Train_CNN_Analog-Readout_Version-Small1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Training\n",
    "\n",
    "Target of this code is to train a CNN network to extract the needle position of an analog needle device.\n",
    "\n",
    "### Preparing the training\n",
    "* First all libraries are loaded\n",
    "    * It is assumed, that they are installed during the Python setup\n",
    "* matplotlib is set to print the output inline in the jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "########### Basic Parameters for Running: ################################\n",
    "    \n",
    "TFliteNamingAndVersion = \"ana1100s1\"   # Used for tflite Filename\n",
    "Training_Percentage = 0.2              # 0.0 = Use all Images for Training\n",
    "Epoch_Anz = 100\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.python import keras\n",
    "from tensorflow.python.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.callbacks import History \n",
    "import math\n",
    "from PIL import Image \n",
    "\n",
    "loss_ges = np.array([])\n",
    "val_loss_ges = np.array([])\n",
    "\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=4)\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load training data\n",
    "* The data is expected in the \"Input_dir\"\n",
    "* Picture size must be 32x32 with 3 color channels (RGB)\n",
    "* The filename contains the informations needed for training in the first 3 digits::\n",
    "* Typical filename: \n",
    "    * x.y-zzzz.jpg \n",
    "    * e.g. \"4.6_Lfd-1406_zeiger3_2019-06-02T050011\"\n",
    "\n",
    "|Place holder | Meaning                     | Usage        |\n",
    "|------------- |-----------------------------|--------------|\n",
    "| **x.y**          | readout value               | **to be learned** |\n",
    "| zzzz        | additional information              | not needed   |\n",
    "\n",
    "* The images are stored in the x_data[]\n",
    "* The expected output for each image in the corresponding y_data[]\n",
    "    * The periodic nature is reflected in a **sin/cos coding**, which allows to restore the angle/counter value with an arctan later on.\n",
    "\n",
    "* The last step is a shuffle (from sklearn.utils) as the filenames are on order due to the encoding of the expected analog readout in the filename "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5761, 32, 32, 3)\n",
      "(5761, 2)\n"
     ]
    }
   ],
   "source": [
    "Input_dir='data_resize_all'\n",
    "\n",
    "files = glob.glob(Input_dir + '/*.*')\n",
    "x_data = []\n",
    "y_data = []\n",
    "\n",
    "for aktfile in files:\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    test_image = np.reshape(test_image, (32,32,3))\n",
    "    base = os.path.basename(aktfile)\n",
    "    target_number = (float(base[0:3])) / 10\n",
    "    target_sin = math.sin(target_number * math.pi * 2)\n",
    "    target_cos = math.cos(target_number * math.pi * 2)\n",
    "\n",
    "    x_data.append(test_image)\n",
    "    zw = np.array([target_sin, target_cos])\n",
    "    y_data.append(zw)\n",
    "\n",
    "x_data = np.array(x_data)\n",
    "y_data = np.array(y_data)\n",
    "print(x_data.shape)\n",
    "print(y_data.shape)\n",
    "\n",
    "x_data, y_data = shuffle(x_data, y_data)\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n",
    "else:\n",
    "    X_train = x_data\n",
    "    y_train = y_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model\n",
    "\n",
    "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n",
    "\n",
    "#### Important\n",
    "* Shape of the input layer: (32, 32, 3)\n",
    "* Shape of the output layer: (2) - sin and cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 32, 32, 3)        12        \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 32, 32, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 8, 8, 32)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 32)          25632     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 4, 4, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 32)          9248      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 2, 2, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 130       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 62,222\n",
      "Trainable params: 62,216\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = tf.keras.Input(shape=(32, 32, 3))\n",
    "inputs2 = tf.keras.layers.BatchNormalization()(inputs)\n",
    "inputs3 = tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=\"relu\")(inputs2)\n",
    "inputs4 = tf.keras.layers.MaxPool2D(pool_size=(4,4))(inputs3)\n",
    "inputs5 = tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=\"relu\")(inputs4)\n",
    "inputs6 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs5)\n",
    "inputs7 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs6)\n",
    "inputs8 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs7)\n",
    "inputs9 = tf.keras.layers.Flatten()(inputs8)\n",
    "inputs10 = tf.keras.layers.Dense(128,activation=\"relu\")(inputs9)\n",
    "inputs11 = tf.keras.layers.Dense(64,activation=\"relu\")(inputs10)\n",
    "output = tf.keras.layers.Dense(2)(inputs11)\n",
    "\n",
    "model = tf.keras.Model(inputs=inputs, outputs=output)\n",
    "\n",
    "model.compile(loss=keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "\n",
