Train_CNN_Analog-Readout_Version-Small0.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 = \"ana1100s0\"     # 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)\n"
   ]
  },
  {
   "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, 64)        4864      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 8, 8, 64)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 32)          51232     \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: 90,254\n",
      "Trainable params: 90,248\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(64, (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",
    "#model = Sequential()\n",
    "#model.add(BatchNormalization(input_shape=(32,32,3)))\n",
    "#model.add(Conv2D(64, (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_13144\\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 [==============================] - 10s 13ms/step - loss: 0.1511 - accuracy: 0.8729 - val_loss: 0.0244 - val_accuracy: 0.9783\n",
      "Epoch 2/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0194 - accuracy: 0.9674 - val_loss: 0.0138 - val_accuracy: 0.9801\n",
      "Epoch 3/30\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 0.0104 - accuracy: 0.9757 - val_loss: 0.0056 - val_accuracy: 0.9766\n",
      "Epoch 4/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0057 - accuracy: 0.9825 - val_loss: 0.0041 - val_accuracy: 0.9870\n",
      "Epoch 5/30\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 0.0040 - accuracy: 0.9823 - val_loss: 0.0033 - val_accuracy: 0.9879\n",
      "Epoch 6/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0030 - accuracy: 0.9870 - val_loss: 0.0044 - val_accuracy: 0.9844\n",
      "Epoch 7/30\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 0.0025 - accuracy: 0.9892 - val_loss: 0.0045 - val_accuracy: 0.9835\n",
      "Epoch 8/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0021 - accuracy: 0.9880 - val_loss: 0.0021 - val_accuracy: 0.9714\n",
      "Epoch 9/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0018 - accuracy: 0.9905 - val_loss: 0.0021 - val_accuracy: 0.9913\n",
      "Epoch 10/30\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 0.0015 - accuracy: 0.9899 - val_loss: 0.0012 - val_accuracy: 0.9887\n",
      "Epoch 11/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0014 - accuracy: 0.9910 - val_loss: 9.3556e-04 - val_accuracy: 0.9948\n",
      "Epoch 12/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0013 - accuracy: 0.9913 - val_loss: 0.0010 - val_accuracy: 0.9913\n",
      "Epoch 13/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0011 - accuracy: 0.9939 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 14/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 0.0011 - accuracy: 0.9917 - val_loss: 7.2462e-04 - val_accuracy: 0.9931\n",
      "Epoch 15/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 9.3463e-04 - accuracy: 0.9934 - val_loss: 8.3444e-04 - val_accuracy: 0.9948\n",
      "Epoch 16/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 9.2807e-04 - accuracy: 0.9934 - val_loss: 0.0010 - val_accuracy: 0.9931\n",
      "Epoch 17/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 8.7116e-04 - accuracy: 0.9932 - val_loss: 7.0241e-04 - val_accuracy: 0.9957\n",
      "Epoch 18/30\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 8.2744e-04 - accuracy: 0.9917 - val_loss: 6.2416e-04 - val_accuracy: 0.9948\n",
      "Epoch 19/30\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.4896e-04 - accuracy: 0.9943 - val_loss: 5.6973e-04 - val_accuracy: 0.9939\n",
      "Epoch 20/30\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 6.9912e-04 - accuracy: 0.9957 - val_loss: 5.2153e-04 - val_accuracy: 0.9939\n",
      "Epoch 21/30\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 6.8340e-04 - accuracy: 0.9941 - val_loss: 5.7535e-04 - val_accuracy: 0.9974\n",
