CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initalizing Variables and Preprocessing Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_wavelets.Layers.DWT as wavelet\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Constents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_DATA_PATH = \"Data/train\"\n",
    "VALID_DATA_PATH = \"Data/valid\"\n",
    "TEST_DATA_PATH  = \"Data/test\"\n",
    "BATCH_SIZE      = 16\n",
    "TARGET_SIZE     = (227,227)\n",
    "INPUT_SHAPE     = [227,227,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Training Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 613 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_generator = tf.keras.preprocessing.image.ImageDataGenerator( rescale = 1.0/255.0,\n",
    "                                                                        horizontal_flip = True,\n",
    "                                                                        fill_mode = 'nearest',\n",
    "                                                                        zoom_range=0.2,\n",
    "                                                                        shear_range = 0.2,\n",
    "                                                                        width_shift_range=0.2,\n",
    "                                                                        height_shift_range=0.2,\n",
    "                                                                        rotation_range=0.4)\n",
    "\n",
    "train_data = train_data_generator.flow_from_directory(  TRAIN_DATA_PATH,\n",
    "                                                        batch_size = BATCH_SIZE,\n",
    "                                                        target_size = TARGET_SIZE,\n",
    "                                                        class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Validation Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 72 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_data_generator = ImageDataGenerator(rescale = 1.0/255.0)\n",
    "\n",
    "validation_data = validation_data_generator.flow_from_directory(     VALID_DATA_PATH,\n",
    "                                                        target_size = TARGET_SIZE,\n",
    "                                                        batch_size = BATCH_SIZE,\n",
    "                                                        class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initalizing Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Abstract Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNNModel:\n",
    "    def __init__(self, input_shape=0, model_type='', model_path=None):\n",
    "        self.model_type = model_type\n",
    "        self.history = None\n",
    "        self.history_accuracy = []\n",
    "        self.history_val_accuracy = []\n",
    "        self.history_loss = []\n",
    "        self.history_val_loss = []\n",
    "        self.input_shape = input_shape\n",
    "        self.model = tf.keras.models.Sequential()\n",
    "        if(model_path==None):\n",
    "            self.model_path = 'savedModel/CNNModel.ckpt'\n",
    "        else:\n",
    "            self.model_path = model_path\n",
    "        self.model_checkpoint = tf.keras.callbacks.ModelCheckpoint(self.model_path,save_weights_only=True,verbose=1)\n",
    "\n",
    "    def fit(self,train_data, validation_data, epochs):\n",
    "        self.history = self.model.fit(x = train_data, validation_data = validation_data, epochs = epochs, callbacks=[self.model_checkpoint])\n",
    "        self.history_accuracy.extend(self.history.history['accuracy'])\n",
    "        self.history_val_accuracy.extend(self.history.history['val_accuracy'])\n",
    "        self.history_loss.extend(self.history.history['loss'])\n",
    "        self.history_val_loss.extend(self.history.history['val_loss'])\n",
    "\n",
    "    def plotAccuracy(self):\n",
    "        if(self.history != None):\n",
    "            print(self.history.history.keys())\n",
    "            plt.plot(self.history_accuracy)\n",
    "            plt.plot(self.history_val_accuracy)\n",
    "            plt.title('model accuracy')\n",
    "            plt.ylabel('accuracy')\n",
    "            plt.xlabel('epoch')\n",
    "            plt.legend(['train','test'], loc='upper left')\n",
    "            plt.show()\n",
    "        \n",
    "    def plotLoss(self):\n",
    "        if(self.history != None):\n",
    "            plt.plot(self.history_loss)\n",
    "            plt.plot(self.history_val_loss)\n",
    "            plt.title('model loss')\n",
    "            plt.ylabel('loss')\n",
    "            plt.xlabel('epoch')\n",
    "            plt.legend(['train', 'test'], loc='upper left')\n",
    "            plt.show()\n",
    "\n",
    "    def compareAccuracy(models):\n",
    "        modelList = []\n",
    "        for model in models:\n",
    "            if(model.history != None):\n",
    "                plt.plot(model.history_accuracy)\n",
    "                modelList.append(model.model_type)\n",
    "        plt.ylabel('accuracy')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(modelList, loc='upper left')\n",
    "        plt.show()\n",
    "\n",
    "    def compareLoss(models):\n",
    "        modelList = []\n",
    "        for model in models:\n",
    "            if(model.history != None):\n",
    "                plt.plot(model.history_loss)\n",
    "                modelList.append(model.model_type)\n",
    "        plt.ylabel('loss')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(modelList, loc='upper left')\n",
    "        plt.show()\n",
    "\n",
    "    def predict(self, path):\n",
    "        classes_dir = [\"Adenocarcinoma\",\"Large cell carcinoma\",\"Normal\",\"Squamous cell carcinoma\"]\n",
    "        image = tf.keras.utils.load_img(path, target_size=TARGET_SIZE)\n",