    "#model = Sequential()\n",
    "#model.add(BatchNormalization(input_shape=(32,32,3)))\n",
    "#model.add(Conv2D(32, (5, 5), input_shape=(32,32,3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(4,4)))\n",
    "#model.add(Conv2D(32, (5, 5), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(4,4)))\n",
    "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(2,2)))\n",
    "#model.add(Flatten())\n",
    "#model.add(Dense(128,activation=\"relu\"))\n",
    "#model.add(Dense(64,activation=\"relu\"))\n",
    "#model.add(Dense(2))\n",
    "#model.summary()\n",
    "\n",
    "#model.compile(loss=keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "The input pictures are randomly scattered for brightness and pixel shift variations. These is implemented with a ImageDataGenerator.\n",
    "\n",
    "\n",
    "The training is splitted into two steps:\n",
    "1. Variation of the brightness only\n",
    "2. Variation of brightness and Pixel Shift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Brigthness scattering only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_8176\\3831122756.py:11: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 13s 16ms/step - loss: 0.1464 - accuracy: 0.8761 - val_loss: 0.0154 - val_accuracy: 0.9714\n",
      "Epoch 2/30\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0176 - accuracy: 0.9712 - val_loss: 0.0091 - val_accuracy: 0.9523\n",
      "Epoch 3/30\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 0.0089 - accuracy: 0.9767 - val_loss: 0.0047 - val_accuracy: 0.9757\n",
      "Epoch 4/30\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 0.0059 - accuracy: 0.9828 - val_loss: 0.0074 - val_accuracy: 0.9818\n",
      "Epoch 5/30\n",
      "721/721 [==============================] - 17s 23ms/step - loss: 0.0045 - accuracy: 0.9833 - val_loss: 0.0040 - val_accuracy: 0.9870\n",
      "Epoch 6/30\n",
      "721/721 [==============================] - 16s 22ms/step - loss: 0.0036 - accuracy: 0.9851 - val_loss: 0.0022 - val_accuracy: 0.9879\n",
      "Epoch 7/30\n",
      "721/721 [==============================] - 16s 23ms/step - loss: 0.0031 - accuracy: 0.9859 - val_loss: 0.0022 - val_accuracy: 0.9801\n",
      "Epoch 8/30\n",
      "721/721 [==============================] - 15s 21ms/step - loss: 0.0026 - accuracy: 0.9882 - val_loss: 0.0025 - val_accuracy: 0.9844\n",
      "Epoch 9/30\n",
      "721/721 [==============================] - 15s 21ms/step - loss: 0.0023 - accuracy: 0.9863 - val_loss: 0.0017 - val_accuracy: 0.9879\n",
      "Epoch 10/30\n",
      "721/721 [==============================] - 19s 26ms/step - loss: 0.0021 - accuracy: 0.9892 - val_loss: 0.0019 - val_accuracy: 0.9835\n",
      "Epoch 11/30\n",
      "721/721 [==============================] - 18s 25ms/step - loss: 0.0019 - accuracy: 0.9894 - val_loss: 0.0018 - val_accuracy: 0.9870\n",
      "Epoch 12/30\n",
      "721/721 [==============================] - 23s 32ms/step - loss: 0.0017 - accuracy: 0.9899 - val_loss: 0.0015 - val_accuracy: 0.9870\n",
      "Epoch 13/30\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 0.0016 - accuracy: 0.9899 - val_loss: 0.0012 - val_accuracy: 0.9948\n",
      "Epoch 14/30\n",
      "721/721 [==============================] - 24s 33ms/step - loss: 0.0014 - accuracy: 0.9892 - val_loss: 0.0018 - val_accuracy: 0.9835\n",
      "Epoch 15/30\n",
      "721/721 [==============================] - 24s 34ms/step - loss: 0.0013 - accuracy: 0.9917 - val_loss: 0.0013 - val_accuracy: 0.9948\n",
      "Epoch 16/30\n",
      "721/721 [==============================] - 24s 33ms/step - loss: 0.0013 - accuracy: 0.9917 - val_loss: 0.0012 - val_accuracy: 0.9983\n",
      "Epoch 17/30\n",
      "721/721 [==============================] - 23s 32ms/step - loss: 0.0012 - accuracy: 0.9908 - val_loss: 0.0013 - val_accuracy: 0.9827\n",
      "Epoch 18/30\n",
      "721/721 [==============================] - 20s 28ms/step - loss: 0.0011 - accuracy: 0.9911 - val_loss: 9.8405e-04 - val_accuracy: 0.9818\n",
      "Epoch 19/30\n",
      "721/721 [==============================] - 23s 31ms/step - loss: 0.0010 - accuracy: 0.9936 - val_loss: 8.3284e-04 - val_accuracy: 0.9931\n",
      "Epoch 20/30\n",
      "721/721 [==============================] - 18s 25ms/step - loss: 0.0010 - accuracy: 0.9920 - val_loss: 0.0014 - val_accuracy: 0.9853\n",
      "Epoch 21/30\n",
      "721/721 [==============================] - 22s 30ms/step - loss: 9.3571e-04 - accuracy: 0.9941 - val_loss: 0.0016 - val_accuracy: 0.9861\n",
      "Epoch 22/30\n",
      "721/721 [==============================] - 20s 27ms/step - loss: 8.8003e-04 - accuracy: 0.9922 - val_loss: 5.6434e-04 - val_accuracy: 0.9939\n",
      "Epoch 23/30\n",
      "721/721 [==============================] - 21s 30ms/step - loss: 8.6814e-04 - accuracy: 0.9944 - val_loss: 7.1075e-04 - val_accuracy: 0.9974\n",
      "Epoch 24/30\n",
      "721/721 [==============================] - 23s 32ms/step - loss: 7.9173e-04 - accuracy: 0.9944 - val_loss: 5.5683e-04 - val_accuracy: 0.9965\n",
      "Epoch 25/30\n",
      "721/721 [==============================] - 23s 32ms/step - loss: 7.8121e-04 - accuracy: 0.9943 - val_loss: 7.1454e-04 - val_accuracy: 0.9983\n",
      "Epoch 26/30\n",
      "721/721 [==============================] - 18s 26ms/step - loss: 7.3703e-04 - accuracy: 0.9957 - val_loss: 7.6106e-04 - val_accuracy: 0.9913\n",