      "Epoch 22/30\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 6.5704e-04 - accuracy: 0.9950 - val_loss: 4.8583e-04 - val_accuracy: 0.9905\n",
      "Epoch 23/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 5.9141e-04 - accuracy: 0.9951 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 24/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 5.9445e-04 - accuracy: 0.9948 - val_loss: 0.0013 - val_accuracy: 0.9913\n",
      "Epoch 25/30\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 5.8247e-04 - accuracy: 0.9957 - val_loss: 5.0012e-04 - val_accuracy: 0.9939\n",
      "Epoch 26/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 5.7603e-04 - accuracy: 0.9936 - val_loss: 4.0355e-04 - val_accuracy: 0.9974\n",
      "Epoch 27/30\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 5.0325e-04 - accuracy: 0.9967 - val_loss: 5.6592e-04 - val_accuracy: 0.9905\n",
      "Epoch 28/30\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 5.0327e-04 - accuracy: 0.9950 - val_loss: 5.4832e-04 - val_accuracy: 0.9965\n",
      "Epoch 29/30\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.8344e-04 - accuracy: 0.9953 - val_loss: 4.9697e-04 - val_accuracy: 0.9965\n",
      "Epoch 30/30\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 5.0037e-04 - accuracy: 0.9960 - val_loss: 4.9730e-04 - val_accuracy: 0.9974\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": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/160\n",
      " 11/721 [..............................] - ETA: 8s - loss: 0.3704 - accuracy: 0.7273"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_13144\\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": [
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0450 - accuracy: 0.9431 - val_loss: 0.0120 - val_accuracy: 0.9722\n",
      "Epoch 2/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0102 - accuracy: 0.9727 - val_loss: 0.0056 - val_accuracy: 0.9783\n",
      "Epoch 3/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0067 - accuracy: 0.9785 - val_loss: 0.0039 - val_accuracy: 0.9818\n",
      "Epoch 4/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0048 - accuracy: 0.9813 - val_loss: 0.0032 - val_accuracy: 0.9905\n",
      "Epoch 5/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0040 - accuracy: 0.9852 - val_loss: 0.0035 - val_accuracy: 0.9809\n",
      "Epoch 6/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0035 - accuracy: 0.9851 - val_loss: 0.0028 - val_accuracy: 0.9844\n",
      "Epoch 7/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0031 - accuracy: 0.9830 - val_loss: 0.0031 - val_accuracy: 0.9853\n",
      "Epoch 8/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0029 - accuracy: 0.9868 - val_loss: 0.0030 - val_accuracy: 0.9879\n",
      "Epoch 9/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0025 - accuracy: 0.9854 - val_loss: 0.0016 - val_accuracy: 0.9887\n",
      "Epoch 10/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0023 - accuracy: 0.9877 - val_loss: 0.0024 - val_accuracy: 0.9818\n",
      "Epoch 11/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0021 - accuracy: 0.9865 - val_loss: 0.0021 - val_accuracy: 0.9853\n",
      "Epoch 12/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0020 - accuracy: 0.9878 - val_loss: 0.0016 - val_accuracy: 0.9931\n",
      "Epoch 13/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0019 - accuracy: 0.9903 - val_loss: 0.0025 - val_accuracy: 0.9870\n",
      "Epoch 14/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0017 - accuracy: 0.9892 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 15/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0016 - accuracy: 0.9924 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 16/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0016 - accuracy: 0.9899 - val_loss: 0.0016 - val_accuracy: 0.9939\n",
