8X3d3dyyCiiqlCfavw8vIiIiLCZFtERAROTk55Ll1gbW2NtbV1WYQnhNlFxKVw8EoM9d0daF7TOdc2KemZvPDHMc7djGPNiRv8svsKr/f3Y0Bzr/smVJci4pm6/CTpmQr/Xohg/lPtaOtbrURi33ounNdXngagWQ0nzt2M44O/zuFoY8HQNrWKdMxbiWkcCIohMj4lx2u1q9vRsW71HMnVsWu3+PyfAOMc1I1nwvh59xXe6O9Hv6aeRU46T4Xe4frtZDrVc8XNoeT+Twq9lcTrK08BMK5LHab2bVToY8QmpTN1+UmOXrvN878fZWqfRkzu1QCtVsOFsDi+2HyRHQFRAPx9Oowfn2qXa6Jy5Ootxi46TEq6ng2nbtKxjitvDfCjXW3XAsVxIuQ2Go2G1j4uebbR6xV+3X+V7/+7zO2kdABa+bgwtU9DElMz2RsYzd7AKEJvJXP46i0OX73F7G2XcLS2oFO96tR1s+PI1ducvn7HZGpAi5rOvPVQY7o2dDMes7GXIxMXH+dieDyD5+7Fxc6SazFJAPi62vFav0YMblkDrbZqTxOoCjrXr05wdCL7g6IlSRdClApFUYwXncuaraWuQN9vxo4dy65du9i1a5ex2OeiRYsYN24cmzZt4r333uPMmTNs3boVHx8fpk2bxsGDB0lMTKRJkybMmjXLZOryvcPdNRoNv/zyCxs3bmTLli3UrFmTr7/+mkceeaRU3nd5VqGSdH9/fzZt2mSybdu2bfj7+5spIiGKT1EUYpPTcbGzKvS+SWkZ7A+MYW9gNPsCo7kcmQCAtYWW357tyAP1qufY57NNFzh3Mw5nW0t0Wg1XohN5aclxWtZy5s3+WUnKvfR6hXfXniE9U0Gn1RARl8oTPx1k5pBmPNHRN0f7uJR07iSm59ju6WyNtYXpupH7AqOZvPQEmXqF4W1r8eVjLZn593l+3X+V11eext7KIkfvbXqmnvDYFJRsiZaCwrWYJPYFRrM3MJpzN/OvQWGh1dDG14UuDdxoUdOZPw+H8u+FCONnOKiFN9svRhIYmcALfxyjja8L0/o2orar6dAsZztLnG3zHgJ7JSqBET8dIC1DHUrdxNuJrg2q06WBG3Xd7NGQ9YdRowFvZ5sC9dynZeh5+c8TxKVk0MrHhekDmmBlUfgefycbS5aOf4CZf59j8cEQZm+7xNkbsdhZ6fjr1E0UBXRaDY29HDl3M45JS4+zaGwHujTI+l05eyOWZxcdISVdT2MvR65EJ3L46i2Gzz9AnyaevPmQX56F2DL1Cl9vDeCHnUFoNbB0/AO5/u4CzNsRyNfb1FED9d3teaN/Y/o3y7p4MqilWlwsJCaJPYFR7AuMZn9QDHeS0o0/W4N67vZ0a+BGTz8PejRyz5Fsd6jjyt8vd+XFxcc4GXqH2OR03BysmPJgQ57o4Fukz1pUTF0auLHkUAj7A2PMHYoQopJKTs+k6QfmWbHq/Mz+JtO68vLtt99y6dIlmjdvzsyZMwE4d+4cAG+//TZfffUV9erVo1q1aoSGhjJw4EA+/fRTrK2t+f333xk8eDABAQH4+ub83mjw0Ucf8cUXX/Dll1/y/fffM3r0aK5du4ara8Eu+FcWGkVRilhmqPgSEhKMS6q0adOG2bNn06tXL1xdXfH19WX69OncuHGD33//HVCXYGvevDmTJk3i2Wef5b///mPKlCls3LixwNXd4+LicHZ2JjY2VpZnEWZ3ICiGzzdf5GToHXo39uCN/n408S7Y72V0QipDf9hH6K1k4zaNBjwcrYmIS8XB2oKl4zvRspaL8fWNp8OYtPQ4AIvGdaBDHde7Q4avGIf7TupVnzf6N85xvmWHQ3h7zRnsrHSsm9SFr7cGsOWcmvSM7uTL9IFNOH39DnsvqxcMztyIzbWImY2llo51qxuT1JR0PU8vOERSWib9m3kyb1RbLHRa9HqF11edYs3xG1jptCwa1wE3B2vjBYlDV2KMMeensZcjDTwcTK4Q6/UKZ2/GGntFs9NpNTzevhZTHmyIt7Mtscnp/Lw7iIV7r+Z5hdvWUsd3T7bJtYdNURRG/XKIA1dicLKxIC4l474x+7ja8lpfPx5plX8v7cd/n2fB3mCcbCzY9Eo3alWzu++x72f5kRDeX3fOODcb1MT39X5++FSzZdLS42w5F4GdlY4lz3eijW81AiMTePynA9xKTKNjHVd+e7Yjd5LT+GbbZVYeC0WvqL+bw9rUYmrfhiZxxial88ryE+y821MP6u/wple65RhxcCAohtH/dxC9Am8PaMzzXesW6GJGpl7h/M049gZGE3IribZ3L8zUKGDBt9SMTObvDMLWUsdTD9Qu8Fz3wpC/TSWvJD/TW4lptP14GwBH3u2Du6OM0BNCFE9KSgrBwcHUrVsXGxsbktIyyn2SDjnnpO/cuZNevXqxbt06hgwZku++zZs358UXX2Ty5MlA7j3p7733Hh9//DGgLtft4ODAP//8w0MPPVS0N1fG7v25ZleYv0tm7Uk/evQovXr1Mj43zB1/5pln+PXXXwkLCyMkJMT4et26ddm4cSNTp07l22+/pVatWvzf//2fLL9Wjh26EsMXWwJo5OlAlwZudK7vhqt94XuMiyomIZV9QTHsuxxNUFQC/Zp5Msa/DjaWuhxtI+NSmLcjkKCoRN5/uCl+Xrn3+sUkpPLhhvOcvxmb47X2tV15+cEG902Wzt6I5YstAey+lJWY/Hcxkh0BkTzauibT+jbCxzXvY+j1ClOXnyT0VjJuDtb0b+ZJt4ZuPFCvOjaWOsYtOsKBKzGMWXiYFS/408jTkavRiby1Wh1OPrFnfWOV4ql9G/G0f22+336Z3w5cY96OIBysLZnYs77xfNEJqcz65yIA0/o2opGnI/NHt+OHnWqv5pJDISw9HMK9l/zsrHRkTzEz9Aop6Xp2X4oyee8A3Rq68d2TbYxJl1ar4YvhLUlIyWDr+QhG/9+hHJ+DtYUWi3uSWBc7KzrXr07XhurvW35fpkNvJd0dHh3NqdA7tKrlwtS+jWjgkVU8zdnWkjf6N+YZ/zp8999l/j4dRnpGVgKbeXd42msrTrJxSrccP7fVx29w4EoMNpZaNk7phq2Vjv13fyf3BUVzOzHNpH16pkLorWReXX6SH3cF8dZDjenp555jGNrWc+Es2BsMwNePty6RBB1gZAdfGnk68tbq03g72/J6Pz9a1MqaOvHdk214/rej7LkczdhFR5gzshXvrj3LrcQ0mtd04v/GtsfWSoetlS2fP9aS8d3r8dWWADafC2f18etsOHWTpx6ozaRe9YlOSGPCH0e5FpOEjaWWjx5pxi97ggmMTGDq8pP8Nq6j8SJFVHwqU5adQK/AY+1q8WKP+nm9hRx0Wg0tajmbvI/CsLbQ8Wqfwk8jEJWHq70VTb2dOB8Wx4ErMTzSqoa5QxJCVDK2ljrOzzRPTmOby/fiwmrf3rSIakJCAh9++CEbN24kLCyMjIwMkpOTTXK73GRfycPe3h4nJyciIyOLHV9FY9aedHOQ3oqy9cjcvZy+npXMajTqfOM+TTx5sUf9XJPlwohNSmfZkRBi7kl0UtIzOXr1NufDcg539na24dU+DRneVi2YFpeSzk+7THtK7ax0fDWiFQNbmK7Le/ZGLC/8cYwbd5JzHNfASqc1JiHVs/UEJqRmcOhKDGtP3ODv02GAOtz6yY6+PNqmJov2BRu3W+o0jO5Umzf6++XaazdvRyBfbgnAxlLL+sldcwwjTkjNYPT/HeJU6B08HK1ZOr4Tryw7ybmbcbSvXY1lEx7ItQfyp11BxmT806HNGd2pNgBTl59k7YkbNKvhxF+Tupjs+9/FCF5ZdpL4lAw8HK3p2kAt1NWlgRtezqZXEBVF4VJEgrE3/OCVGJLSMmnr68Li5zvlehU3NSPTmBTe2wvfxMvJ7POB0zP1PP7TAU6E3KFVLWdWvtjZOAz6VmIaD369k9tJ6bw9oHGBEsvslcPjU9Ve9451XGnt62JsoygKy4+EEpeSwfNd6/Lew01L5b3lF+NT/3eI4yF3jNsaeDiw4gX/PC/CnQy9w+f/XOTAFXW4sL2VDgVISlOXMPvp6XY0r+nMpYh4Hpm7l5R0Pa/3a8Tk3g3J1Cs8s/AwewOjaejhwF+TuxT4in9FIX+bSl6JfqZ6PV+tP8Tcg7d4ooMP/xtunuXghBCVR349ruVZXj3pt2/fxsXFxdjuxRdfZNu2bXz11Vc0aNAAW1tbHnvsMXr27GncN7ee9LVr1/Loo48aj+Pi4sI333zD2LFjy+T9FVel6EkXldvJ0Ducvh6LlYWWUR19ORAUQ0BEPGdvxHH2RhwJKRlFTi6S0zL5df9V5u8MvO/w4cZejnRt4Ia3iy0L9lzhZmwKb60+w8+7r9CvmRd/Hg7hzt0CVG18XbC20HLwyi1eWnKciT3r83o/P3RaDWuOX2f6mjOkZuip62bP+w83MUkUElMz+L89wRy4EsPCfcGsOBrKs13rokGdc30y9A4Z2cZ/D2ldQ53fXF2d39yudjVe6B7LF1susudyNL/uv8qBoBh+erodddyy5kAfuhLD11sDAPh4SPNc5/k6WFvw27gOjPzpIAER8Qz8di9pmXqq2Vny/ag2eQ4RfqFHfeJTMpi7I5D31p3FwdqC6vbWrD1xA60GZg1rkWPf3o092fF6T+4kpVPf3T7fwiMajQY/L0f8vBx5rmtd0jL0XI6Mp4GHQ4556gbWFjoWju1AQLjarrgXdkqapU7L90+2YdB3ezl1PZb//XORDwarv9efbbrA7aR0Gt99vwVhZ2XBpF4NGNXRl/m7gvh1/1VjAbR7tfZx4c2Hck5NKG12VhYsGtuRJ345yIWwOGpVs2Xxc53yHSXT2seFpeM7sTcwms83X+TsDfUCWuf61Zk7qq1x30aejnw8pDlvrDrN7G2XaF/HlSPBt9gbGI2tpY4fRretdAm6KOcubIANr/CMa1vmMo59QdHmjkgIIczGysqKzMz7Tzfct28fY8eOZejQoYDas3716tVSjq7ykG86otT8fuAqAA+39ObDR5oB6pDyDafD+Pjv8yzcF8yjbWrmWYU8N+mZelYcDeXbfy8TeXcJJD9PR7o3cjNJDjUaaOrtlGO48+hOviw+eM04rH3+ziAAGno48EZ/P/o29SRTr/DFlgB+3n2F+TuDOHczjnpu9vy6X30/vfzc+eaJNrkWCuvd2MMkCflu+2WT12tXt6NLAzdGd/KlWY2c77tFLWf+eK4Tey5HMW3FKQLu9ip+92Qbevp5EJ2QNeR3eNtajGif93KCLnZW/PFcR0b8dMA493r2yNZ4O+c/D/e1fo2IS0nn9wPXmLbiFNXvJk9j/OuYzG/Pzs3BukgVy60stLl+Dvey1GkL9XtS1mpVs+PrEa14/vejLNwXTKd6rjjZWLLq2HU0Gvh0aAssC7mEWzV7K94Z2IRxXeqw4sh1EtNML0Y52VgwqlNtsxUvc7azZOnznVh74gYDWnjlGDWRG41GQ7eG7nSp78bW8xFEJaTyZAefHBd+RrT34eCVW6w+fp2Ji48Rm6xeRPv40eY0zKP4nBClxqkmJMXglrEfW+0YQm8lE3orKd8pSUIIUVnVqVOHQ4cOcfXqVRwcHNDr9bm2a9iwIWvWrGHw4MFoNBref//9PNuKnCRJF6XiVmKacej20w/UNm73cLLhua51ORFym79Ph/HO2jOsfakLunuGLCemZvDq8pPsDzTtscjQK6TenQ9c08WW1/o1Ykjrmjn2z4uNpY7nu9Xj8Q4+/N/uK+wLiuGJDj4Ma1vLeAwLnYZ3BjahWQ0n3lp92mT+9Mu9GzC1T6M8h1hnT0I2nQ1jycEQXB2s6NrAja4N3Ar8pa5bQ3djVekTIXcY9+sRXu/nx8ErMUTEpdLAw4GPH2123+N4ONmw+LlOfLThHD39PIzz0POj0Wj4cHAz4lMyWHviBpHxqXg52fBaP5mTm58+TT0Z360uv+wJ5vWVp4w9w6M7+dKudtGXqfN2tuWVPg1LKswSVc3eimcLOEIgO61Ww0PN815rHeDjR5tx6vodAu+uWPBYu1o81q5oS/EJUSzercHeHU1iFI973uC3MF/2B0Uz0jXv6sRCCFFZvf766zzzzDM0bdqU5ORkFi1alGu72bNn8+yzz9K5c2fc3Nx46623iIvLf9UdkUXmpItS8eOuIP73z0Va1HRm/eQuOYZAR8al8ODsXcSnZPDh4KaM7ZL1RT8lPZPnfjvCvjyWuqlub8Xk3g0Y1ck3zyHSJeX8zTheXHyM24lpfDmiJQ81977/TiUoNSOTD9ef48/DocZtec1DL2kZmXpeXX6Sfy9E8MPotvRuLGsD30/2+ekA7o7WbH+tB042eS/PJvJ2KSKeUb8cpFY1O5aOz71mQWUhf5tKXol+pmtfhFN/cqTGU4y4MpAhrWvw7RNtSiZQIUSVVFHnpIv8yZx0UW5l6hUWH7wGwNP+tXOdo+zhZMNbDzXmvXVn+XJLAP2be+HtbEtGprru877AGOytdPz4dLsca1Lnts52aWlaw4n/XutBeqaCrVXZz4W2ttAxa1hLWtR0Ycb6s6RnKnnOQy9pFjotc0e1JTkt0yzvvSLKPj89NjmdGYObSoJeDI08Hdn7Vm+sdFqzFwgUVVyDPnDqT5olHgYGsj8oBkVR8q3BIYQQQhSVJOmixO0MiOT67WScbS3zXaZmVEdfVh+/zomQO3y4/hzzR7fjzVWn2XY+AisLLb88057O9d3KMPLcWei0lNE1gTyN6uRLx7rVCI9NpWvDsv1MJEEvnFrV7Fg9sTOht5MKNL1A5K+8FQoUVVT93qDRYhd7ibqWtwiOd+VyZEKZXDAVQghR9Zin4pCo1H4/oPaiP96+Vr5fsLVajVotXKthyzl1Hew1J26g02r4YVTbcpGglycNPBzLPEEXRdPAw0ESdCEqEztXqNUBgKfdLgHkqJkihBBClBRJ0kWRJKZm8MbKU3y//TIJqVlVp6/FJLLrUhQaDTyVrWBcXhp7OfF8t3oAHLgSg0YDX49oRZ+mMv9ZCCFEOdKwLwA9tacA2BeUe90UIYQQorgkSRdFsuJoKCuPXefrbZfo8cUOFu0LJjUj0zgXvUcjd+P63/fzyoMNqVNdrXo+85FmPNqmZqnFLYQQQhRJAzVJrx13FCvSOXglhoxMWU5ICCFEyZM56aJItp6LAMDOSkdMYhofbTjPgr3BxvWMny5AL7qBrZWO9S93JTIuhQYeMr9PCCFEOeTVEuw90CVG0t0mkH9TmnDuZhytfFzMHZkQQohKRnrSRaHdTkzj8NVbAPz9clc+HdocD0drrt9OJj4lg1rVbOlZyPm4TjaWkqALIYQov7Ra45D3Ec4XAdgXJPPShRBClDxJ0kWh/Xcxkky9QhNvJ+q5OzC6U212vdGLtx5qTGMvR94b1BSdLJckhBCismnQB4COGccAOCDz0oUQQpQCGe4uCm3r+XAA+mUr7mZrpWNiz/pM7FnfXGEJIYQQpat+L9DoqJZ4hZpEceyajvRMPZY66fMQQghRcuSviiiU5LRMdl2KAqBfM6nALoQQogqxrQY+HQEYYHuWpLRMzt6INXNQQghRcdSpU4dvvvnG+Fyj0bBu3bo821+9ehWNRsPJkyeLdd6SOk5ZkSRdFMrewGhS0vXUdLGlqbeTucMRQgghytbdIe8P254F4FDwLXNGI4QQFVpYWBgDBgwo0WOOHTuWRx991GSbj48PYWFhNG/evETPVVpkuHsVFJuUzoEr0ewNjGZfYAwht5JytGnt48KS5zthY6kz2b713N2h7s080Whk3rkQQogqpmE/+O9jmqWexIp0Dgff4sUeMtVLCCGKwsvLq0zOo9PpyuxcJUF60quItAw9fxy4ypC5e2n98VZeXHycxQdDCI5OJFOv5Lgdu3abH3YEmhwjI1PPvxfUpdf6Na04v+RCCCFEifFqAQ5eWGYm00F7kSPBt8jUK+aOSghR0SkKpCWa56YU7P+wn3/+mRo1aqDX6022DxkyhGeffZagoCCGDBmCp6cnDg4OdOjQgX///TffY9473P3w4cO0adMGGxsb2rdvz4kTJ0zaZ2Zm8txzz1G3bl1sbW3x8/Pj22+/Nb7+4Ycf8ttvv/HXX3+h0WjQaDTs3Lkz1+Huu3btomPHjlhbW+Pt7c3bb79NRkaG8fWePXsyZcoU3nzzTVxdXfHy8uLDDz8s0GdVXNKTXsnp9QrrT93k620BhN5KNm5v4OFA1wZudG3gRrOaTuiy9YrvDYxm2opTzN8VxCOtaxiXRjt67Ta3k9JxsbOkQ51qZf5ehBBCCLPTaKBhHzixmP6Wp9mX2oILYXE0r+ls7siEEBVZehJ8VsM8537nJljZ37fZiBEjePnll9mxYwcPPvggALdu3WLz5s1s2rSJhIQEBg4cyKeffoq1tTW///47gwcPJiAgAF9f3/sePyEhgYcffpi+ffuyePFigoODeeWVV0za6PV6atWqxcqVK6levTr79+9nwoQJeHt78/jjj/P6669z4cIF4uLiWLRoEQCurq7cvHnT5Dg3btxg4MCBjB07lt9//52LFy8yfvx4bGxsTBLx3377jWnTpnHo0CEOHDjA2LFj6dKlC3379r3v+ykOSdIrKUVR2BkQxeebL3IxPB4Ad0drXupZnwHNvfFytslz36FtarLxdBjbL0byzpqzLJvwAFqthq3n1F70Bxt7YiGVbIUQQlRVDfrCicX0tTzNB6mjOXglRpJ0IUSlV61aNQYMGMDSpUuNSfqqVatwc3OjV69eaLVaWrVqZWz/8ccfs3btWtavX8/kyZPve/ylS5ei1+tZsGABNjY2NGvWjOvXrzNx4kRjG0tLSz766CPj87p163LgwAFWrFjB448/joODA7a2tqSmpuY7vP2HH37Ax8eHuXPnotFoaNy4MTdv3uStt97igw8+QKtVc52WLVsyY8YMABo2bMjcuXPZvn27JOmiaH7cdYXPN18EwNHaghd71mdclzrYWd3/R67RaPhoSDP2B8Vw+OotVh4L5fH2PllLr0lVdyGEEFVZ/V6gtcA7IxRfTQSHgj15vls9c0clhKjILO3UHm1znbuARo8ezfjx4/nhhx+wtrZmyZIlPPHEE2i1WhISEvjwww/ZuHEjYWFhZGRkkJycTEhISIGOfeHCBVq2bImNTVZnor+/f4528+bNY+HChYSEhJCcnExaWhqtW7cu8HswnMvf39+kxlaXLl1ISEjg+vXrxp7/li1bmuzn7e1NZGRkoc5VFJKkV0Ip6Zn8vDsIgDH+tZnapxHV7K0KdYxa1eyY1rcRn266wGebLlLDxZbrt5OxsdTSvaF7aYQthBBCVAw2zuDrD1f30Ft7gnVXa6HXK2i1UlBVCFFEGk2Bhpyb2+DBg1EUhY0bN9KhQwf27NnDnDlzAHj99dfZtm0bX331FQ0aNMDW1pbHHnuMtLS0Ejv/smXLeP311/n666/x9/fH0dGRL7/8kkOHDpXYObKztLQ0ea7RaHLMyS8NMma5Evr7dBi3k9Kp6WLLjMHNCp2gG4zrUocm3k7EJqfz0uLjAHRv6I6tle4+ewohhBCVXKP+APSxOMmdpHQuRcabOSAhhCh9NjY2DBs2jCVLlvDnn3/i5+dH27ZtAdi3bx9jx45l6NChtGjRAi8vL65evVrgYzdp0oTTp0+TkpJi3Hbw4EGTNvv27aNz58689NJLtGnThgYNGhAUFGTSxsrKiszMzPue68CBAyjZiubt27cPR0dHatWqVeCYS4sk6ZXQHweuAjD6AV90xbiqb6HTMmtYCzQaiE9VKx32ayZV3YUQQggaqkl6J80F7Ejh0BVZL10IUTWMHj2ajRs3snDhQkaPHm3c3rBhQ9asWcPJkyc5deoUo0aNKlSv86hRo9BoNIwfP57z58+zadMmvvrqK5M2DRs25OjRo2zZsoVLly7x/vvvc+TIEZM2derU4fTp0wQEBBAdHU16enqOc7300kuEhoby8ssvc/HiRf766y9mzJjBtGnTjPPRzcn8EYgSdSr0Dqeux2Kl0zKyvU+xj9fax4UxD9QGQKuBBxt7FPuYQgghRIXn1hCq1cGSdLpqz3AoOMbcEQkhRJno3bs3rq6uBAQEMGrUKOP22bNnU61aNTp37szgwYPp37+/sZe9IBwcHNiwYQNnzpyhTZs2vPvuu3z++ecmbV544QWGDRvGyJEj6dSpEzExMbz00ksmbcaPH4+fnx/t27fH3d2dffv25ThXzZo12bRpE4cPH6ZVq1a8+OKLPPfcc7z33nuF/DRKh0ZRCrgwXiURFxeHs7MzsbGxODk5mTucEvf6ylOsOnadoW1qMmdk6xI5ZnxKOq8uO0mzGk5M6+dXIscUQgiRpbL/bTKHMvlMN70Jh3/iz4xefG0ziSPv9jEpQiSEEHlJSUkhODiYunXrmhRKExVbfj/Xwvxdkp70SuR2YhobTqlVIZ/2r11ix3W0sWTB2A6SoAshhBDZNeoHQG/dSaITUgmKSjRzQEIIISoDsyfp8+bNo06dOtjY2NCpUycOHz6cZ9v09HRmzpxJ/fr1sbGxoVWrVmzevLkMoy3fVhwNJTVDT/OaTrTxcTF3OEIIIUTlVrsrWNrhqblNM801GfIuhBCiRJg1SV++fDnTpk1jxowZHD9+nFatWtG/f/8815577733+Omnn/j+++85f/48L774IkOHDuXEiRNlHHn5k6lXWHzoGgBPP1BbhtsJIYQol+bPn0/Lli1xcnLCyckJf39//vnnH+PrKSkpTJo0ierVq+Pg4MDw4cOJiIgwY8T5sLSBej0B6KU9IcXjhBBClAizJumzZ89m/PjxjBs3jqZNm/Ljjz9iZ2fHwoULc23/xx9/8M477zBw4EDq1avHxIkTGThwIF9//XUZR17+7L4UReitZJxtLXmkVU1zhyOEEELkqlatWvzvf//j2LFjHD16lN69ezNkyBDOnTsHwNSpU9mwYQMrV65k165d3Lx5k2HDhpk56nw0NAx5P8Gh4BiqWKkfIYQQpcDCXCdOS0vj2LFjTJ8+3bhNq9XSp08fDhw4kOs+qampOSbg29rasnfv3jzPk5qaSmpqqvF5XFxcMSMvn36/u+zaiHa1ZB1zIYQQ5dbgwYNNnn/66afMnz+fgwcPUqtWLRYsWMDSpUvp3bs3AIsWLaJJkyYcPHiQBx54wBwh5+9ukt5aE0R6XBTXYpKo42Zv5qCEEBWFXNirXErq52m2nvTo6GgyMzPx9PQ02e7p6Ul4eHiu+/Tv35/Zs2dz+fJl9Ho927ZtY82aNYSFheV5nlmzZuHs7Gy8+fgUf1my8iYkJomdl6IAeOqBkisYJ4QQQpSmzMxMli1bRmJiIv7+/hw7doz09HT69OljbNO4cWN8fX3zvIBvkJqaSlxcnMmtTDjXBM8WaDUKPbSnZF66EKJAdDq1Uy0tLc3MkYiSZPh5Gn6+RWW2nvSi+Pbbbxk/fjyNGzdGo9FQv359xo0bl+fweIDp06czbdo04/O4uLhKlagrisKnm86jKNC9kbtcvRdCCFHunTlzBn9/f1JSUnBwcGDt2rU0bdqUkydPYmVlhYuLi0n7/C7gG8yaNYuPPvqoFKPOR6N+EHGG3roT/BsUw8gOvuaJQwhRYVhYWGBnZ0dUVBSWlpZotWav5y2KSa/XExUVhZ2dHRYWxUuzzZaku7m5odPpchSDiYiIwMvLK9d93N3dWbduHSkpKcTExFCjRg3efvtt6tWrl+d5rK2tsba2LtHYy5Pf9l9ly7kILHUaXu/XyNzhCCGEEPfl5+fHyZMniY2NZdWqVTzzzDPs2rWrWMc060X5hv1hz9d0157mk8sR6PUKWq0UcBVC5E2j0eDt7U1wcDDXrl0zdziihGi1Wnx9fYtdxNtsSbqVlRXt2rVj+/btPProo4B69WH79u1Mnjw5331tbGyoWbMm6enprF69mscff7wMIi5/Tl+/w6ebLgDwzsAmtKzlYt6AhBBCiAKwsrKiQYMGALRr144jR47w7bffMnLkSNLS0rhz545Jb3p+F/ANzHpRvlZ7FFtXnJNvUTvpLBfC/WlWw9k8sQghKgwrKysaNmwoQ94rESsrqxIZFWHW4e7Tpk3jmWeeoX379nTs2JFvvvmGxMRExo0bB8CYMWOoWbMms2bNAuDQoUPcuHGD1q1bc+PGDT788EP0ej1vvvmmOd+GWcQmpzNp6XHSMxX6N/NkbOc65g5JCCGEKBK9Xk9qairt2rXD0tKS7du3M3z4cAACAgIICQnB39/fzFHmQ6tD06APnFlBb91J9lyOliRdCFEgWq02R2FsIcyapI8cOZKoqCg++OADwsPDad26NZs3bzYWkwsJCTG5EpGSksJ7773HlStXcHBwYODAgfzxxx855q5Vdoqi8OaqU4TeSsbH1ZYvHmsl66ILIYSoEKZPn86AAQPw9fUlPj6epUuXsnPnTrZs2YKzszPPPfcc06ZNw9XVFScnJ15++WX8/f3LZ2X37PwegjMreFB7nA8vR/Fij/rmjkgIIUQFZfbCcZMnT85zePvOnTtNnvfo0YPz58+XQVTl26/Z5qHPG9UWZ1tLc4ckhBBCFEhkZCRjxowhLCwMZ2dnWrZsyZYtW+jbty8Ac+bMQavVMnz4cFJTU+nfvz8//PCDmaMugAZ9ULQWNOQGkcHnSU7rIEuiCiGEKBKzJ+micLaeC+ezu/PQ35V56EIIISqYBQsW5Pu6jY0N8+bNY968eWUUUQmxcYbaXSB4F905yqHgh+np52HuqIQQQlRAUuu/gtDrFWZvu8SEP46RnqkwqIU3z8g8dCGEEKLc0PgNBKCP9gR7L0ebORohhBAVlSTpFUBcSjoT/jjKd9svAzC2cx2+eaK1zEMXQgghyhO/hwDooL3IiUtXzRuLEEKICkuGu5dzgZHxTPj9GFeiE7Gy0PLZ0BY81q6WucMSQgghxL2q1SHTrQkW0ReoGb2XiLjeeDpJ1WYhhBCFIz3p5diWc+E8Om8/V6IT8Xa2YdWL/pKgCyGEEOWYrvEAAPrqjrFHhrwLIYQoAknSyyG9XmH21gBe+OMYCakZdKzryoaXu0qROCGEEKK8uzsvvYf2NPsv3TRzMEIIISoiGe5ezsSlpDN12Um2X4wE1Pnn7w5qgqVOrqcIIYQQ5V7NdqTbVMcpJYaky3vQ6zug1UoNGSGEEAUnmV85EhgZz6Nz97H9YiRWFlq+HtGKDx9pJgm6EEIIUVFotWj91CHvHdMOcyE8zswBCSGEqGgk+ysnUtIzefKXQ1yJTqSGsw2rX+zMcJl/LoQQQlQ4uiaGpdiOsedSlJmjEUIIUdFIkl5OXI5IICo+FWdbS9a/3JUWtZzNHZIQQgghiqJeTzK1Vvhqo7hy4ai5oxFCCFHBSJJeTlyJTgCgkacDbg7WZo5GCCGEEEVmZU+KTzcAPG/uIDkt08wBCSGEqEgkSS8nrkQlAlDPzcHMkQghhBCiuOyaPwxAT80xDgbHmDkaIYQQFYkk6eVEcLSapNd1tzdzJEIIIYQoLo3fQwC00QRy7NwlM0cjhBCiIpEkvZwwDHev5yZJuhBCCFHhOdUg1qUZWo0Cl/4xdzRCCCEqEEnSywFFUQg2DHeXnnQhhBCiUrBuMQSAtol7uXkn2czRCCGEqCgkSS8HIuNTSUzLRKsBX1dJ0oUQQojKwKblUAC6as9w4FyQmaMRQghRUUiSXg4Yisb5uNphZSE/EiHEPTJSzR2BEKIo3BsRbVcPK00m8ac3mDsaIYQQFYRkhOWAoWiczEcXQuRwYjF8VhO2f2zuSIQQRZDeSK3y7hvxLxmZejNHI4QQoiKQJL0cuBKlFo2rK8uvCSGyO7cW1r8M+nQ4MBcSoswdkRCikDw6jQSgi3KKM8E3zByNEEKIikCS9HJAll8TQuRw+V9YPR4UPeisISMFDv9k7qiEEIWk82pGhGUtrDXp3Dz8l7nDEUIIUQFIkl4OXLmbpNeX4e5CCIBrB2D5U2oPerNhMOxucn74Z0iNN29sQojC0WiI8VXXTK92TZZiE0IIcX+SpJtZeqaekFtJgPSkCyGAmydh6eOQkQwN+8HQn6DJEKjeEFJi4div5o5QCFFI7p1GANA65QixsbFmjkYIIUR5J0m6mYXcSiJTr2BrqcPLycbc4QhRsW19D/4YWnF7mxMiYfFwSI2D2l1gxG9gYQVaLXR9VW1zYF7VqfaelgSLBsKXDUxvX/nBjs9AUcwdoRAF4t6wE2EaD+w0qQTuX2fucIQQQpRzkqSbWfDd5dfqutmj0WjMHI0QFVh8BOyfC0H/Vdze5jOrICka3BvDk8vAyi7rtRaPg2MNiA+DU8vMF2NZurQZru2DxCjTW0I47Pocdnxq7giFKBiNhqseDwKgvbjezMEIIYQo7yRJNzPj8msy1F2I4rn0D3C3Z7Wi9jZf3KjetxsLNk6mr1lYQefJ6uN934I+s0xDM4vsn8fEA1m3/p+p23d/Cfu+M1t4QhSGdathADSK3YuSnmzmaIQQQpRnkqSb2ZVodfk1WSNdiGIyJHSg9jafXm6+WIoiMQZC9quP/Qbm3qbtM2DjAreC4MKGMgvNLDLS4PJW9XHr0eDZNOvmPwkenKG+tu39ijtyQlQpTdv3IkxxxZ4Uwo5LATkhhBB5M3uSPm/ePOrUqYONjQ2dOnXi8OHD+bb/5ptv8PPzw9bWFh8fH6ZOnUpKSkoZRVvyrkTJ8mtCFFtqPFzZqT5uO0a9r2i9zZc2q8utebWAarVzb2PtAJ1eUB/vnVO552Rf3aPOzbf3gJrtc77ebRp0nao+3vAqnF1dpuEJUVg2VpacduwOQMIJ+X0VQgiRN7Mm6cuXL2fatGnMmDGD48eP06pVK/r3709kZGSu7ZcuXcrbb7/NjBkzuHDhAgsWLGD58uW88847ZRx5yTEsv1bPzcHMkQhRgQX+C5lpUL2BOhTaxgViAuHi3+aOTKXXw87PYfvHeSfWhpEAjR/O/1gdXwALWwg7CVd2lGiY+UqJg/VTIHh32ZzP+HkMVAvn5ebBGdD+WUCBNROyLtTk5cpOWDsRYq8XLpaMVNj2Afz+aM7b+pcLdyxRpaU1GgxAzYid6mgRIYQQIhdmTdJnz57N+PHjGTduHE2bNuXHH3/Ezs6OhQsX5tp+//79dOnShVGjRlGnTh369evHk08+ed/e9/IqPiWdqHh13qz0pAtRDMaEbhBYO0LHCerz8tDbrCiwZTrs/Az2fAVB23O2SUtSC97B/ZN0++rQ9mn18YklJRtrfo4ugOO/wcbXS/9cej0EbFIf5/d5aDQw8GtoPhz0GbDri/yPu/kdOLUUfh+iVtIviMwMWP28OjLjyo6ct9CK+fdHmIdfhz5EKc7YKwmkXf7P3OEIIYQop8yWpKelpXHs2DH69OmTFYxWS58+fThw4ECu+3Tu3Jljx44Zk/IrV66wadMmBg7MY/4mkJqaSlxcnMmtvDAUjXNzsMbJxtLM0QhRQWWkwaW7c5cNCV2nu73NN09A8C7zxQaw839w6Mes53u/ydkmaLu6LrpLbfBsdv9jtlDXXObytrLrjTNcCIkOgOjA0j3XzRNqXQErB6jbPf+2Wm3W/PSQA+rc/tzcugKR59THMYHwxzBIvpP/sfV62PAKXFgPOit46H8w7BfTW5+PCvXWRNXW0MuZHRZdALh1cLGZoxFCCFFemS1Jj46OJjMzE09PT5Ptnp6ehIeH57rPqFGjmDlzJl27dsXS0pL69evTs2fPfIe7z5o1C2dnZ+PNx8enRN9HcRgru0vROCGK7tpeSI01nbts75Y1N33vHPPFdmAe7Pqf+rjb66C1VOdahx4xbZd9qHtBlmKs2V59v6mx6vsvbfHhcD1bzAEb825bEgzTFBr2BQvr+7evVludy6/o1bn9uR7zbs+8Z3P1s4s4A0sfh7TE3NsrCmx9F04uBo0WHlsID0yElo+b3vweKvz7E1WWRqMhut6jALiGboPUBPMGJIQQolwye+G4wti5cyefffYZP/zwA8ePH2fNmjVs3LiRjz/+OM99pk+fTmxsrPEWGhpahhHnLyhKll8Totgu3E3o7p273HkyaHTqPOQbx8s+ruN/wJa7FxB7vwcPvg8tR6rP932T1S4zAwLuVnpuPKhgx9Zq1fcLplXtS4th6LlGWzbnLOj8/OwMbfOqQ2A4Ztsx8PRasHGG0EOwbHTuy/Xt+hwO/qA+HjIPmgwueCxC5KNJ254E6z2x0qeglJe6GUIIIcoVsyXpbm5u6HQ6IiIiTLZHRETg5eWV6z7vv/8+Tz/9NM8//zwtWrRg6NChfPbZZ8yaNQu9Xp/rPtbW1jg5OZncygtDT3pd6UkXVVVGGhz6CWKCirZ/fnOXXXyzhoVnT4oLKvk27J9b+CJjAOfWwYYp6uPOL6u96ABdpgAaNZGMClC3heyHlDtgVx18OhX8HMakdKP6OeTmxjHY+j5sfc/0dvLPwr0fQ4Lb4Xn1PvQwxEfk3b44oi+rQ+q1lmpPekEZPo+g/3L2jidEQehB9bHfQPBqDqNXg6W9Oq988XDTz2fti7Bzltp+wBfQelTx35cQd/k3cGMj3QBIOLLUzNEIIYQoj8yWpFtZWdGuXTu2b88qoqTX69m+fTv+/v657pOUlIT2niq/Op0OAMXcxaGKINiwRrq7VHYXVdS+b+CfN9Wq4UVxv7nLXV9V78+vV5O/gkqJgz+GqsOd/y3knOPAf9VCY4peXde878dZQ9jd/bJ6y/d9q94bEuBGA0BnUfDz1O0OVo7q+795IufrGanw5yjY/x3s/970tu5FCD9bsPOkxMGVu/P6O4yHmu0AJeviSEkzfB51u6m93QXl2Uyd05+RklWEz8CwvJ13K3C5O+XJpwM8uVSda351j+nnc+ruRYxe72UteSdECbGx1BFRZwgA9tf3FLyIoRBCiCrDrMPdp02bxi+//MJvv/3GhQsXmDhxIomJiYwbNw6AMWPGMH36dGP7wYMHM3/+fJYtW0ZwcDDbtm3j/fffZ/DgwcZkvaJQFIXgKOlJF1VYWiIcnK8+Dtmv9nYW1v3mLns0UXtOUbKS4vtJT4Y/n8hKfK/uLXiF+GsHYNlToE+HZsPg4Tk555gb1vY+vRzuhJpWpi8MC+usnubchsye+hMSwsHBU+3NN9w8muW9T24ub1XfT/WG4N4oK87SGvJuiKuwn4dGYzq6wOSYeQyfr9cTxm2GLq+YfkadX4bhC6B7GVSyr4JmzZpFhw4dcHR0xMPDg0cffZSAgACTNj179kSj0ZjcXnzxRTNFXPJatWrLCX0DtOjhrKyZLoQQwlQhum1K3siRI4mKiuKDDz4gPDyc1q1bs3nzZmMxuZCQEJOe8/feew+NRsN7773HjRs3cHd3Z/DgwXz66afmegtFFhmfSmJaJjqtBl9XO3OHI0TZO7EYkm+pjw0FvwxLixVUQeYud52q9vqeWga93gGnGnm3zUiDFWPg2j6wdoL0JIi/CbGh6vD5/Nw8qRYiy0iGhv1g6E+gzeXiYa32UKeb2nu7bqJ6bEs7qN/rvm83h8aD4Nwa9XPoMyNruz4z66JEl1fAf1LWa+5L4K+X1GS459v3P8e9FxEaPwzbZ6pV81PiwKYEpxBlL1Dnl/eqHXlqPAgOzlPn+GdmqCMTUhPyX96uVjv1JsrMrl27mDRpEh06dCAjI4N33nmHfv36cf78eeztsy5ajx8/npkzZxqf29lVnr+VvRt78O2aLrTRBpJ24k+sHpho7pCEEEKUI2YvHDd58mSuXbtGamoqhw4dolOnrDmZO3fu5NdffzU+t7CwYMaMGQQGBpKcnExISAjz5s3DxcWl7AMvpqAodai7TzVbrCzM/mMQomxlpqvDigGqN1DvC9szW9C5yz4doXYXtTf4wLy82+kzYe0Las+xhS2MWq4OjwYIOZh/LFGXYPEwSI1TzzXiN7Cwyrt9t2nq/dU96n393mBpm/85ctOwr/r+owNMh/NfWK8uOWZbTR1yn12jh9QCcOFn4Pa1/I+fkaou8wZZCa5bI/VnlpmmDu0vSYYh9DXb5X8xJS8+ndS5/Sl31NEZoCbomalQra46skKY3ebNmxk7dizNmjWjVatW/Prrr4SEhHDs2DGTdnZ2dnh5eRlv5ammTHG52ltxo8YAMhQtVhGnCjcdRwghRKUn2aGZSNE4UWHFh0PIoeId4+xqtQfZ3h2G/qxuC/ov7+WIUuLg7Bo4vSLrZlhvvG73+89dNgwxP/YrJN3K+bqiwMZpaq+01hJGLobancH3bn2MkAN5H/tOCPzxKCTFgHdreHIZWN2nx69er6wLAFD0yuE2zllz8Q0XORQla9m5ji+A9T01L+yrg29n9fH95pUH74G0eHDwujsXnfyHlQOkJak1ALL/rE6vgKAd9582UJSq7tnpLNS5/dmPlX0kQEGWtxNlLjY2FgBXV1eT7UuWLMHNzY3mzZszffp0kpKS8j1OamoqcXFxJrfyrGOLRuzWt1SfnF5h3mCEEEKUK5Kkm8kV4/JrUjROVDArnoGF/dSe2KLQ67MS7AdegpptoVodtbfz3oJfBhtegVXjYM34rNvJxeprjQswLLpBH3V97LQEOLLA9DVFgW0fqAm8RgvDf4GGfdTXfB9Q7/PrSd/4GsTdADc/eGpNwYZ/azRZFw40OnV4fFHdO0f8yg4IO6UOoe84oWD75MU4P/ye5e0MSfTlreoUAYOUOPh1IKx42vRntWa8eiHjys68z5WWmFWgrrDz0bPL/t4y07PWTS9q4i9KlV6v59VXX6VLly40b97cuH3UqFEsXryYHTt2MH36dP744w+eeuqpfI81a9YsnJ2djTcfH5/SDr9Y+jTxZF1mFwD0p1cUvPaFEEKISk+SdDORnnRRIekzswqqFXXt8ctbIOqCOue7w3P375mNvgzn1qqP6/VUe6ENt1ZPQssn7n/O7Enxoflqb6/Bnq/VCugAD38DzYZmveZzN0mPPK8uyXav5DtZFxYe/13tpS6oJo9At9fg4dlg53r/9nkxzN2+fkQd5WDoRW/7TN7xGC5sXNuX+8gCuGd5u3uS5prt1IJ0qXFZQ/bTk+HPJ9XfDxsX05+TYT5/XhdhQF0uTp8OTjXVIfVFVb+XeoEiNhQO/Xh3eTs3ddqDKHcmTZrE2bNnWbZsmcn2CRMm0L9/f1q0aMHo0aP5/fffWbt2LUFBeS/XOH36dGJjY4230NDQ0g6/WOq5OxDo2p1ExRrtnatZ9RiEEEJUeZKkm4khSa/nLkm6qEDuhKg93gDRlwq/v6LAntnq4/bPZg1TNySBlzarvZ/Z7fsWUNRhzGP+gjHrsm5Df8w5nDsvTR9Vl+hKilGL1gEc/gX++1h93O9TaHfP/G0H96w586GHcx7z8jbQZ4B7Y/BoXLA4DLQ6ePADaDe2cPvdy8kbarYHFPjvEwjeDVoL02Jx96pWBzxbZBXsy82NY5AQoV5MqXPP8nZabdbFAUOP9Ypn4NpedVm47D+jMeug5ztq2/xGJBhe832geMPSLW3VOf4AOz5T7/0G5F7ET5jV5MmT+fvvv9mxYwe1atXKt62hXk1gYGCebaytrXFycjK5lXfdmtVms76D+kSGvAshhLhLknQzSEnPJOSW2pNXX4a7i4oke3GjohQ6CjkA1w+DzhqyVzP26aT2dqbcgWv7s7bH3VSrskNWT3hR6Sygy9312Pd/rybqm+4usdX9Teg8Off9jEPec5mXXtTlwkqa4fwn/lDvWzyetR54XprkM3oB7lneLpcieNlHP6x9UR0hYWGjFtyr0ca0reEzvHlC7XHPjeHzNdQBKA5DbOlJps9FuaAoCpMnT2bt2rX8999/1K1b9777nDx5EgBvb+9Sjq5s9W3iyV93h7wrZ1fnvEgphBCiSpIk3QwuRcSTqVeobm+Fh2MuazsLUV5l7z0vSk+6YSh261Hg6JW1XatTezvBNGk8ME8dAu3bGXyzVn4ostaj1WJ1sSHw192e5k4vqkuz5cVYPO6eXuD0lKzq5mZP0u9JQru8UoB97sYcuN10+L/B/dZvr9tN7TVPCIezq9Te+8f/gDpdcratVkctPqdPz32aRGZG1kgFQ0JfHI36q3P9ASztoV6P4h9TlJhJkyaxePFili5diqOjI+Hh4YSHh5OcrF7ACQoK4uOPP+bYsWNcvXqV9evXM2bMGLp3707Lli3NHH3JauNbjQs2bYlSnNEk3yr5FROEEEJUSJKkm8H5m2rF2aY1nNBItWFRkWRPzO9cUxPVgrq4SS00ptFC55dzvp69Z1ZR1LnSx35VtxmWLCsuS1vTHvzWo6H/rPyHVxuS9BvHTN9v8G61EJ1jDfBuk/u+ZcW9EVRvqD72G1SwofeezdW54hnJarG57AI2Q8xltdJ9gzyWt7Owzrb0nQaG/QKN8iiAp9HkPyIh8pz6WVo7gUfT+8d+P3auanV+gAYPFm15O1Fq5s+fT2xsLD179sTb29t4W758OQBWVlb8+++/9OvXj8aNG/Paa68xfPhwNmzYYObIS55Oq6FHE29jATlO/WnegIQQQpQLFuYOoCo6Z0jSvcv/fDkhTGQf4q7o1bW4PQuQVAXvhpVj1cftxkH1+jnb1Ouh9nrGXVerk1/epiZuns3V6uwlpcPzau9x9fowaI5p1fLcuNZTe98ToyDsZFaymVflc3Pp+bZaAO/BDwrW3lCw7+AP6oURQ4/51X2w8u7c/Daj869W7z8Joi5C5ynQfFj+5/P1h/Prcp+Xbtjm07Hk5o53f0Mt9tfl1ZI5nigxyn2qmPv4+LBr164yisb8+jb15Jvj3RhvsQkl4B80ybfBtpq5wxJCCGFG5eCbZdVzPiyrJ12ICsXQk25pb/o8P9ePqVW/M1PVpHDAF7m3s7RVez0BzqxUq7CDOhe9JEec2DjDuE3wyPfqPPX7ya0XWJ+Zd+Vzc2nxGLywu3AF7AyxB/yjDjm/eQKWjoSMFGjYHwZ+lf/+tdrDSweg9ZP3P5fhMww9rH5+2Rnno5fAUHeDej1g4j6o1a7kjilEKejW0I0rurpc0PuiyUyDs2vMHZIQQggzkyS9jOn1ChfCpCddVEBJtyApWn1sSKbvVzwu4jwsGa72iNftAcMX5J8YG4a8H/xBrcLuUlutym5u985Lv35U7Vm3dobaXc0XV3H5PAC2rpB8C47/BouHQ1q8+p4e/w10liV3Ls/mYOUAqbEQeSFru6Jkq+xeAkXjhKhg7Kws6NbQnVWZ3dQNhmKZQgghqixJ0svYtVtJJKVlYm2hlTXSRcViSMidamZV786vJ/3WFfhjqDrkuGZ7eGIpWNrkf45G/dSCX4pefd5lSsF6u0ubsSf9oLp+uGGoe6N+uVc+ryh0FlkF+zZOUy+M1GgDT/5Z8vO4dRZQ6+5SU9nnpd+5BvFh6vz3Gm1L9pxCVBAPNfdifWYXMtGqK2DE5L0evBBCiMpPkvQyZiga19jLEQudfPyiAjEk5G4Nwa2R6bZ7ZaSpvbIJ4eDRDEavLNh65rbVoM7dnml7d7WwW3ng1RIs7dQl4qIulp+l10pC9vfg3hhGr85/Hnpx5FYp3/C4Rmuwsiud8wpRzvVp4sFtbTV2Z7ZQN0hvuhBCVGmSJZax82GxgMxHFxWQMUlvlC1Jv6wOV77XjaNqT7ptNXh6rVptu6A6jgc00Ovd8lOVW2epzr8GdVj4rSugsyrZgnbmUq8XVKur/kyfXgv21UvvXNlHJBiUxnx0ISoYFzsr/OtXZ032Ie96vXmDEkIIYTaSpJex81LZXVRUhuHubo3Ata66LnZ6IsTdzNnWkHjV7Q6OnoU7T5PB8H40tB9XvHhLmqEX+MgC9b5eT7B2NFs4JcbKDl4+BhMPgFON0j1XrfbqdIa463AnVN12zZCky3x0UbX1b+bFVn17EjV2EBsCIfvNHZIQQggzkSS9jElld1FhZe9J11mqva/Zt2dX3EJg5WEe+r0M70Wfrt5XhqHuBlpd2XzmVvbg3Up9HHIAEmMgOkB97tOp9M8vRDnWr6knaRor1qff/bcga6YLIUSVJUl6GYpOSCUiLhWNBvy8JEkXFUhGKty+qj42DHXPPuQ9O70eQg6pjyvTEGZDLzAAGmg0wKzhVFjGeekHIPTu74lbI7B3M19MQpQDHk42tPOtljXk/dxfkJZk3qCEEEKYhSTpZciw9Fqd6vY4WJfDnkIh8nIrGJRMsHIERy91m1tD9f7envSoC+oyW5b24NmibOMsTdaO4HX3/fh0LPwwfqHKPi9d5qMLYeKh5l4