      "Epoch 27/30\n",
      "721/721 [==============================] - 22s 31ms/step - loss: 6.9321e-04 - accuracy: 0.9948 - val_loss: 6.9258e-04 - val_accuracy: 0.9965\n",
      "Epoch 28/30\n",
      "721/721 [==============================] - 23s 32ms/step - loss: 6.9167e-04 - accuracy: 0.9946 - val_loss: 7.6681e-04 - val_accuracy: 0.9965\n",
      "Epoch 29/30\n",
      "721/721 [==============================] - 21s 29ms/step - loss: 6.8439e-04 - accuracy: 0.9957 - val_loss: 6.5337e-04 - val_accuracy: 0.9983\n",
      "Epoch 30/30\n",
      "721/721 [==============================] - 23s 31ms/step - loss: 6.3500e-04 - accuracy: 0.9943 - val_loss: 8.1554e-04 - val_accuracy: 0.9983\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 8\n",
    "Epoch_Anz = 30\n",
    "Shift_Range = 0\n",
    "Brightness_Range = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], height_shift_range=[-Shift_Range,Shift_Range],brightness_range=[1-Brightness_Range,1+Brightness_Range])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, epochs = Epoch_Anz)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Learing result\n",
    " \n",
    "* Visualization of the training and validation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "plt.semilogy(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Brigthness and Pixel Shift scattering\n",
    "Here a higher number of epochs is used to reach the minimum loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_8176\\1492021130.py:11: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 0.0515 - accuracy: 0.9363 - val_loss: 0.0116 - val_accuracy: 0.9653\n",
      "Epoch 2/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 0.0119 - accuracy: 0.9705 - val_loss: 0.0098 - val_accuracy: 0.9696\n",
      "Epoch 3/160\n",
      "721/721 [==============================] - 24s 34ms/step - loss: 0.0086 - accuracy: 0.9764 - val_loss: 0.0054 - val_accuracy: 0.9792\n",
      "Epoch 4/160\n",
      "721/721 [==============================] - 26s 35ms/step - loss: 0.0061 - accuracy: 0.9806 - val_loss: 0.0043 - val_accuracy: 0.9818\n",
      "Epoch 5/160\n",
      "721/721 [==============================] - 21s 29ms/step - loss: 0.0047 - accuracy: 0.9802 - val_loss: 0.0044 - val_accuracy: 0.9792\n",
      "Epoch 6/160\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 0.0045 - accuracy: 0.9819 - val_loss: 0.0030 - val_accuracy: 0.9792\n",
      "Epoch 7/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 0.0039 - accuracy: 0.9826 - val_loss: 0.0024 - val_accuracy: 0.9801\n",
      "Epoch 8/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 0.0032 - accuracy: 0.9856 - val_loss: 0.0022 - val_accuracy: 0.9896\n",
      "Epoch 9/160\n",
      "721/721 [==============================] - 26s 35ms/step - loss: 0.0029 - accuracy: 0.9849 - val_loss: 0.0018 - val_accuracy: 0.9835\n",
      "Epoch 10/160\n",
      "721/721 [==============================] - 26s 35ms/step - loss: 0.0025 - accuracy: 0.9861 - val_loss: 0.0021 - val_accuracy: 0.9861\n",
      "Epoch 11/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 0.0025 - accuracy: 0.9861 - val_loss: 0.0027 - val_accuracy: 0.9905\n",
      "Epoch 12/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0022 - accuracy: 0.9866 - val_loss: 0.0025 - val_accuracy: 0.9818\n",
      "Epoch 13/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0021 - accuracy: 0.9872 - val_loss: 0.0016 - val_accuracy: 0.9853\n",
      "Epoch 14/160\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 0.0019 - accuracy: 0.9896 - val_loss: 0.0021 - val_accuracy: 0.9913\n",
      "Epoch 15/160\n",
      "721/721 [==============================] - 23s 32ms/step - loss: 0.0017 - accuracy: 0.9878 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 16/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0016 - accuracy: 0.9898 - val_loss: 0.0016 - val_accuracy: 0.9835\n",
      "Epoch 17/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 0.0015 - accuracy: 0.9887 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 18/160\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 0.0015 - accuracy: 0.9880 - val_loss: 0.0017 - val_accuracy: 0.9896\n",
      "Epoch 19/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 0.0014 - accuracy: 0.9892 - val_loss: 0.0014 - val_accuracy: 0.9879\n",
      "Epoch 20/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 0.0015 - accuracy: 0.9917 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 21/160\n",
      "721/721 [==============================] - 24s 33ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 22/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 0.0014 - accuracy: 0.9901 - val_loss: 0.0011 - val_accuracy: 0.9905\n",
      "Epoch 23/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0013 - accuracy: 0.9924 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 24/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0012 - accuracy: 0.9910 - val_loss: 9.2467e-04 - val_accuracy: 0.9879\n",
      "Epoch 25/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 0.0012 - accuracy: 0.9918 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 26/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0012 - accuracy: 0.9885 - val_loss: 9.2473e-04 - val_accuracy: 0.9913\n",