      "Epoch 17/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0015 - accuracy: 0.9882 - val_loss: 0.0017 - val_accuracy: 0.9931\n",
      "Epoch 18/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0014 - accuracy: 0.9903 - val_loss: 0.0013 - val_accuracy: 0.9931\n",
      "Epoch 19/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0014 - accuracy: 0.9892 - val_loss: 0.0015 - val_accuracy: 0.9887\n",
      "Epoch 20/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0013 - accuracy: 0.9884 - val_loss: 0.0018 - val_accuracy: 0.9887\n",
      "Epoch 21/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 0.0013 - accuracy: 0.9924 - val_loss: 0.0013 - val_accuracy: 0.9905\n",
      "Epoch 22/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0013 - accuracy: 0.9911 - val_loss: 0.0013 - val_accuracy: 0.9913\n",
      "Epoch 23/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0012 - accuracy: 0.9896 - val_loss: 0.0011 - val_accuracy: 0.9887\n",
      "Epoch 24/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 0.0010 - val_accuracy: 0.9983\n",
      "Epoch 25/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0011 - accuracy: 0.9939 - val_loss: 0.0011 - val_accuracy: 0.9957\n",
      "Epoch 26/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0011 - accuracy: 0.9911 - val_loss: 0.0010 - val_accuracy: 0.9922\n",
      "Epoch 27/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 0.0010 - accuracy: 0.9910 - val_loss: 0.0012 - val_accuracy: 0.9844\n",
      "Epoch 28/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 0.0010 - accuracy: 0.9918 - val_loss: 0.0011 - val_accuracy: 0.9853\n",
      "Epoch 29/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 0.0010 - accuracy: 0.9915 - val_loss: 8.6488e-04 - val_accuracy: 0.9965\n",
      "Epoch 30/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 9.7703e-04 - accuracy: 0.9915 - val_loss: 0.0013 - val_accuracy: 0.9957\n",
      "Epoch 31/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 9.4356e-04 - accuracy: 0.9913 - val_loss: 8.2847e-04 - val_accuracy: 0.9922\n",
      "Epoch 32/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 9.1148e-04 - accuracy: 0.9922 - val_loss: 0.0013 - val_accuracy: 0.9913\n",
      "Epoch 33/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 8.7676e-04 - accuracy: 0.9924 - val_loss: 9.7314e-04 - val_accuracy: 0.9948\n",
      "Epoch 34/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 8.5662e-04 - accuracy: 0.9920 - val_loss: 7.7614e-04 - val_accuracy: 0.9922\n",
      "Epoch 35/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 8.9629e-04 - accuracy: 0.9932 - val_loss: 0.0010 - val_accuracy: 0.9957\n",
      "Epoch 36/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 8.3934e-04 - accuracy: 0.9941 - val_loss: 8.5590e-04 - val_accuracy: 0.9922\n",
      "Epoch 37/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 8.5106e-04 - accuracy: 0.9927 - val_loss: 0.0013 - val_accuracy: 0.9905\n",
      "Epoch 38/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.9529e-04 - accuracy: 0.9929 - val_loss: 8.8161e-04 - val_accuracy: 0.9974\n",
      "Epoch 39/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 8.0297e-04 - accuracy: 0.9943 - val_loss: 8.3724e-04 - val_accuracy: 0.9939\n",
      "Epoch 40/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.7408e-04 - accuracy: 0.9934 - val_loss: 6.6572e-04 - val_accuracy: 0.9983\n",
      "Epoch 41/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 7.6238e-04 - accuracy: 0.9934 - val_loss: 7.7758e-04 - val_accuracy: 0.9896\n",
      "Epoch 42/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.7268e-04 - accuracy: 0.9929 - val_loss: 7.0059e-04 - val_accuracy: 0.9965\n",
      "Epoch 43/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.3785e-04 - accuracy: 0.9932 - val_loss: 6.5599e-04 - val_accuracy: 0.9965\n",