cVRoRofNSl0O8uNHcIQkhhDADSdLLkMxHFxVW9sruGs3dx3lUeDckXj4dyuew9eIwDHFv9aR546jIDAl55Hm4tOXuNpmPLgSo89IVtCxL7axukCHvQghRJUmSXoZkPrqosLLPRzfIa7h7ceejl2ddp8HE/dBurLkjqbgcPMC1vvrYMB9detKFAMDH1Y7mNZ1YbRjyHvSfupqEEEKIKkWS9DIkPemiwjJWdm+Ytc2tgXoffxNS47O2G5P0Sph46SzAs1nWaAJRNLWzXcBx8MoqQiiEYEBzb0IUT07ZdAAUOPijuUMSQghRxiRJLyMp6ZkERSUA0pMuKqDcetJtq4G9x93X7ybxd0IhNlQtsFazfdnGKCqO7KMsfB+Qix5CZNO/mVr3Y3ZCX3XDicWQfNuMEQkhhChrkqSXkYDwePQKVLe3wsPR2tzhCJFT3E3471NIiDLdriima6Rnd++Qd0O1bu+WYO1QerGKis0kSa+E0yKEKIYGHg408HBgV0YzYp38ID0Rji4yd1hCCCHKkCTpZST7fHSN9BqJ8mj/97D7C/jnDdPt8eFqlWGNDlzvGZZ8b4V3Y7VuSbxEPlzrgbMPoIG63cwdjRDlzkPNvAANa2yGqhsO/wwZaWaNSQghRNmRJL2MyHx0Ue7dClbvz/8FMUFZ2w0JeLU6YHHPKJB7K7xX5vnoouRoNDB6JTy1Wp3jL4Qw8VDzu0Pew5qjd/CE+DA4t8bMUQkhhCgrkqSXEansLsq92OvqvaKHfd9mbc9tPrpB9uHuyXcg4pz6XHrSxf14NIEGD5o7CiHKpWY1nKhd3Y74dC0Xfe8u+bh/rjr9SAghRKUnSXoZ0OsVLoRJT7oo52JDsx6f+hPiwtTH2ddIv5dh260guLYfUNTltRw8SjVUIYSozDQaDY+0qgHAj/HdwdIOIs5A8G4zRyaEEKIsSJJeBq7dSiIpLRNrCy113ezNHY4QOaXGQ8od9bFXS8hMg4M/qM/z60l39gELG7X96eXqNulFF0KIYjMk6f9cSSW1+RPqxgNzzRiREEKIslIukvR58+ZRp04dbGxs6NSpE4cPH86zbc+ePdFoNDlugwYNKsOIC8cwH72xtxMWunLxkQthKvaGem/jDL3fUx8fXagu+5NXZXcArRaq3+1Nv7hRvZf56EIIUWwNPR1p4u1EeqbCVqfhgAYub4WoAHOHJoQQopSZPWNcvnw506ZNY8aMGRw/fpxWrVrRv39/IiMjc22/Zs0awsLCjLezZ8+i0+kYMWJEGUdecOfDYgEZ6i7KMcN8dGcfaNgPPJpBWoI6Nz3ubgKf23D37Nv16eq99KQLIUSJMPSmL7msg8Z3OyOkN10IISo9syfps2fPZvz48YwbN46mTZvy448/Ymdnx8KFC3Nt7+rqipeXl/G2bds27OzsyneSflOKxolyzjAf3bmWWnm761T1+f7v1Xs7N7BzzX3f7D3sdm5QvX7pxSmEEFXI4FbeABwKvkVMywnqxlPLsmqGCCGEqJSKlKTv2LGjRE6elpbGsWPH6NOnT1ZAWi19+vThwIEDBTrGggULeOKJJ7C3z32ud2pqKnFxcSa3shYcnQhAQw+HMj+3EAVi7Emvpd43GwouvqDPUJ/nNtTdIHsPu+8DapIvhBCi2GpVs6Nd7WooCqyN8VFHKmWmSW+6EEJUckVK0h966CHq16/PJ598Qmho6P13yEN0dDSZmZl4enqabPf09CQ8PPy++x8+fJizZ8/y/PPP59lm1qxZODs7G28+Pj5FjrcoFEUhIi4VAG9nmzI9txAFlr0nHUBnAZ2nZL2e11B3ME3gZai7EEKUKMOQ9w2nbkK319SNRxdC0i0zRiWEEKI0FSlJv3HjBpMnT2bVqlXUq1eP/v37s2LFCtLS0ko6vnwtWLCAFi1a0LFjxzzbTJ8+ndjYWOOtOBcViiI+NYPk9EwAPBwlSRflVPY56QZtnlKHr0P+SXr1BsDd3nNJ0oUQokQNbOGNTqvh1PVYrrr4qytwpCfBwfnmDk0IIUQpKVKS7ubmxtSpUzl58iSHDh2iUaNGvPTSS9SoUYMpU6Zw6tSpAh9Hp9MRERFhsj0iIgIvL698901MTGTZsmU899xz+baztrbGycnJ5FaWIuNSAHCyscDWSlem5xaiwO7tSQewtIXB30K9XtDi8bz3tbKDXu9C+2ehRpvSjVMIIaoYd0drOtevDsCG02FZvemHf4KUsp/CJ4QQovQVu3Bc27ZtmT59OpMnTyYhIYGFCxfSrl07unXrxrlz5/Ld18rKinbt2rF9+3bjNr1ez/bt2/H3z79HbuXKlaSmpvLUU08V9y2UKsNQd08n6UUX5ZQ+E+Juqo+d75kO0uRhGLMOHD1z7Gaixxvw8Bx1STYhhBAlyjDk/a9TN1GaDFanGaXEqsPehRBCVDpF/kadnp7OqlWrGDhwILVr12bLli3MnTuXiIgIAgMDqV27doEqrk+bNo1ffvmF3377jQsXLjBx4kQSExMZN24cAGPGjGH69Ok59luwYAGPPvoo1atXL+pbKBMRd3vSJUkX5VZChFogTqMDx/xHsAghhCh7/Zt7YWWhJTAygQvhiVkrcByYB+nJ5g1OCCFEibMoyk4vv/wyf/75J4qi8PTTT/PFF1/QvHlz4+v29vZ89dVX1KhR477HGjlyJFFRUXzwwQeEh4fTunVrNm/ebCwmFxISgvae3rmAgAD27t3L1q1bixJ+mTL0pHs4WZs5EiHyYJiP7lQTtDIlQwghyhsnG0t6+bmz5VwE60/dpGm/EbBjFsSGwInF0HG8uUMUQghRgoqUpJ8/f57vv/+eYcOGYW2de/Lp5uZW4KXaJk+ezOTJk3N9befOnTm2+fn5oShKgeM1J+lJF+VebvPRhRBClCtDWtdUk/STN3izvx/aLlNg0+uw71toNxZ0luYOUQghRAkp0nD37du38+STT+aZoANYWFjQo0ePIgdWWUTG303SHaUnXZRT966RLoQQotzp3dgDJxsLbsamsD8oRl2Bw95DvdB6eoW5wxNCCFGCipSkz5o1i4ULcxYrWbhwIZ9//nmxg6pMpHCcKPckSRdCiHLPxlLHI63VaYQrjoaqK3B0vjsK8eB8qCAjDIUQQtxfkZL0n376icaNG+fY3qxZM3788cdiB1WZGIa7e0iSLsorSdKFEGVo1qxZdOjQAUdHRzw8PHj00UcJCAgwaZOSksKkSZOoXr06Dg4ODB8+PMdyrVXR4+3VFTi2nAsnNjkd2jwNOmuIOANhJ80bnBBCiBJTpCQ9PDwcb2/vHNvd3d0JCwsrdlCVhaIoRBp70mW4uyinjHPSffJvJ4QQJWDXrl1MmjSJgwcPsm3bNtLT0+nXrx+JiYnGNlOnTmXDhg2sXLmSXbt2cfPmTYYNG2bGqMuHFjWd8fN0JDVDz4ZTN8HOVV0qE+D4H+YNTgghRIkpUuE4Hx8f9u3bR926dU2279u3r0AV3auKO0nppGXqAXCXOemiNGx4FQK337+dRgP+k6HThJyvSU+6EKIMbd682eT5r7/+ioeHB8eOHaN79+7ExsayYMECli5dSu/evQFYtGgRTZo04eDBgzzwwAPmCLtc0Gg0jGhfi082XmDl0VCeeqC22pt+djWcWQX9P1WHwQshhKjQipSkjx8/nldffZX09HTjH9Dt27fz5ptv8tprr5VogBVZxN2ica72VlhbyNJWooSlxMKxRQVvv+drdZkejSZrW2oCJN9WH0uSLoQwg9jYWABcXV0BOHbsGOnp6fTp08fYpnHjxvj6+nLgwIE8k/TU1FRSU1ONz+Pi4koxavN5tE1N/vfPRU5djyUgPB6/uj3AxRfuhMD59dBqpLlDFEIIUUxFStLfeOMNYmJieOmll0hLSwPAxsaGt956i+nTp5dogBWZcY106UUXpSEhUr23coRn/sq7nT4TFg2EhHC4fRVcs42Aibuh3ls7g41TqYUqhBC50ev1vPrqq3Tp0oXmzZsD6pQ6KysrXFxcTNp6enoSHh6e57FmzZrFRx99VJrhlgtuDtb0buzB1vMRrDwaynsPN1V703d8Cif+kCRdCCEqgSLNSddoNHz++edERUVx8OBBTp06xa1bt/jggw9KOr4KTdZIF6Uq4W4RJUcvqNku75tPR6jRRm0bctD0GLJGuhDCjCZNmsTZs2dZtmxZsY81ffp0YmNjjbfQ0NASiLB8GnG3gNy6kzdIz9RD61GABq7ugVtXzBucEEKIYitSkm7g4OBAhw4daN68eb5rpldVkcYkXT4bUQoMSbqD5/3b+t4dHhpywHS7zEcXQpjJ5MmT+fvvv9mxYwe1amX9H+Tl5UVaWhp37twxaR8REYGXl1eex7O2tsbJycnkVln19HPHzcGK6IQ0dlyMVP8Pb/Cg+uKJxeYNTgghRLEVOUk/evQob775Jk888QTDhg0zuQmVrJEuSpVhuLuDx/3b+vqr9zl60iVJF0KULUVRmDx5MmvXruW///7LUYS2Xbt2WFpasn17VlHMgIAAQkJC8Pf3L+twyyVLnZZhbdX/t1ccvfv/eJun1fuTSyEzw0yRCSGEKAlFStKXLVtG586duXDhAmvXriU9PZ1z587x33//4ezsXNIxVliyRrooVYXpSffppN5HB0BiTNZ2SdKFEGVs0qRJLF68mKVLl+Lo6Eh4eDjh4eEkJycD4OzszHPPPce0adPYsWMHx44dY9y4cfj7+1fpyu73GtFO/X97R0AkUfGp4DcQ7KpDfBgEFWDVDyGEEOVWkZL0zz77jDlz5rBhwwasrKz49ttvuXjxIo8//ji+vr4lHWOFFRF/tyddCseJ0lCYnnT76uDmpz4OPZS13ZikyxrpQoiyMX/+fGJjY+nZsyfe3t7G2/Lly41t5syZw8MPP8zw4cPp3r07Xl5erFmzxoxRlz8NPR1p5eNCpl5h3YkbYGEFLZ9QXzz+u3mDE0IIUSxFStKDgoIYNGgQAFZWViQmJqLRaJg6dSo///xziQZYkUVK4ThRmgrTkw65z0uXwnFCiEL47bff2Lhxo/H5m2++iYuLC507d+batWsFOoaiKLnexo4da2xjY2PDvHnzuHXrFomJiaxZsybf+ehV1ePt1f+7/zwSgqIo0PbukPdLm7Mu5AohhKhwipSkV6tWjfj4eABq1qzJ2bNnAbhz5w5JSUklF10FptcrRN7tSfeQwnGiNBQ6Sb9nXrpeD7F3l2CTJF0IUQCfffYZtra2ABw4cIB58+bxxRdf4ObmxtSpU80cXdXzSKsa2FnpuBKVyIErMeDRBGq2B32GOjddCCFEhVSkJL179+5s27YNgBEjRvDKK68wfvx4nnzySR588MESDbCiiklMI1OvoNGoa5oKUSgxQXDsV3WN87wUZrg7ZPWk3zwB6cmQGAn6dNBowdG7WOEKIaqG0NBQGjRoAMC6desYPnw4EyZMYNasWezZs8fM0VU9jjaWPNqmJgBLDoaoG9uPU+8P/qD+Xy+EEKLCKVKSPnfuXJ54Qp339O677zJt2jQiIiIYPnw4CxYsKNEAKypD0bjq9tZY6oq10p2oiv55Eza8Ape25P66PhMSo9THBe1Jr1YHHLzUxPzG8az56I41QGdR7JCFEJWfg4MDMTFq8cmtW7fSt29fQB2ebij8JsrWU51qA7DlXLg6za7F42qdkYQIOP6HmaMTQghRFIXOHjMyMvj777/R6XTqAbRa3n77bdavX8/XX39NtWrVSjzIiijKUDROhrqLorhzt0ckJjD315NiQNGrveD2bgU7pkZjOi9d5qMLIQqpb9++PP/88zz//PNcunSJgQMHAnDu3Dnq1Klj3uCqqKY1nGjr60KGXmH5kVC1gFzXV9UX930DGanmDE8IIUQRFDpJt7Cw4MUXXyQlJaU04qk0IqRonCiOxGj13tDbfS/DfHQ7N9DqCn7c7PPSZfk1IUQhzZs3D39/f6Kioli9ejXVq1cH4NixYzz55JNmjq7qeuoBtTf9z8MhZOoVaP2UOo0p7obMTRdCiAqoSGNcO3bsyMmTJ6ldu3ZJx1NpRMRJT7oooswMSL6lPr5fkl7Qoe4Gte8m6aGH1eHvIEm6EKLAXFxcmDt3bo7tH330kRmiEQYDW3jz8d/nuRmbwn8XI+nb1BO6vAKb34a9s6HNU6CzNHeYQgghCqhIk6Vfeuklpk2bxty5czlw4ACnT582uQmIiFd70j0cpSddFFJSTNZjw5D0exW2aJyBRzOwcoTUWAhUiz9Kki6EKKjNmzezd+9e4/N58+bRunVrRo0axe3bt80YWdVmY6ljRHsfABYfvLsUXttnwN5dnT51eoUZoxNCCFFYRUrSn3jiCYKDg5kyZQpdunShdevWtGnTxngvZI10UQyGgnBQ8j3pOgvw6aA+vn1VvXf2KdwxhBBV1htvvEFcXBwAZ86c4bXXXmPgwIEEBwczbdo0M0dXtY3q6AvA7stRhMQkgZUddH5ZfXHP1+ooLSGEEBVCkYa7BwcHl3QclY4MdxdFlj1JT74FaYlgZW/apqg96aDOSw/6L+u59KQLIQooODiYpk2bArB69WoefvhhPvvsM44fP24sIifMo46bPd0aurHncjRLDl9j+oAm0P452PsN3AqCc2ug5ePmDlMIIUQBFKknvXbt2vnehBSOE8WQfbg7QOyNnG2K2pMOWRXeDVykJ10IUTBWVlYkJSUB8O+//9KvXz8AXF1djT3swnwMBeRWHr1OakYmWDuA/yT1xd1fgV5vxuiEEEIUVJF60n///fd8Xx8zZkyRgqksMjL1RCeoPeke0pMuCit7Tzqo89LdG5luK05Pes12oLUAfQZYO4GNc9HiFEJUOV27dmXatGl06dKFw4cPs3z5cgAuXbpErVoyKsfcHmzsgbezDWGxKfxzJpxH29SEjhNg/3cQHQDn10HzYeYOUwghxH0UKUl/5ZVXTJ6np6eTlJSElZUVdnZ2VT5Jj0lMQ6+ATquhur0k6aKQciTpucxLL05PupU9eLeCG8dkqLsQolDmzp3LSy+9xKpVq5g/fz41a9YE4J9//uGhhx4yc3TCQqfliQ6+zPn3Egv3BTOkdQ00Nk7wwEuwcxbs/B80HVK4pTuFEEKUuSIl6blVcL18+TITJ07kjTfeKHZQFZ1hqLu7gzU6rcbM0YgKp7STdFDnpUuSLoQoJF9fX/7+++8c2+fMmWOGaERunnrAlx92BnL6eiwHr9zCv351eGAiHJyv9qafXS1z04UQopwr0pz03DRs2JD//e9/OXrZ72fevHnUqVMHGxsbOnXqxOHDh/Ntf+fOHSZNmoS3tzfW1tY0atSITZs2FSf0EidF40SxJEar985qpd4cSXp6CqTEqo+LMtwd1KV5PJpB69FF218IUWVlZmayevVqPvnkEz755BPWrl1LZmamucMSd1V3sOaxduoF2J93B6kbbZyhyxT18c5ZUuldCCHKuRJL0gEsLCy4efNmgdsvX76cadOmMWPGDI4fP06rVq3o378/kZGRubZPS0ujb9++XL16lVWrVhEQEMAvv/xiHG5XXhh60j2kaJwoCkNPeo3W6v29a6Un3v33obMu+nxy90bw0n5o9mjR9hdCVEmBgYE0adKEMWPGsGbNGtasWcNTTz1Fs2bNCAoKMnd44q7x3eqh0cCOgCguRcSrGzu+AHZucOsKnPrTvAEKIYTIV5GGu69fv97kuaIohIWFMXfuXLp06VLg48yePZvx48czbtw4AH788Uc2btzIwoULefvtt3O0X7hwIbdu3WL//v1YWloCUKdOnaK8hVKVtUa69KSLIjD0pNdoAxfW5+xJNxaN8wSNTKcQQpSdKVOmUL9+fQ4ePIirqysAMTExPPXUU0yZMoWNGzeaOUIB6nJsDzXz4p+z4fy8+wpfjWilVnrvOhW2vgu7voCWI8HCytyhCiGEyEWRkvRHH33U5LlGo8Hd3Z3evXvz9ddfF+gYaWlpHDt2jOnTpxu3abVa+vTpw4EDB3LdZ/369fj7+zNp0iT++usv3N3dGTVqFG+99RY6Xe5FUFJTU0lNTTU+L4slYozD3R2lJ10UQfYkHSDuhrpsjvbuwBfjfPQiDnUXQogi2rVrl0mCDlC9enX+97//FeoivSh9E7rX45+z4fx18gav9/PDy9kGOjwH+7+H2BA48Tt0eN7cYQohhMhFkYa76/V6k1tmZibh4eEsXboUb2/vAh0jOjqazMxMPD1NC195enoSHh6e6z5Xrlxh1apVZGZmsmnTJt5//32+/vprPvnkkzzPM2vWLJydnY03H5/SXxM6Il7WSBdFlJ4MaXeHJnq1BI0WMtNMi8kVt2icEEIUkbW1NfHx8Tm2JyQkYGUlvbLlSRvfanSs40p6psKifcHqRktb6Paa+nj3V+rfHCGEEOVOic5JL216vR4PDw9+/vln2rVrx8iRI3n33Xf58ccf89xn+vTpxMbGGm+hoaF5ti0php50WSNdFJqhF11rCXau4Hj3olf2Ie/FWSNdCCGK4eGHH2bChAkcOnQIRVFQFIWDBw/y4osv8sgjj5g7PHGPCd3rAbD0UAjxKenqxnbPgFMtiA+Do4vMGJ0QQoi8FClJHz58OJ9//nmO7V988QUjRowo0DHc3NzQ6XRERESYbI+IiMDLyyvXfby9vWnUqJHJ0PYmTZoQHh5OWlparvtYW1vj5ORkcittWXPSpSddFJKhx9zeXZ1vblgiLXvxOOlJF0KYyXfffUf9+vXx9/fHxsYGGxsbOnfuTIMGDfjmm2/MHZ64R+/GHtR3tyc+NYM/D4eoGy2socfd5XL3zs5aLUQIIUS5UaQkfffu3QwcODDH9gEDBrB79+4CHcPKyop27dqxfft24za9Xs/27dvx9/fPdZ8uXboQGBiIXq83brt06RLe3t7lZphdWoaemET1goEk6aLQDD3p9m7qvTFJl550IYT5ubi48Ndff3Hp0iVWrVrFqlWruHTpEmvXrsXFxcXc4Yl7aLUaY2/6wr1XScu4+/2p9WioVke9MPzLgxAVYL4ghRBC5FCkwnF5zT2ztLQsVGG2adOm8cwzz9C+fXs6duzIN998Q2JiorHa+5gxY6hZsyazZs0CYOLEicydO5dXXnmFl19+mcuXL/PZZ58xZcqUoryNUhGVoA51t9RpqGZnaeZoRIWTZEjS3dX7XJN06UkXQpSdadOm5fv6jh07jI9nz55d2uGIQnq0TU2+2nqJ8LgU/jp5gxHtfUBnCSN+g2WjIOYy/NIbHp0PTWXKghBClAdFStJbtGjB8uXL+eCDD0y2L1u2jKZNmxb4OCNHjiQqKooPPviA8PBwWrduzebNm43F5EJCQtBqszr7fXx82LJlC1OnTqVly5bUrFmTV155hbfeeqsob6NUGNdId7RBI8tjicLKPtwdwPluoUMZ7i6EMJMTJ04UqJ38zSufrC10PN+1LrP+uch3/11mSOuaWFlooUZrmLALVo2Dq3tgxdPqEm293wdt7ivmCCGEKBtFStLff/99hg0bRlBQEL179wZg+/bt/Pnnn6xcubJQx5o8eTKTJ0/O9bWdO3fm2Obv78/BgwcLHXNZkTXSRbEYk/Q8hrsrigx3F0KUqew95aJietq/Nr/sCSb0VjIrj4UyulNt9QUHd3h6Hfw7Aw7Mhb1zIOw0PLlM1lAXQggzKtKc9MGDB7Nu3ToCAwN56aWXeO2117h+/Tr//vtvjjXUqxpjZXdZI10URZ5z0u/2pKfGQYZ6IUiSdCGEEAVhZ2XB5F71Afh+eyAp6ZlZL+osoP+n8NhCsLSDoO1wepmZIhVCCAHFWIJt0KBB7Nu3j8TERKKjo/nvv//o0aNHScZWIUVIT7oojhzD3e8m6UkxkJaU1Ytu7ayudyuEEEIUwJOdfKnhbEN4XApLDoXkbNB8OPR6V3289xvQZ+ZsI4QQokwUKUk/cuQIhw4dyrH90KFDHD16tNhBVWRZa6RLT7oognuTdBsXsHJQH8fdyDYfXXrRhRBCFJy1hY6XH2wIwPydgSSmZuRs1G4s2FaDW0Fw/q+yDVAIIYRRkZL0SZMmERoammP7jRs3mDRpUrGDqsgi4w2F46QnXRRBYox6bxjufu9a6VI0TgghRBE91q4WtavbEZ2Qxm8HruZsYO0AnV5UH++ZrdZBEUIIUeaKlKSfP3+etm3b5tjepk0bzp8/X+ygKjLDcHcvZ+lJF4WkKDl70sG0eJwUjRNCCFFEljotr/ZRe9N/2nWF2OT0nI06TgBLe4g4A4H/lnGEQgghoIhJurW1NRERETm2h4WFYWFRpILxlYZhuLunDHcXhZUaD5nq7w92blnbTZJ06UkXQghRdI+0qklDDwdik9NZsDc4ZwM7V2g/Tn285+uyDU4IIQRQxCS9X79+TJ8+ndjYWOO2O3fu8M4779C3b98SC66iSUnPNF6V9pTq7qKwDL3olvZgZZe1XXrShRBClBCdVsO0vo0AWLDnCrcS03I28p8MOisIOQDX9pdxhEIIIYqUpH/11VeEhoZSu3ZtevXqRa9evahbty7h4eF8/XXVveoaebcX3dpCi5Nt1R5RIIrg3uXXDJx91HuZky6EEKIE9G/mRfOaTiSmZTJn26WcDZy8ofUo9fGe2WUbnBBCiKIl6TVr1uT06dN88cUXNG3alHbt2vHtt99y5swZfHx8SjrGCiMi3rD8mg0ajcbM0YgKJ8mQpLubbpfh7kKISmb37t0MHjyYGjVqoNFoWLduncnrY8eORaPRmNweeugh8wRbCWm1Gt4b1BSAJYeucSEsLmejLq+ARguB2yDsVBlHKIQQVVuR10m3t7ena9euDB48mO7du+Pi4sI///zD+vXrSzK+CkXWSBfFklvROMiWpN+A+HD1sQx3F0JUYImJibRq1Yp58+bl2eahhx4iLCzMePvzzz/LMMLK74F61RnUwhu9Ah9tOIdybyV313rQbJj6eO+csg9QCCGqsCKNyb5y5QpDhw7lzJkzaDQaFEUx6TnOzMwssQArElkjXRSLMUm/Z7i7Yw1AoxaVM7SRnnQhRAU2YMAABgwYkG8ba2trvLy8yiiiqmn6wMb8eyGCg1du8c/ZcAa28DZt0HUqnF0F59ZB+z1Qt5tZ4hRCiKqmSD3pr7zyCnXr1iUyMhI7OzvOnj3Lrl27aN++PTt37izhECuOSENPuhSNE0WRmMdwdwsrcMz2RVWjzZnICyFEJbNz5048PDzw8/Nj4sSJxMTE5Ns+NTWVuLg4k5vIX61qdrzQoz4An268QEr6PZ0sXs2h7TOAAuteghT5TIUQoiwUKUk/cOAAM2fOxM3NDa1Wi06no2vXrsyaNYspU6aUdIwVhgx3F8WSV086ZA15B3V5Nq2ubGISQggzeOihh/j999/Zvn07n3/+Obt27WLAgAH5jtSbNWsWzs7OxltVrpFTGBN71KeGsw037iTz064rORv0/xRcakNsCGx+u+wDFEKIKqhISXpmZiaOjo4AuLm5cfPmTQBq165NQEBAyUVXwUTGyxrpohjympMOpkm6DHUXQlRyTzzxBI888ggtWrTg0Ucf5e+//+bIkSP5jtYzLA1ruIWGhpZdwBWYrZWO6QObADB/VyA37iSbNrB2hKE/ARo4uQQu/F32QQohRBVTpCS9efPmnDqlVvrs1KkTX3zxBfv27WPmzJnUq1evRAOsSAw96R7Sky6KIvHuUM779aRL0TghRBVTr1493NzcCAwMzLONtbU1Tk5OJjdRMA+39KZjXVdS0vX875+LORvU9ocud0dKbpgCCZFlG6AQQlQxRUrS33vvPfR6PQAzZ84kODiYbt26sWnTJr777rsSDbAiMayTLj3pokjy7UnPNmxTetKFEFXM9evXiYmJwdvb+/6NRaFpNBpmDG6KVgMbTt3kyNVbORv1ehc8m0NSDKyfAvdWgxdCCFFiipSk9+/fn2HD1GU5GjRowMWLF4mOjiYyMpLevXuXaIAVRWJqBvGpGYAk6aII9Pq810kH6UkXQlQqCQkJnDx5kpMnTwIQHBzMyZMnCQkJISEhgTfeeIODBw9y9epVtm/fzpAhQ2jQoAH9+/c3b+CVWLMazozs4AvAzA3n0evvScItrNVh71pLuPQPnPjDDFEKIUTVUOR10u/l6upqsgxbVWOYj25vpcPBukgr24mqLPk2KOroFOyq53xd5qQLISqRo0eP0qZNG9q0aQPAtGnTaNOmDR988AE6nY7Tp0/zyCOP0KhRI5577jnatWvHnj17sLaW6WSlaVrfRjhYW3DmRixrT9zI2cCrOfR+V3288XUI/LdsAxRCiCpCsskSklXZXXrRRREYhrrbVgOdZc7XTYa7S0+6EKJi69mzJ0o+w6W3bNlShtEIA3dHayb3bsD//rnIF1suMqCFF3ZW93xV7DwFbhyDCxvgz1EwajnU72WegIUQopIqsZ70qk6KxoliMSTpdnmsf25bDSzt1MfSky6EEKKUjOtSBx9XWyLiUvkxtyXZtDoYvhD8BkJmKvz5BFzZVfaBCiFEJSZJegmRonGiWPKbjw6g0UDjQeDoDV4tyi4uIYQQVYq1hY53BqhLsv20KyjnkmwAFlYw4ldo2B8yUmDpSLi6t2wDFUKISkyS9BIiw91FsSQakvQ8etIBhv8fTD0Hti5lEpIQQoiq6aHmXnSs60pqhp4vNueyJBuoheRG/gEN+kJGMix5HI79BndCyjZYIYSohCRJLyERdwvHeTjKcHdRBPktv5adVlf6sQghhKjSNBoN7w9qikYDf528yfGQ27k3tLCGkYuhXi9IT1TXUP+mBcxpAWtfhON/QFpS2QYvhBCVgCTpJUR60kWxFDRJF0IIIcpAi1rOPNZWXVkk1yXZDCxt4Mk/ocdbULMdaHQQGwKn/oT1k+H3RyAtsQwjF0KIik+S9BISKUm6KA5jkp7PcHchhBCiDL3R3w97Kx0nQ++wcF9w3g0tbaHXOzD+P3g7BJ5aA91eAxtnuH4EVjwDmellF7gQQlRwkqSXAEVRiDAWjpPh7qIICjInXQghhChDHk42vDNILSL3xZYAAiPj77+TtQM0eBAe/ABGrQQLWwjcButeAr2+lCMWQojKoVwk6fPmzaNOnTrY2NjQqVMnDh8+nGfbX3/9FY1GY3KzsTFv73V8agbJ6ZkAeDhKT7oogsT7VHcXQgghzGBUR1+6N3InLUPPaytOkZFZiETbtxM8/jtoLeDMCtjyDih5DJsXQghhZPYkffny5UybNo0ZM2Zw/PhxWrVqRf/+/YmMjMxzHycnJ8LCwoy3a9eulWHEORmGujvaWGBrJYW9RBHInHQhhBDlkEaj4YvhLXGyseDU9Vjm7wwq3AEa9YMhP6iPD82HPV+XfJBCCFHJmD1Jnz17NuPHj2fcuHE0bdqUH3/8ETs7OxYuXJjnPhqNBi8vL+PN09OzDCPOKULWSBfFkZEGKXfUx5KkCyGEKGe8nG2YOaQ5AN9uv8zZG7GFO0CrkdB/lvr4v4/hzKoSjlAIISoXsybpaWlpHDt2jD59+hi3abVa+vTpw4EDB/LcLyEhgdq1a+Pj48OQIUM4d+5cnm1TU1OJi4szuZW0rMruMh9dFEFSjHqv0YGNi1lDEUIIIXIzpHUNBjT3IkOv8NqKU6RmZBbuAP4vQZdX1MebXoeEqJIPUgghKgmzJunR0dFkZmbm6An39PQkPDw81338/PxYuHAhf/31F4sXL0av19O5c2euX7+ea/tZs2bh7OxsvPn4+JT4+zD2pMt8dFEU2Su7a80+uEUIIYTIQaPR8MmjzXFzsCIgIp452y4X/iC93wfPFpB8Gza/VfJBCiFEJVHhMgJ/f3/GjBlD69at6dGjB2vWrMHd3Z2ffvop1/bTp08nNjbWeAsNDS3xmAw96R4y3F0URdLdonF2UtldCCFE+VXdwZrPhrYA4KfdQRy5eqtwB9BZwiPfgUYLZ1dDwOZSiFIIISo+sybpbm5u6HQ6IiIiTLZHRETg5eVVoGNYWlrSpk0bAgMDc33d2toaJycnk1tJi4yX4e6iGGT5NSGEEBVEv2ZePNauFooC01acJCE1o3AHqNkW/CepjzdOg5SSn4YohBAVnVmTdCsrK9q1a8f27duN2/R6Pdu3b8ff379Ax8jMzOTMmTN4e3uXVpj3JYXjRLFIZXchhBAVyIzBTanpYkvorWQ++ft84Q/Q8x2oVgfibsD2j0o8PiGEqOjMPtx92rRp/PLLL/z2229cuHCBiRMnkpiYyLhx4wAYM2YM06dPN7afOXMmW7du5cqVKxw/fpynnnqKa9eu8fzzz5vrLUjhOFE8kqQLIYSoQBxtLPn68VZoNLDsSCjbzkfcf6fsrOxg8Lfq4yP/B9fyLhYshBBVkdmT9JEjR/LVV1/xwQcf0Lp1a06ePMnmzZuNxeRCQkIICwsztr99+zbjx4+nSZMmDBw4kLi4OPbv30/Tpk3NEr+iKETe7Un3kMJxorASIuH8X+pjx4JN8RBCCCHM7YF61RnfrR4A09ecJiYhtXAHqNcT2jylPl7/MqSnlGyAQghRgWkURVHMHURZiouLw9nZmdjY2BKZn347MY02H28DIOCTh7C20BX7mKKKSL4Dvz4MEWfA2Qee3w6OnvfdTQhR+ZT03yYhn2lZSEnPZMjcfQRExNOvqSc/Pd0OjUZT8AMk34Z5nSAhAvwGwYhfwcKq1OIVQghzKszfJbP3pFd0kfHqleNqdpaSoIuCS0uEpY+rCbq9B4z5SxJ0IYQQFYqNpY45I1tjqdOw9XwEK4/mvhxunmyrwfD/AwsbCNgIq5+FzPTSCVYIISoQSdKLKWs+ugx1FwWUkQrLn4LQQ2DjDE+vher1zR2VEEIIUWhNazgxra8fAO//dZaToXcKd4C63eGJJaCzggsbYPXzkFnIivFCCFHJSJJeTLJGuiiUzAz1C0jQf2BpD6NXg1dzc0clhBBCFNmE7vV4sLEHqRl6xv9+lLDY5MIdoEEfGLkEtJZwfh2snSCJuhCiSpMkvZgMw909HaWyuyiAowvhwnq1x+CJJeDTwdwRCSGEEMWi02r49sk2+Hk6EhWfyvjfj5KUVsgku1E/GPmHmqifXQ3rXoTU+NIJWAghyjlJ0otJhruLQjm3Rr3v/T7U72XeWIQQQogS4mBtwf89057q9lacvRHH6ytPodcXsjax3wAYsQi0FnBmJXzbCvZ/D+mF7JkXQogKTpL0YpI10kWBJURByEH1cfNh5o1FCCGEKGE+rnb8+HQ7LHUaNp0J55t/LxX+IE0Gw6gVUL0hJMXA1vfguzbqSDQpKieEqCIkSS+mCMMa6dKTLu7n0j+AAt6twbmWuaMRQgghSlyHOq58NrQFAN/9F8i6EzcKf5AGD8JLB2HIPHWJ0vgw+Hsq/NQDUuJKOGIhhCh/JEkvpkgZ7i4K6uJG9b7xw+aNQwghhChFI9r78EL3egC8vvIUOy5GFv4gOgto8xS8fAwGfAG2rhB5DrZ9UMLRCiFE+SNJejHo9UpW4TgZ7i7yk5oAQTvUx40HmTcWIYQQopS99VBjhrSuQYZeYeKSYxy5eqtoB7Kwhk4vwOO/q8+PLYIru0ouUCGEKIckSS+GW0lpZOgVNBpwc5AkXeQjaDtkpkK1uuDRxNzRCCGEEKVKq9Xw1YhW9G7sQUq6nmd/PcK5m7FFP2DdbtD+OfXx+snqxW8hhKikJEkvBkPRuOr21ljq5KMU+TAOdR8EGo15YxFCCCHKgKVOyw+j29KxjivxKRk8s/Aw/9/efYdHVaZ9HP9OJjOZSe+VhITepAiCAVx1hXXRxbarqKggvriusoqIfVWs7K5lsa671sWGZdFVsSEiCtIFpIYWSIAUEkjvmfP+cchgJGggZWbI73NdzzXJmTPP3CeH8OSep2UWlB9/haPvh7AUKMqCBfe3XqAiIl5GmWUL5JdoqLs0Q30tbP3M/Frz0UVEpANx2Ky8OHEIfRJCKSir4YoXl5NTfJxbqgWEwHlPml+v+DfsWtJ6gYqIeBEl6S2gPdKlWXYvgapiCIyG5KGejkZERKRdhTpszL5mKF2ig9hbVMmVL63gQHnN8VXW9ddw8lXm1/+7AWoqWi9QEREvoSS9BfLUky7N4R7qfg74WT0bi4iIiAdEBwcw+5qhJIQ52J5fxsRXVlBWXXd8lf3mIQhNgoOZ8NWDrRuoiIgXUJLeAnmlZk96bIh60uUoDENbr4mIiACdIgJ57ZphRAbZ+WFPMZP/s4qq2vpjr8gRBmMPDXtf9hxsfL91AxUR8TAl6S1w+2978dnU07h0aLKnQxFvlbMWSvaCLQjSTvd0NCIiXuGbb75h7NixJCYmYrFY+OCDDxo9bxgG9957LwkJCTidTkaNGsW2bds8E6y0qm6xwfzn6qEEB/izdGchU95cQ12969gr6j4a0qeYX7//J9i3tlXjFBHxJCXpLRDmtNErPpSEMKenQxFv1dCL3n0U2DTiQkQEoLy8nAEDBvDss882+fzf//53nnrqKZ5//nmWL19OUFAQZ599NlVVVe0cqbSFkzqF8cJVQ7D7+/Hl5jxue+8HXC7j2Csa/QB0GwV1lTDncijNa/1gRUQ8QEm6SFvSUHcRkSOMGTOGhx56iAsvvPCI5wzDYNasWfzlL3/h/PPPp3///syePZt9+/Yd0eMuviu9axTPXn4yVj8Lc9fsZfp766g91h51Pyv8/iWI6m6OWnt7PNTqgxwR8X1K0kXaSuEOyN8Efv7msDwREflFmZmZ5ObmMmrUKPexsLAwhg0bxtKlS4/6uurqakpKShoV8W6j+8TxxCUDzET9+7386fXVxz5H3RkOl79tzlPfsxI+nmquByMi4sOUpIu0lcxvzMeUdHBGeDYWEREfkZubC0BcXFyj43Fxce7nmjJz5kzCwsLcJTlZ68X4gvMHJvGvKwYT4O/Hl5vzuerlFZRU1R5bJVFd4eJXwWKFdW/BvGlQsL1N4hURaQ9K0kXaSu568zHpZM/GISLSAdx5550UFxe7S3Z2tqdDkmYa1SeO2ZOGEhLgz4rMA1z6r2XsL60+tkq6/hrOfsT8etXL8MxgeHE0rHoFKotaPWYRkbakJF2krTQk6fH9PRuHiIgPiY+PByAvr/EiYHl5ee7nmhIQEEBoaGijIr5jWJco3rr2VKKD7WzKKeHi578jq7DiGCv5I1z6FnT/DVj8YM8Kc/j7Yz3gi79AzTHWJyLiIUrSRdqCqx7yNppfx5/k2VhERHxIWloa8fHxLFiwwH2spKSE5cuXk56e7sHIpK31Swrj3euG0ynCya7CCi7653ds2Fvc/AosFuh1Dox/F6ZthtEPQkxvqK+G756G50fArsVtdwEiIq1ESbpIWziQCbXl4O+EqG6ejkZExKuUlZWxdu1a1q5dC5iLxa1du5asrCwsFgtTp07loYce4sMPP2T9+vVcddVVJCYmcsEFF3g0bml7adFBzP3TcPokhFJQVs24fy1l0db9x15RSDyMuBGuXwqXvwOhSXBgJ7x6Lnw8Daq0sKCIeC8l6SJtIfcH8zGuj7lFjIiIuK1atYpBgwYxaNAgAKZNm8agQYO49957Abjtttv485//zLXXXsspp5xCWVkZn332GQ6Hw5NhSzuJDXXw9h9PZWS3aMpr6rnm1ZW8t3rP8VVmsUCPs+H6ZTD4avPYqpfguXRY/A/Yv7X1AhcRaSUWw+hY+1SUlJQQFhZGcXGx5qtJ2/nyflj8BAyeCGOf9HQ0IuLl1Da1Pv1MfV9NnYvb3lvHB2v3AXDL6B5M+XU3LBbL8Vea+Q18eCMczDx8LKo79DoXep8HnQa3MGoRkaYdS7uknnSRtuBeNE7z0UVERI6H3d+PJy4ZyHWndwXg8flbmTx7NcUVx7hF24+l/Qr+9B387h/QbRT42aBwGyyZBS/+Gv77f1B1DPPgRUTagFck6c8++yypqak4HA6GDRvGihUrmvW6OXPmYLFYNEdNvI9WdhcREWkxPz8Ld4zpxcyLTsLu78eXm/M456lvWZtddPyV2gNhyCS44r9w2074w8vQ9yJzn/X178I/R8Lupa12DSIix8rjSfrbb7/NtGnTuO+++/j+++8ZMGAAZ599Nvn5+T/7ul27djF9+nROO+20dopUpJnK8qEsF7BAbB9PRyMiIuLzLhuawtw/DadzVCB7iyq5+PnveGlxJi2etekIhX6/h4tfgUmfQXhnKM6CV8+Brx6G+rrWuQARkWPg8ST9iSeeYPLkyVx99dX06dOH559/nsDAQF5++eWjvqa+vp7x48dz//3306VLl3aMVqQZGnrRo7pCQLBnYxERETlB9EsK46M/j+Sck+KprTd48ONN/PG11RRV1LTOGyQPhesWw4DLwHDBN3+HV8ZAeWHr1C8i0kweTdJrampYvXo1o0aNch/z8/Nj1KhRLF169GFGDzzwALGxsVxzzTW/+B7V1dWUlJQ0KiJtSvPRRURE2kSow8azl5/M/ef1xW7144tNeYx58luW7WylRNoRChc+D79/CQLCYM8KeOtSqKlonfpFRJrBo0l6QUEB9fX1xMXFNToeFxdHbm5uk69ZvHgxL730Ei+88EKz3mPmzJmEhYW5S3JycovjFvlZStJFRETajMViYcLwVOZeP5wu0UHkFFdx2QvLePyLDOrqXa3zJif9Aa75AhyHEvW5k8FV3zp1i4j8Ao8Pdz8WpaWlXHnllbzwwgtER0c36zV33nknxcXF7pKdnd3GUUqHp0XjRERE2lzD8PeLB3fCMODpr7Zzyb+Wkn2glXq9Y3vBZXPAGgBbPoZPb4OOtXOxiHiIvyffPDo6GqvVSl5eXqPjeXl5xMfHH3H+jh072LVrF2PHjnUfc7nMT0z9/f3JyMiga9eujV4TEBBAQEBAG0SPOfSpcDsERkJYp7Z5D/EtNRXmVi6gnnQREZE2FhTgz6MXD+BXPWK4a+56vs8q4rezvuH2Mb24Ylhn/PxasKc6QOfhcNG/4d2JsPJF8++9kTe3SuwiIkfj0Z50u93O4MGDWbBggfuYy+ViwYIFpKenH3F+r169WL9+PWvXrnWX8847jzPPPJO1a9e2/1D2ebfAv06DtW+17/uK98rfbC42ExQDwXG/fL6IiIi02NgBiXxy02kMTY2kvKaee/+3kUv/vYzMgvKWV973AvjtTPPrL2fAD++0vE4RkZ/h0Z50gGnTpjFhwgSGDBnC0KFDmTVrFuXl5Vx99dUAXHXVVSQlJTFz5kwcDgf9+vVr9Prw8HCAI463i+ju5mPB1vZ/b/FOuT+Yj/EngaWFn96LiIhIsyVHBjLn2lN5fflu/vrpFlbsOsBvZ33D9N/0ZNLINKwt6VU/9U9QvAeWPgPvXwfF2TDiZvDzqZmjIuIjPJ6kjxs3jv3793PvvfeSm5vLwIED+eyzz9yLyWVlZeHnrf8BRvcwH5WkSwMtGiciIuIxfn4WrkpP5cyesdw5dz2Ltxfw8CebeX/NXmac15ehaZHHX/noB6GqGNa8BgsegF2L4cJ/Q3BM612AiAhgMYyOtQJGSUkJYWFhFBcXExoa2rLKCrbBM0PAFgR37VXPqcCLo81VYH//krkyrIhIM7Rq2ySAfqYChmHwzqpsHp63mZKqOsAcFn/nmF4khjuPt1JY8zp8civUVUJwPPz+RUg7rRUjF5ET0bG0S17aRe0jIlLBzx9qy6Fkn6ejEU9z1UPeRvNr9aSLiIh4lMViYdwpKSycfgaXDU3BYoGP1u3j149/zVMLtlFZcxxbqlkscPKVcO1CiOkFZbkw+zz47C7Yr5GVItI6lKS3hNUGkV3MrzXkXQ5kmh/Y+DshqpunoxEREREgKjiAmRedxEdTRnJKagRVtS6emL+V0/6+kJcWZ1JVexzJemxvmPwVDLrCXDB22bPw7Cnwr1/Bd0+r80ZEWkRJektpXro0aFg0Lq4v+Fk9G4uIiIg00i8pjHf+mM5Tlw2iU4STgrJqHvx4E7/6+0JeWXIcybo9CM5/1txLvcdvzdGVOevgi7/AE33gjYth93dtczEickJTkt5SWuFdGmjROBEREa9msVg4b0AiX91yBjMvOomkcCf5pdXc/9EmTn90Ie+szKbedYzLNfUcA5e/DbdshXOfgJR0wIBtX8ArY+ClsyHjM3M+u4hIMyhJbyn1pEsDJekiIiI+we7vx2VDzfnqD1/Yj8QwB3kl1dz23x8496lvWbK94NgrDYqCU66BSZ/Bn7+HwVeD1Q7Zy+CtcfDPEbDz61a/FhE58ShJbyl3kr7Ns3GI57mT9P6ejUNERESaxe7vx/hhnVl46xn85dzehDr82ZJbyvgXl3PNqyvZnl92fBVHdYWxs2Dqehh+I9iDIX8jvHYRrHq5Va9BRE48StJbqmGBsNIcqCrxbCziOWX55gqvWCCuj6ejERERkWMQ4G/l/07rwqJbz2Ti8FT8/Sws2JLPb/6xiBve/J4f9hQdX8Uh8fCbB+HmDdD/UjDq4eOb4Yt7wOVq1WsQkROHkvSWcoZDcJz5daF60zusvd+bj9HdzYVkRERExOdEBNmZcV5fvrj5V4zuE4fLgHk/5HDeM0u49N9LWbglH+N45pY7I+DC5+GMu8zvv3sK3p0AtZWtewEickLw93QAJ4ToHlCWZw55Txrs6WjEE7KWmo/Jwzwbh4iIiLRYl5hgXrhqCJtzSnjhm518uG4fy3YeYNnOA3SJCWJs/0TO7Z9Aj7iQ5ldqscAZt0NEKnw4BTZ/CK/ug2F/hMBICIyCwGgIigGbo82uTUS8n5L01hDdHXZ9q8XjOrKsZeZjSrpn45ATSn19PbW1tZ4OQ1qBzWbDatXWjCK+pndCKE+MG8j0s3vyypJM3lyexc795Ty5YBtPLthGt9hgzjkpgfMHJtI1Jrh5lQ4YB2Gd4O3xsHcVzF3V+Hk/fzh5Avz6L2byLiIdjpL01qAV3ju22irYd2i4e8qpno1FTgiGYZCbm0tRUZGnQ5FWFB4eTnx8PBaLxdOhiMgxSgx3cve5fbjxrO7M35THvB9y+HZbAdvzy3hqwTaeWrCN07pHc/WIVM7oEYuf3y/8nqeOgGu+hMX/gOJsqCg0S3kBuGph1Uuw4b9w5l0wZBJYbe1zoSLiFZSktwb3Xumak94h7VsD9TUQFAuRXTwdjZwAGhL02NhYAgMDldT5OMMwqKioID8/H4CEhAQPRyQixyvEYeOikztx0cmdKKmq5ctNeXz8Qw4LM/L5dlsB324rIDUqkKvSU/nDkE6EOn4muY7uBhc82/iYYcCuxfDZHZC3AT69zVwN/uxHoOuvzSHzInLCU5LeGhp60gt3QH0dWPVj7VAa5qOnnKrGU1qsvr7enaBHRUV5OhxpJU6nE4D8/HxiY2M19F3kBBD6o4Q9+0AFs5fuYs7KbHYVVvDAx5v462dbGN07jvMHJnJGz1js/s1Yr9ligbTT4I/fwPf/gQUPwv4t8PpFEH8SnPJ/cNLFWqRW5ASn1d1bQ2gn8Heaw5OKdns6Gmlvmo8urahhDnpgYKCHI5HW1nBPtc6AyIknOTKQu8/tw7I7z+KhC/rRIy6YmjoX89bncO1rqxn6yJfc9f56Vu460LzV4f2s5jD3G7+HU68HfwfkroePboLHe8Nnd0L+FrPnXUROOOrybQ1+fuaQpdz15rz0qK6ejkjai8sF2Q1JuuajS+vREPcTj+6pyIkvKMCfK07tzPhhKWzcV8IHa/by4bp95JdW8+byLN5cnkVypJMLByZx4cmdSIv+hR5xZwT8dib86lZY+wasfBEO7oJlz5klON6c3955BKSONEd36v8aEZ+nJL21RPc8nKT3HOPpaKS97N8CVcVgC4L4/p6ORkRERLyAxWKhX1IY/ZLCuPOc3izdUcjcNXv4fEMu2Qcqeeqr7Tz11XYGJodzdt94zuwVQ8+4kKN/mBcYCcP/DKfeADsWmMn6jq+gLNdcYG7Df83z/PzNofD2YLAFml8nD4PTboGQuPb7AYhIiyhJby1a4b1japiP3mmI1iIQaUWpqalMnTqVqVOnejoUEZEWsfpZGNk9mpHdo6m4oI75m/J4f81evt1WwNrsItZmF/G3z7aQEObgjJ6xnNkzhpHdowm0N/F3hZ8fdB9tltpK2LMKdi8xF5vbsxLqqszOg6riw6/JWQtrXoP0KWai7whtt2sXkeOjrKK1aIX3jqkhSe883LNxiHiBM844g4EDBzJr1qwW17Vy5UqCgrQwkoicWALt/pw/MInzByaxv7SaTzfk8HXGfr7bUUBOcRVvrcjirRVZOGx+/Kp7DGf3jees3rGEB9qPrMzmNBeZSzvN/L6uBioKoKYcasqgpgLK8+G7Z8z92L/5u7lS/Om3wYDLlKyLeDEl6a2loSd9f4a5iIfmA3UMWZqPLtJchmFQX1+Pv/8vNz0xMTHtEJGIiOfEhARwVXoqV6WnUlVbz7KdhXydsZ8FW/LIPlDJF5vy+GJTHlY/C8PSIjmzZyyn94yhe2xw08Pi/e0Qmnjk8T4XwOaPYMH9ULjd3Nbt09sgKMbcOjayi7meUu/zIKZnm1+3iPwyre7eWqK6AhaoKoKKQk9HI+2hKBuKs8FihaQhno5GTmCGYVBRU+eR0qxViIGJEyeyaNEinnzySSwWCxaLhVdffRWLxcKnn37K4MGDCQgIYPHixezYsYPzzz+fuLg4goODOeWUU/jyyy8b1ZeamtqoR95isfDiiy9y4YUXEhgYSPfu3fnwww9b88csIuIxDpuVM3rGMuO8vnxz65l8cuNp3HhWd3rFh1DvMvhuRyEPf7KZ3/zjG0b89SvunPsDH63bx479ZdS7fuH/aYsF+pwH1y+D3/0DwlLM4+X7IXs5rHsLvnoInh0Kb112uANCRDxGPemtxeaE8BRzC7aCrRAU7emIpK1lLzcfE/pDQLBnY5ETWmVtPX3u/dwj773pgbObnhf5E08++SRbt26lX79+PPDAAwBs3LgRgDvuuIPHHnuMLl26EBERQXZ2Nueccw4PP/wwAQEBzJ49m7Fjx5KRkUFKSspR3+P+++/n73//O48++ihPP/0048ePZ/fu3URGRrbOxYrXmDFjBvfff3+jYz179mTLli0eikik/VgsFvokhtInMZRpo3uwq6Ccr7bk8/XW/SzbWci+4ireWpHNWyuyAXDY/OgZF0Kv+FAGJIdzVu9Y4kIdR1ZstZnbug2ZZM5ZP5AJB3bAgZ3m3Patn0PGJ2ZJPhXSb4CIzuZidA3FHgTBcRoxKtLGlKS3pugeh5N0zVE+8TXMR9f+6CKEhYVht9sJDAwkPj4ewJ1QPfDAA4wePdp9bmRkJAMGDHB//+CDD/L+++/z4YcfMmXKlKO+x8SJE7nssssAeOSRR3jqqadYsWIFv/3tb9viksTD+vbt22iERXOmSYiciFKjg5g0Mo1JI9OorKlneaY5LH5N1kEy8kqpqnWxbk8x6/YU8/aqbHgf+ncKY1TvOEb1jqN3QhOrxjvCIHGgWRoUbIPvnoJ1c8ztZbOP0qMeGAWJgw6XpCFaOV6klanFa03RPWD7fC0e11FoPrq0E6fNyqYHzvbYe7fUkCGNp4OUlZUxY8YM5s2bR05ODnV1dVRWVpKVlfWz9fTvf3ibw6CgIEJDQ8nPz29xfOKd/P393R/4iIjJaTeHxZ/RMxaAepfB7sJytuSWsjmnhMXbzRXjf9hTzA97inli/laig+0M6BTOgORwBiaHM6BTOGGBtiMrj+4O5z0NZ9wFy5+HTf8zV4t31R0q9eaidBWFsP1LswBgMRevG3A59B6r0YUirUBJemtyr/CubdhOeJVFkGcO5SVZSbq0LYvF0qwh597qp6u0T58+nfnz5/PYY4/RrVs3nE4nf/jDH6ipqfnZemy2xn9UWiwWXC5Xq8cr3mHbtm0kJibicDhIT09n5syZPzsdorq6murqavf3JSUl7RGmiEdZ/Sx0iQmmS0ww55yUwC2/6Ul+aRULt+Qzf1M+i7fvp6CshgVb8lmw5fCHmp2jAumbGErfxDD3Y0xIgPlkaAKMvt8sP1VbBfkbYd8as+xdY36f+Y1Z5t1izn/veQ6EJkFIPATHmkPtRaTZfPevPm+kvdI7jj0rAcNcEVVDvEQAsNvt1NfX/+J5S5YsYeLEiVx44YWA2bO+a9euNo5OfMmwYcN49dVX6dmzJzk5Odx///2cdtppbNiwgZCQkCZfM3PmzCPmsYt0RLEhDsadksK4U1Koqq1nU04Ja7OKWLfH3JN9d2GFu3yyPtf9utSoQNK7RpPeNYr0LlGHk/YfszkgabBZGhzcDT+8bS5Ad2Cn+bjurR+9yGIOkY/qBkknm69NHGT+DaW57SJNUpLemhqS9IO7zU8abU0s2iEnBs1HFzlCamoqy5cvZ9euXQQHBx+1l7t79+7MnTuXsWPHYrFYuOeee9QjLo2MGTPG/XX//v0ZNmwYnTt35p133uGaa65p8jV33nkn06ZNc39fUlJCcnJym8cq4s0cNisnp0RwckqE+9jB8ho27ith475i9+POgnJ2FVawq9Dcpx2gW2wwfRND6REXcqgEkxwRiJ/fTxLriM7m3uu/uhWyV5gJeu4PUJoLZXnmUPmKArP8eJ67I9xcfDe2z49KLwho+oM4kY7EK5L0Z599lkcffZTc3FwGDBjA008/zdChQ5s8d+7cuTzyyCNs376d2tpaunfvzi233MKVV17ZzlE3ISja/A+nqshcLTOur6cjkrai+egiR5g+fToTJkygT58+VFZW8sorrzR53hNPPMGkSZMYPnw40dHR3H777RqaLD8rPDycHj16sH379qOeExAQQEBAEz1/ItJIRJCdkd2jGdn98E5EJVW1rMw8wNIdhXy3o5BNOSVszy9je35Zo9c6bVb6JoZyUqcw+ncK46SkcLpEB5mJu8UCKcPM0sDlMuewl+6D/M2w93vYuxpy15t/LzcMk/+xqG6HetsP9brHn6SOL+lwLEZzN8FtI2+//TZXXXUVzz//PMOGDWPWrFm8++67ZGRkEBsbe8T5X3/9NQcPHqRXr17Y7XY+/vhjbrnlFubNm8fZZ//ywkolJSWEhYVRXFxMaGho61/Qi6Nhzwr4wyvQ76LWr188r64a/ppiLqYyZdXhtQhEWkFVVRWZmZmkpaXhcOiPkhPJz93bNm+bfFxZWRkpKSnMmDGDG2+8sVmv0c9U5PgdLK9h9W5z9fhteaVk5JWxY38ZNXVHjnpy2qx0jgokNSqIztGBpEUFkRYdRPe4ECKD7E2/QV0N5G8y1/dxP26Gstwjz7VYzY6woBhz2HxQjDnXPa4vJAw0R7JavaLfUeRnHUu75PF/0U888QSTJ0/m6quvBuD5559n3rx5vPzyy9xxxx1HnH/GGWc0+v6mm27iP//5D4sXL25Wkt7m4vuZSfre1UrST1SZ35gJekiC+WmviIi0qunTpzN27Fg6d+7Mvn37uO+++7Bare4t+ESkbUUE2RnVJ45RfQ6vu1NX7yKzoJz1e82V49fvLWbjvmIqa+vZklvKltzSI+qJDrbTLTaYHnEh9EkIZVBKBN1ig7H624/cAg6gvODQgnSHetz3rjaHyZflmaUp/k6ztz2+H4Qkmgl8SLy5n3twrDnKVT3x4mM8mqTX1NSwevVq7rzzTvcxPz8/Ro0axdKlS3/x9YZh8NVXX5GRkcHf/va3Js9p99VeU9Jh1cuHh0PLiWfLx+Zjz3O04ImISBvYs2cPl112GYWFhcTExDBy5EiWLVtGTEyMp0MT6bD8rX50jwuhe1wIF53cCTAT96wD5iJ0uwrL2V1YQWZBOTv2l7HnYCUFZTUUlB1g2c4D7nqCA/wZkBzGoOQIesaHkBoVREpUIGFOm9lj3n20WQAMw5zbXp4P5fuhvNB8LN4DOevMue81ZWYH2Z4VRw/eGgDOcDNhj+4OvX4HPceYx0S8kEeT9IKCAurr64mLa7w6dlxcHFu2bDnq64qLi0lKSqK6uhqr1cpzzz3H6NGjmzy33Vd7bZijnLMWairAHth+7y1tz+WCLZ+YX/c617OxiIicoObMmePpEESkGfytfu4t4H6qvLqOHfvL2JpXxra8Un7YU8y6PUWUVdexZHshS7YXNjo/PNBG56ggukYH0SUmiK4xwXSNDaZzVCwBoQlNB+ByQeF28+/u/VugNM8cMt/wWF4AGFBffbg3viDD7HDxs0GX06HP+RCRZu4BX1t+6LESorqanW/2oKbfW6QNeXy4+/EICQlh7dq1lJWVsWDBAqZNm0aXLl2OGAoPHljtNSzZ3BeyZK85RCfttLZ7L2l/e1eZn+YGhEKq7q2IiIhIU4IC/OnfKZz+ncLdx+pdBlvzSlmTVcTa7IPs3F/O7gMV7C+tpqiilqKKItZlFzWqx88CSRFOUg/NdU+NCiItJohuMcEkhTvxi+kBMT2aDsLlgppSqCyCqmKoPAC7v4NNH8L+zbD9S7McjZ8/JA2BtF9B6ghzTryf/6FiNYfah8RrZKW0Oo8m6dHR0VitVvLyGs8xycvLIz4+/qiv8/Pzo1s3cy7wwIED2bx5MzNnzmwySW/31V4tFrM3fcN/zSHvStJPLA1D3bv/BvyPshiKiIiIiBzB6mehd0IovRNCuXxYivt4eXXdoWHz5ezYbw6X37G/nJ35ZZRW15F9oJLsA5V8u62gUX1Om5WusWbC3i02mJSoIDpHBtI5KpDwQDv4+YEjzCwNupwBZ94F+7fC5v9BxqdQXWaOfrUHmz3nVhvsWwvF2ea2cdnL4CeL0LsFx0Pn4YdLTG/zfUVawKNJut1uZ/DgwSxYsIALLrgAAJfLxYIFC5gyZUqz63G5XI3mnXtcSvqhJP2X59WLDzEM2HwoSddQdxEREZFWERTg707ef8wwDPaXVpNZUM6uwnIyCyrYVVDOzoIyMgvKqaytZ8PeEjbsPXLNqTCnjeRIJ/GhThLDHcSHOUgIc9ApIpCuMcFExvSAmFvN/d2bYhhwcBfs+tZcNHjPSnMYvKvuUKk3vy/LhY1zzQLg7wD/ALO33WI1HwOjoMdvzLnwiYPU8y6/yOPD3adNm8aECRMYMmQIQ4cOZdasWZSXl7tXe7/qqqtISkpi5syZgDnHfMiQIXTt2pXq6mo++eQTXnvtNf75z3968jIaa5iXnr3C/AX2s3o2HmkdBVvhwA6w2qHbKE9HIyIiInJCs1gsxIY6iA11MKxLVKPnGhat255fxrb8MnbuLyfrgLl4XX5pNcWVtRTvrW0ygQeICLSZ895jgukcHUhSuJPEcCdJ4U5iQwLwt/pBZJpZTr6q6QBrK83prbu/g91LIHulOa+9rqrxeaX7IG89fPu4OS2217lmsm64Gif9VjsEhJjTKh2h5teGYW7/W19t1uuqNxe/C++sZP8E5vEkfdy4cezfv597772X3NxcBg4cyGeffeZeTC4rKwu/Hw0ZKS8v5/rrr2fPnj04nU569erF66+/zrhx4zx1CUeK7WP+clWXmPs+JvT3dETSGhqGuqedbv7HKSIiIiIe8eNF637Tt/FzlTX1ZB2oYM/BCnKKq8gtriKnuIqc4kp2F1awt6iSgxW1rNp9kFW7Dx5Rt9XPQmxIAPFhDuJDHcSFmj3xoQ4bQQFWAu3+BAVYCXXY6Bw/jJDUkeYL62vNIfKu+kOlDox62H9osbptX5rrVq34d8t/AMHxZsdgSjqkDDPzD/92nOIrbcpiGIbh6SDa07FsIt8ir//eXIhizKMw7Nq2ex9pPy/82vy09HezYMjVno5GTlBVVVVkZmaSlpaGw6F9XU8kP3dv261t6kD0MxWRo6moqTu0VVw5O/LLyD5Ywb6iSvYWVZJTVEWd69jSo/hQB91izXnxXWKCSAp3khBmDrMPc9qwNPR411bBzq8hYx4UZZtz3xsWobNYzSS/uuRQKTWLxc/cQs7fbg6lNwxzhXpXXeMg/PwhugfE9TP3jI/pDcEx5lB7Z6Q511497x51LO2Sx3vST1gpp5pJetZSJekngpJ9ZoKOxdwfXURaXWpqKlOnTmXq1KmAOczx/fffd69Z8lO7du0iLS2NNWvWMHDgwON+39aqR0REfEOg3Z++iWH0TQw74rl6lzkPPrekitziSnKLq8gtqSa/pIrS6joqauooq66norqOgxU1FJTVmOeWVLF4e8ER9Tlt1kOr0weSGhVE5+jepPYeTEKYk1CnP6EOGw7bMU6NramAfd+bi1RnLTPny1cVQf4ms6x/58jXWAMgONbcWi6q26HSHcKSwOYEW+Dhx2OZqmsYUHkQyvKhohACgs0PBgKjzPrkuChJbysp6eZj1lLzH68+ufJtW+aZj51OgZA4z8Yi0kHk5OQQERHRqnVOnDiRoqIiPvjgA/ex5ORkcnJyiI6ObtX3EhER32P1s5jD3MMckBz+i+cXV9ayPb+MHfllbN9vzo3PKa4kp7iKA+U1VNbWsz2/jO35ZUetw+7vR6jDRmxIAJ2jAkmJDCQ50nzsHBVIYrgTm/VHK8bbAyF1pFnAzDVK9kLuBnPue95GKNhuJs0VheZ89vpqcyh+cbbZm/9zbEEQdCjRDow2HwFqKw6VSqgpg/JCc+95V+1R6gk0t6jrPRYGXWnOpZdmUZLeVhJPBj8blOZAURZEdPZ0RNISDUm6VnUXaTc/txVna7Jare32XiIicmIJc9oY3DmCwZ2P/FC5qraenOIqsg9UsPtABbvdq9SXs7+0mtLqOgwDaupcFJRVU1BWzaacIxe687NAYriTlEOJe0KYk7jQAOJ+NGc+IjQJS1gn6Pnbxi82DDOxriiEkhwo3N64lOaaC9LVVhx+TW05FJWbOUxzOSPMYfU15VBRYA7Hr62AAzthyZNmSUk3k/W+F5jD7+WolKS3FXsgJA40h59kLVOS7ssqi8ztN8DcOkOkvTU0sJ5gC2zWSKB///vfzJgxgz179jRa7PP8888nKiqKu+++m2nTprFs2TLKy8vp3bs3M2fOZNSoo++U8NPh7itWrOCPf/wjmzdvpl+/ftx9992Nzq+vr+faa6/lq6++Ijc3l5SUFK6//npuuukmAGbMmMF//vMfd90ACxcuJDU19Yjh7osWLeLWW29l3bp1REZGMmHCBB566CH8/c1m84wzzqB///44HA5efPFF7HY71113HTNmzGjWj1VERE58DpuVtOgg0qKbTkhdLoOymjpKKmsprqwlr6SK3YUVZB2oMBP7wgqyD1ZQVetiz8FK9hys5LsdhU3WZbNaiAkOICbUYS56F+ogOdJJSmSQmdxHJRIcnmIuMtcUwzCT9ZoKc+h8xQEz2a4ohPIC828BW6BZ7IceA6PNEaZBMY0XrTMMc159RaHZu7/2Ddj2hTnCOGspfHQjhCQcKvEQmmjuZV9fC/U1hx8doRDT61Dp2aESeyXpbSnl1ENJ+lIY4EWrz8ux2Tbf/DQwphdEd/N0NNIR1VbAI4meee+79jWrUbz44ov585//zMKFCznrrLMAOHDgAJ999hmffPIJZWVlnHPOOTz88MMEBAQwe/Zsxo4dS0ZGBikpKb9Yf1lZGb/73e8YPXo0r7/+OpmZme7ku4HL5aJTp068++67REVF8d1333HttdeSkJDAJZdcwvTp09m8eTMlJSW88sorAERGRrJv375G9ezdu5dzzjmHiRMnMnv2bLZs2cLkyZNxOByNkvD//Oc/TJs2jeXLl7N06VImTpzIiBEjGD169C9ej4iIiJ+fhVCHjVCHjU4RNDlHvmGv+KxDSXvWgQrySqrIKzk8V76wvIbaeoN9xVXsK65q4p1MYU4bUUF2wgNtRATaiQiyExVkJzbUYfbMhzqIDXEQFdyZoMguhxe8O1YWi5l0O8Igsgv0Oc/sxV/3Jqx53exdbxh63/xKITzFLA1z3htKWBJEpJrb0p0gOzApSW9LKenw3dNmT7p4VsUB81O86tJjf+3Wz81HDXUXOaqIiAjGjBnDm2++6U7S33vvPaKjoznzzDPx8/NjwIAB7vMffPBB3n//fT788EOmTJnyi/W/+eabuFwuXnrpJRwOB3379mXPnj386U9/cp9js9m4//773d+npaWxdOlS3nnnHS655BKCg4NxOp1UV1f/7PD25557juTkZJ555hksFgu9evVi37593H777dx7773ukQL9+/fnvvvuA6B79+4888wzLFiwQEm6iIi0mh/vFT8kNbLJc2rqXOwvMxP2/NJq8kuryS2uJOtApbtX/kB5jbl3fOVR5o//hL+fhfBAG6FOG+FOG3GhDpIjA+kU4SQ5IpDkSCdRQQGEOm1Y/ZqRzIcmwGm3wMhp5vz5khxzWnBDqS4194m32swpw1ab2YO/fwvkbzZ79Yt2m+XnOCPNEcyOMHNuvc1p9vzbg83nfjzXPijGHAkQEOp164cpSW9LyYeGk+zfbCaJgU3/YkkbqzwIr/4O8je2rB4l6eIptkCzR9tT791M48ePZ/LkyTz33HMEBATwxhtvcOmll+Ln50dZWRkzZsxg3rx55OTkUFdXR2VlJVlZzZvvtnnzZvfw8gbp6elHnPfss8/y8ssvk5WVRWVlJTU1Nce8YvvmzZtJT09v1IMwYsQIysrK2LNnj7vnv3///o1el5CQQH5+/jG9l4iISEvZ/f1ICneSFH701dRLq2rJKa7iYHkNBytqOVhRY65OX1pDfmkV+SXV5Jeaq9RX1bqocxkUlJmr1/+SkAB/wgJthDlthB96NIvZax/utBEeaCci0EZEkJ1wZzSBsfEEJg3BrzkJPhxO2EtzDy+IV14A5fuheA8c3AWVBw6XY+HvNIfdNwy9j+wCkV3Nx6iuZlLfzkm8kvS2FBRt7ldYsBWyl0PPMZ6OqOOpLoM3LjYT9OA46H3e8dUT2xuSBrdubCLNZbH4xDyssWPHYhgG8+bN45RTTuHbb7/lH//4BwDTp09n/vz5PPbYY3Tr1g2n08kf/vAHamp+ufFvrjlz5jB9+nQef/xx0tPTCQkJ4dFHH2X58uWt9h4/ZrPZGn1vsVhwuVxt8l4iIiItEeKwEeKw/eJ5hmFQVeuiqNLseS+qqKWooubQAniVZB+sODQ/voLSKnOv9tLqOkqr69hzsPKY43LarAQFWAlx2Mw59SGHS2yIOQQ/LtRBfGgYoZ1H/PwQ/KqSQ73tWWYO4F6NvsL8vvKAuSJ9RcGhBL8AqouhrhIOZpqlKQGh8H8LIKbHMV/f8VKS3tZSTjWT9KylStLbW20VzLncXBfAEQ5XfgBxfTwdlcgJy+FwcNFFF/HGG2+wfft2evbsycknnwzAkiVLmDhxIhdeeCFgzjHftWtXs+vu3bs3r732GlVVVe7e9GXLGk8lWrJkCcOHD+f66693H9uxY0ejc+x2O/X19b/4Xv/9738xDMP9x8CSJUsICQmhU6dOzY5ZRETE11gsFpx2K067k4Swn9/nvLbe5V70rqiyluKKWveQ+qKKhuM1FB3quW94LK6sxTDMOipr66msraegrIbMgvKffb8Afz+igwOIPDSvPjLITkSgnehg++HkPjiFmITuRAXbG29bdzQ1FVCWC6V55rD74mxzzvyBnVC4E0r2mIvghbTvLjBK0ttaSjp8P1vz0ttbfR28NwkyF5lzUK6YqwRdpB2MHz+e3/3ud2zcuJErrrjCfbx79+7MnTuXsWPHYrFYuOeee46p1/nyyy/n7rvvZvLkydx5553s2rWLxx57rNE53bt3Z/bs2Xz++eekpaXx2muvsXLlStLS0tznpKam8vnnn5ORkUFUVBRhYUcu0nP99dcza9Ys/vznPzNlyhQyMjK47777mDZtWqOV60VERDoym9WPqOAAooIDfvnkH2norS+vqaOiup7ymjqKKmrd29DtPzSvfn9ptXuRvIMVtVTXudhbVMneoub12IcHmr3z0Yd66JMinO459Z0inCSGO3HYAw8Nb+/SdCW1lWbPfDsvSKckva2lnGo+7v0eNn4AFv2B1y42fQAZ88AaAJe9BZ00VF2kPfz6178mMjKSjIwMLr/8cvfxJ554gkmTJjF8+HCio6O5/fbbKSk5ci/YowkODuajjz7iuuuuY9CgQfTp04e//e1v/P73v3ef88c//pE1a9Ywbtw4LBYLl112Gddffz2ffvqp+5zJkyfz9ddfM2TIEMrKytxbsP1YUlISn3zyCbfeeisDBgwgMjKSa665hr/85S/H/4MRERER4Me99VYIbt5rqmrryS+pprC8moMVNRwor+VgeQ2F5TUUNiT3hxL8wrIa6lzGoaH6tWzLLztqvTarhaAAf4Ls/gQFWAl12NxD7WNDHYe2tYtgWFgdgfb2S50thtEw2KBjKCkpISwsjOLiYkJD2+ETEcOAx3tCWV7bv5c0ZrHCpW9omoH4lKqqKjIzM0lLS2u0SJr4vp+7t+3eNnUA+pmKiHRMLpdBUWWtu1e+oKya/JJq9hysIPvQfPrsA5VU1v789Lcf+/a2M0mObP5iuk05lnZJPeltzWKB3zwEq14BQwsKtRt/O5x6vRJ0EREREZEOxM/PQmSQncggOz3iQpo8xzAMSqrqKK8+VGrqKa82h93vL61yD7dv2NIu+hiH9LeUkvT20P8Ss4iIiIiIiIhHWSwW91Zx3kgTpEVERERERES8hJJ0ERERERERES+hJF1ExAt1sDU9OwTdUxEREWkOJekiIl7EZjPnRlVUVHg4EmltDfe04R6LiIiINEULx4mIeBGr1Up4eDj5+fkABAYGYrFYPByVtIRhGFRUVJCfn094eDhWq9XTIYmIiIgXU5IuIuJl4uPjAdyJupwYwsPD3fdWRERE5GiUpIuIeBmLxUJCQgKxsbHU1tZ6OhxpBTabTT3oIiIi0ixK0kVEvJTValViJyIiItLBaOE4ERERERERES+hJF1ERERERETESyhJFxEREREREfESHW5OumEYAJSUlHg4EhEREVNDm9TQRknLqb0XERFvcixtfYdL0ktLSwFITk72cCQiIiKNlZaWEhYW5ukwTghq70VExBs1p623GB3sY3uXy8W+ffsICQnBYrG0qK6SkhKSk5PJzs4mNDS0lSJsX7oGz/P1+EHX4A18PX7o2NdgGAalpaUkJibi56eZaK1B7f1hvh4/6Bq8ga/HD7oGb+Dr8UP7tPUdrifdz8+PTp06tWqdoaGhPvuPrIGuwfN8PX7QNXgDX48fOu41qAe9dam9P5Kvxw+6Bm/g6/GDrsEb+Hr80LZtvT6uFxEREREREfESStJFREREREREvISS9BYICAjgvvvuIyAgwNOhHDddg+f5evyga/AGvh4/6BrEe/n6ffX1+EHX4A18PX7QNXgDX48f2ucaOtzCcSIiIiIiIiLeSj3pIiIiIiIiIl5CSbqIiIiIiIiIl1CSLiIiIiIiIuIllKSLiIiIiIiIeAkl6S3w7LPPkpqaisPhYNiwYaxYscLTIR3VN998w9ixY0lMTMRisfDBBx80et4wDO69914SEhJwOp2MGjWKbdu2eSbYJsycOZNTTjmFkJAQYmNjueCCC8jIyGh0TlVVFTfccANRUVEEBwfz+9//nry8PA9FfKR//vOf9O/fn9DQUEJDQ0lPT+fTTz91P+/t8f/UX//6VywWC1OnTnUf8/ZrmDFjBhaLpVHp1auX+3lvj7/B3r17ueKKK4iKisLpdHLSSSexatUq9/Pe/Pucmpp6xD2wWCzccMMNgG/cg/r6eu655x7S0tJwOp107dqVBx98kB+vw+rN90COjdr69qO23vuorfccX27rwffbe4+39YYclzlz5hh2u914+eWXjY0bNxqTJ082wsPDjby8PE+H1qRPPvnEuPvuu425c+cagPH+++83ev6vf/2rERYWZnzwwQfGunXrjPPOO89IS0szKisrPRPwT5x99tnGK6+8YmzYsMFYu3atcc455xgpKSlGWVmZ+5zrrrvOSE5ONhYsWGCsWrXKOPXUU43hw4d7MOrGPvzwQ2PevHnG1q1bjYyMDOOuu+4ybDabsWHDBsMwvD/+H1uxYoWRmppq9O/f37jpppvcx739Gu677z6jb9++Rk5Ojrvs37/f/by3x28YhnHgwAGjc+fOxsSJE43ly5cbO3fuND7//HNj+/bt7nO8+fc5Pz+/0c9//vz5BmAsXLjQMAzfuAcPP/ywERUVZXz88cdGZmam8e677xrBwcHGk08+6T7Hm++BNJ/a+valtt67qK33HF9v6w3D99t7T7f1StKP09ChQ40bbrjB/X19fb2RmJhozJw504NRNc9PG26Xy2XEx8cbjz76qPtYUVGRERAQYLz11lseiPCX5efnG4CxaNEiwzDMeG02m/Huu++6z9m8ebMBGEuXLvVUmL8oIiLCePHFF30q/tLSUqN79+7G/PnzjdNPP93dcPvCNdx3333GgAEDmnzOF+I3DMO4/fbbjZEjRx71eV/7fb7pppuMrl27Gi6Xy2fuwbnnnmtMmjSp0bGLLrrIGD9+vGEYvncP5OjU1nuW2nrPUVvvWSdaW28Yvtfee7qt13D341BTU8Pq1asZNWqU+5ifnx+jRo1i6dKlHozs+GRmZpKbm9voesLCwhg2bJjXXk9xcTEAkZGRAKxevZra2tpG19CrVy9SUlK88hrq6+uZM2cO5eXlpKen+1T8N9xwA+eee26jWMF37sG2bdtITEykS5cujB8/nqysLMB34v/www8ZMmQIF198MbGxsQwaNIgXXnjB/bwv/T7X1NTw+uuvM2nSJCwWi8/cg+HDh7NgwQK2bt0KwLp161i8eDFjxowBfOseyNGprfc8tfWeo7bes06kth58s733dFvv3+IaOqCCggLq6+uJi4trdDwuLo4tW7Z4KKrjl5ubC9Dk9TQ8501cLhdTp05lxIgR9OvXDzCvwW63Ex4e3uhcb7uG9evXk56eTlVVFcHBwbz//vv06dOHtWvX+kT8c+bM4fvvv2flypVHPOcL92DYsGG8+uqr9OzZk5ycHO6//35OO+00NmzY4BPxA+zcuZN//vOfTJs2jbvuuouVK1dy4403YrfbmTBhgk/9Pn/wwQcUFRUxceJEwDf+DQHccccdlJSU0KtXL6xWK/X19Tz88MOMHz8e8L3/U6Vpaus9S22956it97wTqa0H32zvPd3WK0kXn3PDDTewYcMGFi9e7OlQjlnPnj1Zu3YtxcXFvPfee0yYMIFFixZ5Oqxmyc7O5qabbmL+/Pk4HA5Ph3NcGj79BOjfvz/Dhg2jc+fOvPPOOzidTg9G1nwul4shQ4bwyCOPADBo0CA2bNjA888/z4QJEzwc3bF56aWXGDNmDImJiZ4O5Zi88847vPHGG7z55pv07duXtWvXMnXqVBITE33uHoh4K7X1nqG23jucSG09+GZ77+m2XsPdj0N0dDRWq/WIFQjz8vKIj4/3UFTHryFmX7ieKVOm8PHHH7Nw4UI6derkPh4fH09NTQ1FRUWNzve2a7Db7XTr1o3Bgwczc+ZMBgwYwJNPPukT8a9evZr8/HxOPvlk/P398ff3Z9GiRTz11FP4+/sTFxfn9dfwU+Hh4fTo0YPt27f7xD0ASEhIoE+fPo2O9e7d2z2Uz1d+n3fv3s2XX37J//3f/7mP+co9uPXWW7njjju49NJLOemkk7jyyiu5+eabmTlzJuA790B+ntp6z1Fb7zlq673DidLWg++2955u65WkHwe73c7gwYNZsGCB+5jL5WLBggWkp6d7MLLjk5aWRnx8fKPrKSkpYfny5V5zPYZhMGXKFN5//32++uor0tLSGj0/ePBgbDZbo2vIyMggKyvLa66hKS6Xi+rqap+I/6yzzmL9+vWsXbvWXYYMGcL48ePdX3v7NfxUWVkZO3bsICEhwSfuAcCIESOO2JJo69atdO7cGfCN32eAV155hdjYWM4991z3MV+5BxUVFfj5NW4+rVYrLpcL8J17ID9PbX37U1vveWrrvcOJ0taD77b3Hm/rW7z0XAc1Z84cIyAgwHj11VeNTZs2Gddee60RHh5u5Obmejq0JpWWlhpr1qwx1qxZYwDGE088YaxZs8bYvXu3YRjmFgLh4eHG//73P+OHH34wzj//fK/axuFPf/qTERYWZnz99deNtnOoqKhwn3PdddcZKSkpxldffWWsWrXKSE9PN9LT0z0YdWN33HGHsWjRIiMzM9P44YcfjDvuuMOwWCzGF198YRiG98fflB+v+GoY3n8Nt9xyi/H1118bmZmZxpIlS4xRo0YZ0dHRRn5+vmEY3h+/YZhb4vj7+xsPP/ywsW3bNuONN94wAgMDjddff919jrf/PtfX1xspKSnG7bfffsRzvnAPJkyYYCQlJbm3ZZk7d64RHR1t3Hbbbe5zvP0eSPOorW9fauu9k9r69ncitPWG4dvtvafbeiXpLfD0008bKSkpht1uN4YOHWosW7bM0yEd1cKFCw3giDJhwgTDMMxtBO655x4jLi7OCAgIMM466ywjIyPDs0H/SFOxA8Yrr7ziPqeystK4/vrrjYiICCMwMNC48MILjZycHM8F/ROTJk0yOnfubNjtdiMmJsY466yz3I22YXh//E35acPt7dcwbtw4IyEhwbDb7UZSUpIxbty4RnuOenv8DT766COjX79+RkBAgNGrVy/j3//+d6Pnvf33+fPPPzeAJmPyhXtQUlJi3HTTTUZKSorhcDiMLl26GHfffbdRXV3tPsfb74E0n9r69qO23juprfcMX2/rDcO323tPt/UWwzCMlvfHi4iIiIiIiEhLaU66iIiIiIiIiJdQki4iIiIiIiLiJZSki4iIiIiIiHgJJekiIiIiIiIiXkJJuoiIiIiIiIiXUJIuIiIiIiIi4iWUpIuIiIiIiIh4CSXpIiIiIiIiIl5CSbqItKuvv/4ai8VCUVGRp0MRERGRNqL2XuT4KUkXERERERER8RJK0kVERERERES8hJJ0kQ7G5XIxc+ZM0tLScDqdDBgwgPfeew84PDRt3rx59O/fH4fDwamnnsqGDRsa1fHf//6Xvn37EhAQQGpqKo8//nij56urq7n99ttJTk4mICCAbt268dJLLzU6Z/Xq1QwZMoTAwECGDx9ORkZG2164iIhIB6L2XsR3KUkX6WBmzpzJ7Nmzef7559m4cSM333wzV1xxBYsWLXKfc+utt/L444+zcuVKYmJiGDt2LLW1tYDZ2F5yySVceumlrF+/nhkzZnDPPffw6quvul9/1VVX8dZbb/HUU0+xefNm/vWvfxEcHNwojrvvvpvHH3+cVatW4e/vz6RJk9rl+kVERDoCtfciPswQkQ6jqqrKCAwMNL777rtGx6+55hrjsssuMxYuXGgAxpw5c9zPFRYWGk6n03j77bcNwzCMyy+/3Bg9enSj1996661Gnz59DMMwjIyMDAMw5s+f32QMDe/x5Zdfuo/NmzfPAIzKyspWuU4REZGOTO29iG9TT7pIB7J9+3YqKioYPXo0wcHB7jJ79mx27NjhPi89Pd39dWRkJD179mTz5s0AbN68mREjRjSqd8SIEWzbto36+nrWrl2L1Wrl9NNP/9lY+vfv7/46ISEBgPz8/BZfo4iISEen9l7Et/l7OgARaT9lZWUAzJs3j6SkpEbPBQQENGq4j5fT6WzWeTabzf21xWIBzPlzIiIi0jJq70V8m3rSRTqQPn36EBAQQFZWFt26dWtUkpOT3ectW7bM/fXBgwfZunUrvXv3BqB3794sWbKkUb1LliyhR48eWK1WTjrpJFwuV6M5byIiItJ+1N6L+Db1pIt0ICEhIUyfPp2bb74Zl8vFyJEjKS4uZsmSJYSGhtK5c2cAHnjgAaKiooiLi+Puu+8mOjqaCy64AIBbbrmFU045hQcffJBx48axdOlSnnnmGZ577jkAUlNTmTBhApMmTeKpp55iwIAB7N69m/z8fC655BJPXbqIiEiHofZexMd5elK8iLQvl8tlzJo1y+jZs6dhs9mMmJgY4+yzzzYWLVrkXuTlo48+Mvr27WvY7XZj6NChxrp16xrV8d577xl9+vQxbDabkZKSYjz66KONnq+srDRuvvlmIyEhwbDb7Ua3bt2Ml19+2TCMwwvJHDx40H3+mjVrDMDIzMxs68sXERHpENTei/gui2EYhic/JBAR7/H1119z5plncvDgQcLDwz0djoiIiLQBtfci3k1z0kVERERERES8hJJ0ERERERERES+h4e4iIiIiIiIiXkI96SIiIiIiIiJeQkm6iIiIiIiIiJdQki4iIiIiIiLiJZSki4iIiIiIiHgJJekiIiIiIiIiXkJJuoiIiIiIiIiXUJIuIiIiIiIi4iWUpIuIiIiIiIh4if8H8FKUKh4nxJAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and Validation accuracy and loss\n",
    "f = plt.figure(figsize = (12,4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('train validation accuracy')\n",
    "plt.plot(history4.history['accuracy'])\n",
    "plt.plot(history4.history['val_accuracy'])\n",
    "plt.legend(['train','validation'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('train validation loss')\n",
    "plt.plot(history4.history['loss'])\n",
    "plt.plot(history4.history['val_loss'])\n",
    "plt.legend(['train','validation'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fa01cbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec2_test = spec2_file['spec_test']\n",
    "y2_test_spec = spec2_file['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f4a75e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "spec2_test_scaled = spec2_test / np.amax(spec2_test)\n",
    "\n",
    "# Reshape the data\n",
    "N,row,col = spec2_test_scaled.shape\n",
    "spec2_test_reshaped = spec2_test_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1bc4e9d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 64ms/step\n"
     ]
    }
   ],
   "source": [
    "y2_prob = model4.predict(spec2_test_reshaped)\n",
    "y2_pred = np.argmax(y2_prob, axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6e24d398",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy\n",
    "acc2_2d = round(accuracy_score(y2_pred,y2_test_spec),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a47fffb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "cm_spec = confusion_matrix(y2_test_spec,y2_pred)\n",
    "cm_2d_spec = ConfusionMatrixDisplay(confusion_matrix = cm_spec, display_labels = ['Angry', 'Happy', 'Relaxed', 'Sad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "89485764",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Angry       1.00      0.90      0.95        10\n",
      "       Happy       0.83      1.00      0.91        10\n",
      "     Relaxed       0.75      0.60      0.67        10\n",
      "         Sad       0.64      0.70      0.67        10\n",
      "\n",
      "    accuracy                           0.80        40\n",
      "   macro avg       0.80      0.80      0.80        40\n",
      "weighted avg       0.80      0.80      0.80        40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y2_test_spec, y2_pred, target_names = ['Angry', 'Happy', 'Relaxed', 'Sad']))\n",
    "cm_2d_spec.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93c86f2",
   "metadata": {},
   "source": [
    "### 4.3 mel spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e9ced243",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load mel data set\n",
    "mel1_file = np.load(\"Features2D/npz_new/mel1_train_test.npz\")\n",
    "\n",
    "mel1_train = mel1_file['mel_train']\n",
    "y1_train = mel1_file['y_train']\n",
    "mel1_train,mel1_val,y1_train,y1_val = train_test_split(mel1_train,y1_train, \n",
    "                                                       train_size = 0.8, \n",
    "                                                       random_state = 13, \n",
    "                                                       stratify= y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bec79f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mel1_train_scaled = (mel1_train - np.mean(mel1_train))/np.std(mel1_train)\n",
    "mel1_val_scaled = (mel1_val - np.mean(mel1_val))/np.std(mel1_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d0c14e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data\n",
    "N,row,col = mel1_train_scaled.shape\n",
    "mel1_train_reshaped = mel1_train_scaled.reshape((N,row,col,1))\n",
    "\n",
    "N,row,col = mel1_val_scaled.shape\n",
    "mel1_val_reshaped = mel1_val_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2356fd9a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CNN2D_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 300, 400, 8)       80        \n",
      "                                                                 \n",
      " MaxPooling2D_1 (MaxPooling2  (None, 150, 200, 8)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN1 (BatchNormalization)    (None, 150, 200, 8)       32        \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 150, 200, 8)       584       \n",
      "                                                                 \n",
      " MaxPooling2D_2 (MaxPooling2  (None, 75, 100, 8)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN2 (BatchNormalization)    (None, 75, 100, 8)        32        \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 75, 100, 16)       3216      \n",
      "                                                                 \n",
      " MaxPooling2D_3 (MaxPooling2  (None, 38, 50, 16)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN3 (BatchNormalization)    (None, 38, 50, 16)        64        \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 30400)             0         \n",
      "                                                                 \n",
      " Dense1 (Dense)              (None, 64)                1945664   \n",
      "                                                                 \n",
      " Dropout1 (Dropout)          (None, 64)                0         \n",
      "                                                                 \n",
      " Dense2 (Dense)              (None, 32)                2080      \n",
      "                                                                 \n",
      " Dropout2 (Dropout)          (None, 32)                0         \n",
      "                                                                 \n",
      " Softmax (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,951,884\n",
      "Trainable params: 1,951,820\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "f1,k1,a1 = 8,3,'relu'\n",
    "mw1,mh1,sw1,sh1 = 2,3,2,2\n",
    "f2,k2,a2 = 8,3,'relu'\n",
    "mw2,mh2,sw2,sh2 = 2,3,2,2\n",
    "f3,k3,a3 = 16,3,'relu'\n",
    "mw3,mh3,sw3,sh3 = 2,3,2,2\n",
    "d1,dr1,da1,r1 = 64,0.2,'relu',0.2\n",
    "d2,dr2,da2,r2 = 32,0.2,'relu',0.2\n",
    "num = 5\n",
    "model5 = modelBuilder3L(mel1_train_reshaped,\n",
    "                        f1,k1,a1,\n",