      "Epoch 27/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 0.0011 - accuracy: 0.9899 - val_loss: 0.0011 - val_accuracy: 0.9853\n",
      "Epoch 28/160\n",
      "721/721 [==============================] - 29s 40ms/step - loss: 0.0011 - accuracy: 0.9884 - val_loss: 0.0013 - val_accuracy: 0.9818\n",
      "Epoch 29/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 9.9067e-04 - accuracy: 0.9924 - val_loss: 0.0010 - val_accuracy: 0.9905\n",
      "Epoch 30/160\n",
      "721/721 [==============================] - 29s 40ms/step - loss: 0.0010 - accuracy: 0.9918 - val_loss: 8.1247e-04 - val_accuracy: 0.9939\n",
      "Epoch 31/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 0.0010 - accuracy: 0.9911 - val_loss: 0.0010 - val_accuracy: 0.9939\n",
      "Epoch 32/160\n",
      "721/721 [==============================] - 29s 40ms/step - loss: 9.5485e-04 - accuracy: 0.9913 - val_loss: 8.7358e-04 - val_accuracy: 0.9939\n",
      "Epoch 33/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 9.3403e-04 - accuracy: 0.9925 - val_loss: 8.8438e-04 - val_accuracy: 0.9913\n",
      "Epoch 34/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 9.2796e-04 - accuracy: 0.9915 - val_loss: 7.3085e-04 - val_accuracy: 0.9939\n",
      "Epoch 35/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 8.9348e-04 - accuracy: 0.9910 - val_loss: 0.0012 - val_accuracy: 0.9887\n",
      "Epoch 36/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 8.7606e-04 - accuracy: 0.9950 - val_loss: 6.9878e-04 - val_accuracy: 0.9965\n",
      "Epoch 37/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 8.5106e-04 - accuracy: 0.9931 - val_loss: 6.4345e-04 - val_accuracy: 0.9887\n",
      "Epoch 38/160\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 8.3969e-04 - accuracy: 0.9917 - val_loss: 8.9194e-04 - val_accuracy: 0.9913\n",
      "Epoch 39/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 7.9617e-04 - accuracy: 0.9929 - val_loss: 7.9334e-04 - val_accuracy: 0.9844\n",
      "Epoch 40/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 7.9489e-04 - accuracy: 0.9944 - val_loss: 6.1541e-04 - val_accuracy: 0.9931\n",
      "Epoch 41/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 8.0355e-04 - accuracy: 0.9917 - val_loss: 6.4730e-04 - val_accuracy: 0.9922\n",
      "Epoch 42/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 8.0499e-04 - accuracy: 0.9939 - val_loss: 7.2381e-04 - val_accuracy: 0.9965\n",
      "Epoch 43/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 7.9936e-04 - accuracy: 0.9929 - val_loss: 7.0128e-04 - val_accuracy: 0.9957\n",
      "Epoch 44/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 7.5756e-04 - accuracy: 0.9931 - val_loss: 7.5290e-04 - val_accuracy: 0.9905\n",
      "Epoch 45/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 7.2414e-04 - accuracy: 0.9908 - val_loss: 6.7195e-04 - val_accuracy: 0.9887\n",
      "Epoch 46/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 7.3321e-04 - accuracy: 0.9939 - val_loss: 6.5586e-04 - val_accuracy: 0.9939\n",
      "Epoch 47/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 6.9704e-04 - accuracy: 0.9941 - val_loss: 7.1244e-04 - val_accuracy: 0.9922\n",
      "Epoch 48/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 7.4302e-04 - accuracy: 0.9934 - val_loss: 9.3747e-04 - val_accuracy: 0.9870\n",
      "Epoch 49/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 7.0761e-04 - accuracy: 0.9950 - val_loss: 5.4655e-04 - val_accuracy: 0.9922\n",
      "Epoch 50/160\n",
      "721/721 [==============================] - 24s 33ms/step - loss: 6.8693e-04 - accuracy: 0.9946 - val_loss: 0.0010 - val_accuracy: 0.9887\n",
      "Epoch 51/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 7.0529e-04 - accuracy: 0.9960 - val_loss: 5.4450e-04 - val_accuracy: 0.9931\n",
      "Epoch 52/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 6.7291e-04 - accuracy: 0.9953 - val_loss: 5.6576e-04 - val_accuracy: 0.9861\n",
      "Epoch 53/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 7.0268e-04 - accuracy: 0.9924 - val_loss: 6.2167e-04 - val_accuracy: 0.9896\n",
      "Epoch 54/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 6.6604e-04 - accuracy: 0.9941 - val_loss: 5.1820e-04 - val_accuracy: 0.9948\n",
      "Epoch 55/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 6.4392e-04 - accuracy: 0.9948 - val_loss: 7.3397e-04 - val_accuracy: 0.9879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 6.4505e-04 - accuracy: 0.9939 - val_loss: 6.2130e-04 - val_accuracy: 0.9948\n",
      "Epoch 57/160\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 6.2751e-04 - accuracy: 0.9915 - val_loss: 5.9381e-04 - val_accuracy: 0.9957\n",
      "Epoch 58/160\n",
      "721/721 [==============================] - 25s 34ms/step - loss: 6.4489e-04 - accuracy: 0.9955 - val_loss: 5.5194e-04 - val_accuracy: 0.9939\n",
      "Epoch 59/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 6.5105e-04 - accuracy: 0.9944 - val_loss: 5.2932e-04 - val_accuracy: 0.9931\n",
      "Epoch 60/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 6.4087e-04 - accuracy: 0.9938 - val_loss: 6.2474e-04 - val_accuracy: 0.9939\n",
      "Epoch 61/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.9956e-04 - accuracy: 0.9950 - val_loss: 6.0461e-04 - val_accuracy: 0.9957\n",
      "Epoch 62/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.9865e-04 - accuracy: 0.9944 - val_loss: 8.9512e-04 - val_accuracy: 0.9887\n",