      "Epoch 44/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 7.4865e-04 - accuracy: 0.9934 - val_loss: 6.5328e-04 - val_accuracy: 0.9939\n",
      "Epoch 45/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.1063e-04 - accuracy: 0.9932 - val_loss: 6.3389e-04 - val_accuracy: 0.9957\n",
      "Epoch 46/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.5194e-04 - accuracy: 0.9944 - val_loss: 6.4864e-04 - val_accuracy: 0.9939\n",
      "Epoch 47/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 7.0147e-04 - accuracy: 0.9931 - val_loss: 5.9827e-04 - val_accuracy: 0.9974\n",
      "Epoch 48/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 7.0014e-04 - accuracy: 0.9943 - val_loss: 7.6335e-04 - val_accuracy: 0.9983\n",
      "Epoch 49/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.3307e-04 - accuracy: 0.9936 - val_loss: 0.0010 - val_accuracy: 0.9983\n",
      "Epoch 50/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.9258e-04 - accuracy: 0.9950 - val_loss: 5.5049e-04 - val_accuracy: 0.9948\n",
      "Epoch 51/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 6.5882e-04 - accuracy: 0.9938 - val_loss: 6.3922e-04 - val_accuracy: 0.9965\n",
      "Epoch 52/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.3937e-04 - accuracy: 0.9939 - val_loss: 7.6322e-04 - val_accuracy: 0.9948\n",
      "Epoch 53/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.8835e-04 - accuracy: 0.9944 - val_loss: 4.8049e-04 - val_accuracy: 0.9948\n",
      "Epoch 54/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 6.6451e-04 - accuracy: 0.9948 - val_loss: 6.2479e-04 - val_accuracy: 0.9948\n",
      "Epoch 55/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.3391e-04 - accuracy: 0.9950 - val_loss: 5.9607e-04 - val_accuracy: 0.9974\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.2755e-04 - accuracy: 0.9948 - val_loss: 5.8901e-04 - val_accuracy: 0.9905\n",
      "Epoch 57/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 6.2516e-04 - accuracy: 0.9944 - val_loss: 5.8634e-04 - val_accuracy: 0.9939\n",
      "Epoch 58/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.8521e-04 - accuracy: 0.9955 - val_loss: 5.3607e-04 - val_accuracy: 0.9965\n",
      "Epoch 59/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.8227e-04 - accuracy: 0.9950 - val_loss: 6.5549e-04 - val_accuracy: 0.9879\n",
      "Epoch 60/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 5.9916e-04 - accuracy: 0.9950 - val_loss: 5.6862e-04 - val_accuracy: 0.9983\n",
      "Epoch 61/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 6.1211e-04 - accuracy: 0.9941 - val_loss: 5.8916e-04 - val_accuracy: 0.9974\n",
      "Epoch 62/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.8417e-04 - accuracy: 0.9950 - val_loss: 6.4900e-04 - val_accuracy: 0.9939\n",
      "Epoch 63/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 5.7265e-04 - accuracy: 0.9944 - val_loss: 8.4276e-04 - val_accuracy: 0.9905\n",
      "Epoch 64/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.6602e-04 - accuracy: 0.9934 - val_loss: 5.8316e-04 - val_accuracy: 0.9974\n",
      "Epoch 65/160\n",
      "721/721 [==============================] - 10s 15ms/step - loss: 5.6761e-04 - accuracy: 0.9934 - val_loss: 4.8365e-04 - val_accuracy: 0.9957\n",
      "Epoch 66/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 5.5846e-04 - accuracy: 0.9964 - val_loss: 4.8948e-04 - val_accuracy: 0.9957\n",
      "Epoch 67/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.6047e-04 - accuracy: 0.9951 - val_loss: 5.2702e-04 - val_accuracy: 0.9991\n",
      "Epoch 68/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.5485e-04 - accuracy: 0.9950 - val_loss: 5.0398e-04 - val_accuracy: 0.9965\n",
      "Epoch 69/160\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 5.3985e-04 - accuracy: 0.9939 - val_loss: 5.9531e-04 - val_accuracy: 0.9957\n",