    "        normalizedImage = tf.keras.utils.img_to_array(image)/255\n",
    "        imageArray = np.array([normalizedImage])\n",
    "        predtionResult = np.argmax(self.model.predict(imageArray))\n",
    "        print(classes_dir[predtionResult])\n",
    "\n",
    "    def load(self,model_path=None):\n",
    "        if(model_path==None):\n",
    "            self.model.load_weights(self.model_path)\n",
    "        else:\n",
    "            self.model.load_weights(model_path)\n",
    "            \n",
    "    def summary(self):\n",
    "        print(self.model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### LeNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(CNNModel):\n",
    "    def __init__(self,input_shape=0,model_path='savedModel/LeNet.ckpt'):\n",
    "\n",
    "        super().__init__(input_shape=input_shape,model_type=\"LeNet\",model_path=model_path)\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=6, kernel_size=5, activation='tanh', strides=1, input_shape=input_shape, padding=\"same\"))\n",
    "        self.model.add(tf.keras.layers.AveragePooling2D(pool_size=2,strides=2, padding=\"valid\"))\n",
    "        \n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=16, kernel_size=5, strides=1, activation='tanh', padding=\"valid\"))\n",
    "        self.model.add(tf.keras.layers.AveragePooling2D(pool_size=2,strides=2, padding=\"valid\"))\n",
    "        \n",
    "        self.model.add(tf.keras.layers.Flatten())\n",
    "        self.model.add(tf.keras.layers.Dense(units=120, activation='tanh'))\n",
    "        self.model.add(tf.keras.layers.Dense(units=84, activation='tanh'))\n",
    "        self.model.add(tf.keras.layers.Dense(units=4, activation='softmax'))\n",
    "        self.model.compile(loss='categorical_crossentropy', optimizer=\"rmsprop\", metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AlexNet(CNNModel):\n",
    "    def __init__(self,input_shape=0,model_path='savedModel/AlexNet.ckpt'):\n",
    "\n",
    "        super().__init__(input_shape=input_shape,model_type=\"AlexNet\",model_path=model_path)\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=96, kernel_size=11, activation='relu', strides=4, input_shape=input_shape))\n",
    "        self.model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=5, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2))\n",
    "        self.model.add(tf.keras.layers.Flatten())\n",
    "        self.model.add(tf.keras.layers.Dense(units=4096, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Dropout(0.5))\n",
    "        self.model.add(tf.keras.layers.Dense(units=4096, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Dropout(0.5))\n",
    "        self.model.add(tf.keras.layers.Dense(units=4, activation='softmax'))\n",
    "        self.model.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Wavelet AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WaveletAlexNet(CNNModel):\n",
    "    def __init__(self,input_shape=0,model_path='savedModel/WaveletAlexNet.ckpt'):\n",
    "\n",
    "\n",
    "        super().__init__(input_shape=input_shape,model_type=\"WaveletAlexNet\",model_path=model_path)\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=5, activation='relu', strides=4, input_shape=input_shape))\n",
    "\n",
    "        self.model.add(wavelet.DWT(name='bd2'))\n",
    "        self.model.add(tf.keras.layers.Reshape((112, 112, 96)))\n",
    "\n",
    "        self.model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=5, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Conv2D(filters=384, kernel_size=3, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.MaxPool2D(pool_size=3,strides=2))\n",
    "        self.model.add(tf.keras.layers.Flatten(input_shape = (2,2,384)))\n",
    "        self.model.add(tf.keras.layers.Dense(units=4096, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Dropout(0.5))\n",
    "        self.model.add(tf.keras.layers.Dense(units=4096, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Dropout(0.5))\n",
    "        self.model.add(tf.keras.layers.Dense(units=4, activation='softmax'))\n",
    "        self.model.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ResNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(CNNModel):\n",
    "    def __init__(self,input_shape=0,model_path='savedModel/ResNet.h5'):\n",
    "        if(input_shape==0):\n",
    "            super().__init__(input_shape=input_shape,model_type=\"ResNet\",model_path=model_path)\n",
    "        else:\n",
    "            super().__init__(input_shape=input_shape,model_type=\"ResNet\")\n",
    "            pretrained_model= tf.keras.applications.ResNet50(include_top=False,\n",
    "            input_shape=INPUT_SHAPE,\n",
    "            pooling='avg',classes=5,\n",
    "            weights='imagenet')\n",
    "\n",
    "        for layer in pretrained_model.layers:\n",
    "            layer.trainable=True\n",
    "\n",
    "        self.model.add(pretrained_model)\n",
    "        self.model.add(tf.keras.layers.Flatten())\n",
    "        self.model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Dense(4, activation='softmax'))\n",
    "        self.model.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics = ['accuracy'])\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Modified ResNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HybridResNet(CNNModel):\n",
    "    def __init__(self,input_shape=0,model_path='savedModel/HybridResNet.h5'):\n",
    "        if(input_shape==0):\n",
    "            super().__init__(input_shape=input_shape,model_type=\"HybridResNet\",model_path=model_path)\n",
    "        else:\n",
    "            super().__init__(input_shape=input_shape,model_type=\"HybridResNet\")\n",
    "            pretrained_model= tf.keras.applications.ResNet50(include_top=False,\n",
    "            input_shape=INPUT_SHAPE,\n",
    "            pooling='max',classes=5,\n",
    "            weights='imagenet')\n",