      "Epoch 63/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 6.2094e-04 - accuracy: 0.9941 - val_loss: 6.7191e-04 - val_accuracy: 0.9887\n",
      "Epoch 64/160\n",
      "721/721 [==============================] - 22s 31ms/step - loss: 6.2837e-04 - accuracy: 0.9943 - val_loss: 7.4884e-04 - val_accuracy: 0.9827\n",
      "Epoch 65/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 5.8244e-04 - accuracy: 0.9950 - val_loss: 5.5981e-04 - val_accuracy: 0.9974\n",
      "Epoch 66/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.7322e-04 - accuracy: 0.9944 - val_loss: 5.2823e-04 - val_accuracy: 0.9905\n",
      "Epoch 67/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.6343e-04 - accuracy: 0.9948 - val_loss: 4.7071e-04 - val_accuracy: 0.9974\n",
      "Epoch 68/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 5.5796e-04 - accuracy: 0.9944 - val_loss: 5.6767e-04 - val_accuracy: 0.9939\n",
      "Epoch 69/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.6536e-04 - accuracy: 0.9948 - val_loss: 4.9914e-04 - val_accuracy: 0.9922\n",
      "Epoch 70/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.8199e-04 - accuracy: 0.9946 - val_loss: 5.8125e-04 - val_accuracy: 0.9948\n",
      "Epoch 71/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 5.5191e-04 - accuracy: 0.9943 - val_loss: 6.7393e-04 - val_accuracy: 0.9844\n",
      "Epoch 72/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.5121e-04 - accuracy: 0.9958 - val_loss: 6.7250e-04 - val_accuracy: 0.9905\n",
      "Epoch 73/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 5.3418e-04 - accuracy: 0.9943 - val_loss: 4.9384e-04 - val_accuracy: 0.9931\n",
      "Epoch 74/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 6.3228e-04 - accuracy: 0.9943 - val_loss: 4.7011e-04 - val_accuracy: 0.9965\n",
      "Epoch 75/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 5.3700e-04 - accuracy: 0.9944 - val_loss: 6.8441e-04 - val_accuracy: 0.9957\n",
      "Epoch 76/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.2974e-04 - accuracy: 0.9962 - val_loss: 5.3606e-04 - val_accuracy: 0.9887\n",
      "Epoch 77/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 5.1049e-04 - accuracy: 0.9941 - val_loss: 4.9804e-04 - val_accuracy: 0.9957\n",
      "Epoch 78/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 5.2627e-04 - accuracy: 0.9958 - val_loss: 5.3534e-04 - val_accuracy: 0.9939\n",
      "Epoch 79/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 5.3412e-04 - accuracy: 0.9941 - val_loss: 4.5567e-04 - val_accuracy: 0.9957\n",
      "Epoch 80/160\n",
      "721/721 [==============================] - 24s 33ms/step - loss: 5.1519e-04 - accuracy: 0.9951 - val_loss: 4.4373e-04 - val_accuracy: 0.9957\n",
      "Epoch 81/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 4.9589e-04 - accuracy: 0.9951 - val_loss: 5.3525e-04 - val_accuracy: 0.9931\n",
      "Epoch 82/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 5.1541e-04 - accuracy: 0.9946 - val_loss: 5.1392e-04 - val_accuracy: 0.9913\n",
      "Epoch 83/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.2091e-04 - accuracy: 0.9958 - val_loss: 7.4577e-04 - val_accuracy: 0.9905\n",
      "Epoch 84/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 5.0352e-04 - accuracy: 0.9944 - val_loss: 5.5971e-04 - val_accuracy: 0.9905\n",
      "Epoch 85/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 5.3308e-04 - accuracy: 0.9950 - val_loss: 5.0532e-04 - val_accuracy: 0.9965\n",
      "Epoch 86/160\n",
      "721/721 [==============================] - 24s 33ms/step - loss: 5.1006e-04 - accuracy: 0.9946 - val_loss: 5.1691e-04 - val_accuracy: 0.9913\n",
      "Epoch 87/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.0334e-04 - accuracy: 0.9953 - val_loss: 4.5080e-04 - val_accuracy: 0.9974\n",
      "Epoch 88/160\n",
      "721/721 [==============================] - 29s 39ms/step - loss: 5.0212e-04 - accuracy: 0.9948 - val_loss: 3.5376e-04 - val_accuracy: 0.9939\n",
      "Epoch 89/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 5.0436e-04 - accuracy: 0.9953 - val_loss: 4.1121e-04 - val_accuracy: 0.9931\n",
      "Epoch 90/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 4.8820e-04 - accuracy: 0.9936 - val_loss: 4.2388e-04 - val_accuracy: 0.9965\n",
      "Epoch 91/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 4.7736e-04 - accuracy: 0.9951 - val_loss: 4.4806e-04 - val_accuracy: 0.9991\n",
      "Epoch 92/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 4.7709e-04 - accuracy: 0.9939 - val_loss: 4.1889e-04 - val_accuracy: 0.9965\n",
      "Epoch 93/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.9295e-04 - accuracy: 0.9958 - val_loss: 4.2677e-04 - val_accuracy: 0.9965\n",
      "Epoch 94/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.8683e-04 - accuracy: 0.9965 - val_loss: 3.4728e-04 - val_accuracy: 0.9974\n",
      "Epoch 95/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 4.7099e-04 - accuracy: 0.9964 - val_loss: 4.2644e-04 - val_accuracy: 0.9965\n",
      "Epoch 96/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.5606e-04 - accuracy: 0.9967 - val_loss: 4.5745e-04 - val_accuracy: 0.9948\n",
      "Epoch 97/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 4.7302e-04 - accuracy: 0.9960 - val_loss: 4.1841e-04 - val_accuracy: 0.9957\n",