    "                        mw1,mh1,sw1,sh1,\n",
    "                        f2,k2,a2,\n",
    "                        mw2,mh2,sw2,sh2,\n",
    "                        f3,k3,a3,\n",
    "                        mw3,mh3,sw3,sh3,\n",
    "                        d1,dr1,da1,r1,\n",
    "                        d2,dr2,da2,r2,\n",
    "                        num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ce17673d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "29/29 [==============================] - 20s 618ms/step - loss: 33.4322 - accuracy: 0.3763 - val_loss: 30.8477 - val_accuracy: 0.2500\n",
      "Epoch 2/80\n",
      "29/29 [==============================] - 16s 541ms/step - loss: 28.2444 - accuracy: 0.4286 - val_loss: 26.0775 - val_accuracy: 0.2500\n",
      "Epoch 3/80\n",
      "29/29 [==============================] - 16s 549ms/step - loss: 23.6201 - accuracy: 0.4495 - val_loss: 22.5402 - val_accuracy: 0.2500\n",
      "Epoch 4/80\n",
      "29/29 [==============================] - 16s 538ms/step - loss: 20.0100 - accuracy: 0.4704 - val_loss: 19.8260 - val_accuracy: 0.2500\n",
      "Epoch 5/80\n",
      "29/29 [==============================] - 15s 534ms/step - loss: 17.1801 - accuracy: 0.4564 - val_loss: 17.0021 - val_accuracy: 0.2500\n",
      "Epoch 6/80\n",
      "29/29 [==============================] - 17s 588ms/step - loss: 14.9620 - accuracy: 0.4599 - val_loss: 15.8493 - val_accuracy: 0.2500\n",
      "Epoch 7/80\n",
      "29/29 [==============================] - 16s 538ms/step - loss: 13.2009 - accuracy: 0.5348 - val_loss: 13.5176 - val_accuracy: 0.2500\n",
      "Epoch 8/80\n",
      "29/29 [==============================] - 16s 538ms/step - loss: 11.7856 - accuracy: 0.5889 - val_loss: 11.9361 - val_accuracy: 0.2500\n",
      "Epoch 9/80\n",
      "29/29 [==============================] - 15s 523ms/step - loss: 10.7554 - accuracy: 0.5889 - val_loss: 10.7770 - val_accuracy: 0.2569\n",
      "Epoch 10/80\n",
      "29/29 [==============================] - 15s 535ms/step - loss: 9.8415 - accuracy: 0.6167 - val_loss: 10.2014 - val_accuracy: 0.2778\n",
      "Epoch 11/80\n",
      "29/29 [==============================] - 18s 622ms/step - loss: 9.1244 - accuracy: 0.6341 - val_loss: 9.5558 - val_accuracy: 0.3125\n",
      "Epoch 12/80\n",
      "29/29 [==============================] - 16s 549ms/step - loss: 8.4856 - accuracy: 0.6185 - val_loss: 8.6412 - val_accuracy: 0.4375\n",
      "Epoch 13/80\n",
      "29/29 [==============================] - 15s 527ms/step - loss: 7.9677 - accuracy: 0.6638 - val_loss: 8.1034 - val_accuracy: 0.4167\n",
      "Epoch 14/80\n",
      "29/29 [==============================] - 16s 537ms/step - loss: 7.5119 - accuracy: 0.6951 - val_loss: 7.6997 - val_accuracy: 0.4722\n",
      "Epoch 15/80\n",
      "29/29 [==============================] - 15s 527ms/step - loss: 7.1844 - accuracy: 0.6864 - val_loss: 7.3292 - val_accuracy: 0.4931\n",
      "Epoch 16/80\n",
      "29/29 [==============================] - 15s 523ms/step - loss: 6.8029 - accuracy: 0.7143 - val_loss: 6.9373 - val_accuracy: 0.5556\n",
      "Epoch 17/80\n",
      "29/29 [==============================] - 15s 531ms/step - loss: 6.4539 - accuracy: 0.7491 - val_loss: 6.7501 - val_accuracy: 0.5486\n",
      "Epoch 18/80\n",
      "29/29 [==============================] - 15s 523ms/step - loss: 6.1722 - accuracy: 0.7613 - val_loss: 6.4319 - val_accuracy: 0.5903\n",
      "Epoch 19/80\n",
      "29/29 [==============================] - 15s 532ms/step - loss: 5.9550 - accuracy: 0.7334 - val_loss: 6.2729 - val_accuracy: 0.5139\n",
      "Epoch 20/80\n",
      "29/29 [==============================] - 17s 602ms/step - loss: 5.6803 - accuracy: 0.7648 - val_loss: 6.0475 - val_accuracy: 0.5764\n",
      "Epoch 21/80\n",
      "29/29 [==============================] - 20s 686ms/step - loss: 5.4798 - accuracy: 0.7735 - val_loss: 5.8354 - val_accuracy: 0.5833\n",
      "Epoch 22/80\n",
      "29/29 [==============================] - 20s 701ms/step - loss: 5.1596 - accuracy: 0.8275 - val_loss: 5.6765 - val_accuracy: 0.5764\n",
      "Epoch 23/80\n",
      "29/29 [==============================] - 17s 597ms/step - loss: 5.0398 - accuracy: 0.8136 - val_loss: 5.5355 - val_accuracy: 0.5486\n",
      "Epoch 24/80\n",
      "29/29 [==============================] - 16s 548ms/step - loss: 4.9012 - accuracy: 0.8118 - val_loss: 5.2867 - val_accuracy: 0.5625\n",
      "Epoch 25/80\n",
      "29/29 [==============================] - 15s 527ms/step - loss: 4.7062 - accuracy: 0.8502 - val_loss: 5.2597 - val_accuracy: 0.5278\n",
      "Epoch 26/80\n",
      "29/29 [==============================] - 18s 617ms/step - loss: 4.5074 - accuracy: 0.8624 - val_loss: 5.0695 - val_accuracy: 0.5556\n",
      "Epoch 27/80\n",
      "29/29 [==============================] - 16s 552ms/step - loss: 4.3875 - accuracy: 0.8380 - val_loss: 4.9457 - val_accuracy: 0.5278\n",
      "Epoch 28/80\n",
      "29/29 [==============================] - 15s 522ms/step - loss: 4.1938 - accuracy: 0.8763 - val_loss: 4.7695 - val_accuracy: 0.5556\n",
      "Epoch 29/80\n",
      "29/29 [==============================] - 15s 524ms/step - loss: 4.0058 - accuracy: 0.9251 - val_loss: 4.7092 - val_accuracy: 0.5556\n",
      "Epoch 30/80\n",
      "29/29 [==============================] - 16s 535ms/step - loss: 3.8690 - accuracy: 0.9129 - val_loss: 4.5373 - val_accuracy: 0.5486\n",
      "Epoch 31/80\n",
      "29/29 [==============================] - 17s 591ms/step - loss: 3.7554 - accuracy: 0.8815 - val_loss: 4.4256 - val_accuracy: 0.5764\n",
      "Epoch 32/80\n",
      "29/29 [==============================] - 18s 619ms/step - loss: 3.6173 - accuracy: 0.8972 - val_loss: 4.3315 - val_accuracy: 0.5556\n",
      "Epoch 33/80\n",
      "29/29 [==============================] - 16s 559ms/step - loss: 3.5213 - accuracy: 0.9181 - val_loss: 4.1842 - val_accuracy: 0.5486\n",
      "Epoch 34/80\n",
      "29/29 [==============================] - 17s 581ms/step - loss: 3.3651 - accuracy: 0.9373 - val_loss: 4.2227 - val_accuracy: 0.5486\n",
      "Epoch 35/80\n",
      "29/29 [==============================] - 16s 560ms/step - loss: 3.2477 - accuracy: 0.9268 - val_loss: 4.0876 - val_accuracy: 0.5764\n",
      "Epoch 36/80\n",
      "29/29 [==============================] - 16s 541ms/step - loss: 3.1690 - accuracy: 0.9338 - val_loss: 3.9796 - val_accuracy: 0.5208\n",
      "Epoch 37/80\n",
      "29/29 [==============================] - 15s 518ms/step - loss: 3.0632 - accuracy: 0.9268 - val_loss: 3.8439 - val_accuracy: 0.5208\n",
      "Epoch 38/80\n",
      "29/29 [==============================] - 15s 521ms/step - loss: 2.9604 - accuracy: 0.9303 - val_loss: 3.7915 - val_accuracy: 0.5208\n",
      "Epoch 39/80\n",
      "29/29 [==============================] - 15s 528ms/step - loss: 2.8265 - accuracy: 0.9477 - val_loss: 3.6861 - val_accuracy: 0.5625\n",
      "Epoch 40/80\n",
      "29/29 [==============================] - 15s 521ms/step - loss: 2.7095 - accuracy: 0.9582 - val_loss: 3.5280 - val_accuracy: 0.5417\n",
      "Epoch 41/80\n",
      "29/29 [==============================] - 15s 525ms/step - loss: 2.6558 - accuracy: 0.9338 - val_loss: 3.5424 - val_accuracy: 0.5764\n",
      "Epoch 42/80\n",
      "29/29 [==============================] - 15s 515ms/step - loss: 2.5497 - accuracy: 0.9530 - val_loss: 3.4469 - val_accuracy: 0.5486\n",
      "Epoch 43/80\n",
      "29/29 [==============================] - 15s 532ms/step - loss: 2.4724 - accuracy: 0.9512 - val_loss: 3.4020 - val_accuracy: 0.5833\n",
      "Epoch 44/80\n",
      "29/29 [==============================] - 15s 526ms/step - loss: 2.3744 - accuracy: 0.9669 - val_loss: 3.2865 - val_accuracy: 0.5625\n",
      "Epoch 45/80\n",
      "29/29 [==============================] - 18s 612ms/step - loss: 2.3395 - accuracy: 0.9355 - val_loss: 3.2477 - val_accuracy: 0.5417\n",
      "Epoch 46/80\n",
      "29/29 [==============================] - 16s 565ms/step - loss: 2.2442 - accuracy: 0.9547 - val_loss: 3.1202 - val_accuracy: 0.5486\n",
      "Epoch 47/80\n",
      "29/29 [==============================] - 17s 600ms/step - loss: 2.1847 - accuracy: 0.9443 - val_loss: 2.9915 - val_accuracy: 0.5833\n",
      "Epoch 48/80\n",
      "29/29 [==============================] - 16s 567ms/step - loss: 2.0941 - accuracy: 0.9582 - val_loss: 3.0251 - val_accuracy: 0.5486\n",
      "Epoch 49/80\n",
      "29/29 [==============================] - 15s 508ms/step - loss: 2.0562 - accuracy: 0.9460 - val_loss: 2.9128 - val_accuracy: 0.5556\n",
      "Epoch 50/80\n",
      "29/29 [==============================] - 15s 510ms/step - loss: 1.9624 - accuracy: 0.9739 - val_loss: 2.8717 - val_accuracy: 0.5347\n",
      "Epoch 51/80\n",
      "29/29 [==============================] - 15s 506ms/step - loss: 1.9024 - accuracy: 0.9564 - val_loss: 2.8080 - val_accuracy: 0.5417\n",
      "Epoch 52/80\n",
      "29/29 [==============================] - 15s 513ms/step - loss: 1.8513 - accuracy: 0.9634 - val_loss: 2.7152 - val_accuracy: 0.5417\n",
      "Epoch 53/80\n",
      "29/29 [==============================] - 14s 493ms/step - loss: 1.7675 - accuracy: 0.9756 - val_loss: 2.7249 - val_accuracy: 0.5625\n",
      "Epoch 54/80\n",
      "29/29 [==============================] - 14s 490ms/step - loss: 1.7289 - accuracy: 0.9599 - val_loss: 2.5709 - val_accuracy: 0.5833\n",
      "Epoch 55/80\n",
      "29/29 [==============================] - 14s 494ms/step - loss: 1.6955 - accuracy: 0.9495 - val_loss: 2.5569 - val_accuracy: 0.5347\n",
      "Epoch 56/80\n",
      "29/29 [==============================] - 14s 495ms/step - loss: 1.6287 - accuracy: 0.9843 - val_loss: 2.5241 - val_accuracy: 0.5486\n",
      "Epoch 57/80\n",
      "29/29 [==============================] - 14s 494ms/step - loss: 1.5620 - accuracy: 0.9739 - val_loss: 2.5241 - val_accuracy: 0.5556\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/80\n",
      "29/29 [==============================] - 14s 488ms/step - loss: 1.5449 - accuracy: 0.9547 - val_loss: 2.4790 - val_accuracy: 0.5417\n",
      "Epoch 59/80\n",
      "29/29 [==============================] - 14s 489ms/step - loss: 1.4881 - accuracy: 0.9634 - val_loss: 2.4589 - val_accuracy: 0.5625\n",
      "Epoch 60/80\n",
      "29/29 [==============================] - 14s 498ms/step - loss: 1.4355 - accuracy: 0.9791 - val_loss: 2.3801 - val_accuracy: 0.5556\n",
      "Epoch 61/80\n",
      "29/29 [==============================] - 14s 495ms/step - loss: 1.3852 - accuracy: 0.9704 - val_loss: 2.3123 - val_accuracy: 0.5486\n",
      "Epoch 62/80\n",
      "29/29 [==============================] - 14s 490ms/step - loss: 1.3557 - accuracy: 0.9739 - val_loss: 2.3460 - val_accuracy: 0.5139\n",
      "Epoch 63/80\n",
      "29/29 [==============================] - 14s 498ms/step - loss: 1.2985 - accuracy: 0.9774 - val_loss: 2.2120 - val_accuracy: 0.5625\n",
      "Epoch 64/80\n",
      "29/29 [==============================] - 14s 492ms/step - loss: 1.2671 - accuracy: 0.9808 - val_loss: 2.3168 - val_accuracy: 0.5486\n",
      "Epoch 65/80\n",
      "29/29 [==============================] - 14s 496ms/step - loss: 1.2188 - accuracy: 0.9826 - val_loss: 2.2545 - val_accuracy: 0.5625\n",
      "Epoch 66/80\n",
      "29/29 [==============================] - 14s 497ms/step - loss: 1.1823 - accuracy: 0.9808 - val_loss: 2.0630 - val_accuracy: 0.5625\n",
      "Epoch 67/80\n",
      "29/29 [==============================] - 16s 565ms/step - loss: 1.1426 - accuracy: 0.9774 - val_loss: 2.0286 - val_accuracy: 0.5764\n",
      "Epoch 68/80\n",
      "29/29 [==============================] - 15s 521ms/step - loss: 1.0869 - accuracy: 0.9808 - val_loss: 2.0235 - val_accuracy: 0.5347\n",
      "Epoch 69/80\n",
      "29/29 [==============================] - 16s 547ms/step - loss: 1.0442 - accuracy: 0.9826 - val_loss: 1.9952 - val_accuracy: 0.5486\n",
      "Epoch 70/80\n",
      "29/29 [==============================] - 16s 555ms/step - loss: 1.0137 - accuracy: 0.9861 - val_loss: 2.0452 - val_accuracy: 0.5347\n",
      "Epoch 71/80\n",
      "29/29 [==============================] - 16s 534ms/step - loss: 0.9993 - accuracy: 0.9826 - val_loss: 2.0196 - val_accuracy: 0.5694\n",
      "Epoch 72/80\n",
      "29/29 [==============================] - 15s 528ms/step - loss: 0.9797 - accuracy: 0.9843 - val_loss: 1.9144 - val_accuracy: 0.5694\n",
      "Epoch 73/80\n",
      "29/29 [==============================] - 15s 506ms/step - loss: 0.9471 - accuracy: 0.9791 - val_loss: 1.9048 - val_accuracy: 0.5764\n",
      "Epoch 74/80\n",
      "29/29 [==============================] - 15s 501ms/step - loss: 0.9279 - accuracy: 0.9826 - val_loss: 1.9066 - val_accuracy: 0.5556\n",
      "Epoch 75/80\n",
      "29/29 [==============================] - 15s 502ms/step - loss: 0.9131 - accuracy: 0.9791 - val_loss: 1.8970 - val_accuracy: 0.5625\n",
      "Epoch 76/80\n",
      "29/29 [==============================] - 15s 504ms/step - loss: 0.8922 - accuracy: 0.9791 - val_loss: 1.8040 - val_accuracy: 0.5486\n",
      "Epoch 77/80\n",
      "29/29 [==============================] - 15s 507ms/step - loss: 0.8634 - accuracy: 0.9791 - val_loss: 1.8304 - val_accuracy: 0.5694\n",
      "Epoch 78/80\n",
      "29/29 [==============================] - 15s 508ms/step - loss: 0.8429 - accuracy: 0.9826 - val_loss: 1.8076 - val_accuracy: 0.5625\n",
      "Epoch 79/80\n",
      "29/29 [==============================] - 15s 507ms/step - loss: 0.8205 - accuracy: 0.9791 - val_loss: 1.8543 - val_accuracy: 0.5625\n",
      "Epoch 80/80\n",
      "29/29 [==============================] - 15s 523ms/step - loss: 0.8080 - accuracy: 0.9808 - val_loss: 1.7931 - val_accuracy: 0.5694\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd00f55a8f0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.fit(mel1_train_reshaped,y1_train,validation_data=(mel1_val_reshaped,y1_val),batch_size=20,epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8f9cfb3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load mel data set\n",
    "mel2_file = np.load(\"Features2D/npz_new/mel2_train_test.npz\")\n",
    "\n",
    "mel2_train = mel2_file['mel_train']\n",
    "y2_train = mel2_file['y_train']\n",
    "mel2_train,mel2_val,y2_train,y2_val = train_test_split(mel2_train,y2_train, \n",
    "                                                       train_size = 0.8, \n",
    "                                                       random_state = 13, \n",
    "                                                       stratify= y2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0396f7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mel2_train_scaled = (mel2_train - np.mean(mel2_train)) / np.std(mel2_train)\n",
    "mel2_val_scaled = (mel2_val - np.amin(mel2_val)) / np.std(mel2_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a9f041c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the data\n",
    "N,row,col = mel2_train_scaled.shape\n",
    "mel2_train_reshaped = mel2_train_scaled.reshape((N,row,col,1))\n",
    "\n",
    "N,row,col = mel2_val_scaled.shape\n",
    "mel2_val_reshaped = mel2_val_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cb76884e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"CNN2D_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 300, 400, 8)       80        \n",
      "                                                                 \n",
      " MaxPooling2D_1 (MaxPooling2  (None, 150, 200, 8)      0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN1 (BatchNormalization)    (None, 150, 200, 8)       32        \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 150, 200, 8)       584       \n",
      "                                                                 \n",
      " MaxPooling2D_2 (MaxPooling2  (None, 75, 100, 8)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN2 (BatchNormalization)    (None, 75, 100, 8)        32        \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 75, 100, 16)       1168      \n",
      "                                                                 \n",
      " MaxPooling2D_3 (MaxPooling2  (None, 38, 50, 16)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " BN3 (BatchNormalization)    (None, 38, 50, 16)        64        \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 30400)             0         \n",
      "                                                                 \n",
      " Dense1 (Dense)              (None, 64)                1945664   \n",
      "                                                                 \n",
      " Dropout1 (Dropout)          (None, 64)                0         \n",
      "                                                                 \n",
      " Dense2 (Dense)              (None, 32)                2080      \n",
      "                                                                 \n",
      " Dropout2 (Dropout)          (None, 32)                0         \n",
      "                                                                 \n",
      " Softmax (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,949,836\n",
      "Trainable params: 1,949,772\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "f1,k1,a1 = 8,3,'relu'\n",
    "mw1,mh1,sw1,sh1 = 2,3,2,2\n",
    "f2,k2,a2 = 8,3,'relu'\n",
    "mw2,mh2,sw2,sh2 = 2,3,2,2\n",
    "f3,k3,a3 = 16,3,'relu'\n",
    "mw3,mh3,sw3,sh3 = 2,3,2,2\n",
    "d1,dr1,da1,r1 = 64,0.2,'relu',0.2\n",
    "d2,dr2,da2,r2 = 32,0.2,'relu',0.2\n",
    "num = 6\n",
    "model6 = modelBuilder3L(mel2_train_reshaped,\n",
    "                        f1,k1,a1,\n",
    "                        mw1,mh1,sw1,sh1,\n",
    "                        f2,k2,a2,\n",
    "                        mw2,mh2,sw2,sh2,\n",
    "                        f3,k3,a3,\n",
    "                        mw3,mh3,sw3,sh3,\n",
    "                        d1,dr1,da1,r1,\n",
    "                        d2,dr2,da2,r2,\n",
    "                        num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "4fe71fb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "15/15 [==============================] - 13s 662ms/step - loss: 34.8135 - accuracy: 0.4236 - val_loss: 33.2904 - val_accuracy: 0.5833\n",
      "Epoch 2/80\n",
      "15/15 [==============================] - 9s 608ms/step - loss: 31.7204 - accuracy: 0.5868 - val_loss: 30.4967 - val_accuracy: 0.4722\n",
      "Epoch 3/80\n",
      "15/15 [==============================] - 8s 519ms/step - loss: 28.7805 - accuracy: 0.6910 - val_loss: 27.7586 - val_accuracy: 0.3750\n",
      "Epoch 4/80\n",
      "15/15 [==============================] - 8s 529ms/step - loss: 26.0131 - accuracy: 0.7361 - val_loss: 25.2757 - val_accuracy: 0.3889\n",
      "Epoch 5/80\n",
      "15/15 [==============================] - 8s 541ms/step - loss: 23.5495 - accuracy: 0.7778 - val_loss: 23.0970 - val_accuracy: 0.3333\n",
      "Epoch 6/80\n",
      "15/15 [==============================] - 9s 563ms/step - loss: 21.3652 - accuracy: 0.7951 - val_loss: 21.2679 - val_accuracy: 0.2500\n",
      "Epoch 7/80\n",
      "15/15 [==============================] - 7s 494ms/step - loss: 19.5391 - accuracy: 0.8368 - val_loss: 19.7662 - val_accuracy: 0.2500\n",
      "Epoch 8/80\n",
      "15/15 [==============================] - 8s 514ms/step - loss: 17.9098 - accuracy: 0.8646 - val_loss: 18.3608 - val_accuracy: 0.2500\n",
      "Epoch 9/80\n",
      "15/15 [==============================] - 7s 493ms/step - loss: 16.5234 - accuracy: 0.8924 - val_loss: 16.9684 - val_accuracy: 0.2917\n",
      "Epoch 10/80\n",
      "15/15 [==============================] - 7s 497ms/step - loss: 15.2242 - accuracy: 0.9410 - val_loss: 15.9562 - val_accuracy: 0.2778\n",
      "Epoch 11/80\n",
      "15/15 [==============================] - 8s 522ms/step - loss: 14.2170 - accuracy: 0.9201 - val_loss: 14.9395 - val_accuracy: 0.2917\n",
      "Epoch 12/80\n",
      "15/15 [==============================] - 8s 551ms/step - loss: 13.2641 - accuracy: 0.9375 - val_loss: 14.2747 - val_accuracy: 0.2639\n",
      "Epoch 13/80\n",
      "15/15 [==============================] - 8s 545ms/step - loss: 12.4511 - accuracy: 0.9410 - val_loss: 13.4018 - val_accuracy: 0.3194\n",
      "Epoch 14/80\n",
      "15/15 [==============================] - 8s 524ms/step - loss: 11.6766 - accuracy: 0.9653 - val_loss: 12.8285 - val_accuracy: 0.3194\n",
      "Epoch 15/80\n",
      "15/15 [==============================] - 7s 490ms/step - loss: 11.0503 - accuracy: 0.9549 - val_loss: 12.1234 - val_accuracy: 0.3333\n",
      "Epoch 16/80\n",
      "15/15 [==============================] - 7s 470ms/step - loss: 10.4687 - accuracy: 0.9688 - val_loss: 11.6306 - val_accuracy: 0.3333\n",
      "Epoch 17/80\n",
      "15/15 [==============================] - 11s 740ms/step - loss: 9.9802 - accuracy: 0.9410 - val_loss: 11.4643 - val_accuracy: 0.2778\n",
      "Epoch 18/80\n",
      "15/15 [==============================] - 9s 619ms/step - loss: 9.5167 - accuracy: 0.9688 - val_loss: 10.9586 - val_accuracy: 0.2778\n",
      "Epoch 19/80\n",
      "15/15 [==============================] - 9s 603ms/step - loss: 9.0902 - accuracy: 0.9618 - val_loss: 10.9358 - val_accuracy: 0.2500\n",
      "Epoch 20/80\n",
      "15/15 [==============================] - 7s 485ms/step - loss: 8.6947 - accuracy: 0.9722 - val_loss: 10.2826 - val_accuracy: 0.3056\n",
      "Epoch 21/80\n",
      "15/15 [==============================] - 7s 476ms/step - loss: 8.3601 - accuracy: 0.9618 - val_loss: 9.6542 - val_accuracy: 0.3472\n",
      "Epoch 22/80\n",
      "15/15 [==============================] - 8s 562ms/step - loss: 8.0644 - accuracy: 0.9583 - val_loss: 9.2492 - val_accuracy: 0.4583\n",
      "Epoch 23/80\n",
      "15/15 [==============================] - 7s 479ms/step - loss: 7.7567 - accuracy: 0.9757 - val_loss: 8.9773 - val_accuracy: 0.4583\n",
      "Epoch 24/80\n",
      "15/15 [==============================] - 7s 459ms/step - loss: 7.5123 - accuracy: 0.9514 - val_loss: 8.7854 - val_accuracy: 0.4306\n",
      "Epoch 25/80\n",
      "15/15 [==============================] - 7s 463ms/step - loss: 7.2381 - accuracy: 0.9826 - val_loss: 8.4335 - val_accuracy: 0.4722\n",