      "Epoch 70/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.3138e-04 - accuracy: 0.9950 - val_loss: 4.5799e-04 - val_accuracy: 0.9896\n",
      "Epoch 71/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 5.3341e-04 - accuracy: 0.9955 - val_loss: 5.3960e-04 - val_accuracy: 0.9965\n",
      "Epoch 72/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.5323e-04 - accuracy: 0.9943 - val_loss: 5.6037e-04 - val_accuracy: 0.9983\n",
      "Epoch 73/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.5423e-04 - accuracy: 0.9960 - val_loss: 3.9940e-04 - val_accuracy: 0.9983\n",
      "Epoch 74/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 5.2901e-04 - accuracy: 0.9955 - val_loss: 4.9764e-04 - val_accuracy: 0.9983\n",
      "Epoch 75/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 5.0752e-04 - accuracy: 0.9960 - val_loss: 6.3241e-04 - val_accuracy: 0.9957\n",
      "Epoch 76/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 4.9376e-04 - accuracy: 0.9957 - val_loss: 4.3622e-04 - val_accuracy: 0.9983\n",
      "Epoch 77/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.2023e-04 - accuracy: 0.9953 - val_loss: 4.7616e-04 - val_accuracy: 0.9905\n",
      "Epoch 78/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.1814e-04 - accuracy: 0.9946 - val_loss: 5.8389e-04 - val_accuracy: 0.9991\n",
      "Epoch 79/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.4608e-04 - accuracy: 0.9932 - val_loss: 5.8211e-04 - val_accuracy: 0.9948\n",
      "Epoch 80/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 4.9535e-04 - accuracy: 0.9969 - val_loss: 4.8977e-04 - val_accuracy: 0.9948\n",
      "Epoch 81/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.0581e-04 - accuracy: 0.9955 - val_loss: 6.0478e-04 - val_accuracy: 0.9965\n",
      "Epoch 82/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.0547e-04 - accuracy: 0.9967 - val_loss: 6.8367e-04 - val_accuracy: 0.9922\n",
      "Epoch 83/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 5.0676e-04 - accuracy: 0.9965 - val_loss: 5.4814e-04 - val_accuracy: 0.9939\n",
      "Epoch 84/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 4.8123e-04 - accuracy: 0.9964 - val_loss: 4.1940e-04 - val_accuracy: 0.9983\n",
      "Epoch 85/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 4.8883e-04 - accuracy: 0.9964 - val_loss: 3.9578e-04 - val_accuracy: 0.9965\n",
      "Epoch 86/160\n",
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      "Epoch 87/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 4.7813e-04 - accuracy: 0.9965 - val_loss: 5.6189e-04 - val_accuracy: 0.9974\n",
      "Epoch 88/160\n",
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      "Epoch 89/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 4.7551e-04 - accuracy: 0.9960 - val_loss: 6.6506e-04 - val_accuracy: 0.9939\n",
      "Epoch 90/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 4.6849e-04 - accuracy: 0.9951 - val_loss: 5.2882e-04 - val_accuracy: 0.9983\n",
      "Epoch 91/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.9961e-04 - accuracy: 0.9969 - val_loss: 4.1399e-04 - val_accuracy: 0.9948\n",
      "Epoch 92/160\n",
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      "Epoch 93/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.5892e-04 - accuracy: 0.9953 - val_loss: 5.8368e-04 - val_accuracy: 0.9974\n",
      "Epoch 94/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.6005e-04 - accuracy: 0.9960 - val_loss: 3.9044e-04 - val_accuracy: 0.9965\n",
      "Epoch 95/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.4998e-04 - accuracy: 0.9964 - val_loss: 4.5754e-04 - val_accuracy: 0.9974\n",
      "Epoch 96/160\n",
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      "Epoch 97/160\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 4.4931e-04 - accuracy: 0.9946 - val_loss: 4.4676e-04 - val_accuracy: 0.9983\n",