      "Epoch 98/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 4.6521e-04 - accuracy: 0.9955 - val_loss: 3.5983e-04 - val_accuracy: 0.9957\n",
      "Epoch 99/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.4558e-04 - accuracy: 0.9967 - val_loss: 4.2898e-04 - val_accuracy: 0.9931\n",
      "Epoch 100/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.5417e-04 - accuracy: 0.9955 - val_loss: 4.1460e-04 - val_accuracy: 0.9991\n",
      "Epoch 101/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 4.5630e-04 - accuracy: 0.9951 - val_loss: 4.4090e-04 - val_accuracy: 0.9974\n",
      "Epoch 102/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.7351e-04 - accuracy: 0.9941 - val_loss: 3.6843e-04 - val_accuracy: 0.9948\n",
      "Epoch 103/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.5505e-04 - accuracy: 0.9955 - val_loss: 4.2656e-04 - val_accuracy: 0.9939\n",
      "Epoch 104/160\n",
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      "Epoch 105/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.5050e-04 - accuracy: 0.9962 - val_loss: 3.6137e-04 - val_accuracy: 0.9983\n",
      "Epoch 106/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 4.3625e-04 - accuracy: 0.9965 - val_loss: 3.6187e-04 - val_accuracy: 0.9957\n",
      "Epoch 107/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.5884e-04 - accuracy: 0.9964 - val_loss: 4.3816e-04 - val_accuracy: 0.9948\n",
      "Epoch 108/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.5674e-04 - accuracy: 0.9950 - val_loss: 3.5305e-04 - val_accuracy: 0.9957\n",
      "Epoch 109/160\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 27s 37ms/step - loss: 4.3814e-04 - accuracy: 0.9962 - val_loss: 3.4573e-04 - val_accuracy: 0.9948\n",
      "Epoch 110/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.2307e-04 - accuracy: 0.9960 - val_loss: 4.2351e-04 - val_accuracy: 0.9965\n",
      "Epoch 111/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.3209e-04 - accuracy: 0.9967 - val_loss: 4.3516e-04 - val_accuracy: 0.9948\n",
      "Epoch 112/160\n",
      "721/721 [==============================] - 29s 40ms/step - loss: 4.1199e-04 - accuracy: 0.9964 - val_loss: 5.8843e-04 - val_accuracy: 0.9853\n",
      "Epoch 113/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 4.3094e-04 - accuracy: 0.9964 - val_loss: 4.0414e-04 - val_accuracy: 0.9965\n",
      "Epoch 114/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.2694e-04 - accuracy: 0.9970 - val_loss: 4.0987e-04 - val_accuracy: 0.9948\n",
      "Epoch 115/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 4.1554e-04 - accuracy: 0.9960 - val_loss: 4.7333e-04 - val_accuracy: 0.9939\n",
      "Epoch 116/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.2591e-04 - accuracy: 0.9955 - val_loss: 3.3648e-04 - val_accuracy: 0.9957\n",
      "Epoch 117/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.1432e-04 - accuracy: 0.9962 - val_loss: 3.2760e-04 - val_accuracy: 0.9965\n",
      "Epoch 118/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.2080e-04 - accuracy: 0.9953 - val_loss: 3.3914e-04 - val_accuracy: 0.9974\n",
      "Epoch 119/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 4.1492e-04 - accuracy: 0.9962 - val_loss: 3.4920e-04 - val_accuracy: 0.9965\n",
      "Epoch 120/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.2399e-04 - accuracy: 0.9965 - val_loss: 3.3458e-04 - val_accuracy: 0.9974\n",
      "Epoch 121/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 4.1102e-04 - accuracy: 0.9964 - val_loss: 4.5944e-04 - val_accuracy: 0.9948\n",
      "Epoch 122/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 4.1044e-04 - accuracy: 0.9969 - val_loss: 3.3874e-04 - val_accuracy: 0.9965\n",
      "Epoch 123/160\n",
      "721/721 [==============================] - 25s 35ms/step - loss: 3.9399e-04 - accuracy: 0.9970 - val_loss: 2.7827e-04 - val_accuracy: 0.9965\n",
      "Epoch 124/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.1719e-04 - accuracy: 0.9958 - val_loss: 3.3646e-04 - val_accuracy: 0.9974\n",
      "Epoch 125/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.9290e-04 - accuracy: 0.9962 - val_loss: 3.8413e-04 - val_accuracy: 0.9991\n",
      "Epoch 126/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 4.0171e-04 - accuracy: 0.9967 - val_loss: 3.8029e-04 - val_accuracy: 0.9983\n",
      "Epoch 127/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.9341e-04 - accuracy: 0.9965 - val_loss: 3.6204e-04 - val_accuracy: 0.9965\n",
      "Epoch 128/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 3.9368e-04 - accuracy: 0.9957 - val_loss: 5.0163e-04 - val_accuracy: 0.9974\n",
      "Epoch 129/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 3.9010e-04 - accuracy: 0.9967 - val_loss: 4.0204e-04 - val_accuracy: 0.9991\n",
      "Epoch 130/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 4.0609e-04 - accuracy: 0.9960 - val_loss: 3.4633e-04 - val_accuracy: 0.9974\n",
      "Epoch 131/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 4.0928e-04 - accuracy: 0.9958 - val_loss: 3.0983e-04 - val_accuracy: 0.9965\n",
      "Epoch 132/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 4.0886e-04 - accuracy: 0.9958 - val_loss: 4.2859e-04 - val_accuracy: 0.9991\n",
      "Epoch 133/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 4.0736e-04 - accuracy: 0.9970 - val_loss: 3.7095e-04 - val_accuracy: 0.9974\n",