      "Epoch 26/80\n",
      "15/15 [==============================] - 7s 480ms/step - loss: 7.0045 - accuracy: 0.9722 - val_loss: 8.2808 - val_accuracy: 0.4722\n",
      "Epoch 27/80\n",
      "15/15 [==============================] - 9s 634ms/step - loss: 6.7815 - accuracy: 0.9861 - val_loss: 7.7800 - val_accuracy: 0.5694\n",
      "Epoch 28/80\n",
      "15/15 [==============================] - 10s 689ms/step - loss: 6.6033 - accuracy: 0.9757 - val_loss: 7.4587 - val_accuracy: 0.5556\n",
      "Epoch 29/80\n",
      "15/15 [==============================] - 13s 862ms/step - loss: 6.4040 - accuracy: 0.9826 - val_loss: 7.3199 - val_accuracy: 0.5417\n",
      "Epoch 30/80\n",
      "15/15 [==============================] - 8s 531ms/step - loss: 6.2354 - accuracy: 0.9757 - val_loss: 7.1083 - val_accuracy: 0.5694\n",
      "Epoch 31/80\n",
      "15/15 [==============================] - 7s 496ms/step - loss: 6.0498 - accuracy: 0.9826 - val_loss: 7.0360 - val_accuracy: 0.5556\n",
      "Epoch 32/80\n",
      "15/15 [==============================] - 7s 485ms/step - loss: 5.9025 - accuracy: 0.9722 - val_loss: 7.0732 - val_accuracy: 0.5278\n",
      "Epoch 33/80\n",
      "15/15 [==============================] - 10s 681ms/step - loss: 5.7764 - accuracy: 0.9653 - val_loss: 6.6036 - val_accuracy: 0.5972\n",
      "Epoch 34/80\n",
      "15/15 [==============================] - 9s 533ms/step - loss: 5.6218 - accuracy: 0.9896 - val_loss: 6.7812 - val_accuracy: 0.5278\n",
      "Epoch 35/80\n",
      "15/15 [==============================] - 8s 525ms/step - loss: 5.4967 - accuracy: 0.9792 - val_loss: 6.7835 - val_accuracy: 0.5139\n",
      "Epoch 36/80\n",
      "15/15 [==============================] - 8s 504ms/step - loss: 5.3703 - accuracy: 0.9792 - val_loss: 6.1075 - val_accuracy: 0.6111\n",
      "Epoch 37/80\n",
      "15/15 [==============================] - 7s 466ms/step - loss: 5.2537 - accuracy: 0.9722 - val_loss: 5.8192 - val_accuracy: 0.7500\n",
      "Epoch 38/80\n",
      "15/15 [==============================] - 7s 484ms/step - loss: 5.1476 - accuracy: 0.9861 - val_loss: 5.8437 - val_accuracy: 0.6528\n",
      "Epoch 39/80\n",
      "15/15 [==============================] - 7s 481ms/step - loss: 5.0272 - accuracy: 0.9861 - val_loss: 5.6429 - val_accuracy: 0.6806\n",
      "Epoch 40/80\n",
      "15/15 [==============================] - 8s 560ms/step - loss: 4.9188 - accuracy: 0.9896 - val_loss: 5.4769 - val_accuracy: 0.7083\n",
      "Epoch 41/80\n",
      "15/15 [==============================] - 7s 474ms/step - loss: 4.8138 - accuracy: 0.9896 - val_loss: 5.5646 - val_accuracy: 0.6528\n",
      "Epoch 42/80\n",
      "15/15 [==============================] - 7s 476ms/step - loss: 4.7043 - accuracy: 0.9931 - val_loss: 5.7083 - val_accuracy: 0.5694\n",
      "Epoch 43/80\n",
      "15/15 [==============================] - 7s 487ms/step - loss: 4.6158 - accuracy: 0.9757 - val_loss: 5.3179 - val_accuracy: 0.6806\n",
      "Epoch 44/80\n",
      "15/15 [==============================] - 8s 540ms/step - loss: 4.5137 - accuracy: 0.9826 - val_loss: 5.2066 - val_accuracy: 0.6944\n",
      "Epoch 45/80\n",
      "15/15 [==============================] - 8s 533ms/step - loss: 4.4259 - accuracy: 0.9861 - val_loss: 4.9319 - val_accuracy: 0.7917\n",
      "Epoch 46/80\n",
      "15/15 [==============================] - 7s 489ms/step - loss: 4.3426 - accuracy: 0.9861 - val_loss: 4.9002 - val_accuracy: 0.7361\n",
      "Epoch 47/80\n",
      "15/15 [==============================] - 7s 494ms/step - loss: 4.2331 - accuracy: 0.9931 - val_loss: 4.8112 - val_accuracy: 0.7500\n",
      "Epoch 48/80\n",
      "15/15 [==============================] - 7s 457ms/step - loss: 4.2031 - accuracy: 0.9688 - val_loss: 4.7286 - val_accuracy: 0.7500\n",
      "Epoch 49/80\n",
      "15/15 [==============================] - 8s 572ms/step - loss: 4.1199 - accuracy: 0.9861 - val_loss: 4.8490 - val_accuracy: 0.6806\n",
      "Epoch 50/80\n",
      "15/15 [==============================] - 9s 574ms/step - loss: 4.0337 - accuracy: 0.9826 - val_loss: 4.5911 - val_accuracy: 0.7222\n",
      "Epoch 51/80\n",
      "15/15 [==============================] - 7s 490ms/step - loss: 3.9543 - accuracy: 0.9826 - val_loss: 4.3858 - val_accuracy: 0.8194\n",
      "Epoch 52/80\n",
      "15/15 [==============================] - 8s 523ms/step - loss: 3.8738 - accuracy: 0.9896 - val_loss: 4.4113 - val_accuracy: 0.7361\n",
      "Epoch 53/80\n",
      "15/15 [==============================] - 9s 564ms/step - loss: 3.8081 - accuracy: 0.9931 - val_loss: 4.2772 - val_accuracy: 0.8194\n",
      "Epoch 54/80\n",
      "15/15 [==============================] - 7s 457ms/step - loss: 3.7255 - accuracy: 0.9896 - val_loss: 4.2112 - val_accuracy: 0.8194\n",
      "Epoch 55/80\n",
      "15/15 [==============================] - 7s 469ms/step - loss: 3.6809 - accuracy: 0.9861 - val_loss: 4.1422 - val_accuracy: 0.7917\n",
      "Epoch 56/80\n",
      "15/15 [==============================] - 7s 487ms/step - loss: 3.6056 - accuracy: 0.9826 - val_loss: 4.1296 - val_accuracy: 0.7778\n",
      "Epoch 57/80\n",
      "15/15 [==============================] - 7s 482ms/step - loss: 3.5372 - accuracy: 0.9826 - val_loss: 4.0369 - val_accuracy: 0.7917\n",
      "Epoch 58/80\n",
      "15/15 [==============================] - 7s 473ms/step - loss: 3.4554 - accuracy: 0.9896 - val_loss: 4.2993 - val_accuracy: 0.6111\n",
      "Epoch 59/80\n",
      "15/15 [==============================] - 7s 477ms/step - loss: 3.4006 - accuracy: 0.9965 - val_loss: 4.1197 - val_accuracy: 0.6806\n",
      "Epoch 60/80\n",
      "15/15 [==============================] - 7s 467ms/step - loss: 3.3358 - accuracy: 0.9931 - val_loss: 3.9010 - val_accuracy: 0.7778\n",
      "Epoch 61/80\n",
      "15/15 [==============================] - 7s 503ms/step - loss: 3.2757 - accuracy: 0.9896 - val_loss: 3.6786 - val_accuracy: 0.8333\n",
      "Epoch 62/80\n",
      "15/15 [==============================] - 7s 487ms/step - loss: 3.2147 - accuracy: 0.9931 - val_loss: 3.6784 - val_accuracy: 0.8194\n",
      "Epoch 63/80\n",
      "15/15 [==============================] - 7s 476ms/step - loss: 3.1685 - accuracy: 0.9826 - val_loss: 3.6407 - val_accuracy: 0.8333\n",
      "Epoch 64/80\n",
      "15/15 [==============================] - 7s 476ms/step - loss: 3.1148 - accuracy: 0.9896 - val_loss: 3.5147 - val_accuracy: 0.8194\n",
      "Epoch 65/80\n",
      "15/15 [==============================] - 8s 559ms/step - loss: 3.0625 - accuracy: 0.9861 - val_loss: 3.5427 - val_accuracy: 0.8056\n",
      "Epoch 66/80\n",
      "15/15 [==============================] - 8s 507ms/step - loss: 3.0009 - accuracy: 0.9931 - val_loss: 3.5052 - val_accuracy: 0.8194\n",
      "Epoch 67/80\n",
      "15/15 [==============================] - 7s 471ms/step - loss: 2.9774 - accuracy: 0.9792 - val_loss: 3.4390 - val_accuracy: 0.7917\n",
      "Epoch 68/80\n",
      "15/15 [==============================] - 7s 466ms/step - loss: 2.9140 - accuracy: 0.9896 - val_loss: 3.5248 - val_accuracy: 0.7500\n",
      "Epoch 69/80\n",
      "15/15 [==============================] - 9s 610ms/step - loss: 2.8510 - accuracy: 1.0000 - val_loss: 3.3645 - val_accuracy: 0.7917\n",
      "Epoch 70/80\n",
      "15/15 [==============================] - 8s 553ms/step - loss: 2.7798 - accuracy: 0.9965 - val_loss: 3.6096 - val_accuracy: 0.6667\n",
      "Epoch 71/80\n",
      "15/15 [==============================] - 7s 499ms/step - loss: 2.7352 - accuracy: 0.9896 - val_loss: 3.3727 - val_accuracy: 0.7639\n",
      "Epoch 72/80\n",
      "15/15 [==============================] - 8s 541ms/step - loss: 2.6742 - accuracy: 0.9861 - val_loss: 3.1417 - val_accuracy: 0.8194\n",
      "Epoch 73/80\n",
      "15/15 [==============================] - 8s 520ms/step - loss: 2.6377 - accuracy: 0.9896 - val_loss: 3.1703 - val_accuracy: 0.7639\n",
      "Epoch 74/80\n",
      "15/15 [==============================] - 7s 482ms/step - loss: 2.5950 - accuracy: 0.9931 - val_loss: 3.1514 - val_accuracy: 0.7778\n",
      "Epoch 75/80\n",
      "15/15 [==============================] - 8s 501ms/step - loss: 2.5321 - accuracy: 0.9826 - val_loss: 3.0937 - val_accuracy: 0.7361\n",
      "Epoch 76/80\n",
      "15/15 [==============================] - 8s 516ms/step - loss: 2.4886 - accuracy: 0.9896 - val_loss: 3.0164 - val_accuracy: 0.7917\n",
      "Epoch 77/80\n",
      "15/15 [==============================] - 8s 511ms/step - loss: 2.4764 - accuracy: 0.9792 - val_loss: 2.9259 - val_accuracy: 0.8056\n",
      "Epoch 78/80\n",
      "15/15 [==============================] - 8s 509ms/step - loss: 2.4208 - accuracy: 0.9826 - val_loss: 2.9255 - val_accuracy: 0.7778\n",
      "Epoch 79/80\n",
      "15/15 [==============================] - 7s 496ms/step - loss: 2.3533 - accuracy: 1.0000 - val_loss: 2.8512 - val_accuracy: 0.8056\n",
      "Epoch 80/80\n",
      "15/15 [==============================] - 9s 576ms/step - loss: 2.3264 - accuracy: 0.9861 - val_loss: 2.9394 - val_accuracy: 0.7639\n"
     ]
    }
   ],
   "source": [
    "history6 = model6.fit(mel2_train_reshaped,y2_train,validation_data=(mel2_val_reshaped,y2_val),batch_size=20,epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "41b20697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'loss')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dN998M08//bRpCZXmzZubvd5+/frh7u7OQw89xFNPPYVGo+G7774rtkysR48erFq1itmzZ9OrVy+cnJzMfs0v7IMPPmD06NGEhoYydepU0xIqrq6uvP7665V6TTeqyHv2v//9jwEDBtC9e3ceffRRWrRowaVLl1i7di1HjhwB4N1332Xjxo0MHjyYRx99lHbt2hETE8NPP/3Ezp07cXNzY+TIkQQGBjJ16lSef/55rKysWLJkCd7e3kV+ACjJyJEjsbW1ZezYsTz22GOkpaXx1Vdf0aRJE2JiYkz7ubi4sGDBAh555BF69erFhAkTcHd35+jRo2RkZJj9MGBjY8N9993HokWLsLKy4v7776/amyuEEA2QfH8oW2P4/lCcsWPHMnToUF5++WUuXbpEly5d2LhxI7/99hvPPPOM6Uf6N998k3/++YdbbrmF5s2bc/XqVT799FP8/f0ZMGAAoH7O+/r60r9/f3x8fDh9+jSLFi3illtuKTLnXYgy1VIXeSGqxVtvvaU0a9ZM0Wq1ZkuCAMqMGTOKfc7XX3+thISEKDqdTmnbtq2ydOnSci1HYlxC5calubZu3VqhZa6++uorBVCcnZ2LLKmlKIpy6tQpZfjw4YqTk5Pi5eWlTJs2TTl69GiR5UnKE7OiKMqxY8eUwYMHK3Z2dkqzZs2Ut956S/n666+LLKGya9cupW/fvoq9vb3i5+envPDCC8qGDRuKvLa0tDRlwoQJipubmwKYli4pbgkVRVGUzZs3K/3791fs7e0VFxcXZezYscqpU6fM9jG+lhuXMiluSbPilPc9UxRFOXHihHLHHXcobm5uip2dndKmTRvl1VdfNdsnIiJCmTRpkuLt7a3odDqlZcuWyowZM5Ts7GzTPgcPHlT69Omj2NraKoGBgcr8+fNLXILtlltuKTbu33//XencubNiZ2enBAUFKe+9956yZMmSYl/z77//rvTr18/0Pvbu3Vv54YcfihzTuCzPyJEjS33PhBCiMZPvD/L9QVGKLsGmKIqSmpqqzJo1S/Hz81NsbGyUkJAQ5YMPPjBbPm/Lli3KuHHjFD8/P8XW1lbx8/NT7r//fuXs2bOmfb744gtl0KBBiqenp6LT6ZTg4GDl+eefV5KTk0uNSYjiaBSlhjosCCGEqHFHjx6la9eufPvttzz44IOWDkcIIYQQQlSRzEkXQoh67KuvvsLJyYk777zT0qEIIYQQQohqIHPShRCiHvrjjz84deoUX375JTNnzqyxzrdCCCGEEKJ2Sbm7EELUQ0FBQcTFxTFq1Ci+++47aUojhBBCCNFASJIuhBBCCCGEEELUETInXQghhBBCCCGEqCMkSRdCCCGEEEIIIeqIRtc4zmAwcOXKFZydndFoNJYORwghhEBRFFJTU/Hz80Orld/Pq4N83gshhKhLKvJZ3+iS9CtXrhAQEGDpMIQQQogioqKi8Pf3t3QYDYJ83gshhKiLyvNZ3+iSdGMH5KioKFxcXCwcjRBCCAEpKSkEBARIl/5qJJ/3Qggh6pKKfNY3uiTdWPLm4uIiH9pCCCHqFCnLrj7yeS+EEKIuKs9nvUx8E0IIIYQQQggh6ghJ0oUQQgghhBBCiDpCknQhhBBCCCGEEKKOaHRz0oUQQgghhBCiLtDr9eTm5lo6DFFNbGxssLKyqvJxJEkXQgghhBBCiFqWlpbG5cuXURTF0qGIaqLRaPD398fJyalKx5EkXQghhBC15rPPPuOzzz7j0qVLAHTo0IHXXnuN0aNHAzBkyBC2b99u9pzHHnuMzz//vLZDFUKIGqPX67l8+TIODg54e3vL6h4NgKIoxMfHc/nyZUJCQqo0oi5JuhBCCCFqjb+/P//9738JCQlBURS++eYbxo0bx+HDh+nQoQMA06ZN48033zQ9x8HBwVLhCiFEjcjNzUVRFLy9vbG3t7d0OKKaeHt7c+nSJXJzc6uUpFu0cdw///zD2LFj8fPzQ6PRsGbNmjKfs23bNrp3745Op6NVq1YsW7asxuMUQgghRPUYO3YsY8aMISQkhNatW/POO+/g5OTEnj17TPs4ODjg6+trusg650KIhkpG0BuW6vp7WjRJT09Pp0uXLixevLhc+1+8eJFbbrmFoUOHcuTIEZ555hkeeeQRNmzYUMORCiGEEKK66fV6Vq5cSXp6OqGhoabty5cvx8vLi44dOzJnzhwyMjLKPFZ2djYpKSlmFyGEEKI+smi5++jRo01z0Mrj888/p0WLFnz00UcAtGvXjp07d7JgwQJGjRpVU2EKIYSoguvpOZyKSaFLgBtOuvo5yyoyMQN7Wyu8nXWWDqVBOH78OKGhoWRlZeHk5MSvv/5K+/btAZgwYQLNmzfHz8+PY8eO8eKLLxIWFsYvv/xS6jHnzZvHG2+8Ue2xJmXkcCjyOhqNhqFtmlT78YUQQogb1atvS7t372b48OFm20aNGsUzzzxT4nOys7PJzs423Zdf1oUQovYcirzO9O8PEpeSjbVWQ9cAN/q38mJAiBddA9ywsbJoQVe5HLh0jfu/2oPO2ooV0/rQ2d/N0iHVe23atOHIkSMkJyezevVqHnroIbZv30779u159NFHTft16tSJpk2bMmzYMM6fP09wcHCJx5wzZw6zZ8823U9JSSEgIKDKse4MT2DmisN0DXCTJF0IIapRUFAQzzzzTKm5XGNVr5L02NhYfHx8zLb5+PiQkpJCZmZmsU0XauqXdSGEqGmJadks2hrO6oOX8XWxo38rLwaGeNGnpWe9GJH+YV8kr/12gly9goOtFRk5eg5EXOdAxHUWbjmHh6MtTwwJ5oG+zbGzqfqaojXhWnoOT/5wmFy9Qq4+j4eW7OPHx0IJ8XG2dGj1mq2tLa1atQKgR48e7N+/n4ULF/LFF18U2bdPnz4AhIeHl5qk63Q6dLrqr3Ro11SdDx8Wm4reoGCllfmjQojGa8iQIXTt2pWPP/64ysfav38/jo6OVQ+qAar73/KqqKZ+WRdCFC86KZNd5xLYcyGREB9npg8p+Ut1ZWXm6PluzyUiEjN4aXRbnO1sqv0chWXn6TkcmcSu8ATOxqXS0c+V/iFedG7minUNjASnZefxfzsu8NU/F0jP0QOQmpXGuatpLPv3Upkj0ilZuey9cI1d4QmkZOXywqi2+LraVSmmfRevsXxvBLd0asrIDr6l7puTZ+D1P06yYm8kADd38OXDe7twPT2HXeEJ7AxPYFd4AtfSc3h77WmW7rrEM8NDuLO7f4USIL1B4eSVZHacS+D45WTu7uHP8PY+ZT+xnAwGhWd/PEJMchYtvRxxtrPm6OVkHvh6L6sf70eAh3Qcry4Gg8Gs6q2wI0eOANC0adNajKhAkKcjdjZaMnP1RF7LoIWXfKEUQoiSKIqCXq/H2rrsNNPb27sWIqqf6lWS7uvrS1xcnNm2uLg4XFxcSly6oKZ+WReiLjh5JZnsPAPdA91r5XyZOXr2XbrGhfg0s+2KAhcT0tkVnsCFhHSzx7ycbLmnZ/X8MJarN/DTgcss3HKWuBT1C72jzpr/jGlXLccvLDkjl9WHLrPjXDx7L1wjM1dvemzDyTg+2nQWZztrQlt6MiDEi/6tvGjp5Vilrp7ZeXpW7I1k0d/hJKbnANCxmQuzR7QmO9fAzvwENyIxw2xE2tHWir4tPQlu4sSBS9c4ejkZvUExHXfHuQQ+m9idnkEeRc6pKAo7wxNIz9bTt6UHbg62Zo+fjknhgw1h/H3mKgB/Hovh8wd6MKKEZPhqShbTlx/iYMR1NBp4bmQbnhgSjEajwUlnzX29A7mvdyB5egOrD17m483niE7K5PnVx/jynws8MTSYwa2b4OFoW+TYiqIQkZhhSvL/PZ9Icmau6fH1J2N56qZWPDO8Ndpikv1LCekci07m5g6+2FqX/ePKF/9cYGtYPDprLYsndsfXxY7xX+7mbFwaE/9vL6sfD6WJS9V+/GiM5syZw+jRowkMDCQ1NZUVK1awbds2NmzYwPnz51mxYgVjxozB09OTY8eOMWvWLAYNGkTnzp0tEq+VVkMbH2eOXk7mdEyKJOlCiBqhKIrZd43aZG9jVa7vL5MnT2b79u1s376dhQsXArB06VKmTJnCunXreOWVVzh+/DgbN24kICCA2bNns2fPHtLT02nXrh3z5s0zm7p8Y7m7RqPhq6++Yu3atWzYsIFmzZrx0Ucfcdttt9XI667L6lWSHhoayrp168y2bdq0yawjrBC1IStXj5VWY9H5tFHXMrjj03/JyTPw6KCWvDCqTZVHdeNTs8nMMf+ASEjP5t/85PBQRBI5ekOpx7DSauji74qXk46Np+J49bcTdPZ3o41v+cqDFUUhJjmLPL1itv1YdBLzN541/Qjg5WRLQloOy/69xOR+Qfi5Ff9DXVauHoOi4GBbvv/dZeboWfbvJT7bFk5KVp5pu5eTLf1bedGuqQtHo5JMCeLGU3FsPKX+eOjnamca3e4X7FXuJmN6g8JvR6KZv+ksl69nAtDCy5FnR7ZmTMempoRzdCd1JDHqmnmiei09hy1nrrIlP5E2Pr9fsCcHLl0nLC6V+7/aw9yxHZjYJ9D0QbzzXALvbzjDscvJAGg00LmZK/1bedGjuTt/HothzZFoFEX9u7b1debklRRmrDjE0sm96N/Ky+x1HIxQ559fTc3G2c6a/93XjaFti5/Da22l5b7egdzerRnf/HuJT7ed59zVNGatOgpABz8XBrTyol8rL1Iyc9kVnsCOcwlEJ2WaHcdZZ01osCeOOmt+PRzN//4O5+SVFBbc1xWX/AqL2OQsFm45x48HotAbFHo0d+ezid1LTbD3X7rGhxvDAHjjtg6mkufvpvbhns93E3ktgwe+3suqR0NxL+YHBVGyq1evMmnSJGJiYnB1daVz585s2LCBESNGEBUVxebNm/n4449JT08nICCAu+66i1deecWiMbdr6mJK0sd0ssyIvhCiYcvM1dP+NcusWHXqzVHl+p60cOFCzp49S8eOHXnzzTcBOHnyJAAvvfQSH374IS1btsTd3Z2oqCjGjBnDO++8g06n49tvv2Xs2LGEhYURGBhY4jneeOMN3n//fT744AM++eQTJk6cSEREBB4eRQcaGjKNoihK2bvVjLS0NMLDwwHo1q0b8+fPZ+jQoXh4eBAYGMicOXOIjo7m22+/BdQl2Dp27MiMGTN4+OGH+fvvv3nqqadYu3Ztubu7p6Sk4OrqSnJysqy7Kiol/GoqE77ai0YDiycUPzpZG55ZeZg1R66Y7g9o5cUn93erVMKgKArvrD3N/+28WOa+zdzs6RLgipXW/AcBT0db+gV70jfYExc7GwwGhYeW7mPHuQSCvR35feYAHEuYRx2bnMWu/KRzZ3gCV1OLL3sF8HC0ZebQVkzsG8iDX+9j38Vr3NvTn/fv7lJk37iULG5fvIurqdkF5eGt1PLwG0dSixulb+3jxL09AxgQ4kUbH2ezX5kLl1rvPJfAwYjrRX7AaOvrbDpn7xYeRV6/oij8feYq768PIywuFYAmzjqeHh7CvT0DyvUjkMGgcComhV3hCVxKzKBbgBv9Wnni766WYqdn5/HC6mOsPR4DwH29Arinpz8LNp1jZ3gCAI62VjR1syf8alqx57ilc1OeG9mGAHd7Zqw4xIaTcTjYWrH8kT50y6/iWLE3krm/q/PPQ5o48eWknhUacUzOzGXJzotsOBnLmdjUEvezsdLQPdDd9GNI4SkHqw9e5j+/Hicnz0BLL0c+uKczm05dZemui2TnqX8bW2stOXkGmjjr+OyBHvRoXrQK5Vp6DmMW7iA2JYvbu/qxYHxXs7991LUM7v78X+JSsuni78ryaX2r3CNAPpuqX3W+p8t2XeT1P04xvF0T/u+hXtUUoRCiMcvKyuLixYu0aNECOzs7MnLy6nySDkXnpG/bto2hQ4eyZs0axo0bV+pzO3bsyOOPP87MmTOB4kfSX3nlFd566y1AXa7bycmJv/76i5tvvrlyL66W3fh3Lawin0sWHUk/cOAAQ4cONd03zh1/6KGHWLZsGTExMURGRpoeb9GiBWvXrmXWrFksXLgQf39//u///k+WXxPV5qcDUWw4Gcvzo9oWO/IbdS2Dif+315REFjc6CWritPZ4DN/uvkTXADemD2lVbPluenYeS3Ze5J9z8cwY2ooh5ewcfOpKCr8dVRP0WcNb8/n28+wMT2Dsop18+WBP2vtV7Avpgk1nTQm6o615Ay87Gyt6BXnQP8SLga28aO7pUK6SKK1Ww4LxXbnlfzs4H5/Oq2tO8NG9XUzPNRgUfjsazRfbLxRJyqy1GnQ3JNEOOmsm9A5k2qCWpoTopdFtufPTf1l98DLTBrY0a+aVpzfw1A+HiUnOAtRR3oMR1/nflnM42FrR9IY52smZeSSkqX/XZm72zB7Rmtu7NStxjrSVVkNnfzc6+7sxY2grMnP0HIi4ppakn0vg5JUUzsSmciY2la93XsRaqyHQw4HCb11WrsE0MuxiZ830Ia2Y3C8Ie9vyN1HTajV0bOZKx2auxT7uqLNm0YRudNzuyvsbzrByfxQr90cBasL7QN/mzBjaCi8nHXEp6o8lO88lcCDiOi29HXl2RBs6+Rcc+3/3d+ORbw6w41wCk5fu5/upfVixL5If9qn/rx7d0ZcP7ulS4aTV1d6GWSNaM2tEa66mZrH7fCI7zyWw52IijrbWDGjlRf8QL/q08Cjxi8TdPfxp4+PMY98d4EJCOnd9ttv0WM/m7rw4ui1eTjoe/fYA566mcd+Xu3lzXEfu7x1o+rFjZ3gCaw5HE5uSRUtvR965o1ORf+8BHg58P7UP936xmwAPB2zrQYd6UTXGSorTMSX/gCSEEFVhb2PFqTctk9PYV0Pz1p49e5rdT0tL4/XXX2ft2rXExMSQl5dHZmamWW5XnMJTmxwdHXFxceHq1aulPKNhsmiSPmTIEEobyF+2bFmxzzl8+HANRiUaq5SsXOb+fpKMHD3/nk/kw3u6mJU1Xk3J4oGv9xKXkk1rHydaNXFi3fFYXllzghPRybwxrgO2Vlp25JcQn4hWl/vbf+k6K/dF8eigljw8oAWOOmty8gys3B/J/7aEmxLDKcv2m83fLc37G86gKDC2ix9PDw9hVEcfHv32IJHXMrjzs13c1yvQbKRYo4H+wWpn8BuP/dU/F/jf32pFy1vjOvBgaFB1vJ0AeDnp+N993bj/qz38cjiavi09uaenP1vD1NFjY3Ku0UCnZq4MyB917t7cvVzdvrsHujOyvQ8bT8Xx/oYwvppU8AGxcMs59l68hqOtFf/3UC8ir6WzMzzR1LDsfHx6keN5Otoy86ZWTOgTiM66Yh9Y9rZWDAzxZmCIN4xWO7PvvqAmmsYy7Rvn6wPorLVM6d+C6YODcXWomQZ4Go2G6UOCae/nwpMrDpGanccd3Zoxa3hrs+ZnPi523Nndnzu7+5d4LJ21FV882IMH/m8vhyKTuG3xThSFIvPPq6KJsx3jujZjXNdmFX5uJ39Xfn9yADOWH2LvxWu08XHmhZvbcFPbJqa4fp3Rn+d+PMr6k7HM+eU4vxy6TPjVNK5nFMxvd7C1YvGE7iVWf4T4OLNmRn/83R2k23cj0DY/SY9OyiQ5MxdX+5ptVimEaHw0Gk25R7Prohu7tD/33HNs2rSJDz/8kFatWmFvb8/dd99NTk5OqcexsTH//6tGo8FgKH2qZUNUf/8lCFEBiqJw7moarbydim0oBfDzwctk5OjRaiAjR88Tyw/xxJBgnh3ZhtSsXB78eh8RiRkEejjw3dQ+NHHW8fn2C6bRyTOxqdjbWLH7QiIATjprJvYJZGe4Oqr60aazfLM7gvG9/PnjaAyR1zIAaO7pQEc/V9Yej+GDDWGciE7mw3u6lJgc7D6fyLaweKy1Gp4d0RqAtr4u/D6zP0+tPMI/Z+NZ9u+lIs/7YvsF+rTw4MXRbU2N5lbui+SddacBeH5Um2pN0I36tPTk2ZFt+GBDGK/+doIfD0RxIOI6AM521jw+OJiJfQKLNCwrrxdubsPm03FsOhXHwYhr9GjuwT9n41m0Vf3h4d07OxEa7ElosCfje6kjpmevppJUKCED0Go0dPBzKfF9ryhPJx23dvbj1s5+KIpC5LUM06h+YSFNnPB0qp3mloNbe7P9+aGk5+SZyuErw8HWmqWTe3PfV3s4HZOCi501C+/vVmfWkPZy0rFiWl/OXU0lpIlzkSTaSWfNZw9059Nt5/lwYxj7L6n/Hh1trQgN9qR/Ky9GdvClWQl9Doyae0oDscbC1d6GZm72RCdlciYmhT4tPS0dkhBCWIStrS16fdkN7nbt2sXkyZO54447AHVk/dKlSzUcXcMhSbpoFN768zRLdl3kyZta8ezINkUeVxSF7/ZEAPDare25kpzFl/9c4NNt5zl5JYXkzFzC4lJp4qxj+SN98MlvOFV4dPJIVBIAtlZaHgxVS4g9HG0xGBT+PB7DRxvDiEjMYPHW8wB4O+t4algI9/VS5x73z5/T+9eJWM7Hp/Hlgz0JumFOr6Io/Hf9GQDu7x1o9ribgy1LJ/fixwNRXLxhxDYpI4c1R66w9+I17vz0X0a296F3Cw9Tgv7Y4JY8UQNLpRlNHxzMnguJ7Mgvo9ZZa5ncP4jpg4MrnZwbtWrizD09Alh1IIr3/grjkwndmLXqCIoCE/oEFhmN1Wo1tPWt3Tm/Go2G5p6OdSKpc3e0rZZGZ64ONix/pA+/HLrMyPa+BHrWreXIrMr4O2s0GmYMbUW3QDcORybRp4UHXW5Yyk6Iwto1dVaT9NhUSdKFEI1WUFAQe/fu5dKlSzg5OZU4yh0SEsIvv/zC2LFj0Wg0vPrqq41yRLyyJEkX9ca5uFSeW30MDwcb3r+7S7k7Z/91PIYlu9T51l/+c4EJfQJp6mo+QrYrPJEL8ek46ay5u2cATjprOvi58OLPx9h+Nh4ANwcbvn+kT5G1kQe39uaPJwcw9/eT+LrY8eSwELMROK1Ww21d/Bjd0ZdV+6P4+dBlhrfzYUr/ILOypgl9Amnj68z07w9yNi6NsYt28vSwEB7o29xU+r3hZCxHo5JwsLXiyWGtirxWK62G+3sX3zHz6eGtWbj5LKsPXjbrSH5/70BeurltlUuUS6PVavh4fFde/PkYPi52zLypVZG/QVU8MyKENUei2XfpGnd//i+J6Tm0a+rCa7e2r7ZziKI8HG15ZGBLS4dRJf2C1U78QpSlXVMXNp++yumYFEuHIoQQFvPcc8/x0EMP0b59ezIzM1m6dGmx+82fP5+HH36Yfv364eXlxYsvvkhKivz/s7ws2t3dEqSDbu0Ki01l+d4Ipg8JLjUp2xp2lX0XrzGhd2CRJBhg/YlYnv3xCOn5y4P5utjx+YM96BrgVur5IxMzuOV/O0jNzsPB1oqMHD339Qrgv3eZr7f76LcH2HgqjkmhzXlzXEfT9lNXUpi+/CDJmbl8M6U3Xco4X3WIS8niifx1pkFd1uuZEa0Z19WP0Qt3cCE+naduasXsYioCyiP8aiofbAhjw8k47ujWjA/v6dIg5tTOW3eaL/65AKhly388OYCW3k4WjkqI8pHPpupX3e/puuMxPLH8EF38Xflt5oBqiFAI0ZiV1gVc1F8Noru7qN+ORCUR6OFQbNdyUJPB+7/aw7X0HBLTc1g8oXux+yVl5DBz+SHSc/R8veMiE/sGMnNoKzyddOgNCh9vPssn+Y3N+rTwICEtm/Px6dz7+W7evr0j9/YKKPa42Xl6ZuQ3yerR3J0XRrVh/Jd7+PFAFI8MbEmrJmoCdyUpk82n1VHlB/s2NztGez8X/n52CDl5hgp13K4KHxc7Vj3al18ORbNg81muJGfxwupjvL/+DAlpOXg42jJtUOVHL1s1ceaLB3uSkpWLs866RkfQa9P0IcGs3B9FcmYu797ZSRJ0IUT1MOghMZzOWnXJwrC4VPQGpUH8uCmEEKJuksl3olJ+P3qF2xfvYvj87fx7PqHI41HXMnjg//ZxLV3t4PjX8Zgi86SNvtsdQXqOHp21lhy9gaW7LjHo/a0s2HSWR77Zb0rQH+7fguWP9GHNjP4Mb+dDjt7ACz8f49U1J8jJKzrHZd66MxyPTsbdwYZP7u9Gn5aejGjvg0GBDzacMe23Ym8kBgX6tvQwW8LLyEqrqbUE3cjaSsu9vQLY+twQXh7TDjcHGxLS1PdyxtBWONtVvbOwi51Ng0nQQZ2T/9PjoXw/tU+luoILIUSxjv0Ii3vTbO9b2NtYkZVr4FJi8Z9nQgghRHWQJF1UWHaenvfzm5ddS8/hwa/38fXOi6bl9IxLlcWmZBHSxIm+LT0wKOp88Btl5uhZmt+J/P27O/Pd1N50bOZCeo6ehVvOsTUsHp21lvn3duG1se2xttLibGfDlw/2YNZwtbP5d3si6Pffv3lm5WF+OhBFTHImfx2PMXU4n39vV/zy54i/MKoNWg1sOBnHocjrZOfpWblfXa9xUg10Nq8qOxsrpg1qyT8vDGXW8NZMHdCCB/oWP+dcQGsfZwaEyPxiIUQ18usKgCbmGO181OlYMi9dCCFETZJyd1FhK/ZGcvl6Jt7OOvoHe7LmyBXe+vMUJ6KTefHmtjy0RF2qLMDDnu+m9iHqegb3fL6bnw9eZtbwEJq4FMzP+PFAFNfScwjwsOeWTk2xttLSP9iLdSdiWLDpLHqDwqIJ3enYzNUsBq1Ww9PDQ+jg58Jzq4+SkJbNmiNXWHPkCoCpDPHxwcEMbVuwLFSIjzN3dffnp4OXee+vM0zoE0hCWg4+LjpGtPephXevclzsbHh6eIilwxBCiMbHqzXYOEBuOgM9rnPosjWnY1K4tbOfpSMTQgjRQEmSLiokLTuPRfnl508PC2Fin0A6+7vxzrrT/Ho4mrXHYsjRG9Slyqb2xdfVDl9XO3o2d+dAxHW+3nmROWPaAZCrN5hG1x8dFIx1/tJHWq3GbH3p0kqyh7f3Ye9/hnEoIold4QnsDE/g2OUk9AaFns3deXZk6yLPmTWiNb8dVZcjOx+vlixO6N1cll4SQghRlNYKmnaByN300kUCLTkTk2rpqIQQQjRgkpWICvnqnwskpufQwsuR8b0C0Gg0PDygBd9P7YOHoy05eoNpqbLC6yZPz1+D+/s9ESRn5ALwx9ErRCdl4uVkyz09/Is9X3nmTOusrQgN9uS5UW1YM6M/h18dyfJH+vDt1N7FJt5+bvZM7hcEQEJaNtZaDff3Lr75nBBCCEHTrgCE5Kk/Uku5uxBCiJokSboot4S0bP5vhzry/dzINmYJcGiwJ388OYAZQ4NZ9WgorW9owDa0TRPa+DiTnqPn+70RGAwKn28/D8CU/i1M64BXB1cHG/q38jJbg/xGTwwJxtlOfXxUR1+zEnwhhBDCjF83ADxTTgFwJTmLpIwcS0YkhBCiAZMkXZTbor/DSc/R09nflTGdfIs83szNnudHtaWNb9EO6VqthseHqMuGLdl5kXUnYjgbl4aTzpoHblj2rDa4Odjy5rgOtPV15ulhMtdbCCFEKfKTdOurJ2jupi47elpK3oUQQtQQSdJFuUQmZrB8bwQAL93ctlJLd43t7Ie/uz2J6Tk8/9MxACb2DcTVvurLiVXGHd38Wf/MoCKj/kIIIYQZz1Zg6wS5GQzxSgLgTKyUvAshhKgZkqQ3ImnZeYRfTavw8/L0Bt7fcIZcvcLAEC/6tarcElfWVloeHaSOpmfm6rG10jK1f4tKHUsIIYSoNVqt2jwOCLVTl+2UeelCCFFxQUFBfPzxx6b7Go2GNWvWlLj/pUuX0Gg0HDlypErnra7j1Bbp7t6IzF51hI2n4vh0YnfGdGpa4n6KonA+Pp2d5+LZGZ7InguJpGXnAfDizW2rFMM9PQJYuPkciek53NXDX+aCCyGEqB/8ukHELtoZwoG2Uu4uhBDVICYmBnd392o95uTJk0lKSjJL/gMCAoiJicHLq3KDjbVNkvRGIjNHz7aweADm/n6S/q28ii0zT8/OY9KSfRyMuG623c3BhieGBBdZr7yi7G2tePfOTqw+eJlnZN1vIYQQ9UV+h3ef9DDgVs7GpZKnN5iWDxVCCFFxvr5F+1zVBCsrq1o7V3WQT5ZGYt+la+ToDQDEp2bzwYYzRfZRFIVX1pzgYMR1bK21DGjlxYs3t+WPmQM4+MoIHh0UXC2xjOrgy1eTeuIjo+hCCCHqi/zmcbrEk7jYKmTnGbiUmG7hoIQQDYaiQE66ZS6KUq4Qv/zyS/z8/DAYDGbbx40bx8MPP8z58+cZN24cPj4+ODk50atXLzZv3lzqMW8sd9+3bx/dunXDzs6Onj17cvjwYbP99Xo9U6dOpUWLFtjb29OmTRsWLlxoevz111/nm2++4bfffkOj0aDRaNi2bVux5e7bt2+nd+/e6HQ6mjZtyksvvUReXp7p8SFDhvDUU0/xwgsv4OHhga+vL6+//nq53quqkpH0RmJXeAIA7Zq6cDomheV7I7mjmz89mheUl/x4IIpfD0djpdWw/JE+9ArysFS4QgghRN3i0RJ0LmiyUxjmeZ1fYzw4FZNKqybSfFQIUQ1yM+BdP8uc+z9XwNaxzN3uuecennzySbZu3cqwYcMAuHbtGuvXr2fdunWkpaUxZswY3nnnHXQ6Hd9++y1jx44lLCyMwMDAMo+flpbGrbfeyogRI/j++++5ePEiTz/9tNk+BoMBf39/fvrpJzw9Pfn333959NFHadq0Kffeey/PPfccp0+fJiUlhaVLlwLg4eHBlStXzI4THR3NmDFjmDx5Mt9++y1nzpxh2rRp2NnZmSXi33zzDbNnz2bv3r3s3r2byZMn079/f0aMGFHm66kKGUlvJHacU5P0xwe35J4e/igK/OeX4+Tmj66fiU3htd9OAvDsyNaSoAshhBCFFWoeN8DxMiDN44QQjYu7uzujR49mxYoVpm2rV6/Gy8uLoUOH0qVLFx577DE6duxISEgIb731FsHBwfz+++/lOv6KFSswGAx8/fXXdOjQgVtvvZXnn3/ebB8bGxveeOMNevbsSYsWLZg4cSJTpkzhxx9/BMDJyQl7e3t0Oh2+vr74+vpia2tb5FyffvopAQEBLFq0iLZt23L77bfzxhtv8NFHH5lVCnTu3Jm5c+cSEhLCpEmT6NmzJ1u2bKnM21chMpLeCMSnZpu+SPRv5cWgEG+2nLlKWFwq/7fjIpNCm/PE8kNk5xkY3Nqbx6uprF0IIYRoUPy6wqUddNRcADpLki6EqD42DuqItqXOXU4TJ05k2rRpfPrpp+h0OpYvX859992HVqslLS2N119/nbVr1xITE0NeXh6ZmZlERkaW69inT5+mc+fO2NkVTIkNDQ0tst/ixYtZsmQJkZGRZGZmkpOTQ9euXcv9GoznCg0NNVtWun///qSlpXH58mXTyH/nzp3Nnte0aVOuXr1aoXNVhiTpjcC/5wtK3b2cdAC8PKYdz/50lIVbzrLvYiIX4tPxdbFj/r1d0Gorvga6EEII0eDlz0v3zwwD4OQVSdKFENVEoylXybmljR07FkVRWLt2Lb169WLHjh0sWLAAgOeee45Nmzbx4Ycf0qpVK+zt7bn77rvJycmptvOvXLmS5557jo8++ojQ0FCcnZ354IMP2Lt3b7WdozAbG/NG2xqNpsic/JogSXojsDO/1H1gSMGSA3d2b8bqg5fZfSGRrWHxWGk1fDKhG575SbwQQgghbpCfpDtcP41Ok0d8KlxNyZLlRIUQjYadnR133nkny5cvJzw8nDZt2tC9e3cAdu3axeTJk7njjjsAdY75pUuXyn3sdu3a8d1335GVlWUaTd+zZ4/ZPrt27aJfv3488cQTpm3nz58328fW1ha9Xl/muX7++WcURTGNpu/atQtnZ2f8/f3LHXNNkTnpDZyiKKamcf1bFSTpGo2Gd+7oiG3+0jEyD10IIYQog3sLsHNFo89hqEciIKPpQojGZ+LEiaxdu5YlS5YwceJE0/aQkBB++eUXjhw5wtGjR5kwYUKFRp0nTJiARqNh2rRpnDp1inXr1vHhhx+a7RMSEsKBAwfYsGEDZ8+e5dVXX2X//v1m+wQFBXHs2DHCwsJISEggNze3yLmeeOIJoqKiePLJJzlz5gy//fYbc+fOZfbs2Wi1lk+RLR+BqFEXEtK5kpyFrZWW3jck4S29nfhyUg/euK2DzEMXQgghyqLRmNZLH+IcDcDJK8kWDEgIIWrfTTfdhIeHB2FhYUyYMMG0ff78+bi7u9OvXz/Gjh3LqFGjTKPs5eHk5MQff/zB8ePH6datGy+//DLvvfee2T6PPfYYd955J+PHj6dPnz4kJiaajaoDTJs2jTZt2tCzZ0+8vb3ZtWtXkXM1a9aMdevWsW/fPrp06cLjjz/O1KlTeeWVVyr4btQMjaKUc2G8BiIlJQVXV1eSk5NxcXGxdDg17pt/LzH395P0C/ZkxbS+lg5HCCFEMRrbZ1NtqLH3dNNc2PUxp/3uZPSFuxnd0ZfPHuhRfccXQjQKWVlZXLx4kRYtWpg1ShP1W2l/14p8LslIegO3s5hSdyGEEEJUUv689ICss4CUuwshhKh+Fk/SFy9eTFBQEHZ2dvTp04d9+/aVuG9ubi5vvvkmwcHB2NnZ0aVLF9avX1+L0dYveXoDe86rc+YKN40TQgghRCX5dQXAMSkMW3KJvJZBSlbR+Y5CCCFEZVk0SV+1ahWzZ89m7ty5HDp0iC5dujBq1KgS15575ZVX+OKLL/jkk084deoUjz/+OHfccQeHDx+u5cjrh6OXk0jNzsPNwYYOfq6WDkcIIYSo/9yag707GkMuA13U7yunZDRdCCFENbJokj5//nymTZvGlClTaN++PZ9//jkODg4sWbKk2P2/++47/vOf/zBmzBhatmzJ9OnTGTNmDB999FEtR14/7DynjqL3C/bEStY+F0IIIapOozGVvA91UZvHnYiW5nFCCCGqj8WS9JycHA4ePMjw4cMLgtFqGT58OLt37y72OdnZ2UUm4Nvb27Nz584Sz5OdnU1KSorZpbHYGR4PwIBW3haORAghhGhA8ju8d7W6CMhIuhCi8hpZD+8Gr7r+nhZL0hMSEtDr9fj4+Jht9/HxITY2ttjnjBo1ivnz53Pu3DkMBgObNm3il19+ISYmpsTzzJs3D1dXV9MlICCgWl9HXZWWncfhyCRA5qMLIYSoOz777DM6d+6Mi4sLLi4uhIaG8tdff5kez8rKYsaMGXh6euLk5MRdd91FXFycBSMuRv5IevPsMECaxwkhKs7KygpQBy5Fw2H8exr/vpVlXR3B1JaFCxcybdo02rZti0ajITg4mClTppRYHg8wZ84cZs+ebbqfkpLSKBL1vRcSyTMoBHo4EODhYOlwhBBCCAD8/f3573//S0hICIqi8M033zBu3DgOHz5Mhw4dmDVrFmvXruWnn37C1dWVmTNncueddxa7zq3FNFPX/XVKPocd2YTHa8jK1WNnU7UvZUKIxsPa2hoHBwfi4+OxsbFBq7V4P29RRQaDgfj4eBwcHLC2rlqabbEk3cvLCysrqyK/jsfFxeHr61vsc7y9vVmzZg1ZWVkkJibi5+fHSy+9RMuWLUs8j06nQ6fTVWvs9cHWMLWZzQAZRRdCCFGHjB071uz+O++8w2effcaePXvw9/fn66+/ZsWKFdx0000ALF26lHbt2rFnzx769u1riZCLcmkGjk3QpF+lr0M02zJaEhabSpcAN0tHJoSoJzQaDU2bNuXixYtERERYOhxRTbRaLYGBgWg0VesHZrEk3dbWlh49erBlyxZuv/12QP31YcuWLcycObPU59rZ2dGsWTNyc3P5+eefuffee2sh4vrjenoOvxxSm9nc3KH4HzyEEEIIS9Pr9fz000+kp6cTGhrKwYMHyc3NNetX07ZtWwIDA9m9e3epSXp2djbZ2dmm+zXag0ajUUfTz65nmIuapJ+8kiJJuhCiQmxtbQkJCZGS9wbE1ta2WqoiLFruPnv2bB566CF69uxJ7969+fjjj0lPT2fKlCkATJo0iWbNmjFv3jwA9u7dS3R0NF27diU6OprXX38dg8HACy+8YMmXUed8uzuCjBw97Zq6yHx0IYQQdc7x48cJDQ0lKysLJycnfv31V9q3b8+RI0ewtbXFzc3NbP/S+tUYzZs3jzfeeKMGo76BXzc4u55uVheBgZy4Ih3ehRAVp9VqizTGFsKiSfr48eOJj4/ntddeIzY2lq5du7J+/XpTM7nIyEizXyKysrJ45ZVXuHDhAk5OTowZM4bvvvuuyId5Y5aRk8eyf9Vus9OHBFe51EIIIYSobm3atOHIkSMkJyezevVqHnroIbZv316lY9Z6Dxo/dV56kDSPE0IIUc0s3jhu5syZJZa3b9u2zez+4MGDOXXqVC1EVX+t2h/F9Yxcmns6MKajlLoLIYSoe2xtbWnVqhUAPXr0YP/+/SxcuJDx48eTk5NDUlKS2Q/wpfWrMar1HjTG5nFpF3EmgzMxWvL0BqytpPmTEEKIqpFPkgYkV2/gq38uAPDooJbyRUEIIUS9YDAYyM7OpkePHtjY2LBlyxbTY2FhYURGRhIaGmrBCIvh6AWugQD0tI0gO8/AhYR0CwclhBCiIbD4SLqoPr8fucKV5Cy8nHTc1d3f0uEIIYQQRcyZM4fRo0cTGBhIamoqK1asYNu2bWzYsAFXV1emTp3K7Nmz8fDwwMXFhSeffJLQ0NC609m9ML+ukBzJTS7RbE1ox8krybT2cbZ0VEIIIeo5SdIbCINB4fPt5wGYOqCFrNUqhBCiTrp69SqTJk0iJiYGV1dXOnfuzIYNGxgxYgQACxYsQKvVctddd5Gdnc2oUaP49NNPLRx1CZp1h9O/091a7QVzMjqFO7pZOCYhhBD1niTpDcSWM1c5dzUNZ501E/sGWjocIYQQolhff/11qY/b2dmxePFiFi9eXEsRVcENzeOkw7sQQojqIEl6A6AoCp9uCwfggdDmuNjZWDgiIYQQohHw6wqAY+YVPEjh1BVrFEWRlVWEEEJUiXQWawD2XbzG4cgkbK21TOkfZOlwhBBCiMbBzhU8QwDoZn2RlKw8Ll/PtHBQQggh6jtJ0huAz/Lnot/dw58mznYWjkYIIYRoRPzUSehDnS8DcFJK3oUQQlSRJOn13KkrKWwLi0ergccGtbR0OEIIIUTjkr9eurF53InoFEtGI4QQogGQJL2eM3Z0v6WzH809HS0cjRBCCNHIGJvH5ZwFFI5Hy0i6EEKIqpHGcfVYZGIGfx67AsDjg2UUXQghhKh1vp1AY4VDdgK+XON4tE6axwkhhKgSGUmvx77ccR6DAkPaeNPBz9XS4QghhBCNj60DNGkHqCXv19JziE6S5nFCCCEqT5L0eio+NZsfD6hNaqYPDrZwNEIIIUQjlt88bnB+87hjl6XkXQghROVJkl5PLdl1kZw8A90D3ejdwsPS4QghhBCNl7F5nJXaPE6SdCGEEFUhSXo9lJKVy/e7IwCYPqSVzHsTQgghLCm/eVxgdhhq87gki4YjhBCifpPGcfXQ8j2RpGbnEdLEiWFtm1g6HCGEEKJxa9IerHToclNoronj2GUbaR4nhBCi0mQkvZ7JytXz9U61nO7xwcFotfIFQAghhLAoa1vw7QhAd+tLpGblcSkxw8JBCSGEqK8kSa9n/joRQ0JaNn6udtzW1c/S4Qgh6pK87LL3MRhAn1vzsQjR2OSXvA91igTg2OUkCwYjhBCiPpMkvZ5ZfyIWgLt7+GNjJX8+IUS+fz6Aef5w+PuS90lPgC8Hw8edIO5k7cUmRGMQ0BuAbpqzAByX5nFCCCEqSbK8eiQzR8/2s/EAjOzga+FohBB1yrGfQJ8Dvz8JJ38t+nhWMnx3B8Qeg9QY+PZ2SDxf62EK0WDlJ+nNMs+iI4dj0ZKkCyGEqBxJ0uuRneEJZOUaaOZmTwc/F0uHI4SoK9ITISFMva0Y4OdpcG5zweM5GbBivJqgO3ipTa7Sr6qJenK0RUIWosFxaw5OvmiVPDprLnAiOhm9QbF0VEIIIeohSdLrkY0n1VL3Ee19pGOsEKJA1F712qs1dLgDDLmw6gGI2A15OfDjgxC5G3Su8OAvMOk38GgJyZHw3e1qGbwQomo0GtNoel+bcDJy9FyIT7NwUEIIIeojSdLriTy9gc2n4wAYJaXuQojCIner1837wR1fQqsRkJcJK+6FH+6D8M1g4wATf4SmXcCpiZqou/hDwln4/k61HF4IUTUBfQAYZH8BgGMyL10IIUQlSJJeTxyMuM71jFzcHGzoFeRu6XCEaFhSY+GLQbDvq9L3O7cJFnSCD1qZXxb1tuz87sg96nVgqLoU1L3fQmA/yE6B81tAawPjv4fAvgXPcQuESWvU8veYo7DiPrUsviTRB+GzAWW/R9UpN0st09/8Ru2dU4iqyE/S2+vPAArHZV66EEKISpAkvZ7YeEodRR/W1gdr6eouRPU6t0lNVPf/X+n7Hf5OLRFPjze/JITB9vdrJ9Yb5WbClcPqbWMSbusAE1ZCsx5gZQt3fw2thhV9rlcIPPirWgYf+a9aFp+XU3S/q6fh+7sg7jhsfh0yr9fYyzFzaQecXQ+7FkK2lA2LeqBpZ7DS4ZiXRJAmlqOyDJsQQohKkGyvHlAUhY2n1PnoIzv4WDgaIRqg65fU68TzoM8reb+Ec+r1rR/D9N3q5b4f1G3Hf4KkyJqMsnjRB9U56M5N1cZVRnauMHUzPHcO2o8r+flNO6tl8Nb2aln8L9PAoC94/NpFtcGcMTHPSYP9X9fISynCWMav6CH6QO2cU4iqsNZBM3W99J7as5y6kkKu3mDhoIQQQtQ3kqTXA2diU4m6lomdjZZBId6WDkeIhuf6RfXakAtJEcXvY9BDYrh6O3go+LRXL23HQMuhaiL57ye1E29hxkQ2sK/auKowrRbs3co+RmBfuO97tSz+1Br442lQFEiJgW/HQVosNOkAo/OrBfZ8po7g1zRjGf+Nt4Woy4zN46zDyc4zcC5OqkCEEEJUjMWT9MWLFxMUFISdnR19+vRh3759pe7/8ccf06ZNG+zt7QkICGDWrFlkZWXVUrSWsfGkWuo+MMQbe1srC0cjRANkHEkHtZFacZIi1HXIre3ANcD8sQGz1OtD30JafI2EWKLC89GrotVwtSxeo1XL+tc9p3Z+T4oA9xZqWXzPqepc9owEOPx9lUMvVV62WiVgZPwxQoi6Ln9eeh8b9Ue949FJFgxGCCFEfWTRJH3VqlXMnj2buXPncujQIbp06cKoUaO4evVqsfuvWLGCl156iblz53L69Gm+/vprVq1axX/+859ajrx2mUrd20upuxA1ojxJurHU3bMVaG/4sazFIPDrDnlZsO+LGgmxWAY9ROX/sFm4KVxltR8Ht+VXA+z/P4g/A85+aid4Zx+wsoZ+T6mP7/of6HOrfs6SxBxV30+ttXo/an/pUxEq69C38MczpVcGGAzw99tl9ywQAsBfHUkPyIvAhXTp8C6EEKLCLJqkz58/n2nTpjFlyhTat2/P559/joODA0uWLCl2/3///Zf+/fszYcIEgoKCGDlyJPfff3+Zo+/12eXrGZy8koJWA8PaSZIuRLXLSoGMxIL7JSbp+du9Qoo+ptEUjKbv+xKyU6s3xpJcPaV2cLd1VsvRq0O3B+Dm/6q3HTzVDvDuhea6d52odoRPjoSTv1bPOYtjHDkPGanOr89NVxvXVaf9/we/PwkHl8LBZSXvF7YO/vkA1j5X+5USov5x8gaPlgB004ZLki6EEKLCLJak5+TkcPDgQYYPH14QjFbL8OHD2b27+LLGfv36cfDgQVNSfuHCBdatW8eYMWNKPE92djYpKSlml/rEWOreK8gDD0dbC0cjRANUeBQdCkbMb2RK0lsX/3jbW8EzRF1vvLSErzoZS90Deqmj3NWl73SYthWe2APebcwfs3VQHwfYuUCdu14TjK+teT8I6Gu+rToc+1FNuo3+XVR8Z3tFgZ3zjXfUbvNClCX/32wPbRhnYlPIztOX8QQhhBCigMWS9ISEBPR6PT4+5qPDPj4+xMbGFvucCRMm8OabbzJgwABsbGwIDg5myJAhpZa7z5s3D1dXV9MlICCgxH3rooKu7r4WjkSIBsqYpNs6q9fxYcUnnsbkvaQkXauF/k+rt3cvVudU1zRT07h+1X/sZt3BqUnxj/V6RH2/rp6Ccxur/9wGg/lce2Mpf8S/1XP8sL/g18cBBXo+DE4+kHIZTqwuuu+lneZz48+srZ4YRMOW3zyuj3U4uXqFsNhaqq4RQgjRIFi8cVxFbNu2jXfffZdPP/2UQ4cO8csvv7B27VreeuutEp8zZ84ckpOTTZeoqKhajLhq4lOz2XfxGiDz0YWoMcbO7i0Hq9dZSebl70allbsbdb5XncOdGgNHV1ZrmEUoCkQU6uxem+zdoOcU9fbOBdV//MRzkHlNXRbOt3NBU7zIPVUfub/4D/z4kNqNv8v9MOYj6PuE+tjOj9UfCAozvr6gger1+b9lzXZRtvzmcV004Vih52hUkmXjEUIIUa9YLEn38vLCysqKuLg4s+1xcXH4+hY/avzqq6/y4IMP8sgjj9CpUyfuuOMO3n33XebNm4fhxi9W+XQ6HS4uLmaX+sBgUHhh9VEMCnTxdyXAw8HSIQlRO7JT1SW+jOty1zTjSHqTduAaqN6+cV56emJB4u7ZquRjWesgdIZ6e9dC8/XGb6QoaiJ/cUelwiY5ClKvqI3VmvWo3DGqou8TYGWrjuZHlNF5PSEcDiwtmgCXxFgh4N8TrG3Br5t6rvSrcO1C5WOOPgQ/3A/6bHV6wm2L1AqIng+DzhUSwtT550ZXjsD5LaCxgnGL1C73+mw1UReiNN5tQeeCnZJFW00UByNq6f9nQgghGgSLJem2trb06NGDLVu2mLYZDAa2bNlCaGjxSwllZGSg1ZqHbGWldllWampepIV8ueMCW8Pi0Vlr+e9dnS0djhC1Z8/nsP4ltZt2bTAm6e5BBaPkNybpifml7q4BYOtY+vF6PAR2bnDtPPz5TMkjv1vehF8fg58mVypsUzl4067qPPHa5tIUutyn3j7wden7rp6ivhfhm8t3bFOpe36FgI2d2j2/8GOVseUNyEmDlkPg7iUF8/jtXKDXVPX2zvkFf7NdH6vXHe9S/320vUW9f+bPyscgGgetFvx7AdBde5YDkqQLIYSoAIuWu8+ePZuvvvqKb775htOnTzN9+nTS09OZMkUto5w0aRJz5swx7T927Fg+++wzVq5cycWLF9m0aROvvvoqY8eONSXrDcH+S9f4YEMYAG/c1oF2TevH6L8Q1eLqKfW6siPMFXUtv9zdvUVBk7Qbm8eVp9TdSOesLmOm0arLe218pWiivnNBQTOyjITiG5aVJdJCpe6FdZ2oXp/dWPJybNcuQuyx/NvlHAUv7rUZb1d2vXR9LkTuVW/f/F+16qGwvtPBSqfOP7+0ExLPw6nf1McGPKNet71VvT67vmaXnxMNQ/6/2V7as1y+nklcSpaFAxJCCFFfVGM74IobP3488fHxvPbaa8TGxtK1a1fWr19vaiYXGRlpNnL+yiuvoNFoeOWVV4iOjsbb25uxY8fyzjvvWOolVLtr6Tk8ueIweoPC7V39GN+rfjW6E6LKjCPbCWFqmbmjZ82dS5+nlo1D6SPpZXV2v1H722Ds/+D3mbB7kTqHe9Dz6mMHlsDm1833z7yurkNeEYUbq1mKfy9w9Ib0eDWxDR5adJ/C5eOpV8o+ZkqM+m9AozWtNw2or3PXx5UfSY85BnmZYO8OXm2KPu7URF1+7sDX6o8obgGgGCBkFPjkL28X0Ftdfi4jASJ2qSPyQpTE2DzOJhxy4WDEdcZ0amrhoIQQQtQHFm8cN3PmTCIiIsjOzmbv3r306dPH9Ni2bdtYtmyZ6b61tTVz584lPDyczMxMIiMjWbx4MW5ubrUfeA0wGBRm/3iE2JQsWno78s4dndBoNJYOS4jaVXhJtKi9NXuulGgw5KkjqM5NC5LwIkm6sbN7OUbSjbo/CKPmqbf/fhv2fgnHV8Ofs9VtA59VE0ZQm6RVROb1gooDS46ka62gzWj1dkldzwtvT4kp+5hR+Um4T0e1DN0oP+Eh8RykJ1Q8VuMIfEBftRS5OP2eVH8cOL8FDn+vbhswq+Dx8rxeUaZ58+bRq1cvnJ2dadKkCbfffjthYWFm+wwZMgSNRmN2efzxxy0UcSU16wEaLT6Gq/hwjQOXpORdCCFE+Vg8SRcFvvjnAtvy56EvntAdR51FCx2EqH1ZyeYJa2VLm8vL2NndvbmauBmT9OsRkFuoNLWiI+lGoU/A4JfU2389D788CijqEmY3vQr2HupjFW2SZyzb9gwBR6+KPbe6GUvAz6wtWtafnmD+N0wtR5JeUoWAgwd4tzPfx8ighwvb1KaDJR63HNMDPFpAhzvzj5mnJvTNb4ijtNcrymX79u3MmDGDPXv2sGnTJnJzcxk5ciTp6elm+02bNo2YmBjT5f3337dQxJWkczZVYfTQnuVgpCTpQgghykeS9DriamoWH22UeeiikSs8ig5VaxJWkfO5B6nXjt5g5wooauM3UNc7N+5X0SQdYMhL0Ge6elvRQ+fxMPoD0GgKjaRX8Mv75f3qdWCf0verDS0Gg42jWsp+5bD5Y2fXqyXjVrbq/XIl6aUk08XNSzfo1R8/vh0HG/5T/DEVpfzTA4zzz8F8FN2oZf7rTYmGmCOlH0sUa/369UyePJkOHTrQpUsXli1bRmRkJAcPHjTbz8HBAV9fX9OlvqzOYiawHwB9tac5GZ1MZk4pKz4IIYQQ+SRJryM2n7pKnkGhs7+rzEMXjZcxGXbKX4bxymHIzaz58xmTdI2maMn7tQtqoqlzAacKzhs3HnPUuzBsLgx8DsYtLii3dsgfSc+oYLm78QeEJh0qHk91s7GDkOHq7RtLwI3329+uXqfElD76nJ0KscfV28Um6YXWSwf1WOuegxOr1funfiu+oVvieXUeuZUO/LqW/np8O6nTFAa9ACEjiz5uYw+thqm3peS9WiQnJwPg4eFhtn358uV4eXnRsWNH5syZQ0ZGRqnHyc7OJiUlxexicS0GATDQ+hR5BoVjl5MsG48QQoh6QZL0OmLjqVgARnXwlXnoovEydloPGqAm6oZcdW3rmj6fe4uCbaYkPX8eeuHO7pX9b1OrhYGzYdirYGVTsL2yI+mmuIMqF091aztWvS6ctOakF6wn3nuaep2bDtmlJE6X96s/iLg1Bxe/oo8bE/eYI5CToTbgO7AE0ICNgzpdImJX0ecZR96b9Sja1b04oU/ATS+XPHe9cMm7qBKDwcAzzzxD//796dixo2n7hAkT+P7779m6dStz5szhu+++44EHHij1WPPmzcPV1dV0CQioAz94Bw0AjZYWRKvz0mUpNiGEEOUgSXodkJqVy7/hiQCM6lCJkTohGgrjyLZHi6ovuVWR8xVOdm/s8F7Z+ejlYZqTXsGR9OLitqSQEaC1hvjT6qg1qAl6Xha4Bapd4O1c1e2psSUfp6ySdLdAcPZT54v/Mq1gHfOxC9W1zKH4xPnGdderqvVI0FipzfuMr1dUyowZMzhx4gQrV6402/7oo48yatQoOnXqxMSJE/n222/59ddfOX++5Pd7zpw5JCcnmy5RUVE1HX7Z7N2gaRcAQrWnOChJuhBCiHKQJL0O2H42nhy9gZZejgR7O1k6HCEsx5R8tiha2lyj5wsq2HZjuXtlOruXV2VG0jOvQ1aSeruuJOn2bhA0UL1tTJKN121vVSsQnPOXnkopZRm2iH/V65KSaY2m4LEzf6rXI9+GHg+V3tDNNM+9mpars3dXR0jBfIk5USEzZ87kzz//ZOvWrfj7+5e6r3Hll/Dw8BL30el0uLi4mF3qhPyS937akxyKvI7BIA0HhRBClE6S9Dpg48k4AEZ08JFSd9G4FU6ajclY1D61OVh1M0t2mxdsL1zubjBAfJj59upUmTnppnn7PmDrUO0hVVrbW9TrM2vV9efD/srfnp88G5P0kprH6fMgOr9xWGnJdOHHBj2vLpsGJTd0S7uaP4dfAwG9KvKKSicl75WmKAozZ87k119/5e+//6ZFixZlPufIkSMANG1aD9cZbzEYgP5WJ0nKyOFCQpqFAxJCCFHXSZJuYTl5BraeuQrAyPa+Fo5GCAvS50Fyfnmqe5C6TratE2Qnw9XT1X8+s2TXsWC7e5Baup2boSZ8ppH0mih3r8RIel0rdTdqM0a9jtoLp9aoP4A4eEJAfgd64xzzkpL0lMvqe26lK/297nA7NO2qNnYb+nLB9pIauhkrMZq0L3i/q0PbMeq/E2ud+m9XlNuMGTP4/vvvWbFiBc7OzsTGxhIbG0tmptok8vz587z11lscPHiQS5cu8fvvvzNp0iQGDRpE586dLRx9JQT2Ba0NzTQJBGquSsm7EEKIMkmSbmF7LiSSmp2Ht7OObgFulg5HCMtJuazONbbSqaOuVtbqXGaomXnpJSW7Vjbg0VK9fXG72uxMY2XeXK66VClJr4F4qsK1Gfh1B5SCpdBaj1b/jgDO+T9CppSQpJteV/OSG7YBODWBx7arjd1urDwqbnS7uuejG7n6wwsXYdJvBa9RlMtnn31GcnIyQ4YMoWnTpqbLqlWrALC1tWXz5s2MHDmStm3b8uyzz3LXXXfxxx9/WDjySrJ1NP2/rJ/2JAcuSZIuhBCidPLNwsKMXd1HtPdBq5VSd9GIFZekBYbCha1qomXsEF5dSuuQ7tVanZNuTPY8WoC1bfWeHyqXpNe1zu6Ftb0FrhyCtLiC+0ZllbtXx+u6saGbZ3D1z0cvzK6OzHmuZ5TSluEDAgIC2L59ey1FU0taDobIf+mvPcECGUkXQghRBhlJtyCDQWHTKfXL7Mj20tVdNHLFJWmmDu810DyutBFpY5M44xJiNVHqDlWbk14nk/RbC27bOEDw0IL7ZZW7V8frurGhW046xBxV71f3SLoQFZHfPC5Ue4oLCWlcS8+xcEBCCCHqMknSLehYdDJxKdk46awJDfa0dDiiMYk5CvPbw+HvLR1JgeKSZv+e6rzflMuQVGg5JYMB1j4Hb3nDGx7ml0/7qclZuc8XVPQxY1Kel5V/vwY6u0PBSHpeJuRmlu85dTlJ924DHsHq7eCb1HniRqbu7mUl6VUs4y9c8n75ACh6cPEHtzqwZrZovJr1BBsHvDQptNZclnnpQgghSiVJugVtPKmWug9p443O2srC0YhG5dwmtSna8dWWjqRAccmnraNpjWHTaLqiwLpnYf9XoM9Rk7DCl6sn1Y7wZZ6vjHL30u5XF52LWp4N5St51+dC8mX1tkcdm5MO6hzxPo+DRgu9HjF/zJikp8UV362/tL9HRbTNb2AXuQdO589hbl4Dpe5CVIS1rWnKRX/tCQ5EVKB6RgghRKMjSboFbTSWuneQru6ilqWpKwqYEuO6oKQkzbReev7c4i1vwIElgAZu/xyeDSu4GDuMxx4v/VxlJbuerczv11SSrtFUbF56cpT6Q4S1ndqVvi7qPQ1eu2Ze6g5qwzeNlRp/enzR51VXhYCrv9r9HQUOLlO3Sam7qAtM66Wf4pCMpAshhCiFJOkWcj4+jfCradhYaRjSxtvS4YjGxtjYKzmq7iwfZUzSbkyaC89L3zEfdi5Q79+6ALrer3YNN16a9VAfKytJT44CxVBysmvvZr79xqS9OhmT9PLMSy+cyN7Y2byu0GiKj01rVfCeplwxfyzzOmQlq7ero4zfWPJuyFWva6JpnBAVlZ+k99Ge4sTla2TnFVNRIoQQQiBJusUYG8aFBnvhYmdj4WhEo2McSTfkqfO9La1wkubW3PyxgPwk/epJdRQdYMSb0HNK0eP45q+hXFaSXrhJXUnJrnH03NG7oMFbTTAeuzwj6aa462Cpe3kYl2G7sXmc8XU5+YCtQ9XPU7irvM4VvNtV/ZhCVFXTLih2rrhoMmmtP8+J6GRLRySEEKKOkiTdQozz0aWru7AI40g61I2S99KSNCdv85Hsgc9C/6eLP45vJ/U64WzpjdjKU1ptbBZXU6XuRqZy9wqOpNdHJXV4r+7X1aRdwQ8ZgX1KX3ddiNqitUITNBBQS973XJB56UIIIYon31wsICEtm8NRSYC6ProQtc44kg51I0kvq7N3yCj1utc0uOnVko/j7AsOXvkN5E5X/nwAAX3yr3uXvE91sK/ASHp9T9JL6vBeXZ3djTQa6DpBvd1mdPUcU4jqYFqK7ST/nk+wcDBCCCHqKmtLB9AY/XM2HkWBDn4u+LjYWToc0djkpENOasF94yi2JZWVfA6fq84/9+lY+lxsjUYdTb+wVS15b9a9hPOVo5N4p3uhSXvwbltG8FVUoTnp+XHXxc7u5VFSuXtN/Pgw8Dm1kaBPh+o7phBV1WIwAL20YTxx6SpZuXrsbGR1FyGEEOZkJN0CtoapnY2Htmli4UhEo1R4FB3qyEh6GUmztU5NvsvTLM1Y8l7avPTyJIVaLTTtrC6dVJMcytndXVHgeoR6u76OpJdY7l5Ny68VptWCbxk/6ghR27zboDj5YK/JoZPhDIcjkywdkRBCiDpIkvRapjco/HM2P0lvK13dhQXUyST9knpdHSPEZTWPK5zs1oUR6fIuwZZ5HbJT1NtugTUbU00pq9y9Lvw9hKhJGg2a4GEADNceYreUvAshhCiGJOm17EjUdZIzc3FzsKFrgLulwxGNkbFpnM5Fva5LSXp1jKQaR9LjToDBUPTxjGt1K9kt75x047QE56ZgY1+zMdUUY5KeWmgJtsJr1tfXCgEhKqLtGABGaA+wK1ySdCGEEEVJkl7Ltp5RR9EHhXhjpZUyTGEBxiTdv6d6nZVUvqZlNaW6kzTPVur65zlpBWXUhcUeVa9dmtWNZLe8I+nX6/nyawAu+Ul6VjLkZKi3kyLz16y3L37NeiEamuCbMFjZEaiNJzP6OGnZeZaOSAghRB0jSXot2xqmlhpLqbuwGGO5u3sLcMzvi2DJ0fTqTtKsrNWGb1B8yfuZdep18E1VP1d1MK6TXlbjuPre2R3U6g0bR/W2cV564dcl88dFY2DriDZ4CAA3cYD9l2QpNiGEEOYkSa9FcSlZnLySgkajjqQLYRHGkXQnn4I5wJZM0msiSSupeZyiwJm16u22t1bPuaqq8Ei6opS8X000V6ttGk3RDu8N4ccHISqqTX7Ju9VBdp9PtHAwQggh6hpJ0mvR9vyu7p393fB00lk4GtFoGUfSnZoUJEaWXIatJpK0kpL0K4fV+dA2jtBySPWdryqMc9L12ZCbUfJ+danZXVWYOrzHqtcN4ccHISqqzWgUNHTRXiDs7BlLRyOEEKKOkSS9FplK3dvIKLqwoMIj6cbEqCIj6dcjIHxL9cVTE2t/l9Th3TiKHjIcbOyq73xVYesIWhv1dmnz0hvKiLOpw3t+8zjp7C4aI6cm5DXtAUBA/HaSMnIsHJAQQoi6pE4k6YsXLyYoKAg7Ozv69OnDvn37Stx3yJAhaDSaIpdbbrmlFiOuuFy9gR3n1C6usj66sCjTSLpPQROy8ibp+lz4Zix8fydE7a+eeGoi+fRpD2jUUfP0Qt2T61qpO6gl4GXNS8/Lbjgd0KXcXQgAbDqo/x8aoT3IngtS8i6EEKKAxZP0VatWMXv2bObOncuhQ4fo0qULo0aN4urVq8Xu/8svvxATE2O6nDhxAisrK+65555ajrxiDly6Tlp2Hp6OtnRq5mrpcERjpSiFRtILlbsX1wW9OCd+gaT8susL26onpppI0nTO4NFSvR17TL1OPA/xp0FrDSEjqu9c1aGsDu9JUYCiluk71vNKHGO5e8oV9d/jtUvqfUnSRWPTRh1cCNWe5ODZSAsHI4QQoi6xeJI+f/58pk2bxpQpU2jfvj2ff/45Dg4OLFmypNj9PTw88PX1NV02bdqEg4NDnU/St51Vf3QY3MYbrSy9Jiwl8zoYctXbhZP05MvqKHlpDAbYuaDgfuTuqsdTk0najfPSjaPoQQMKkuK6wpSklzCS3pA6oJvWSo9VKwdyUtX7bs0tF5MQluDdmnSnIGw1egznNls6GiGEEHVIpZL0rVu3VsvJc3JyOHjwIMOHDy8ISKtl+PDh7N5dvgTg66+/5r777sPR0bHYx7Ozs0lJSTG7WMK2/PXRh0ipu7AkY6m7nRtY69TSY2s7dQm05KjSn3tuQ8FINEDUPjDoqxZPTSZpJSXpdanU3cjYPK6kkfSG1FzNlKRfKfjxwdmv7vQIEKIWWbVX/3/UKW0nV1OyLByNEEKIuqJSSfrNN99McHAwb7/9NlFRZXyxL0VCQgJ6vR4fH/O1kX18fIiNjS3z+fv27ePEiRM88sgjJe4zb948XF1dTZeAgIBKx1tZ0UmZhMWlotXAoBCvWj+/ECaFm8aBOipbng7vigI75qu3+05X17vOSYW4k1WLpyaTtMLN49KuQtRe9X6b0dV7nupgHEkvaU56Q5q37VJoJP3aBfV2Q3hdQlSCXcexAAzVHmFPeNnfe4QQQjQOlUrSo6OjmTlzJqtXr6Zly5aMGjWKH3/8kZyc2u1O+vXXX9OpUyd69+5d4j5z5swhOTnZdKnKjwqVtS2/q3v3QHfcHGxr/fxCmBRefs2oPB3eI3fD5X1gpYPQmRCQ/99c5J6qxXPmD/MYqpNxJD3hLJz8FVDArxu4+lf/uarKoYw56Q2pA7pTfuM4fQ5cOaTebgivS4jK8O9FurU7rpoMYo/9belohBBC1BGVStK9vLyYNWsWR44cYe/evbRu3ZonnngCPz8/nnrqKY4ePVru41hZWREXF2e2PS4uDl9f31Kfm56ezsqVK5k6dWqp++l0OlxcXMwutW1rfqn70LZS6i4s7MaRdChfh3fjXPSuE9QS+cC+6v2qzEvf+0XBcbs9UPnjlMTZFxy81FL+XQvVbW3r6CoQZTWOa0gj6da26t8FIOJf9bohvC4hKkNrRUrgMADco2ReuhBCCFWVG8d1796dOXPmMHPmTNLS0liyZAk9evRg4MCBnDxZeimsra0tPXr0YMuWgjWXDQYDW7ZsITQ0tNTn/vTTT2RnZ/PAAzXw5b6anbqSDECfFh4WjkQ0esUm6UHqdUkd3mNPwLmNoNFCvyfVbYH5/31G7lZL4SvqyA/w1wvq7SH/gW4TK36Msmg0BaPpKdHqdV2cjw6lz0lXlIKpCA0lmTWWvBs77zeU1yVEJbh3vx2A0Ly9XIpPs2wwQggh6oRKJ+m5ubmsXr2aMWPG0Lx5czZs2MCiRYuIi4sjPDyc5s2bl6vj+uzZs/nqq6/45ptvOH36NNOnTyc9PZ0pU6YAMGnSJObMmVPkeV9//TW33347np6elX0JtSInz0BsfjOYQE8HC0cjGr3KlLvv+li9bj8OPIPV237dQWujrnVtXJKtvE7/Ab89od7uOwMGv1Cx51eEMUkHdUk277Y1d66qKG1OenoC5KYDGnALrNWwaoxz/jJsikG9liRdNGJ2rYeRpdHhr0ng6N4tZT9BCCFEg2ddmSc9+eST/PDDDyiKwoMPPsj7779Px44dTY87Ojry4Ycf4ufnV+axxo8fT3x8PK+99hqxsbF07dqV9evXm5rJRUZGotWa/5YQFhbGzp072bhxY2XCr1WxyVkYFNBZa/F20lk6HNHYFTeSbpwPfD1CHbUtvMTXtYtw4mf1dv9nCrbbOoBfV7i8X52XXt4k6/zfsPphNTnr9gCMeqdmlxQzNo8DtdS9ri5f5lDKSLrxxxOXZmpH/obA+YbpTO4yJ100YrYORPsMIzh2HXanfoRbx1k6IiGEEBZWqST91KlTfPLJJ9x5553odMV/afTy8ir3Um0zZ85k5syZxT62bdu2ItvatGmDUpkSWwu4fD0DgGbu9mjqaoIgGo/iRtKNo7PZKWqS6FBoWsbuRWpCHXyTmpQXFtg3P0nfDV3uK/vcqXGw8gG1YVj7cTD2fzWfNBceSa+rpe5Q+jrpDWn5NSOXQj/g2jiCo6x6IRo3l9CH4Nd19E3/m2vJKXi41n7/HCGEEHVHpcrdt2zZwv33319igg5gbW3N4MGDKx1YQ3H5eiYA/u5S6i7qAONIeuGRTBv7grWrCy/DlnYVDn+v3h4wq+ixTPPSy9nhPfqAWrbt2Qru/Aq0VhWLvTK8QqDFIPXi36vmz1dZheek3/gD5NXT6rVXq9qNqSYZ/72B+uOD/IApGjnvTiO5qvHGVZNB2PZVlg5HCCGEhVUqSZ83bx5Lliwpsn3JkiW89957VQ6qITGOpPu721s4EtHo6XMhI1G9XbjcHQp1eC+UpO/9HPKyoFlPCBpY9HgBfdTr+DMlr+9dWPJl9bpJ+9or29ZawUN/qJfa+FGgsowj6YY8yLmhcVTscfW6cFVAfVc4SZfl14QArZYLzdQ1051PS5IuhBCNXaWS9C+++IK2bYs2YOrQoQOff/55lYNqSApG0iVJFxaWngAooLEqGLk1urF5XFYK7Ps/9faAWcWPdDp6gVdr9XbU3rLPnxylXrsGVDDwRsDWAazt1Ns3/uBhStI702C43DCSLoTAo99kANplHCArMcqywQghhLCoSiXpsbGxNG3atMh2b29vYmJiqhxUQyLl7qLOMDWNawI3NGMssgzbwaWQnawm4W3GlHzMiqyXbhxJd/Uvd8iNSnFrpaddhbRYQAM+HSwSVo1wLjQnXZJ0IQAIadeZo5p2WGkUorYts3Q4QgghLKhSSXpAQAC7du0qsn3Xrl3l6ujemEi5u6gzimsaZ1S4w3tuFuxerN7v/0zRhL6wisxLlyS9dKZ56YVG0o2j6J6twNax9mOqKQ4eYGWr3pbO7kIAoNFouBSgdnZ3PftT0f4UQgghGo1KJenTpk3jmWeeYenSpURERBAREcGSJUuYNWsW06ZNq+4Y663Ca6RLki4srrjl14wKl7sfW6nu69IMOt1T+jGNI+nRhyA3s/R9JUkvXXEj6Q1xPjqo0yea9VRL/Js2oDJ+US7z5s2jV69eODs706RJE26//XbCwsLM9snKymLGjBl4enri5OTEXXfdRVxcnIUirj1N+t5PhqKjSXYE+qgDlg5HCCGEhVQqSX/++eeZOnUqTzzxBC1btqRly5Y8+eSTPPXUU8yZM6e6Y6y3ZI10UacULne/kTFJT74MOxeot0NngrVt6cd0b6Em/YZcuHK45P3yciA1Vr0tc9KLZ++mXmcUM5Le0JJ0gAd/hWdOFP/vUTRo27dvZ8aMGezZs4dNmzaRm5vLyJEjSU9PN+0za9Ys/vjjD3766Se2b9/OlStXuPPOOy0Yde3o2SaQzRq1Kee1XUUb9AohhGgcKrVOukaj4b333uPVV1/l9OnT2NvbExISUuqSbI2RrJEu6hRTuXsxI+mO3up61bnp6mi6vTv0eKjsY2o06mj6qd/UeenN+xW/X+oVQAErnayJXRLj+vSZSQXbGmLTOCMbO/UiGp3169eb3V+2bBlNmjTh4MGDDBo0iOTkZL7++mtWrFjBTTfdBMDSpUtp164de/bsoW/fvsUeNzs7m+zsbNP9lJSUmnsRNcTGSkuE/+1w+R+cw3+H3PnqMplCCCEalUqNpBs5OTnRq1cvOnbsKAl6MaRpnKhTSit312jMG3j1ebz8c6DLMy+9cKm7/GBVPFO5e/5Iek4GJJ5TbzfEkXQh8iUnJwPg4aH+UHXw4EFyc3MZPny4aZ+2bdsSGBjI7t0lN6mcN28erq6upktAQP2s2gnqOYrLihd2+jQ4s9bS4QghhLCASo2kAxw4cIAff/yRyMhIcnJyzB775ZdfqhxYQyBN40SdUlrjOFCT9KsnwcYBej9a/uOaOrzvBYOh+EZzMh+9bKbGcflz0q+eAsUAjk3AuZgfVoRoAAwGA8888wz9+/enY8eOgLqCjK2tLW5ubmb7+vj4EBsbW+Kx5syZw+zZs033U1JS6mWiPqStD98YBjHT6hfS932HY6e7LR2SEEKIWlapkfSVK1fSr18/Tp8+za+//kpubi4nT57k77//xtXVtbpjrLdkjXRRp5Q2kg7g11W97vlwQel1efh0UhP77GS4dr74fWSN9LIZR9KNc9Jjj6nXMoouGrAZM2Zw4sQJVq5cWeVj6XQ6XFxczC71kbOdDRf9bwPAIWo7JEVaOCIhhBC1rVJJ+rvvvsuCBQv4448/sLW1ZeHChZw5c4Z7772XwMDA6o6x3pJyd1GnlDYnHdRGcRNXw/A3KnZcK2t1iTCAhHPF7yMj6WVzuGEkvSE3jRMCmDlzJn/++Sdbt27F37/g/w2+vr7k5OSQlJRktn9cXBy+vr61HKVldO3SnV36DmhQ4NB3lg5HCCFELatUkn7+/HluueUWAGxtbUlPT0ej0TBr1iy+/PLLag2wPpNyd1Fn5KRDTqp6u6Ryd1sHCBmhJt0V5d1GvU44W/zjkqSX7cY56ZKkizrom2++Ye3agnnSL7zwAm5ubvTr14+IiIhyHUNRFGbOnMmvv/7K33//TYsWLcwe79GjBzY2NmzZssW0LSwsjMjISEJDQ6vnhdRxI9r5sEI/DAD9wW9Bn2fhiIQQQtSmSiXp7u7upKaqX/ibNWvGiRMnAEhKSiIjI6P6oqvHZI10UacYR9FtHMDWqfqP79VavZaR9MorPCfdoIe4k+r9htjZXdRb7777Lvb26mfa7t27Wbx4Me+//z5eXl7MmjWrXMeYMWMG33//PStWrMDZ2ZnY2FhiY2PJzFSrz1xdXZk6dSqzZ89m69atHDx4kClTphAaGlpiZ/eGxtfVjmsBw4lXXLBKj4Wz68t+khBCiAajUkn6oEGD2LRpEwD33HMPTz/9NNOmTeP+++9n2LBh1RpgfSVrpIs6pXDTuJroru4Vol4XN5KuKIWSdJmTXiLTSPp1SAyH3AywtgfPYMvGJUQhUVFRtGqlTm9Zs2YNd911F48++ijz5s1jx44d5TrGZ599RnJyMkOGDKFp06amy6pVq0z7LFiwgFtvvZW77rqLQYMG4evr2+ia0o7u2pzV+sHqnYNLLRuMEEKIWlWp7u6LFi0iK0sdJX755ZexsbHh33//5a677uKVV16p1gDrK1kjXdQpZTWNqyrTSPpZNSkv/G8+Kwly0tTbrs1q5vwNgTFJVwwQsUu97dMBtFaWi0mIGzg5OZGYmEhgYCAbN240dVO3s7MzjYSXRVGUMvexs7Nj8eLFLF68uErx1mdjOjXlnj+GMZ0/UMK3oLkeAe7NLR2WEEKIWlDhJD0vL48///yTUaNGAaDVannppZeqPbD6TprGiTrFlKSXMB+9qjyCAY2akKcngJN3wWPGUXQHL7CRqR8lsrFTpyPkZsCF7eo2mY8u6pgRI0bwyCOP0K1bN86ePcuYMWMAOHnyJEFBQZYNroHxctLhH9yeHZc6MtDqBBz6Foa9aumwhBBC1IIKl7tbW1vz+OOPm0bSRfGkaZyoU8rq7F5VNnYFIzw3lrwnGZdfk/noZTLOS7+UXzYsSbqoYxYvXkxoaCjx8fH8/PPPeHp6AnDw4EHuv/9+C0fX8NzWxc/UQE45/B3ocy0ckRBCiNpQqXL33r17c+TIEZo3l7Krksga6aJOqelyd1BL3q9fUpP0oP4F26VpXPnZu0PKZchIVO9L0zhRx7i5ubFo0aIi2994o4JLN4pyGdnBl7m/9iJeccU7LQ7C/oL2t1k6LCGEEDWsUo3jnnjiCWbPns2iRYvYvXs3x44dM7sIKXcXdUzhxnE1paQO78nGkXRpGlcmB/dCdzTg095ioQhRnPXr17Nz507T/cWLF9O1a1cmTJjA9evXLRhZw+Rqb8OANk35URrICSFEo1KpJP2+++7j4sWLPPXUU/Tv35+uXbvSrVs307WQcndRx9TKSHoJHd6NI+lukqSXyb5Qku7ZCmwdLReLEMV4/vnnSUlJAeD48eM8++yzjBkzhosXL5qayInqdVtXP37Q34QBDZz/G65dtHRIQgghalilyt0vXpQPiNLIGumizqnVkfQSknQpdy+bcU46QFMpdRd1z8WLF2nfXq3w+Pnnn7n11lt59913OXTokKmJnKhew9r68IJNU3boOzHY6pjaQG74XEuHJYQQogZVKkmXueilkzXSRZ2iKLU3Jx0gKRJyMws6uUuSXn6FR9KlaZyog2xtbcnIUCvFNm/ezKRJkwDw8PAwjbCL6mVva8WI9j6sOHaTmqQf/g6GzAFrW0uHJoQQooZUKkn/9ttvS33c+KHdWMka6aJOybwOhvyOwI7epe9bFQ6eapKZeR0Sz4NvR7UTcWqM+rjMSS+bJOmijhswYACzZ8+mf//+7Nu3j1WrVgFw9uxZ/P3lh7iaclsXPx470p143PFOj4ewtdDhDkuHJYQQooZUKkl/+umnze7n5uaSkZGBra0tDg4OkqRL0zhRlxhH0e3dwboGKzs0GnU0PWqvWvLu2xFSrgAKWOnUddJF6RwKlbtLZ3dRBy1atIgnnniC1atX89lnn9GsWTMA/vrrL26++WYLR9dwDQzxxtHenhW5Q3ja+lc4sESSdCGEaMAqlaQX18H13LlzTJ8+neeff77KQdV30jRO1Cm1Uepu5BWSn6Tnd3g3lbo3A22l+lQ2LsaRdCefmu0fIEQlBQYG8ueffxbZvmDBAgtE03jYWmsZ3dGXVfuH8qT1b2gv/gMJ4eDVytKhCSGEqAHV9q05JCSE//73v0VG2cuyePFigoKCsLOzo0+fPuzbt6/U/ZOSkpgxYwZNmzZFp9PRunVr1q1bV5XQq51xJD1ARtJFXZCUvwSai1/Nn+vG5nEyH71iAkOhSXvo9YilIxGiRHq9np9//pm3336bt99+m19//RW9Xm/psBq827r6cQUv/qGrukGWYxNCiAarUiPpJR7M2porV66Ue/9Vq1Yxe/ZsPv/8c/r06cPHH3/MqFGjCAsLo0mToqNIOTk5jBgxgiZNmrB69WqaNWtGREQEbm5u1fgqqq6g3F1G0kUdcP2Seu3eoubPVSRJlzXSK8TBA57YbekohChReHg4Y8aMITo6mjZt2gAwb948AgICWLt2LcHBwRaOsOHq28KT5p4OfHN9GENsD8GR5XDTq2BjZ+nQhBBCVLNKJem///672X1FUYiJiWHRokX079+/3MeZP38+06ZNY8qUKQB8/vnnrF27liVLlvDSSy8V2X/JkiVcu3aNf//9FxsbGwCCgoIq8xJqlJS7izrlev6Sie5BNX8uY5KeGA4Gg4ykC9HAPPXUUwQHB7Nnzx48PNQeComJiTzwwAM89dRTrF271sIRNlxarYYJvQN57680rmq9aZIZD6d+gy7jLR2aEEKIalapJP322283u6/RaPD29uamm27io48+KtcxcnJyOHjwIHPmzDFt02q1DB8+nN27ix9J+v333wkNDWXGjBn89ttveHt7M2HCBF588UWsrKyKfU52djbZ2dmm+zW9RIz5GulS7i7qAONIukctjKS7NQetDeRmQEq0JOlCNDDbt283S9ABPD09+e9//1uhH+lF5dzdw5+PNp7l2+whPGfzk9pATpJ0IYRocCo1J91gMJhd9Ho9sbGxrFixgqZNm5brGAkJCej1enx8zJtZ+fj4EBsbW+xzLly4wOrVq9Hr9axbt45XX32Vjz76iLfffrvE88ybNw9XV1fTJSCgZstuC6+R7uUka5iKarT5DVh2K+SkV+x5pnL3oOqOqCgra/DML3dNOCtJuhANjE6nIzU1tcj2tLQ0bG3lM6+meTrpGN3Jl1X6Ieixgqg9EHfK0mEJIYSoZvWq3bLBYKBJkyZ8+eWX9OjRg/Hjx/Pyyy/z+eefl/icOXPmkJycbLpERUXVaIyFS91ljXRRbfR5sHsxXNoBl3aV/3lZKZCRqN6ujSQd1A7vkJ+ky5x0IRqSW2+9lUcffZS9e/eiKAqKorBnzx4ef/xxbrvtNkuH1yhM7NOceNzZrPRUN0gDOSGEaHAqlaTfddddvPfee0W2v//++9xzzz3lOoaXlxdWVlbExcWZbY+Li8PX17fY5zRt2pTWrVublba3a9eO2NhYcnJyin2OTqfDxcXF7FKTZI10USMSz4E+f9pG7LHyP884iu7gBTrnag+rWMZ56ZcPQE6aetulWe2cWwhRo/73v/8RHBxMaGgodnZ22NnZ0a9fP1q1asXHH39s6fAahV5B7rT2ceLb3JvUDUdXVrzCSgghRJ1WqST9n3/+YcyYMUW2jx49mn/++adcx7C1taVHjx5s2bLFtM1gMLBlyxZCQ0OLfU7//v0JDw/HYDCYtp09e5amTZvWmTI7aRonakTs8eJvl6U2S92NjEn6ha3qtYMn2MqPVkI0BG5ubvz222+cPXuW1atXs3r1as6ePcuvv/5a51Zaaag0Gg0T+zTnX0MHrmibQnYKnPjZ0mEJIYSoRpVqHFfS3DMbG5sKNWabPXs2Dz30ED179qR37958/PHHpKenm7q9T5o0iWbNmjFv3jwApk+fzqJFi3j66ad58sknOXfuHO+++y5PPfVUZV5GjZCRdFEjCo+eVyhJr8XO7kbGcndjmb3MRxeiXps9e3apj2/dutV0e/78+TUdjgDu6N6M//51hmXZQ/iPzQ+w63/Q5X6wsrF0aEIIIapBpZL0Tp06sWrVKl577TWz7StXrqR9+/blPs748eOJj4/ntddeIzY2lq5du7J+/XpTM7nIyEi02oLB/oCAADZs2MCsWbPo3LkzzZo14+mnn+bFF1+szMuour1fwPHV0G0i9JgMwNVUtSTZx0VnmZhEw1Q4Mb92AbJTy1e+bomRdM8Q8/syH12Ieu3w4cPl2k/6sNQeFzsbbuvix4oDw3jS7i+cE8/BwWXQe5qlQxNCCFENKpWkv/rqq9x5552cP3+em25S50Rt2bKFH374gZ9++qlCx5o5cyYzZ84s9rFt27YV2RYaGsqePXsqHHONSLkCl/dBs+6mTYnp6tx4D8e6UX4vGgBFKUjSNVpQDGo338A+ZT+3NpdfM7JzAeemkBqj3peRdCHqtcIj5aLumNg3kFUHovgo505et1oC2+ZB53vBztXSoQkhhKiiSs1JHzt2LGvWrCE8PJwnnniCZ599lsuXL7N58+Yia6g3aMbkw7jMFHA9P0n3dJSRdFFNUmPU0nGNFQQNVLeVt3mcJUbSoaDkHSRJF0KIGtDZ341OzVz5Lnco1x2C1M+JHTLdQAghGoJKL8F2yy23sGvXLtLT00lISODvv/9m8ODB1Rlb3Wcs481fZkpRFK7lJ+nujjIvTFSTmPyE3LsN+PdSb5cnSdfnQVKkervWk/TWBbclSRdCiBrxQN9A9FjxX/0EdcOez+B6hGWDEkIIUWWVStL379/P3r17i2zfu3cvBw4cqHJQ9cYNI+npOXpy9GrneSl3F9XGWOru20m9FN5WmpRoMOSBlS04+9VcfMUxS9JlTroQQtSE27o0w83BhlXJHUj07qMu1bnlTUuHJYQQoooqlaTPmDGDqKioItujo6OZMWNGlYOqN4xJekYi5GRwLU0dRbez0eJgW6np/qK+OL66ckveZFyDrfMg8Xz5n2McNS+cpMedUkfKS2MsdXdrDtpKF81UjpS7CyFEjbO3teL+3oGAhg94ENDAidVw+aClQxNCCFEFlfrmfurUKbp3715ke7du3Th16lSVg6o37FzBNr/Ddko01zLym8Y5yCh6g5aeCL9Mg9UPw+5PK/bczXNh+39h6Ri4drF8zyk8ku7eAmyd1NGSxHOlP88Sy68ZNWmvNrnTuYBjk9o/vxBCNBIP9m2OlVbDyigPklrfpW7c+LLadFQIIUS9VKkkXafTERcXV2R7TEwM1taNaARZoylU8h7FtXR1+TUPJ0nSG7Rr59UO6wAb5sDh78v3vJQrcOQH9XZaLHw7DlJiSn9OVkpBsu3TSR0R9+mo3i+r5N0Snd2NnH1h/HK4/4faH8UXQohGxM/Nnps7+gKwWHMfWNtD5G44/YeFIxNCCFFZlfr2PHLkSObMmUNycrJpW1JSEv/5z38YMWJEtQVXLxSal34tPRcAdxlJb9iMya9V/t/59yfh1G9lP2/Pp2DIBb9u6oh4UgR8d7taAl+SuJPqtUszcPRUb5vmpZfRPM5Snd2N2o6BoAGWObcQQjQiD/cPAuCbk3lk9Jyubtw8F/JyLBeUEEKISqtUkv7hhx8SFRVF8+bNGTp0KEOHDqVFixbExsby0UcfVXeMdVuhJL1g+TVJ0hs0Y5l6p3uh24PqqPrqqRC+ueTnZF6HA0vV20P+A5PWqGuJx5+B7+9UR8yLU7jU3ai8zeOuWbDcXQghRK3pHuhOF39XcvIMfKsZp04zunYBDiyxdGhCCCEqoVJJerNmzTh27Bjvv/8+7du3p0ePHixcuJDjx48TENDIOjkXStITTcuvSZLeoBUuIx+7ENrfro6Qr3wAIvcU/5z9/wc5adCkA4SMUBPnB9eAvQdcOQw/3A+5mUWfV7hpnFHhJL20OYemkXQLlLsLIYSoNRqNhin91f/XLzmQQN7gOeoD2/8LmUmWC0wIIUSlVHqyqKOjIwMGDGDs2LEMGjQINzc3/vrrL37//ffqjK/uK7RWuoykNxKFy8i1VnDnV9BqOORlwvJ7Ieao+f45GbDnc/X2gFlqLwOAJm3hgZ/V5oMRO2HngqLnKm4kvUl70FipqwqkljCnPfM6ZCXlx9m8Ei9SCCFEfTKmU1OaOOu4mprNOuth4N1W/SzY0cgqHIUQogGoVJJ+4cIFunTpQseOHbnlllu4/fbbueOOO0yXRkVG0hufG0eorW3h3u8gMBSyk+G7OyGhUOf1I8shI0FdCq3DDf99NOsOty1Ub+/9ArJTCx7T58LV0+rtwkm6jR14t1Fvl1TybozRsQnYOlb0FQohhKhnbK21PNBX/VH263+jUEbkr5e+93O4HmHByIQQQlRUpZL0p59+mhYtWnD16lUcHBw4ceIE27dvp2fPnmzbtq2aQ6zjTEl6NEnpWYAswdag5WZC6hX1duGu6bYOMGEVNO2iJuTf3g5JUWqivet/6j79nwKrYlY/aH87eLZSR74PflOwPeGcutSarTO4BZk/p6zmcZZuGieEEKX4559/GDt2LH5+fmg0GtasWWP2+OTJk9FoNGaXm2++2TLB1iMT+gRia6Xl6OVkDtn2ghaDQZ8DW960dGhCCCEqoFJJ+u7du3nzzTfx8vJCq9ViZWXFgAEDmDdvHk899VR1x1i3ufgBGtBnY0hLAMBDRtIbrqRI9VrnAvbu5o/ZucIDv4BXa0i5rC6xtvcLSI4ER2/oOrH4Y2qtoP/T6u3diyBPXcqvoNS9Y9FlzMpqHmfJ5deEEKIM6enpdOnShcWLF5e4z80330xMTIzp8sMPP9RihPWTl5OOcV39AFi0NRxGvg1o4MRquHzQssEJIYQot0ol6Xq9HmdnZwC8vLy4ckUdWWzevDlhYWHVF119YGWjdukG7DLU90GS9AbMNELdvGBueWGOXmpDONdAdT31jS+r2/tOBxv7ko/bebz67yg1Bo79qG4rrmmcUXmTdBlJF0LUQaNHj+btt98udYqcTqfD19fXdHF3dy9xX4Ds7GxSUlLMLo3RE0NbYa3VsDUsnh1pTaHL/eoDG18pvdmoEEKIOqNSSXrHjh05elRtjtWnTx/ef/99du3axZtvvknLli2rNcB6Ib/k3SUnDpAkvUErz7Jmrs3UJdYcm6j3bZ2h59TSj2utg9AZ6u1dC8GgL75pnJFP/rZrF8znsVckTiGEqMO2bdtGkyZNaNOmDdOnTycxMbHU/efNm4erq6vp0uhWm8nXwsuRB0PVuenvrD2NfujLYG0Pkf/C0ZUWjk4IIUR5VCpJf+WVVzAYDAC8+eabXLx4kYEDB7Ju3Tr+97//VWuA9UJ+kt5Mk4BGA24yJ73hKu+yZp7BaqIe0AdGvgX2bmUfu8dktWQ+8Ryc+bP0JN3RE1yaqbfjTlY+TiGEqINuvvlmvv32W7Zs2cJ7773H9u3bGT16NHq9vsTnzJkzh+TkZNMlKiqqFiOuW54eFoKLnTVnYlNZfc4AA2erD/zxNETts2xwQgghylRMF6uyjRo1ynS7VatWnDlzhmvXruHu7o6muBLghi4/SffTJOJmb4OVthG+B41FRcrIfTrA1I3lP7bOGXo/Cv98AJteg8xr6lJr3u2K39+3E6REq8l8YN+C7fpcSL5c/jiFEKKOue+++0y3O3XqROfOnQkODmbbtm0MGzas2OfodDp0Ol1thVinuTnY8tSwEN5ee5oPN57l1tlP43jlCISthR/uh2lb5PNBCCHqsEqvk34jDw+Pxpmgg2mtdD9Ngiy/1tBdr+Ey8j6Pq2WJxh8DvNuoS64Vp6QO78lRoOjB2g6cfGomTiGEqEUtW7bEy8uL8PBwS4dSb0wKDaK5pwPxqdl8seMS3PUV+HZWVyBZMR6yki0dohBCiBJUW5LeqBUaSfeUJL3hUpSa75ru6AXdJxXcL67U/cbHbmweV3i0/8au8EIIUQ9dvnyZxMREmjZtaulQ6g1bay0v3dwWgC93XCAmU6suFercFOLPwE+TQZ9n2SCFEEIUS77BVwdTkp6Au8xHb7jS4iAvCzRaU/VEjeg3Uy1zh/Il6XGnICWmYLt0dhdC1HFpaWkcOXKEI0eOAHDx4kWOHDlCZGQkaWlpPP/88+zZs4dLly6xZcsWxo0bR6tWrcym24my3dzRl15B7mTlGvhgQ5i6bOz9K8HGAc7/DX+9IB3fhRCiDpIkvTrkJ+nemhSa2MuHXYNlTH5d/dWl92qKW6CaqNu5QpsxpewXpM5X12fDd3dAxjV1u3R2F0LUcQcOHKBbt25069YNgNmzZ9OtWzdee+01rKysOHbsGLfddhutW7dm6tSp9OjRgx07dsic8wrSaDS8ckt7AH45FM3xy8ng1xXu+j9AAwe+Llj2UwghRJ0hSXp1sHcnR6uugd3c+rqFgxE1xpT81kLH9OFvwIsRapf4kmi1MGFlfuniafj+LnU5NhlJF0LUcUOGDEFRlCKXZcuWYW9vz4YNG7h69So5OTlcunSJL7/8Eh8f6bFRGV0C3Li9qx8Ac38/gcGgQNtbYOh/1B3WvwRp8RaMUAghxI0kSa8OGg3XrNU1sZtpS1/HVdRjtZn8ajTqpSzuQfDgGrD3gCuHYMV9kHA2/zFZfk0IIQS8NLodDrZWHIpM4tfD0erGAbPAp5O6ksj6Fy0boBBCCDOSpFeTq1pvAHyUBAtHImpMXR2hbtIWHvgZbJ0hYqfaEAjqXpxCCCEswtfVjidvCgFg3l9nSM3KVadtjftE7bNy4mcI+8vCUQohhDCSJL2aRBs8AfDIu2rhSESNMS6/VlOd3auiWXe19N260HJt7s0tF48QQog65eEBQbT0ciQhLZuFm8+pG/26QehM9fafs2VZNiGEqCMkSa8mkXoPAFxzYi0ciagxdXUk3ShoANz7HWht1M7vNvaWjkgIIUQdobO24rWxahO5Zf9e4lxcqvrAkDng0RJSr8Dm1y0XoBBCCJM6kaQvXryYoKAg7Ozs6NOnD/v27Stx32XLlqHRaMwudnZ2Je5fGxRF4XyOOwAOmZKkN0g5GeoSbFB3k3SA1iPhmWMweZ2lIxFCCFHHDGnThBHtfcgzKLz+x0kURQFbBxj7P3WHA0vg0k7LBimEEMLySfqqVauYPXs2c+fO5dChQ3Tp0oVRo0Zx9WrJZeMuLi7ExMSYLhEREbUYcVEZOXrTSLpterRFYxE1xDiKbucG9u6WjKRsLn5g52LpKIQQQtRBr93aHltrLbvCE/nrRP7AQouB0GOyevv3JyEzyVLhCSGEoA4k6fPnz2fatGlMmTKF9u3b8/nnn+Pg4MCSJUtKfI5Go8HX19d0sfSyLNfSc4hW1DnpmpRoUGSt9Aanrpe6CyGEEOUQ4OHA44PV5T3f/vMUGTl56gMj3gRnP7h2QV3SMyvFglEKIUTjZtEkPScnh4MHDzJ8+HDTNq1Wy/Dhw9m9e3eJz0tLS6N58+YEBAQwbtw4Tp48WeK+2dnZpKSkmF2q27X0HOIUDwxo0ORlQYYsw9bgSJIuhBCigZg+OJhmbvZcSc7ivb/yVwSxc4WJP6rVYtEHYPndkJ1q2UCFEKKRsmiSnpCQgF6vLzIS7uPjQ2xs8XO727Rpw5IlS/jtt9/4/vvvMRgM9OvXj8uXLxe7/7x583B1dTVdAgICqv11XEvPIQcbrmvyy6CTo6r9HMLCjEl6XezsLoQQQlSAva0V8+7sBMA3uyP452y8+oBvJ3hwjZqwR+2F5fdAdprlAhVCiEbK4uXuFRUaGsqkSZPo2rUrgwcP5pdffsHb25svvvii2P3nzJlDcnKy6RIVVf0J9LX0HPXaJv/HhuTifzAQ9Zhx+TUZSRdCCNEADGrtzUOh6lKdz68+SlKG+l0Gv65qoq5zhcjd8MN9avNUIYQQtcaiSbqXlxdWVlbExcWZbY+Li8PX17dcx7CxsaFbt26Eh4cX+7hOp8PFxcXsUt2u53+wperyY5YkveGRcnchhBANzEuj29HS25G4lGxeXnNC7fYO0Kw7PPgL2DrDpR2w8n7Q51o2WCGEaEQsmqTb2trSo0cPtmzZYtpmMBjYsmULoaGh5TqGXq/n+PHjNG3atKbCLFNi/kh6pn1+DElS7t6gGAxwPX8FAUnShRBCNBD2tlYsuLcrVloNa4/F8PvRKwUP+veEB34GWye4sA02/MdicQohRGNj8XL32bNn89VXX/HNN99w+vRppk+fTnp6OlOmTAFg0qRJzJkzx7T/m2++ycaNG7lw4QKHDh3igQceICIigkceecRSL4Hr+Ul6npOfukHmpDcsqTGgzwatNbj4WzoaIYQQotp0CXDjqZtCAHhlzQmuJGUWPBjYB+78Sr2970s4vNwCEQohRONj8SR9/PjxfPjhh//f3n3HN1ntDxz/JGmTdKZ70tKy95BlQX6KwkVQ3IqKCqK4wCsiXvA6wMm9Ttzc6wBxoXIdKCoiAspGBAQsZbUU6KJ7r+T5/XHalEKBLpqk/b5fr+eV9MmTJ9+H0J58c875Hp544gn69evHjh07+PHHH+3F5JKTk0lNTbUfn5OTw5QpU+jevTtjx44lPz+fDRs20KNHD0ddgr0nXbNUFaWT4e6tS/VQd0sUGNwcGooQQgjR3KaO6EjfKD8KSit5eOlObLYTlpLtNhYunK3uf/cgHNvmmCCFEKINcXiSDjBt2jQOHz5MWVkZmzdvZsiQIfbH1qxZw6JFi+w/v/LKK/Zj09LSWL58Of3793dA1DWqe9LdAiRJb5VkProQQohWzM2g55Ub+mJ217P+QBbv/Hao9gEXzoKuY9WosiW3QGGGYwIVQog2wimSdFdXXd3dHKiqpFKUARWlDoxINKvqyu6y/JoQQohWqkOwN3PG9QTghRUJ7DiSW/OgXg9X/wcCO0NBCnw+ESrLHROoEEK0AZKkN4PsquruvgEh4O6pduYfc2BEollJT7oQQog24MZBUVzWO5xKm8bfP91OQekJFd3NvnDjJ6rie/IG+P4hVVhVCCFEs5MkvYkqrTZyi1UjFuBtAktVYTEZ8u6aSvPgwCrY/3PNlrZbPSZJuhBCiFZMp9Px3DW9ifTzIDm7mMdOXJYNILgLXFtVSO6PxfDFRKgoqftkQgghGk2S9CbKLan5ltnPw/2EJF0qvLukz2+Dj66Bj6+t2Y7Hq8f8Zbi7EEKI1s3i4c5rN6ll2b7ZkcL//jhpZGDXMXDte2AwQvwy+OAKKMpyTLBCCNFKSZLeRNVF4/w83XEz6MEvuuqBww6MSjRKQRocWqvuh/WB8L41W78JENrLsfEJIYQQLWBA+wAeHKmWZXvim90cOl5Y+4De18GtX4HZAke3wHsjIeugAyIVQojWSdaTaqLq5dcCPI1qR3Vva/U8ZuE6En4ANIg4D+5a7ehohBBCCIe596JOrD+QxcZDWUz7ZDtf3BOHl+mEj40xF8AdK+Gj6yD7ELw3CsY8D51HqeRdCCFEo0lPehNV96T7e1Un6TFVDyQ5JB7RBHuXq9vulzs2DiGEEMLBDHod82/sR6CXkb9S87n34z+osJ5UKC64K9z5M4T3g+Is+N8d8HwHWHQ5bHgdMvc7JHYhhHB1kqQ3kb0nXZJ051JWoIavn7iV5Jz++NJ8SKwa6t5NknQhhBAi1NfMuxMH4uFu4Nd9x5m19E9sNq32QT6hMGk5XPCgWqLNVglJv8FPj8EbA+GbqWCtqPsFhBBC1EmGuzdRzinD3WPUbVEGlBWCydsxgbVlR3+HhWPAWscarpe/AgMnn7r/wM/q+MBOENTl3McohBBCuID+0f68dct53PnB73y5/RjBviYeGdO99kEmbxg5V21ZB2H/T7Bvhfrye/tH6ovy6z+Qz0RCCFFP0pPeRPaedO+qJN3DDzz81f1cKR7nEIm/1iToOkPVVvVffc2/obLs1OdUD3XvdhnodC0TpxBCCOECRnQN4d/X9gHgP2sP8d66xNMfHNgRzr8XbvsabloCbh7qi/APLofC4y0TsBBCuDhJ0psop/iknnSQIe+OVr1G/f89DHOy1fZoOvhEQGEa7FxS+/jKcvWtP8hQdyGEEKIO1w1oxz8u7QrA09/9xTc7jp3lGUCX0TDxW/AIgJTt8P7fVJE5IYQQZyRJehNlnzwnHWqS9OwzfNMszp3qJL16zXoANyMMnabur38VbNaax5J+g7J88AqByIEtF6cQQgjhQu69sCOThsYA8NDnO/lpT9rZnxQ1CO74SS1Rm30I3h0JSybA/6bAtw/Aj4/ApgVQUXJugxdCCBciSXoT1Z2kyzJsDlVXkg5w3kQw+0H2QYhfVrPfPtR9LOjlV0IIIYSoi06n44nLe3B1/0gqbRpTP/mD1QkZZ39iUGe1XFtYb1UFfu93sOtz2LYINr0FP86CL6eAzXbWUwkhRFsgGUkT5ZypJ12SdMewJ+lRtfebvGHI3er+uldA09QHgoTv1T4Z6i6EEEKckV6v44Xr+nBZ73AqrBp3f7iN9Qcyz/5EnzCYvAKuWwiXvQx/exZGPApx08BghPhvYeXj5/4ChBDCBUh19ybQNO3UJdjghCRdhru3uNI8KMtT930jT3188N2w/jVI3QmHVoPJAgWpYPSG2P9r2ViFEEIIF+Rm0DP/xn6UW22s/CudOz7Yyge3D2ZIh8AzP9HoBb2uOXV/RH+1xvrGN9RnqMFTzkncQgjhKqQnvQlKKqyUVaqhWf51Jem5ybXnPotzL6+qkI2Hf91LvXgFwoCJ6v66V9SQO4DOo8DN1DIxCiGEEC7O3aDnjZv7c1HXYEorbExetJVth7Mbd7Le18HFVb3oP/xDLd8mhBBtmCTpTZBVqHrRjW56vIyGmgcs7UDvppYBK0h1UHRt1Onmo58obpp6fxJ/hW0L1T4Z6i6EEEI0iMnNwIJbBjCsUyBF5VYmvLuZFfUpJleX4Q9B/1tBs8EXt6sRb0II0UZJkt4EJy6/pjtxbW29QVUxBZmX3tLyjqjbk+ejn8gvCnrfoO6X5IDeXfWkCyGEaBG//vor48aNIyIiAp1Ox9dff13rcU3TeOKJJwgPD8fDw4ORI0eyf/9+xwQrzsjsbuCd2wbae9Tv+WjbmddRPx2dDi5/BTpcBBVF8Ml4yD3S7PEKIYQrkCS9Ceqcj15NlmFzjPr0pAMMe6DmfuxwMFvOXUxCCCFqKSoqom/fvrz55pt1Pv7888/z2muvsWDBAjZv3oyXlxejR4+mtLS0hSMV9eFpdOPd2wYyYUg0mqbWUZ+7bA9Wm9awExnc4YbFENJDjUT86FoobuQQeiGEcGGSpDdBnZXdq8kybI5R3yQ9pBt0v0Ld73XduY1JCCFELWPGjOGZZ57h6quvPuUxTdOYP38+jz32GFdeeSV9+vRh8eLFpKSknNLjLpyHm0HPM1f14pEx3QBYtCGJuz/8naKyyoadyGyBCV+o4q+ZCfDpjbKGuhCizZEkvQnqXCO9mizD5hj1TdIBrl4Aty2Dfjef25iEEELUW2JiImlpaYwcOdK+z2KxMGTIEDZu3Hja55WVlZGfn19rEy1Lp9Nx94UdeWvCeZjc9Pwcn8H1CzZyLLeBSbalHdzyP5WwH9kMSyeDtYHJvhBCuDBJ0pugfkm6DHdvUadbI70uRi/ocKGaByeEEMIppKWpwmOhoaG19oeGhtofq8u8efOwWCz2LSqqHu2AOCfG9g7nkynnE+Rt5K/UfK54fR1bkxo4bD2kO9z0GbiZIeF7+P4h0Bo4fF4IIVyUJOlNYNPAy2ioO0kPkOHuLc5mhfyqJdjq05MuhBCi1XjkkUfIy8uzb0eOSNExRxrQ3p9vpl1Aj3BfsorKufmdTXy2NblhJ2kfB9e+Bzo9bFsEq5+TRF0I0SZIkt4Es8d0Y89TlzJtRKdTH/Rrr26Ls6BUhty1iII00KxqeTXv0LMfL4QQwumEhYUBkJ6eXmt/enq6/bG6mEwmfH19a23CsSL9PFh6bxyX9Q6nwqox63+7mLtsD5VWW/1P0v1yGPuiuv/r8/DZLVJMTgjR6kmS3gz0+jqGS5t9wTNQ3c893LIBtVXVQ919I9QyeEIIIVxObGwsYWFhrFq1yr4vPz+fzZs3ExcX58DIRGN4Gt144+b+zBjVBVAF5W5+dzPp+Q2o1D/oDhjzvFoyde938PYwOLT2HEUshBCOJ0n6uSTLsLWs+qyRLoQQwuEKCwvZsWMHO3bsAFSxuB07dpCcnIxOp2P69Ok888wzLFu2jF27dnHbbbcRERHBVVdd5dC4RePodDr+fklnFtwyAG+TG1sSsxn76m/8tv94/U8y5G6YsgoCO0NBCiy+ElbOgcrycxe4EEI4iCTp55Isw9ayGlLZXQghhMP8/vvv9O/fn/79+wMwY8YM+vfvzxNPPAHAP/7xD+6//37uuusuBg0aRGFhIT/++CNms9mRYYsmurRXGN/efwHdq+ap3/b+Fl5eua/+66mH94W718J5EwEN1s+H//wf7Fqq6tIIIUQr4RRJ+ptvvklMTAxms5khQ4awZcuWej1vyZIl6HQ65/1mXZZha1mSpAshhEu46KKL0DTtlG3RokWA6nl96qmnSEtLo7S0lJ9//pkuXbo4NmjRLGKDvPjqvqHcNDgaTYPXVu3n1vcaMPzd6AVXvAY3fAhmPzgeD/+7A94cDNs/AmvFOY1fCCFagsOT9M8++4wZM2YwZ84c/vjjD/r27cvo0aPJyMg44/OSkpKYOXMmw4cPb6FIG0GWYWtZkqQLIYQQTs/sbmDeNb2ZP74fnkYDGw5mMXr+r/ywK7X+J+lxBTywAy76p0rWsw7AN1Phtf6wbj7kSD0gIYTrcniS/vLLLzNlyhRuv/12evTowYIFC/D09OT9998/7XOsVisTJkzgySefpEOHDi0YbQPJMmwtqyFrpAshhBDCoa7qH8myaRfQK9KX3OIK7v34D2Z+sZOC0nr2hnv4w0Wz4MHdMOop8ApR9Wl+ngOv9oF3LoGNb0LesXN7IUII0cwcmqSXl5ezbds2Ro4cad+n1+sZOXIkGzduPO3znnrqKUJCQrjjjjvO+hplZWXk5+fX2lpMdU96brLMlWoJUjhOCCGEcCmdQrz58t5h3HdRR/Q6WLrtKGNe/Y2tSQ1YZs3kA8MegOl/wrhXIWY4oINjv8OKf8IrPeB/d0LhmUdpCiGEs3Bokp6ZmYnVaiU0tPaa1qGhoaSlpdX5nHXr1vHee+/xzjvv1Os15s2bh8VisW9RUS2YwPmEg8EItsqaXl5xbpQVQGmuum+JdGgoQgghhKg/o5uef1zajc/ujqOdvwdHc0q44T8beeTLP8kuakD1dncPGDAJJn0HDyXAmBcgumrZvl1fwBsD4feFYGvAOu1CCOEADh/u3hAFBQXceuutvPPOOwQFBdXrOY888gh5eXn27ciRI+c4yhPoDeAXre7LkPdzq3oom9lPfaMuhBBCCJcyKCaAHx4YzvUD2qFp8OmWI4x4cQ0fbkyqfwX4aj6hMOQumPwjTFmtKsOX5sF302HhpZD+1zm5BiGEaA5ujnzxoKAgDAYD6enptfanp6cTFhZ2yvEHDx4kKSmJcePG2ffZqr4NdXNzIyEhgY4dO9Z6jslkwmQynYPo68k/VhUzyUkCLqzZX1GietgloWweMh9dCCGEcHk+ZndeuL4v1w+MYs6yPcSn5vP4N3v4ZMsRnryiJ4NjAxp+0sjz4M5fYOs78MszcGQz/Gc4xFwAHS+GDiMgtBfoXarvSgjRijn0r5HRaGTAgAGsWrXKvs9ms7Fq1Sri4uJOOb5bt27s2rWLHTt22LcrrriCESNGsGPHjpYdyl5fdS3DlntELRUyvw+U5DogqFbIPh9dKrsLIYQQrm5wbADfThvG01f2xNfsRnxqPjf8ZyOTF21lT0pew09ocIPz74WpW6Db5aqj5NAaWPmESthf7Axf3SNF5oQQTsGhPekAM2bMYOLEiQwcOJDBgwczf/58ioqKuP322wG47bbbiIyMZN68eZjNZnr16lXr+X5+fgCn7HcaJy/DVpgBi69UxeQADm+AbmMdElqrIsuvCSGEEK2Km0HPrXExjO0dzksr9/HZ1iP8sjeDX/ZmcHmfcGaM6kKHYO+GndQSCTd+DMf3wcFf4NBqSPwNijNh56eQ+CtMWAqhPc7NRQkhRD04PEkfP348x48f54knniAtLY1+/frx448/2ovJJScno3fl4UcnLsNWkgMfXgPZB2seT94oSXpzkCRdCCGEaJUCvU08d3Vv7rwglld+3s+3O1P47s9UftidxvUD2vHgqC6E+pobdtLgLmo7/x6oLFdD4Jc/BJkJ8P6lKpGPHX5uLkgIIc5Cp2laAytxuLb8/HwsFgt5eXn4+vqe+xdM3wNvDwWTBYK7wtEtah3PARPh1xeg3WC4c+W5j6O1W3gZHF4H174Hva9zdDRCCNEgLd42tQHyb9p67UnJ4+Wf9rFqr1pSzeyu584LOnD3hR3wMbs3/sTF2bDkZtWBYjDC1Qug17XNFLUQoq1rSLvkwl3ULsKvvboty1MJutkPbvsa+k1Q+1O2qyJyomlkjXQhhBCiTegZYeG9SYNYek8cA9r7U1ph443VB7jwhTV8sCGJ8spGLrHmGQC3fg3drwBrOSydDBteh7bVnyWEcAKSpJ9rJm/Vcw7g7lU1z6mnmqvuHQa2Cjj2h0NDdHk2K+SnqPsy3F0IIYRoEwbGBLD0njgW3DKADkFeZBeVM2fZHv7v+dW8ufpAw9ZYr+ZuhusXweC71c8/PQYLx8hnNSFEi5IkvSV0GQ0mX7jpE4gapPbpdBB9vrqfvNFxsbUGhRnqyw6dAXxOXbpPCCGEEK2TTqfj0l5hrHjw/3jmql4E+5hIyy/lhRUJxM1bxaylfxKfmt+wk+oNMObfcOm/wM1DfU57ZwR8dS/kp56bCxFCiBNIkt4SrnwDHj4IHS6qvT+6apm55E0tHlKrUl00zjdSNaxCCCGEaFPcDXpuOb8962aN4OUb+tI70kJZpY3Pfj/CmFd/49b3NrMlMbv+J9Tp1JJt92+DPjeqfTs/gdfPg5VzIGkdVJSem4sRQrR5Dq/u3ma4GU/dV92TfmSLGrItCWbjyBrpQgghhABMbgauOa8dV/ePZNvhHBauT+LHPWn8tj+T3/ZnMiQ2gPsv7sywToHodLqzn9ASCdf8BwbfBT/OVvWF1s9Xm8EE7QZC+2HQaSREDVbJvRBCNJEk6Y4U2guM3qqoXEY8hDnpWu/OTpZfE0IIIcQJdDodA2MCGBgTwJHsYt5ac5Cl246wOTGbze9tpn+0H3de0IG/9QzF3VCPgaXtBsAdP8Ff36jt8HooTFe3h9fDr89DQAfod7PqefeTQrZCiMaTJN2RDG7QbhAcWq3mO0mS3jiSpAshhBDiNKICPJl3TW/+fkkn/rP2EJ9uSWZ7ci5TP/mDEB8TNw6O5qbBUYRbPM58Ip0Oel6lNk2DrINq+dfE3yDhB8g+BL88A788Cx0uhA4jwL+9WunHPwY8/NU5KkqgJBdKc8FWCSE9QS8zUIUQNSRJd7TouKokfRMMnuLoaFyTJOlCCCGEOItwiwdzr+jJfSM68uHGw3y65QgZBWW8tmo/b64+wKjuodw0JJrhnYLQ688ybF2ng6BOahswCcoKIf5b2PExJP0Gh9ao7UTuXiopt5bV3t/+Ahj/oVoCTgghkCTd8ewV3qV4XKPJGulCCCGEqKcQHzMP/a0r91/cmZ/+SuPDjYfZnJjNj3vS+HFPGpF+Htw4KIrrB0YRZjHX76Qmb+h3k9pyDsPupWoqY85hyE2GwjSoKKo5XqcHswXKi1Vv/Dsj4KbPIKTbubloIYRLkSTd0doNVEuH5R+F3CMyh6kxpCddtFJWq5WKigpHhyGagbu7OwaDFAcVwpkY3fRc3ieCy/tEsC+9gE82J/PlH0c5llvCSyv38crP+xjRNYQxvcO5pFsI/l51FAGui397GP5Q7X0VJZCfAno38PADo48a4p4RD5+Mh5wkeG8UXPc+dB6lnqNpkLId9n4HqX+CZlX70NRtcDcYOQeMXs34ryKEcAY6TdM0RwfRkvLz87FYLOTl5eHr6+vocJT/joCUP+Cad6HP9Y6OxrWUF8FzEer+7CNgdpL3VIgm0DSNtLQ0cnNzHR2KaEZ+fn6EhYXVWVHaKdsmFyf/pqIxSius/LA7lU+3HKm1ZJtBr2NwTACje4byt55hRPidZf56QxRlwWe3QPIG1cM+fKaar753OeQfO/Nze1wJ1y2SOe1CuICGtEvSk+4MouNUkp68UZL0hsqrarxMFknQRatRnaCHhITg6elZv2WChNPSNI3i4mIyMjIACA8Pd3BEQojTMbsbuLp/O67u346Dxwv5dmcKK/akE5+az8ZDWWw8lMXcb/9icGwAV/aLYGyv8Pr3sJ+OVyDc9g0snwHbP1SV4qu5e0HnkaoInbsHoFPz4Uvz4MdHVKX5NfPg4kebFoMQwqlIku4Mos+HTW/KvPTGSN+tbv2iHRuHEM3EarXaE/TAwEBHhyOaiYeH6nXLyMggJCREhr4L4QI6BnszfWQXpo/sQnJWMT/9lcaKPWlsTcphS2I2WxKzmbtsDxd2CebyPhFc1DUYP89GJuxuRrjidbU879Z3IXoIdBsHHS4C99PMi3f3hG/uU0l9UBfp6BGiFZEk3RlUF4/L+AtKctQSHaJ+Er5Xtx1HODYOIZpJ9Rx0T09PB0cimlv1e1pRUSFJuhAuJjrQkzuHd+DO4R1IyS3h250pfLMjhb9S8/k5PoOf4zPQ62Bg+wAu7h7CyO4hdAz2bthIKJ0Ozr9HbfXRfwJkJsD6V+GbqRAQq2odCSFcniTpzsA7BAI6QvZBOLIVuvzN0RG5hspy2PeTut/tcsfGIkQzkyHurY+8p0K0DhF+Htx9YUfuvrAj+9ML+GZHCiv/SichvYAtSdlsScrmXz/sJTbIizG9whjbO5yeEb7n5m/AJXMgc7/qtPj0JrhrtRTSFaIVkCoTziI6Tt0mb3RsHK7k8DooywOvEGg3yNHRCCGEEKKN6Rzqw8zRXVnx4P/x2z9G8NSVPfm/LsEYDXoSM4t4a81BLn99HRe9uIZ//bCXLYnZlFfami8AvQGu+S+E9ISiDFh8FexfWVUFXgjhqqQn3VlEnw87PnLteekVpRC/DIqza+83ekGva8HYzMN39y5Xt93GSlVTIVqZmJgYpk+fzvTp0x0dihBC1EtUgCe3xcVwW1wMhWWV/LI3gx92pbI6IYPDWcUsWHuQBWsP4uFuYFBsAMM6BjK0YxDdw31wMzThc4zJB25eAu+OhKz98PF1EDUERvwTYi9Uw+iFEC5FknRnUT0v/dg2sFaCwcXemspytXzIgZV1P16YBv/3cPO9ns0Ge6vmo8tQdyGcwkUXXUS/fv2YP39+k8+1detWvLxk7V8hhGvyNrlxRd8IrugbQXF5JWsSjvPj7jQ2HMwks7CcX/cd59d9xwHwMhroG+XHedH+DGjvT/9ov4YXoPOLhns3wLpXVOG5I5th8ZXQfhh0Ga3WaS8vUltlGYT1gp7XgE/oObh6IURTuVgm2IoFdAQ3M1SWQu5hCOzo6Ijqz2aFr+5SCbqbB3QdU/Otbd4xOLIJEn9t3iQ9dTsUpIDRG2L/r/nOK4Q4ZzRNw2q14uZ29qYnODi4BSISQohzz9Poxtje4YztHY6maSSkF7D+QBYbDmSyOTGbwrJKNhzMYsPBLPtzekX6MrxzMMM7BzGgvT8mt3oUm/QKgtHPwtD74beXYdtCOLxebXVZ8U9VPb73DdD9ctUj3xDF2ZC0DjQbdLsMDO4Ne74Q4rQkSXcWej0Edob0XaoAiKsk6ZoG302HPV+B3h1u/Ag6jax5PCMe3jofjv4O1orm+wNePdS98yhwMzXPOYVwUpqmUVJhdchre7gb6lXsaNKkSaxdu5a1a9fy6quvArBw4UJuv/12vv/+ex577DF27drFTz/9RFRUFDNmzGDTpk0UFRXRvXt35s2bx8iRNX87Th7urtPpeOedd1i+fDkrVqwgMjKSl156iSuuuOKcXLcQQpwLOp2ObmG+dAvz5Y4LYrHaNPZnFLDtcA5/HM5le3IOhzKL2H0sn93H8nl7jRoeP6RDAAOi/TmvvT99o/zwNp3hI7xPGIx9Hob9HTYvgMIMNfXQ3VPd6vRw4Gc4uhUO/qK27zyg48WqeHHnv4FvRO1zahrkp0DqTkj6DRJ/q1oGt2rue+QAuPq/ENTpnP3bCdGWSJLuTIKqk/R90PVSR0dzdpoGPz0GfyxWf/Cvfbd2gg4Q1BXMflCaC2l/qj/izSH+O3UrQ91FG1BSYaXHEysc8tp/PTUaT+PZm4pXX32Vffv20atXL5566ikA9uzZA8Ds2bN58cUX6dChA/7+/hw5coSxY8fy7LPPYjKZWLx4MePGjSMhIYHo6OjTvsaTTz7J888/zwsvvMDrr7/OhAkTOHz4MAEBAc1zsUII0cIM+pqkfcKQ9gBkFJSy/kAmv+3L5Nf9mWQWlrEm4ThrEtTweL0OuoT6cF57f86L9ue8aD9ig7xO/ULV0g7+9kzdL3zRbMg6CLuWwq7PIesAJCxXG0BYb4gZDkXH1efSzANQUXTqeYK7q5GNx7bBggvgb0/DoDtlHrwQTSRJujMJ6qJuM/c5No665Car7UQHVsHGN9T9ca9Bz6tOfZ5er+bb7/tRFcVrjiQ9c79aF1TvrnrShRAOZ7FYMBqNeHp6EhYWBsDevXsBeOqppxg1quZ3NSAggL59+9p/fvrpp/nqq69YtmwZ06ZNO+1rTJo0iZtuugmA5557jtdee40tW7Zw6aUu8KWmEELUU4iPmav7t+Pq/u2w2TT2phWw6VAW24/k8sfhHI7llrA3rYC9aQV8sll9NvP3dOe8aDWfvU87P/q0s5x9XntgR7hoFlz4D0jbBftWwP4VavRj2i61nUjvpqZnth8KscNVEu8doqY2fjMVDq2G72eq5eCueAMskefoX0iI1k+SdGcS1FndZu53bBwnyzsGrw8Ea1ndj4+eB+fdevrn25P0jRA3tenxVA91jx0OZkvTzyeEk/NwN/DXU6Md9tpNNXDgwFo/FxYWMnfuXJYvX05qaiqVlZWUlJSQnJx8mjMoffr0sd/38vLC19eXjIyMJscnnM/cuXN58skna+3r2rWr/YsfIdoKvV5HjwhfekT42vdl5JfyR3IO25Nz2XY4hz+P5ZFTXMGqvRms2lvzNzE6wJM+7Sz0jrTQI8KXnhEWArzqSNx1Ogjvo7YLH4aiTLWMW8p28A1XnUhBXcA/pu5pi5ZIuOVL2PoOrHxCDZ9/pScEdoKIfhDeV22RA9RweyHEWUmS7kyctSc9ca1K0I3eteco6d1gwO0w5K4zPz96qLpN3qSGyDd1CJR96bXLmnYeIVyETqer15BzZ3VylfaZM2eycuVKXnzxRTp16oSHhwfXXXcd5eXlZzyPu3vtD4c6nQ6brRnXGxZOpWfPnvz888/2n+tTcFCItiDE18ylvcK5tFc4AOWVNv5KzWfb4Rx2Hsnlz6O5JGUVk5yttu/+TLU/N8zXTI8IX3pHWugX5UffKL9TE3evIOh3k9rqS6+HIXdDhxGwbJqqLp+1X227vlDHmC0w5B61eco0JSHORFo8ZxJYVWyjJBuKssAr0LHxVEveqG4H3QGjnmr48yP6gcGk5jVlH2paUbyCNFXoBKDr2MafRwjR7IxGI1br2QvcrV+/nkmTJnH11VcDqmc9KSnpHEcnXI2bm5t96oQQ4vSMbnr6RfnRL8rPvi+vuII/j+Xy59E89qTk8VdKPklZxaTll5KWX8ovJ/W494vyo1ekL70iLPSMsGDxbGSh3+AucMdPUHhcFZlL3aG2o9vU3PW1/4aNb8LgKXD+VPCWlTyEqIsk6c7E6AmWaMhLVr3pXnGOjkhJ3qRuoxsZj5tJDXFK3qAS/qYk6Qk/AJo638mVR4UQDhUTE8PmzZtJSkrC29v7tL3cnTt35ssvv2TcuHHodDoef/xx6REXp9i/fz8RERGYzWbi4uKYN2/eGQsLlpWVUVZWMy0rPz+/JcIUwilZPN2rlnCrSYILyyrZm5rP7mN5/Hk0jx1Hczl0vMje475sZ4r92Eg/D3pWDZHvHu5DjwhfIv086rXaB6CS784j1QZgs0H8Mvj1RVUked0rsGkBtBuoethNPmDyBbOvWvM9qKuaBlrfHndNU0vB6Zs+RUsIZ+AUSfqbb77JCy+8QFpaGn379uX1119n8ODBdR775Zdf8txzz3HgwAEqKiro3LkzDz30ELfeeoY50a4kqHNNkt7eCZL0osya4fdRQxp/nujza5L0/rc0/jwy1F0IpzVz5kwmTpxIjx49KCkpYeHChXUe9/LLLzN58mSGDh1KUFAQs2bNkoRK1DJkyBAWLVpE165dSU1N5cknn2T48OHs3r0bH5+613KeN2/eKfPYhRA1vE1uDIwJYGBMTeJb3eO+80guu4/lsyc1jyPZJRzLVdtPf6Xbj/U1u9nntveKVLcdgrxwM+jP/uJ6vSow3ONK1eHy6/NqznvSb2d+nmcQBHdVHTwBHdVtYCfwCFCrBh39XVWWP7YNyoug73gY+veaOk9CuCidpmmaIwP47LPPuO2221iwYAFDhgxh/vz5fPHFFyQkJBASEnLK8WvWrCEnJ4du3bphNBr57rvveOihh1i+fDmjR5+9sFJ+fj4Wi4W8vDx8fX3PenyL+2E2bH4b4qbB6GcdHY1KipfcDMHdYOrmxp9n30/wyfXqD+zf/2jcOcoK4PkOYC2HqVvUH20hWpnS0lISExOJjY3FbDY7OhzRjM703jp92+Rgubm5tG/fnpdffpk77rijzmPq6kmPioqSf1MhGiivpIK/UvLZk5JHfGoBf6Xmsz+9gErbqSmD2V1P1zBfOgV70zHEi47B3nQM9qZ9oCfuZ0reNU1NX8xNhrJ8KM1Xn/NKctTUyMz9kH+08RfRdSwMe0B1EgnhJBrS1ju8J/3ll19mypQp3H777QAsWLCA5cuX8/777zN79uxTjr/oootq/fzAAw/wwQcfsG7dunol6U7PXuHdSYrHVc9Hb+ofuahBgA6yD0Jhhlqyo6GObFEJul90TZE9IYQQrZ6fnx9dunThwIEDpz3GZDJhMplaMCohWieLhztxHQOJ61hTG6ms0sqBjEL2pOTbE/g9KfkUl1vZeUT1xJ/I3aCjU4gP3cN96B7mS7dwH7qG+hDsY1JD5nU6iBqsttMpK1Trt2fuU2u6Zx9UP2cdVIl9YCeIHKiGzEeeBxWlamnghO9rtsgB0O9m6HmNFKsTLsWhSXp5eTnbtm3jkUcese/T6/WMHDmSjRs3nvX5mqbxyy+/kJCQwL///e86j3G5OWrOVuG9qfPRq3n4Q0gPyNijztnjiibEMrTpFeKFEEK4jMLCQg4ePNh6prYJ4WJMbgZ6VhWVq2azaSRlFbE3rYCDGYUcPF7IweNFHDxeSHG5lfjUfOJT84Fj9uf4mNzoEFzV4x7iTYcgL2KCvIgJ9MLDeNJ8cpO3Kj4c0a/2fk1TnTZudXwpFzMMju+Dja/DziU1Q+F/mA1dRkOf8dBuEFQUq+Hx5UXqvm+Emgevr8fQfSFagEOT9MzMTKxWK6GhobX2h4aGnnEt1Ly8PCIjIykrK8NgMPDWW28xatSoOo91uTlq1Ul6zmH1jaC7A4e7lhdDyg51vzmGC0Wf38QkvZl69YUQQji1mTNnMm7cONq3b09KSgpz5szBYDBw000NWBJKCHFO6fU6OgR70yHYu9Z+TdM4mlNCfGo+e9MK2JuWT3xqAYeziigoq2Tn0Tx2Hs075XwRFjOxwV50DfVVleYjTzPnXaerO0GvFtwFrngdLn4c/vwMdn6mitXt/U5tp+PhrzqlqreQbqqg3bmQuV+NBgjvL18MiDo5fLh7Y/j4+LBjxw4KCwtZtWoVM2bMoEOHDqcMhQd45JFHmDFjhv3n6jlqTss7RFW5LM1Tw3pCezoulpQ/wFYBPuHg177p54uOg9/fq0m2G8JaoYqDVJ9HCCFEq3X06FFuuukmsrKyCA4O5oILLmDTpk0EB8tyTUI4O51OR1SAJ1EBnvytZ80yimWVVg5nFXOousc9o5BDmUUkZhaRV1JBSl4pKXmlrD+QZX+O2V1P11AfAr1NeJnc8Da54W0yYPFwJyrAkw5B3sQEeeJjrmPJOO8QGHq/2tL3qJ71XUuhIBWMXjWbm4eaB1+SUzNM3h6ABSxRYGlXtUWBX5RajckvCrxC6p9ka5oqlLduPhxcpfZ5hUC3sdB9HMT8H7gZz3gK0XY4NEkPCgrCYDCQnp5ea396evoZ10bV6/V06qTWFO/Xrx/x8fHMmzevziTd5eao6XSqN/3oVjXkvbFJesIPapmLUU+poT91sVnhx9nqD9P1H6hhRSey91zHNc/w8uoe8NSdaniR0av+z03dCZUl6ltOmY8uhBCt2pIlSxwdghCimZncDHQJ9aFL6Km90zlF5RzKVEPlq+e8/5WST1G5tc5e95MF+5iIDfKiQ5CXfTh9h2Bvovw9VE98aE/429PqczGc+rnWWqE+ax7eoEZ8HtkMxZmq06w0D9J31/3CejdwM4PBHQxGtbl7qhpTwV1V4eXgrpCTpJLzlKriyTo9GL2hKAO2LVKbyaJWLzrv1ub77C1clkOTdKPRyIABA1i1ahVXXXUVADabjVWrVjFt2rR6n8dms9Wad+7y7En6/safY+3z6g/BJzfAxGWqcMaJNA2+mw5/LFY/b1sEQ0/6N2+u+ejV/KLAt52q1nn0d+hwYf2fW/2FQdT5MixICCGEEKIV8fcyMsDLyID2/vZ91XPe96UXkF9SSWFZzZZdVM7hLNULn1lYzvGCMo4XlLElMbvWed30qle/faAnMYFetW6jAk6oQG9wVwXo2g2EYX9X+8oKIO8Y5B2FvCNV21FVkT73CBSkgK0SygtPvaDMhLqH1ruZ1VLEcdPAN1L1rMd/q1ZTKsqAnZ+oLbCTOq7vzeATeup5RKvn8OHuM2bMYOLEiQwcOJDBgwczf/58ioqK7NXeb7vtNiIjI5k3bx6g5pgPHDiQjh07UlZWxvfff8+HH37I22+/7cjLaF5NrfCed6zmm7ryQvjoWrj9BwjprvZpGvz0WE2CDqoa5uApNXN8bFZVTR2adw549Pmwe6n6AqBBSXrVFwbOsHa8EEIIIYQ4p0435/1k+aUVJFUNm68uXHfoeBGJmYWUVthIrHoMjtd6nkGvI9LPg/aBnkQHeBLh50GEn5kIiwcRfh6EW7xwC+mm5qbXxVqhViyylqn71nJ1W5KjOtqO74XjCXA8XvWcD5wMg+8G7xOm7XS6RG2XvaR673d8DLu/UlXsf54Lq56GLpfCgInQaSToDXXHIlodhyfp48eP5/jx4zzxxBOkpaXRr18/fvzxR3sxueTkZPQn9JwWFRVx3333cfToUTw8POjWrRsfffQR48ePd9QlNL+mVnivnksT3lcNwzm2DRZfBZN/hIBYNQx+4xvqmMtehl9fUPNz/vxcDbEByPhLFbQw+jTvvHh7kt6AeemaVnvovRBCCCGEEICv2Z0+7fzo086v1n6bTSO9oJSkzGIOZxWRlFVMUmYRSVlFHM4qpqTCSnJ2McnZxXWe192gq5r3rqrPxwR5EexjItDLiL+XkQBPIxafCPT6Ooald7qk5r5Wtb78mYav6w3QfqjaLv0X7PlKdaYd3QoJy9XmG6l61/vfqkanilZNp2nV/3PahoYsIu8wmfvhjYFqTssjxxo+vHvxVXBotZp30/9WWHSZSrr92qu1IteoUQmMngdx98GG11XPemBnmLpZ/aHY8g58PxM6XgK3ftl815a2GxYMU/NwZh0GQz2+J6r+93Azw+zkM1f0FMLFlZaWkpiYSGxsLGazA1d3EM3uTO+tS7RNLkb+TYUQp6NpGhkFZfak/VhOCcdyS0nNKyElt4SU3FLKrbaznqe6Nz66qlBedNUW6e9BpJ8HQd5GtS58Y2XEwx8fws5PoeSEofyGkwvM6cDsC2Y/8PBTt17BENEfogZBaC81pL+l5adC2i6IGqziauMa0i45vCdd1ME/RvWAVxSr+S6WdvV/bkmumt8C0O1y8AyAW7+C9y+FnMSaBP3C2SpBBxgwSfWmZ+1Xc2J6XHHueq5DuqvCGGV58PG16ouIapYoVdTj5CS8OpbIAZKgC9GKxcTEMH36dKZPnw6oCsFfffWVvWbJyZKSkoiNjWX79u3069ev0a/bXOcRQgjhGnQ6HaG+ZkJ9zQzpEHjK4zabRmp+KYnHi0jMKiIps4jDWWr+e05xOdlF5RSUVmK1aWfsjTe56Yn08yDS34PYqh752Kq14dv5e9TMiT+dkO5w6XMwco6au/7HB5D4qxpaf7Ki42o70c5P1K2bB0Sep0brVpaq+fblRWparKbVJPbVt94htSvae/ir52UdVPlC5gHIPQyBHaHTKDXq9sQvI45ug81vqxEBtkr1eb/PeDW11pErV7kQSdKdkcEdAjqo4e6Z+xqWpO//Sf0yBHdTvzgAPmFw2zcqUS9IgSH3wkWza55j8oHBd6lEfd0rahmIw+doTXK9AWKHq2Iah9ac+nhwVxh0R+199gJ2sj66EG1Jamoq/v7+Zz+wASZNmkRubi5ff/21fV9UVBSpqakEBQU162sJIYRwTfqqHvJIPw8u6Fx321BeaSOzsIyjOSX2RP1I1e2xnBLSC0opq7RxKLOIQ5lF/LY/85RzBHgZCfExEexjIsTHTJjFpObGWzwI9zMT4eeBr9lddVL1vk5txdlQUVL7RLZKNU21JBdKc9Vt/jE1XP7oVlWh/vB6tTWGm4dK0qljAPbPc8EnQg3xD+sDu76Ao1tqHvcKqapiv1Bt7YfBoDtVZ6IsOXdakqQ7q6AuVUn6fuh4cf2fV11Jsttltff7t4e7f1VLSMReeOq8mCH3wIY3VMG57R+pZF7vdmpV+OYw7lXoOkb9QamWsl1VmN/wGpw3sfYweJmPLkSbdKalOJuTwWBosdcSQgjROhjd9FXF5jwYHBtwyuPllTbS8ko5mquS98TMmjnxiZlFlFXayC5SvfJ70wpO+zpeRgOhFjNhvlWbpWrzNRNu8SDMYibQx1j33HgAm031fh/ZoirU29eI96lZftme3Oeo+4VpVVXtj6re+cqqLwXMFjU9Nqiz6mFP3QGJv6m8YfuHNa+pd4de18L590B4P0haB1vfgfjvar4s8AqB825TI3pljv0pJEl3Vo2p8F5RCvt/VvdPTtJBVZP0HlH3c72CVNG4Lf9Va6eD+qUyetZ9fFN4BanCFyfqfYMaxpOTBH99rb4pBChIV+u4o4N2g5o/FiFcgaap6S+O4O5Zr7Va//vf/zJ37lyOHj1aq9jnlVdeSWBgII8++igzZsxg06ZNFBUV0b17d+bNm8fIkSNPe86Th7tv2bKFu+++m/j4eHr16sWjjz5a63ir1cpdd93FL7/8QlpaGtHR0dx333088MADAMydO5cPPvjAfm6A1atXExMTc8pw97Vr1/Lwww+zc+dOAgICmDhxIs888wxubqrZvOiii+jTpw9ms5l3330Xo9HIPffcw9y5c+v1zyqEEKJ1M7rpiQ70JDrQEzrWfsxm08gpLiejoExt+aVkFJSRnl9qnxOfkldCbnEFReVWDh0v4tDxotO+lpteR4CXsdYW5K166IO9TQT7+hMcegUhnUwEeBnV2vH1VVGqeuVNvuoz/MmfCSpKVdJ94GdI/RNiLlCV7E9cOi52uNryjqlOuT8+gMJ0+O1FWPcydB6tOvCs5VVD8QuhrBA0q6pJ5WYG96pbjwA1StgnHHzD1fD8VrimvCTpzqoxFd4T10JFkRpyEt6/4a8ZNw22vlez3mNLDi83eqre/NXPwrr56ts3nQ6OVA11D+0pBSdE21VRDM9FOOa1/5mivnE/i+uvv57777+f1atXc8klqqptdnY2P/74I99//z2FhYWMHTuWZ599FpPJxOLFixk3bhwJCQlER0ef9fyFhYVcfvnljBo1io8++ojExER78l3NZrPRrl07vvjiCwIDA9mwYQN33XUX4eHh3HDDDcycOZP4+Hjy8/NZuHAhAAEBAaSkpNQ6z7Fjxxg7diyTJk1i8eLF7N27lylTpmA2m2sl4R988AEzZsxg8+bNbNy4kUmTJjFs2DBGjRp11usRQgjRdun1OgK9TQR6m+gefvrjissrScsrJS2/lPT8UlLzStXPeTU/Hy8so9Km2RP+s9HpIPDEJN7HRLjFTJjFg/CqnvoALyOeRgNmdwMmNxO6wI6nP6G7uWYpubOxRMLFj8KF/1B1sH5/T82x3/eD2hrDzax69QM6qFWs/GPVbVBnVTTbRZetkyTdWdmT9P31f86JQ90bWhEe1JD43tfBn5+pn1t6ePmgO1WCnr4LDqyCziNlProQLsLf358xY8bwySef2JP0pUuXEhQUxIgRI9Dr9fTt29d+/NNPP81XX33FsmXLmDZt2lnP/8knn2Cz2Xjvvfcwm8307NmTo0ePcu+999qPcXd358knn7T/HBsby8aNG/n888+54YYb8Pb2xsPDg7KysjMOb3/rrbeIiorijTfeQKfT0a1bN1JSUpg1axZPPPGEfaRAnz59mDNnDgCdO3fmjTfeYNWqVZKkCyGEaBaeRrezrhVfYbVxvKCM7KJysorKyS4qI6tQ3c+sStyPF5RxvLCMrMIybBpkFpaTWXjmYfbV9DrwcDcQ4G0k3OJBhMVMuJ8H4RY13D7coubO+3u617+SvcEdel6ltsz9qnf9+N7aw/CNXqAzqLnwlaVqHn5lKRRnQUGaWj66JKeqoN1+tZ3yOiaVrAd1UbW6DKZT4/CLUkW7/WNVgTydTg35zzpQUx8s+xBct6hx+VUjSZLurAI7qduCVCjNV8sqnInNCglV30DVNdS9voY9oJJ0naHlE2PPABh4u1rDfd0rVUm6zEcXAndP1aPtqNeupwkTJjBlyhTeeustTCYTH3/8MTfeeCN6vZ7CwkLmzp3L8uXLSU1NpbKykpKSEpKTk+t17vj4ePvw8mpxcaf+XXjzzTd5//33SU5OpqSkhPLy8gZXbI+PjycuLq7Wh41hw4ZRWFjI0aNH7T3/ffr0qfW88PBwMjIyGvRaQgghRFO4G2rmxp+N1aaRXVTO8YIyMgurhtoXqJ756l761LwS8ksq7UvQ2TQoKrdSlF3CkeyS057b5KYn3GKmnb8n7fw9qja1HF1g1RB8X7P7qXPngzrD6Gcbd/EVpWo+fM5htYpVdmLNbdYBlcCn71ZbfRh91MiAk6vkg5rP79++cXE2giTpzsrDD7xD1XyNrP1nL+B2dKv6D2WyqLkgjRXaE25YDDq9mnfS0s6/Dzb/Bw6vg4O/qLktID3pom3T6eo15NzRxo0bh6ZpLF++nEGDBvHbb7/xyiuvADBz5kxWrlzJiy++SKdOnfDw8OC6666jvLyOZWQaacmSJcycOZOXXnqJuLg4fHx8eOGFF9i8eXOzvcaJ3N1rrzmr0+mw2c6+rq4QQgjhCAa9zj7E/WwqrDZKKqyUlFspLreSWVhGSm6JPZlPyS0hLb+UlNxSMgvLKKu0kZRVTFLW6WvoGPQ6/D3dCfAyEmbxIMzXpIbZW8wEe5uweLpj8XDH16xuze76M/fOu5urhrl3AE6qu2WzQm4yHE+AzASVyGvWky6yRB2Tk6Q6RssL1AZq+nB1L3xQZzCefjTDuSBJujML6qKS9Mx6JOnVQ927jFZDN5qix5VNe35TWCKh742qQuTXU9UvkyWqYcvQCSEcwmw2c8011/Dxxx9z4MABunbtynnnnQfA+vXrmTRpEldffTWg5pgnJSXV+9zdu3fnww8/pLS01N6bvmnTplrHrF+/nqFDh3LffffZ9x08eLDWMUajEav1pEa6jtf63//+h6Zp9g8H69evx8fHh3bt5G+REEKI1s/doMfdoFdLwAGxQafvLCirtJKRX8ax3BKO5ZRwJKeYozklHM0pJiW3lJyicgrK1Lry1UPt96UXnjUGo0GPv5c7/p5GtXm5E+xtOmW4fZC3CQ/jSXPP9QY1Nz0gFrpeevYLriiB3COqDlBgR7VEtQNJku7MgjpD0m9nLx6naWpJA2jaUHdnMeyBmmXgQIa6C+FCJkyYwOWXX86ePXu45ZaaVRw6d+7Ml19+ybhx49DpdDz++OMN6nW++eabefTRR5kyZQqPPPIISUlJvPjii7WO6dy5M4sXL2bFihXExsby4YcfsnXrVmJjY+3HxMTEsGLFChISEggMDMRisZzyWvfddx/z58/n/vvvZ9q0aSQkJDBnzhxmzJhRq3K9EEIIIcDkZiAqwJOogNNPkSurtJJbXEF2UTmZhWX2Anhp+er2eGEZ+SUV5JdWkldSgdWmUW61kZ5fRnr+2Qvimd31BHga8fcy4ufpjtnNgJtBh5tBj7FqC/IxEuJjJsTHRIiviVBfM6G+ZtwNenD3gOAuzfnP0iSSpDuz6uJxSevhr2WnP644U82/MJjqV1nR2QV1hu7jIL7qmmWouxAu4+KLLyYgIICEhARuvvlm+/6XX36ZyZMnM3ToUIKCgpg1axb5+fn1Pq+3tzfffvst99xzD/3796dHjx78+9//5tprr7Ufc/fdd7N9+3bGjx+PTqfjpptu4r777uOHH2oqxk6ZMoU1a9YwcOBACgsL7UuwnSgyMpLvv/+ehx9+mL59+xIQEMAdd9zBY4891vh/GCGEEKINM7kZCPU1EOprPuuxmqZRVG4lt7jcntjnFJeTU6SWrUs9Ybh9al4p5ZU2SitspOSVkpJX2qC49DoI9TUT6edBpL8Hob5mvIxueJkMeFbdehndGNopEE9jy6XOOk3TtBZ7NSeQn5+PxWIhLy8PX9+zFGNztAOr4KNr6n9859Ew4fNzF09LOrYN3rlY3b93g5orL0QbUFpaSmJiIrGxsbWKpAnXd6b31qXaJhch/6ZCCNH6aZpGYVllrWQ+t7iC8kob5VYblVYbFVaN0gorxwvLyMhXxfLSq24rrPVLhTfMvrhexfnOpCHtkvSkO7OY4dD3JlWh8GzcTHDRrHMfU0uJHACXzFFrtof0cHQ0QgghhBBCCCej0+nwMbvjY3Y/43D7uthsGpmFZRzNLSGlaj798YIyiiusFJdVUlhmpbi8kqJyK16mlk2bJUl3Zm5GuHqBo6NwnOEzHB2BEEIIIYQQohXS63WE+JoJ8TVzXrS/o8OpRSrgCCGEEEIIIYQQTkKSdCGEEEIIIYQQwklIki6EEE6ojdX0bBPkPRVCCCFEfUiSLoQQTsTd3R2A4uJiB0cimlv1e1r9HgshhBBC1EUKxwkhhBMxGAz4+fmRkZEBgKenJzqdzsFRiabQNI3i4mIyMjLw8/PDYDA4OiQhhBBCODFJ0oUQwsmEhYUB2BN10Tr4+fnZ31shhBBCiNORJF0IIZyMTqcjPDyckJAQKioqHB2OaAbu7u7Sgy6EEEKIepEkXQghnJTBYJDETgghhBCijZHCcUIIIYQQQgghhJOQJF0IIYQQQgghhHASkqQLIYQQQgghhBBOos3NSdc0DYD8/HwHRyKEEEIo1W1SdRslmk7aeyGEEM6kIW19m0vSCwoKAIiKinJwJEIIIURtBQUFWCwWR4fRKkh7L4QQwhnVp63XaW3sa3ubzUZKSgo+Pj7odLomnSs/P5+oqCiOHDmCr69vM0XYsuQaHM/V4we5Bmfg6vFD274GTdMoKCggIiICvV5mojUHae9ruHr8INfgDFw9fpBrcAauHj+0TFvf5nrS9Xo97dq1a9Zz+vr6uux/smpyDY7n6vGDXIMzcPX4oe1eg/SgNy9p70/l6vGDXIMzcPX4Qa7BGbh6/HBu23r5ul4IIYQQQgghhHASkqQLIYQQQgghhBBOQpL0JjCZTMyZMweTyeToUBpNrsHxXD1+kGtwBq4eP8g1COfl6u+rq8cPcg3OwNXjB7kGZ+Dq8UPLXEObKxwnhBBCCCGEEEI4K+lJF0IIIYQQQgghnIQk6UIIIYQQQgghhJOQJF0IIYQQQgghhHASkqQLIYQQQgghhBBOQpL0JnjzzTeJiYnBbDYzZMgQtmzZ4uiQTuvXX39l3LhxREREoNPp+Prrr2s9rmkaTzzxBOHh4Xh4eDBy5Ej279/vmGDrMG/ePAYNGoSPjw8hISFcddVVJCQk1DqmtLSUqVOnEhgYiLe3N9deey3p6ekOivhUb7/9Nn369MHX1xdfX1/i4uL44Ycf7I87e/wn+9e//oVOp2P69On2fc5+DXPnzkWn09XaunXrZn/c2eOvduzYMW655RYCAwPx8PCgd+/e/P777/bHnfn3OSYm5pT3QKfTMXXqVMA13gOr1crjjz9ObGwsHh4edOzYkaeffpoT67A683sgGkba+pYjbb3zkbbecVy5rQfXb+8d3tZrolGWLFmiGY1G7f3339f27NmjTZkyRfPz89PS09MdHVqdvv/+e+3RRx/VvvzySw3Qvvrqq1qP/+tf/9IsFov29ddfazt37tSuuOIKLTY2VispKXFMwCcZPXq0tnDhQm337t3ajh07tLFjx2rR0dFaYWGh/Zh77rlHi4qK0latWqX9/vvv2vnnn68NHTrUgVHXtmzZMm358uXavn37tISEBO2f//yn5u7uru3evVvTNOeP/0RbtmzRYmJitD59+mgPPPCAfb+zX8OcOXO0nj17aqmpqfbt+PHj9sedPX5N07Ts7Gytffv22qRJk7TNmzdrhw4d0lasWKEdOHDAfowz/z5nZGTU+vdfuXKlBmirV6/WNM013oNnn31WCwwM1L777jstMTFR++KLLzRvb2/t1VdftR/jzO+BqD9p61uWtPXORdp6x3H1tl7TXL+9d3RbL0l6Iw0ePFibOnWq/Wer1apFRERo8+bNc2BU9XNyw22z2bSwsDDthRdesO/Lzc3VTCaT9umnnzogwrPLyMjQAG3t2rWapql43d3dtS+++MJ+THx8vAZoGzdudFSYZ+Xv76+9++67LhV/QUGB1rlzZ23lypXahRdeaG+4XeEa5syZo/Xt27fOx1whfk3TtFmzZmkXXHDBaR93td/nBx54QOvYsaNms9lc5j247LLLtMmTJ9fad80112gTJkzQNM313gNxetLWO5a09Y4jbb1jtba2XtNcr713dFsvw90boby8nG3btjFy5Ej7Pr1ez8iRI9m4caMDI2ucxMRE0tLSal2PxWJhyJAhTns9eXl5AAQEBACwbds2Kioqal1Dt27diI6OdsprsFqtLFmyhKKiIuLi4lwq/qlTp3LZZZfVihVc5z3Yv38/ERERdOjQgQkTJpCcnAy4TvzLli1j4MCBXH/99YSEhNC/f3/eeecd++Ou9PtcXl7ORx99xOTJk9HpdC7zHgwdOpRVq1axb98+AHbu3Mm6desYM2YM4FrvgTg9aesdT9p6x5G23rFaU1sPrtneO7qtd2vyGdqgzMxMrFYroaGhtfaHhoayd+9eB0XVeGlpaQB1Xk/1Y87EZrMxffp0hg0bRq9evQB1DUajET8/v1rHOts17Nq1i7i4OEpLS/H29uarr76iR48e7NixwyXiX7JkCX/88Qdbt2495TFXeA+GDBnCokWL6Nq1K6mpqTz55JMMHz6c3bt3u0T8AIcOHeLtt99mxowZ/POf/2Tr1q38/e9/x2g0MnHiRJf6ff7666/Jzc1l0qRJgGv8HwKYPXs2+fn5dOvWDYPBgNVq5dlnn2XChAmA6/1NFXWTtt6xpK13HGnrHa81tfXgmu29o9t6SdKFy5k6dSq7d+9m3bp1jg6lwbp27cqOHTvIy8tj6dKlTJw4kbVr1zo6rHo5cuQIDzzwACtXrsRsNjs6nEap/vYToE+fPgwZMoT27dvz+eef4+Hh4cDI6s9mszFw4ECee+45APr378/u3btZsGABEydOdHB0DfPee+8xZswYIiIiHB1Kg3z++ed8/PHHfPLJJ/Ts2ZMdO3Ywffp0IiIiXO49EMJZSVvvGNLWO4fW1NaDa7b3jm7rZbh7IwQFBWEwGE6pQJienk5YWJiDomq86phd4XqmTZvGd999x+rVq2nXrp19f1hYGOXl5eTm5tY63tmuwWg00qlTJwYMGMC8efPo27cvr776qkvEv23bNjIyMjjvvPNwc3PDzc2NtWvX8tprr+Hm5kZoaKjTX8PJ/Pz86NKlCwcOHHCJ9wAgPDycHj161NrXvXt3+1A+V/l9Pnz4MD///DN33nmnfZ+rvAcPP/wws2fP5sYbb6R3797ceuutPPjgg8ybNw9wnfdAnJm09Y4jbb3jSFvvHFpLWw+u2947uq2XJL0RjEYjAwYMYNWqVfZ9NpuNVatWERcX58DIGic2NpawsLBa15Ofn8/mzZud5no0TWPatGl89dVX/PLLL8TGxtZ6fMCAAbi7u9e6hoSEBJKTk53mGupis9koKytzifgvueQSdu3axY4dO+zbwIEDmTBhgv2+s1/DyQoLCzl48CDh4eEu8R4ADBs27JQlifbt20f79u0B1/h9Bli4cCEhISFcdtll9n2u8h4UFxej19duPg0GAzabDXCd90CcmbT1LU/aeseTtt45tJa2Hly3vXd4W9/k0nNt1JIlSzSTyaQtWrRI++uvv7S77rpL8/Pz09LS0hwdWp0KCgq07du3a9u3b9cA7eWXX9a2b9+uHT58WNM0tYSAn5+f9s0332h//vmnduWVVzrVMg733nuvZrFYtDVr1tRazqG4uNh+zD333KNFR0drv/zyi/b7779rcXFxWlxcnAOjrm327Nna2rVrtcTERO3PP//UZs+erel0Ou2nn37SNM3546/LiRVfNc35r+Ghhx7S1qxZoyUmJmrr16/XRo4cqQUFBWkZGRmapjl//JqmlsRxc3PTnn32WW3//v3axx9/rHl6emofffSR/Rhn/322Wq1adHS0NmvWrFMec4X3YOLEiVpkZKR9WZYvv/xSCwoK0v7xj3/Yj3H290DUj7T1LUvaeuckbX3Law1tvaa5dnvv6LZekvQmeP3117Xo6GjNaDRqgwcP1jZt2uTokE5r9erVGnDKNnHiRE3T1DICjz/+uBYaGqqZTCbtkksu0RISEhwb9Anqih3QFi5caD+mpKREu++++zR/f3/N09NTu/rqq7XU1FTHBX2SyZMna+3bt9eMRqMWHBysXXLJJfZGW9OcP/66nNxwO/s1jB8/XgsPD9eMRqMWGRmpjR8/vtaao84ef7Vvv/1W69Wrl2YymbRu3bpp//3vf2s97uy/zytWrNCAOmNyhfcgPz9fe+CBB7To6GjNbDZrHTp00B599FGtrKzMfoyzvwei/qStbznS1jsnaesdw9Xbek1z7fbe0W29TtM0ren98UIIIYQQQgghhGgqmZMuhBBCCCGEEEI4CUnShRBCCCGEEEIIJyFJuhBCCCGEEEII4SQkSRdCCCGEEEIIIZyEJOlCCCGEEEIIIYSTkCRdCCGEEEIIIYRwEpKkCyGEEEIIIYQQTkKSdCGEEEIIIYQQwklIki6EaFFr1qxBp9ORm5vr6FCEEEIIcY5Iey9E40mSLoQQQgghhBBCOAlJ0oUQQgghhBBCCCchSboQbYzNZmPevHnExsbi4eFB3759Wbp0KVAzNG358uX06dMHs9nM+eefz+7du2ud43//+x89e/bEZDIRExPDSy+9VOvxsrIyZs2aRVRUFCaTiU6dOvHee+/VOmbbtm0MHDgQT09Phg4dSkJCwrm9cCGEEKINkfZeCNclSboQbcy8efNYvHgxCxYsYM+ePTz44IPccsstrF271n7Mww8/zEsvvcTWrVsJDg5m3LhxVFRUAKqxveGGG7jxxhvZtWsXc+fO5fHHH2fRokX259922218+umnvPbaa8THx/Of//wHb2/vWnE8+uijvPTSS/z++++4ubkxefLkFrl+IYQQoi2Q9l4IF6YJIdqM0tJSzdPTU9uwYUOt/XfccYd20003aatXr9YAbcmSJfbHsrKyNA8PD+2zzz7TNE3Tbr75Zm3UqFG1nv/www9rPXr00DRN0xISEjRAW7lyZZ0xVL/Gzz//bN+3fPlyDdBKSkqa5TqFEEKItkzaeyFcm/SkC9GGHDhwgOLiYkaNGoW3t7d9W7x4MQcPHrQfFxcXZ78fEBBA165diY+PByA+Pp5hw4bVOu+wYcPYv38/VquVHTt2YDAYuPDCC88YS58+fez3w8PDAcjIyGjyNQohhBBtnbT3Qrg2N0cHIIRoOYWFhQAsX76cyMjIWo+ZTKZaDXdjeXh41Os4d3d3+32dTgeo+XNCCCGEaBpp74VwbdKTLkQb0qNHD0wmE8nJyXTq1KnWFhUVZT9u06ZN9vs5OTns27eP7t27A9C9e3fWr19f67zr16+nS5cuGAwGevfujc1mqzXnTQghhBAtR9p7IVyb9KQL0Yb4+Pgwc+ZMHnzwQWw2GxdccAF5eXmsX78eX19f2rdvD8BTTz1FYGAgoaGhPProowQFBXHVVVcB8NBDDzFo0CCefvppxo8fz8aNG3njjTd46623AIiJiWHixIlMnjyZ1157jb59+3L48GEyMjK44YYbHHXpQgghRJsh7b0QLs7Rk+KFEC3LZrNp8+fP17p27aq5u7trwcHB2ujRo7W1a9fai7x8++23Ws+ePTWj0agNHjxY27lzZ61zLF26VOvRo4fm7u6uRUdHay+88EKtx0tKSrQHH3xQCw8P14xGo9apUyft/fff1zStppBMTk6O/fjt27drgJaYmHiuL18IIYRoE6S9F8J16TRN0xz5JYEQwnmsWbOGESNGkJOTg5+fn6PDEUIIIcQ5IO29EM5N5qQLIYQQQgghhBBOQpJ0IYQQQgghhBDCSchwdyGEEEIIIYQQwklIT7oQQgghhBBCCOEkJEkXQgghhBBCCCGchCTpQgghhBBCCCGEk5AkXQghhBBCCCGEcBKSpAshhBBCCCGEEE5CknQhhBBCCCGEEMJJSJIuhBBCCCGEEEI4CUnShRBCCCGEEEIIJ/H/OKXxhlLFnbUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and Validation accuracy and loss\n",
    "f = plt.figure(figsize = (12,4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('train validation accuracy')\n",
    "plt.plot(history6.history['accuracy'])\n",
    "plt.plot(history6.history['val_accuracy'])\n",
    "plt.legend(['train','validation'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('train validation loss')\n",
    "plt.plot(history6.history['loss'])\n",
    "plt.plot(history6.history['val_loss'])\n",
    "plt.legend(['train','validation'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2e3f9d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel2_test = mel2_file['mel_test']\n",
    "y2_test_mel = mel2_file['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1d6bb499",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mel2_test_scaled = (mel2_test - np.mean(mel2_test)) / np.std(mel2_test)\n",
    "\n",
    "# Reshape the data\n",
    "N,row,col = mel2_test_scaled.shape\n",
    "mel2_test_reshaped = mel2_test_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "02712725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 78ms/step\n"
     ]
    }
   ],
   "source": [
    "y3_prob = model6.predict(mel2_test_reshaped)\n",
    "y3_pred = np.argmax(y3_prob, axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4c035393",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy\n",
    "acc3_2d = round(accuracy_score(y3_pred,y2_test_mel),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3182272f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "cm_mel = confusion_matrix(y2_test_mel,y3_pred)\n",
    "cm_2d_mel = ConfusionMatrixDisplay(confusion_matrix = cm_mel, display_labels = ['Angry', 'Happy', 'Relaxed', 'Sad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "13a65fe6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Angry       0.91      1.00      0.95        10\n",
      "       Happy       1.00      0.90      0.95        10\n",
      "     Relaxed       0.69      0.90      0.78        10\n",
      "         Sad       0.86      0.60      0.71        10\n",
      "\n",
      "    accuracy                           0.85        40\n",
      "   macro avg       0.86      0.85      0.85        40\n",
      "weighted avg       0.86      0.85      0.85        40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y2_test_mel, y3_pred, target_names = ['Angry', 'Happy', 'Relaxed', 'Sad']))\n",
    "cm_2d_mel.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "95875072",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.save(os.getcwd() + \"/Features2D/Model/Conv2D_mfcc.h5\") \n",
    "model4.save(os.getcwd() + \"/Features2D/Model/Conv2D_spec.h5\") \n",
    "model6.save(os.getcwd() + \"/Features2D/Model/Conv2D_mel.h5\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd18d7c",
   "metadata": {},
   "source": [
    "## 5. Ensemble 3 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4984d32d",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_file_ense = np.load(os.getcwd() + \"/Features2D/npz_new/spec2_train_test.npz\")\n",
    "spec_ense_train = spec_file_ense['spec_train']\n",
    "spec_ense_test = spec_file_ense['spec_test']\n",
    "y_ense_train = spec_file_ense['y_train']\n",
    "y_ense_test = spec_file_ense['y_test']\n",
    "model_spec = load_model(os.getcwd() + \"/Features2D/Model/Conv2D_spec.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9e4a5935",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfcc_file_ense = np.load(os.getcwd() + \"/Features2D/npz_new/mfcc2_train_test.npz\")\n",
    "mfcc_ense_train = mfcc_file_ense['mfcc_train']\n",
    "mfcc_ense_test =mfcc_file_ense['mfcc_test']\n",
    "y1_ense_train = mfcc_file_ense['y_train']\n",
    "y1_ense_test = mfcc_file_ense['y_test']\n",
    "model_mfcc = load_model(os.getcwd() + \"/Features2D/Model/Conv2D_mfcc.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7a4e7602",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel_file_ense = np.load(os.getcwd() + \"/Features2D/npz_new/mel2_train_test.npz\")\n",
    "mel_ense_train = mel_file_ense['mel_train']\n",
    "mel_ense_test =mel_file_ense['mel_test']\n",
    "y_ense_train = mel_file_ense['y_train']\n",
    "y_ense_test = mel_file_ense['y_test']\n",
    "model_mel = load_model(os.getcwd() + \"/Features2D/Model/Conv2D_mel.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "227bfa6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "mfcc_ense_train_scaled = (mfcc_ense_train - np.mean(mfcc_ense_train))/np.std(mfcc_ense_train)\n",
    "spec_ense_train_scaled = spec_ense_train / np.amax(spec_ense_train)\n",
    "mel_ense_train_scaled = (mel_ense_train - np.mean(mel_ense_train))/np.std(mel_ense_train)\n",
    "\n",
    "mfcc_ense_test_scaled = (mfcc_ense_test - np.mean(mfcc_ense_test))/np.std(mfcc_ense_test)\n",
    "spec_ense_test_scaled = spec_ense_test / np.amax(spec_ense_test)\n",
    "mel_ense_test_scaled = (mel_ense_test - np.mean(mel_ense_test))/np.std(mfcc_ense_test)\n",
    "\n",
    "\n",
    "# Reshape the data\n",
    "N,row,col = mfcc_ense_train_scaled.shape\n",
    "mfcc_ense_train_reshaped = mfcc_ense_train_scaled.reshape((N,row,col,1))\n",
    "N,row,col = spec_ense_train_scaled.shape\n",
    "spec_ense_train_reshaped = spec_ense_train_scaled.reshape((N,row,col,1))\n",
    "N,row,col = mel_ense_train_scaled.shape\n",
    "mel_ense_train_reshaped = mel_ense_train_scaled.reshape((N,row,col,1))\n",
    "\n",
    "N,row,col = mfcc_ense_test_scaled.shape\n",
    "mfcc_ense_test_reshaped = mfcc_ense_test_scaled.reshape((N,row,col,1))\n",
    "N,row,col = spec_ense_test_scaled.shape\n",
    "spec_ense_test_reshaped = spec_ense_test_scaled.reshape((N,row,col,1))\n",
    "N,row,col = mel_ense_test_scaled.shape\n",
    "mel_ense_test_reshaped = mel_ense_test_scaled.reshape((N,row,col,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "04894359",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_majority(pred) :\n",
    "    N = len(pred[0])\n",
    "    vote = []\n",
    "    for i in range(N) :\n",
    "        elements = [j[i] for j in pred]\n",
    "        elements = np.array(elements)\n",
    "        uniq, freq = np.unique(elements, return_counts= True)\n",
    "        vote.append(uniq[np.argmax(freq)])\n",
    "    \n",
    "    vote = np.array(vote)\n",
    "    return vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d2af8729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 3s 238ms/step\n",
      "12/12 [==============================] - 2s 188ms/step\n",
      "12/12 [==============================] - 4s 256ms/step\n"
     ]
    }
   ],
   "source": [
    "# Train 3 models\n",
    "y_ense_prob1 = model_spec.predict(spec_ense_train_reshaped)\n",
    "y_ense_pred1 = np.argmax(y_ense_prob1, axis= -1)\n",
    "\n",
    "y_ense_prob2 = model_mfcc.predict(mfcc_ense_train_reshaped)\n",
    "y_ense_pred2 = np.argmax(y_ense_prob2, axis= -1)\n",
    "\n",
    "y_ense_prob3 = model_mel.predict(mel_ense_train_reshaped)\n",
    "y_ense_pred3 = np.argmax(y_ense_prob3, axis= -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d950deb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 different predictions\n",
    "train_pred = [y_ense_pred1, y_ense_pred2, y_ense_pred3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "dc679b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = get_majority(train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b5d90b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9667"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "train_corr = len(y_train_pred) - np.count_nonzero(y_train_pred - y_ense_train)\n",
    "train_acc = np.round((train_corr/ len(y_train_pred)),4)\n",
    "train_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "bd3637cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 71ms/step\n",
      "2/2 [==============================] - 0s 55ms/step\n",
      "2/2 [==============================] - 1s 267ms/step\n"
     ]
    }
   ],
   "source": [
    "# Test ensembled models\n",
    "y_test_prob1 = model_spec.predict(spec_ense_test_reshaped)\n",
    "y_test_pred1 = np.argmax(y_test_prob1, axis= -1)\n",
    "\n",
    "y_test_prob2 = model_mfcc.predict(mfcc_ense_test_reshaped)\n",
    "y_test_pred2 = np.argmax(y_test_prob2, axis= -1)\n",
    "\n",
    "y_test_prob3 = model_mel.predict(mel_ense_test_reshaped)\n",
    "y_test_pred3 = np.argmax(y_test_prob3, axis= -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7c4f3dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = [y_test_pred1, y_test_pred2, y_test_pred3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b1542c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = get_majority(test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "27a0d193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "test_corr = len(y_test_pred) - np.count_nonzero(y_test_pred - y_ense_test)\n",
    "test_acc = np.round((test_corr/ len(y_test_pred)),4)\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ac31a9b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Angry       0.67      1.00      0.80        10\n",
      "       Happy       0.91      1.00      0.95        10\n",
      "     Relaxed       0.86      0.60      0.71        10\n",
      "         Sad       0.86      0.60      0.71        10\n",
      "\n",
      "    accuracy                           0.80        40\n",
      "   macro avg       0.82      0.80      0.79        40\n",
      "weighted avg       0.82      0.80      0.79        40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "cm_ense = confusion_matrix(y_ense_test,y_test_pred)\n",
    "cm_display_ense = ConfusionMatrixDisplay(confusion_matrix = cm_ense, display_labels = ['Angry', 'Happy', 'Relaxed', 'Sad'])\n",
    "print(classification_report(y_ense_test, y_test_pred, target_names = ['Angry', 'Happy', 'Relaxed', 'Sad']))\n",
    "cm_display_ense.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa240803",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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