      "Epoch 98/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.4642e-04 - accuracy: 0.9960 - val_loss: 3.7058e-04 - val_accuracy: 0.9983\n",
      "Epoch 99/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.5134e-04 - accuracy: 0.9953 - val_loss: 4.0687e-04 - val_accuracy: 0.9957\n",
      "Epoch 100/160\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 4.3133e-04 - accuracy: 0.9964 - val_loss: 4.3760e-04 - val_accuracy: 0.9948\n",
      "Epoch 101/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.3113e-04 - accuracy: 0.9962 - val_loss: 3.9103e-04 - val_accuracy: 0.9974\n",
      "Epoch 102/160\n",
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      "Epoch 103/160\n",
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      "Epoch 104/160\n",
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      "Epoch 105/160\n",
      "721/721 [==============================] - 13s 17ms/step - loss: 4.2310e-04 - accuracy: 0.9951 - val_loss: 3.7425e-04 - val_accuracy: 0.9965\n",
      "Epoch 106/160\n",
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      "Epoch 107/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.2897e-04 - accuracy: 0.9967 - val_loss: 4.0830e-04 - val_accuracy: 0.9957\n",
      "Epoch 108/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.2780e-04 - accuracy: 0.9969 - val_loss: 3.8018e-04 - val_accuracy: 0.9974\n",
      "Epoch 109/160\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 12s 17ms/step - loss: 4.2000e-04 - accuracy: 0.9965 - val_loss: 3.8849e-04 - val_accuracy: 0.9957\n",
      "Epoch 110/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.1062e-04 - accuracy: 0.9960 - val_loss: 3.6232e-04 - val_accuracy: 0.9974\n",
      "Epoch 111/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.9594e-04 - accuracy: 0.9969 - val_loss: 3.7162e-04 - val_accuracy: 0.9974\n",
      "Epoch 112/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.0631e-04 - accuracy: 0.9969 - val_loss: 3.8879e-04 - val_accuracy: 0.9939\n",
      "Epoch 113/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.9019e-04 - accuracy: 0.9957 - val_loss: 4.0167e-04 - val_accuracy: 0.9957\n",
      "Epoch 114/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.1357e-04 - accuracy: 0.9964 - val_loss: 3.3567e-04 - val_accuracy: 0.9983\n",
      "Epoch 115/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.9511e-04 - accuracy: 0.9951 - val_loss: 3.9402e-04 - val_accuracy: 0.9965\n",
      "Epoch 116/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.2150e-04 - accuracy: 0.9967 - val_loss: 3.6825e-04 - val_accuracy: 0.9957\n",
      "Epoch 117/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.9747e-04 - accuracy: 0.9955 - val_loss: 3.8277e-04 - val_accuracy: 0.9957\n",
      "Epoch 118/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 4.1033e-04 - accuracy: 0.9958 - val_loss: 2.9320e-04 - val_accuracy: 0.9974\n",
      "Epoch 119/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.9166e-04 - accuracy: 0.9957 - val_loss: 3.4133e-04 - val_accuracy: 0.9991\n",
      "Epoch 120/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.2010e-04 - accuracy: 0.9960 - val_loss: 2.8687e-04 - val_accuracy: 0.9991\n",
      "Epoch 121/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 4.0856e-04 - accuracy: 0.9964 - val_loss: 2.9005e-04 - val_accuracy: 0.9983\n",
      "Epoch 122/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 4.0154e-04 - accuracy: 0.9967 - val_loss: 3.3664e-04 - val_accuracy: 0.9965\n",
      "Epoch 123/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 3.9945e-04 - accuracy: 0.9974 - val_loss: 3.4357e-04 - val_accuracy: 0.9974\n",
      "Epoch 124/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 3.8006e-04 - accuracy: 0.9962 - val_loss: 3.8243e-04 - val_accuracy: 0.9957\n",