      "Epoch 134/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.9020e-04 - accuracy: 0.9972 - val_loss: 3.9082e-04 - val_accuracy: 0.9983\n",
      "Epoch 135/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.9440e-04 - accuracy: 0.9967 - val_loss: 3.5208e-04 - val_accuracy: 0.9948\n",
      "Epoch 136/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 3.9309e-04 - accuracy: 0.9962 - val_loss: 3.3328e-04 - val_accuracy: 0.9983\n",
      "Epoch 137/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 3.8767e-04 - accuracy: 0.9965 - val_loss: 3.3539e-04 - val_accuracy: 0.9965\n",
      "Epoch 138/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.9157e-04 - accuracy: 0.9977 - val_loss: 2.8659e-04 - val_accuracy: 0.9991\n",
      "Epoch 139/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.7185e-04 - accuracy: 0.9970 - val_loss: 4.1663e-04 - val_accuracy: 0.9922\n",
      "Epoch 140/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.7427e-04 - accuracy: 0.9969 - val_loss: 3.2086e-04 - val_accuracy: 0.9965\n",
      "Epoch 141/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 3.8990e-04 - accuracy: 0.9970 - val_loss: 2.9793e-04 - val_accuracy: 0.9983\n",
      "Epoch 142/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 3.9448e-04 - accuracy: 0.9958 - val_loss: 3.5300e-04 - val_accuracy: 0.9931\n",
      "Epoch 143/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.8351e-04 - accuracy: 0.9964 - val_loss: 2.8733e-04 - val_accuracy: 0.9957\n",
      "Epoch 144/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.8736e-04 - accuracy: 0.9965 - val_loss: 4.5700e-04 - val_accuracy: 0.9957\n",
      "Epoch 145/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.9337e-04 - accuracy: 0.9957 - val_loss: 4.0782e-04 - val_accuracy: 0.9991\n",
      "Epoch 146/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.8573e-04 - accuracy: 0.9957 - val_loss: 2.7751e-04 - val_accuracy: 0.9974\n",
      "Epoch 147/160\n",
      "721/721 [==============================] - 26s 37ms/step - loss: 3.7680e-04 - accuracy: 0.9972 - val_loss: 3.8156e-04 - val_accuracy: 0.9983\n",
      "Epoch 148/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 3.7545e-04 - accuracy: 0.9969 - val_loss: 3.9573e-04 - val_accuracy: 0.9991\n",
      "Epoch 149/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 3.6231e-04 - accuracy: 0.9967 - val_loss: 3.4503e-04 - val_accuracy: 0.9983\n",
      "Epoch 150/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 3.7437e-04 - accuracy: 0.9969 - val_loss: 3.2138e-04 - val_accuracy: 0.9957\n",
      "Epoch 151/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.6094e-04 - accuracy: 0.9955 - val_loss: 3.0595e-04 - val_accuracy: 0.9983\n",
      "Epoch 152/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.7058e-04 - accuracy: 0.9965 - val_loss: 5.6372e-04 - val_accuracy: 0.9861\n",
      "Epoch 153/160\n",
      "721/721 [==============================] - 28s 38ms/step - loss: 3.8752e-04 - accuracy: 0.9974 - val_loss: 3.5534e-04 - val_accuracy: 0.9965\n",
      "Epoch 154/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.6363e-04 - accuracy: 0.9969 - val_loss: 3.3202e-04 - val_accuracy: 0.9983\n",
      "Epoch 155/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.6875e-04 - accuracy: 0.9967 - val_loss: 4.3228e-04 - val_accuracy: 0.9974\n",
      "Epoch 156/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 3.6350e-04 - accuracy: 0.9962 - val_loss: 2.9742e-04 - val_accuracy: 0.9974\n",
      "Epoch 157/160\n",
      "721/721 [==============================] - 26s 36ms/step - loss: 3.5978e-04 - accuracy: 0.9964 - val_loss: 2.6598e-04 - val_accuracy: 0.9983\n",
      "Epoch 158/160\n",
      "721/721 [==============================] - 27s 37ms/step - loss: 3.6651e-04 - accuracy: 0.9972 - val_loss: 3.1828e-04 - val_accuracy: 0.9974\n",
      "Epoch 159/160\n",
      "721/721 [==============================] - 27s 38ms/step - loss: 3.6740e-04 - accuracy: 0.9958 - val_loss: 3.1489e-04 - val_accuracy: 0.9965\n",
      "Epoch 160/160\n",
      "721/721 [==============================] - 28s 39ms/step - loss: 3.5411e-04 - accuracy: 0.9969 - val_loss: 3.3255e-04 - val_accuracy: 0.9948\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 8\n",
    "Epoch_Anz = 160\n",
    "Shift_Range = 3\n",
    "Brightness_Range = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], height_shift_range=[-Shift_Range,Shift_Range],brightness_range=[1-Brightness_Range,1+Brightness_Range])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, epochs = Epoch_Anz)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Learing results (Step 1 & Step 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "\n",
    "plt.semilogy(loss_ges)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the model by hand\n",
    "\n",
    "* The following code uses the trained model to check the deviation for each picture.\n",
    "* The evaluation takes the periodic character of the results into account (dev1 ... dev2).\n",
    "* Images, that have a bigger deviation as the parameter \"deviation_max_list\" are printed in a list to check the picture and labeling itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_resize_all\\3.7_1830_zeiger1_2020-06-15_19-06-47.jpg 0.37 0.4280338775059098 -0.058033877505909826\n",