      "Epoch 125/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 3.8635e-04 - accuracy: 0.9967 - val_loss: 3.6076e-04 - val_accuracy: 0.9957\n",
      "Epoch 126/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.8698e-04 - accuracy: 0.9972 - val_loss: 4.0317e-04 - val_accuracy: 0.9965\n",
      "Epoch 127/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 3.7140e-04 - accuracy: 0.9969 - val_loss: 3.7620e-04 - val_accuracy: 0.9974\n",
      "Epoch 128/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.7594e-04 - accuracy: 0.9972 - val_loss: 6.0576e-04 - val_accuracy: 0.9983\n",
      "Epoch 129/160\n",
      "721/721 [==============================] - 13s 19ms/step - loss: 3.7389e-04 - accuracy: 0.9972 - val_loss: 3.0437e-04 - val_accuracy: 0.9957\n",
      "Epoch 130/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 3.9959e-04 - accuracy: 0.9958 - val_loss: 3.7620e-04 - val_accuracy: 0.9983\n",
      "Epoch 131/160\n",
      "721/721 [==============================] - 13s 18ms/step - loss: 3.6551e-04 - accuracy: 0.9969 - val_loss: 3.9044e-04 - val_accuracy: 0.9913\n",
      "Epoch 132/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.7001e-04 - accuracy: 0.9965 - val_loss: 5.2771e-04 - val_accuracy: 0.9922\n",
      "Epoch 133/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.8667e-04 - accuracy: 0.9976 - val_loss: 3.8264e-04 - val_accuracy: 0.9974\n",
      "Epoch 134/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.7418e-04 - accuracy: 0.9964 - val_loss: 3.8322e-04 - val_accuracy: 0.9957\n",
      "Epoch 135/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 3.7967e-04 - accuracy: 0.9955 - val_loss: 3.2255e-04 - val_accuracy: 0.9948\n",
      "Epoch 136/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.7084e-04 - accuracy: 0.9958 - val_loss: 4.8469e-04 - val_accuracy: 0.9957\n",
      "Epoch 137/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.6415e-04 - accuracy: 0.9965 - val_loss: 3.4206e-04 - val_accuracy: 0.9948\n",
      "Epoch 138/160\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 3.6338e-04 - accuracy: 0.9951 - val_loss: 3.8781e-04 - val_accuracy: 0.9948\n",
      "Epoch 139/160\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 3.8969e-04 - accuracy: 0.9951 - val_loss: 3.8594e-04 - val_accuracy: 0.9965\n",
      "Epoch 140/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.6741e-04 - accuracy: 0.9983 - val_loss: 3.0080e-04 - val_accuracy: 0.9983\n",
      "Epoch 141/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.6567e-04 - accuracy: 0.9958 - val_loss: 3.3176e-04 - val_accuracy: 0.9974\n",
      "Epoch 142/160\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 3.7670e-04 - accuracy: 0.9965 - val_loss: 3.2022e-04 - val_accuracy: 0.9974\n",
      "Epoch 143/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.5923e-04 - accuracy: 0.9958 - val_loss: 2.7012e-04 - val_accuracy: 0.9974\n",
      "Epoch 144/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.5360e-04 - accuracy: 0.9969 - val_loss: 2.8009e-04 - val_accuracy: 0.9983\n",
      "Epoch 145/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.6873e-04 - accuracy: 0.9965 - val_loss: 4.8131e-04 - val_accuracy: 0.9957\n",
      "Epoch 146/160\n",
      "721/721 [==============================] - 11s 16ms/step - loss: 3.6588e-04 - accuracy: 0.9972 - val_loss: 3.9026e-04 - val_accuracy: 0.9965\n",
      "Epoch 147/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.5914e-04 - accuracy: 0.9969 - val_loss: 4.0951e-04 - val_accuracy: 0.9965\n",
      "Epoch 148/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.5122e-04 - accuracy: 0.9962 - val_loss: 3.1055e-04 - val_accuracy: 0.9983\n",
      "Epoch 149/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.5295e-04 - accuracy: 0.9969 - val_loss: 2.5388e-04 - val_accuracy: 0.9983\n",
      "Epoch 150/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.4026e-04 - accuracy: 0.9960 - val_loss: 3.0740e-04 - val_accuracy: 0.9965\n",
      "Epoch 151/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.6111e-04 - accuracy: 0.9970 - val_loss: 3.2313e-04 - val_accuracy: 0.9965\n",
      "Epoch 152/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.4564e-04 - accuracy: 0.9970 - val_loss: 3.3885e-04 - val_accuracy: 0.9983\n",
      "Epoch 153/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.4099e-04 - accuracy: 0.9962 - val_loss: 3.0916e-04 - val_accuracy: 0.9983\n",
      "Epoch 154/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.4617e-04 - accuracy: 0.9976 - val_loss: 3.5489e-04 - val_accuracy: 0.9983\n",
      "Epoch 155/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.4881e-04 - accuracy: 0.9969 - val_loss: 3.4108e-04 - val_accuracy: 0.9974\n",
      "Epoch 156/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.8430e-04 - accuracy: 0.9967 - val_loss: 3.9915e-04 - val_accuracy: 0.9957\n",
      "Epoch 157/160\n",
      "721/721 [==============================] - 11s 15ms/step - loss: 3.4533e-04 - accuracy: 0.9970 - val_loss: 2.9391e-04 - val_accuracy: 0.9974\n",
      "Epoch 158/160\n",
      "721/721 [==============================] - 12s 16ms/step - loss: 3.4145e-04 - accuracy: 0.9970 - val_loss: 3.4300e-04 - val_accuracy: 0.9965\n",
      "Epoch 159/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.3224e-04 - accuracy: 0.9976 - val_loss: 2.7631e-04 - val_accuracy: 1.0000\n",
      "Epoch 160/160\n",
      "721/721 [==============================] - 12s 17ms/step - loss: 3.4096e-04 - accuracy: 0.9969 - val_loss: 2.9363e-04 - val_accuracy: 1.0000\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\\4.3_PRODUCED_ANALOG_2.jpg 0.43 0.37357564088853645 0.05642435911146354\n",
      "data_resize_all\\4.4_PRODUCED_ANALOG.jpg 0.44000000000000006 0.38769515972276836 0.0523048402772317\n",
      "data_resize_all\\4.4_PRODUCED_ANALOG_2.jpg 0.44000000000000006 0.37801598481345716 0.061984015186542896\n",
      "data_resize_all\\5.6_2760_zeiger4_2020-04-29_13-09-02.jpg 0.5599999999999999 0.6429735280154505 -0.08297352801545055\n",
      "data_resize_all\\5.9.jpg 0.5900000000000001 0.8549936989100476 -0.26499369891004754\n",
      "data_resize_all\\6.4_PRODUCED_ANALOG_2.jpg 0.64 0.6947819168533267 -0.05478191685332667\n",
      "data_resize_all\\6.5_3349_zeiger2_2020-04-29_13-48-02.jpg 0.65 0.7017860861196978 -0.051786086119697816\n",
      "data_resize_all\\9.4_PRODUCED_ANALOG_2.jpg 0.9400000000000001 1.012748291953735 -0.07274829195373489\n",
      "data_resize_all\\9.6_PRODUCED_ANALOG.jpg 0.96 1.0192373001309416 -0.05923730013094164\n",
      "data_resize_all\\9.7_PRODUCED_ANALOG_2.jpg 0.97 1.0214016554554814 -0.051401655455481476\n",
      "data_resize_all\\9.8_PRODUCED_ANALOG.jpg 0.9800000000000001 1.0440388873886313 -0.06403888738863117\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": [
      "[-0.0005  0.0134 -0.265   0.062 ]\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\\tmpyyz9s2z0\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpyyz9s2z0\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "365580"
      ]
     },
     "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\\tmpkblcs12g\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpkblcs12g\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ana1100s0.tflite\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100080"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import tensorflow as tf\n",
    "\n",
    "FileName = TFliteNamingAndVersion + \".tflite\"\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",
    "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, \"wb\").write(tflite_quant_model)\n",
    "print(FileName)\n",
    "Path(FileName).stat().st_size"
   ]
  },
  {
   "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
}