      "data_resize_all\\3.8_PRODUCED_ANALOG_2.jpg 0.38 0.43832374721801093 -0.058323747218010924\n",
      "data_resize_all\\8.2_4110_zeiger1_2019-09-15_06-20-12.jpg 0.82 0.8777023418939259 -0.05770234189392598\n",
      "data_resize_all\\8.5_4203_zeiger3_2019-06-05T165009.jpg 0.85 0.907313962994347 -0.05731396299434699\n"
     ]
    }
   ],
   "source": [
    "Input_dir='data_resize_all'\n",
    "#Input_dir='test_result'\n",
    "files = glob.glob(Input_dir + '/*.*')\n",
    "res = []\n",
    "stat_Anz = []\n",
    "stat_Abweichung = []\n",
    "i = 0\n",
    "deviation_max_list = 0.05\n",
    "\n",
    "for i in range(100):\n",
    "    stat_Anz.append(0)\n",
    "    stat_Abweichung.append(0)\n",
    "\n",
    "for aktfile in sorted(files):\n",
    "    base = os.path.basename(aktfile)\n",
    "    target = (float(base[0:3])) / 10\n",
    "    \n",
    "    target_sin = math.sin(target * math.pi * 2)\n",
    "    target_cos = math.cos(target * math.pi * 2)\n",
    "\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    img = np.reshape(test_image,[1,32,32,3])\n",
    "    classes = model.predict(img, verbose=0)\n",
    "    \n",
    "    out_sin = classes[0][0]  \n",
    "    out_cos = classes[0][1]\n",
    "    out_target = (np.arctan2(out_sin, out_cos)/(2*math.pi)) % 1\n",
    "\n",
    "    dev_sin = target_sin - out_sin\n",
    "    dev_cos = target_cos - out_cos\n",
    "    dev_target = target - out_target\n",
    "    \n",
    "    if abs(dev_target + 1) < abs(dev_target):\n",
    "        out_target = out_target - 1\n",
    "        dev_target = target - out_target\n",
    "    else:\n",
    "        if abs(dev_target - 1) < abs(dev_target):\n",
    "            out_target = out_target + 1\n",
    "            dev_target = target - out_target\n",
    "            \n",
    "    target_int = int ((float(base[0:3])) * 10)\n",
    "    stat_Abweichung[target_int] = stat_Abweichung[target_int] + dev_target  \n",
    "    stat_Anz[target_int] = stat_Anz[target_int] + 1\n",
    "               \n",
    "    res.append(np.array([target, out_target, dev_target, out_sin, out_cos, i]))\n",
    "    if abs(dev_target) > deviation_max_list:\n",
    "        print(aktfile + \" \" + str(target) + \" \" + str(out_target) +  \" \" + str(dev_target))\n",
    "\n",
    "    \n",
    "for i in range(100):\n",
    "    stat_Abweichung[i] = stat_Abweichung[i] / stat_Anz[i]\n",
    "\n",
    "res = np.asarray(res)\n",
    "res_step_1 = res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:,3])\n",
    "plt.plot(res[:,4])\n",
    "plt.title('Result')\n",
    "plt.ylabel('value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['sin', 'cos'], loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:,0])\n",
    "plt.plot(res[:,1])\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stat_Abweichung)\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stat_Anz)\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deviation from Expected Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.0021  0.0134 -0.0583  0.047 ]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(res[:,2])\n",
    "plt.title('Deviation')\n",
    "plt.ylabel('Deviation from expected value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Deviation'], loc='upper left')\n",
    "#plt.ylim(-0.3, 0.3)\n",
    "plt.show()\n",
    "\n",
    "statistic = np.array([np.mean(res[:,2]), np.std(res[:,2]), np.min(res[:,2]), np.max(res[:,2])])\n",
    "print(statistic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model\n",
    "\n",
    "* Save the model to the file with the \"h5\" file format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpy68912pm\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpy68912pm\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "253476"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileName = TFliteNamingAndVersion\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "open(FileName + \".tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpiticd4eq\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpiticd4eq\\assets\n",
      "C:\\Users\\Muell\\anaconda3\\envs\\py39-tf-opencv\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
      "  warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "71208"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileName = TFliteNamingAndVersion + \"q\"\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "def representative_dataset():\n",
    "    for n in range(x_data[0].size):\n",
    "      data = np.expand_dims(x_data[n], axis=0)\n",
    "      yield [data.astype(np.float32)]\n",
    "        \n",
    "        \n",
    "converter2 = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter2.representative_dataset = representative_dataset\n",
    "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter2.representative_dataset = representative_dataset\n",
    "tflite_quant_model = converter2.convert()\n",
    "\n",
    "open(FileName + \".tflite\", \"wb\").write(tflite_quant_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
   "pygments_lexer": "ipython3",
   "version": "3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:05:37) [Clang 13.0.1 ]"
  },
  "vscode": {
   "interpreter": {
    "hash": "3431513051aa6ef6a02e4978d18932220794b1f41fa21fbae2ad068757488314"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}