    "\n",
    "        for layer in pretrained_model.layers:\n",
    "            layer.trainable=True\n",
    "\n",
    "        self.model.add(pretrained_model)\n",
    "        self.model.add(tf.keras.layers.Flatten())\n",
    "        self.model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
    "        self.model.add(tf.keras.layers.Dense(4, activation='softmax'))\n",
    "        self.model.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Initialize Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "alexNet = AlexNet(INPUT_SHAPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "leNet = LeNet(INPUT_SHAPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "waveletAlexNet = WaveletAlexNet(INPUT_SHAPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "resNet = ResNet(INPUT_SHAPE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Summary Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_5 (Conv2D)           (None, 227, 227, 6)       456       \n",
      "                                                                 \n",
      " average_pooling2d (AverageP  (None, 113, 113, 6)      0         \n",
      " ooling2D)                                                       \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 109, 109, 16)      2416      \n",
      "                                                                 \n",
      " average_pooling2d_1 (Averag  (None, 54, 54, 16)       0         \n",
      " ePooling2D)                                                     \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 46656)             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 120)               5598840   \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 84)                10164     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 4)                 340       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,612,216\n",
      "Trainable params: 5,612,216\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "leNet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " resnet50 (Functional)       (None, 2048)              23587712  \n",
      "                                                                 \n",
      " flatten_3 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 512)               1049088   \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 4)                 2052      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 24,638,852\n",
      "Trainable params: 24,585,732\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "resNet.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training Modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.5552 - accuracy: 0.2675\n",
      "Epoch 1: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 6s 131ms/step - loss: 1.5552 - accuracy: 0.2675 - val_loss: 1.3645 - val_accuracy: 0.3194\n",
      "Epoch 2/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.4460 - accuracy: 0.2594\n",
      "Epoch 2: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 1.4460 - accuracy: 0.2594 - val_loss: 1.4303 - val_accuracy: 0.2083\n",
      "Epoch 3/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.4819 - accuracy: 0.2757\n",
      "Epoch 3: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.4819 - accuracy: 0.2757 - val_loss: 1.4176 - val_accuracy: 0.3194\n",
      "Epoch 4/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.4153 - accuracy: 0.2545\n",
      "Epoch 4: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.4153 - accuracy: 0.2545 - val_loss: 1.4034 - val_accuracy: 0.2917\n",
      "Epoch 5/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.4028 - accuracy: 0.2675\n",
      "Epoch 5: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.4028 - accuracy: 0.2675 - val_loss: 1.4763 - val_accuracy: 0.2083\n",
      "Epoch 6/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3725 - accuracy: 0.3100\n",
      "Epoch 6: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 1.3725 - accuracy: 0.3100 - val_loss: 1.4171 - val_accuracy: 0.3194\n",
      "Epoch 7/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3234 - accuracy: 0.3883\n",
      "Epoch 7: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 1.3234 - accuracy: 0.3883 - val_loss: 1.5544 - val_accuracy: 0.3472\n",
      "Epoch 8/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3217 - accuracy: 0.3980\n",
      "Epoch 8: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.3217 - accuracy: 0.3980 - val_loss: 1.4701 - val_accuracy: 0.2639\n",
      "Epoch 9/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2379 - accuracy: 0.4095\n",
      "Epoch 9: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.2379 - accuracy: 0.4095 - val_loss: 1.3614 - val_accuracy: 0.4167\n",
      "Epoch 10/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2229 - accuracy: 0.4584\n",
      "Epoch 10: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.2229 - accuracy: 0.4584 - val_loss: 1.3877 - val_accuracy: 0.3889\n",
      "Epoch 11/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1989 - accuracy: 0.4339\n",
      "Epoch 11: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.1989 - accuracy: 0.4339 - val_loss: 1.2738 - val_accuracy: 0.3750\n",
      "Epoch 12/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1711 - accuracy: 0.4943\n",
      "Epoch 12: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1711 - accuracy: 0.4943 - val_loss: 1.8261 - val_accuracy: 0.1806\n",
      "Epoch 13/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1781 - accuracy: 0.4747\n",
      "Epoch 13: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.1781 - accuracy: 0.4747 - val_loss: 1.3179 - val_accuracy: 0.4444\n",
      "Epoch 14/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1778 - accuracy: 0.4649\n",
      "Epoch 14: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1778 - accuracy: 0.4649 - val_loss: 1.3124 - val_accuracy: 0.3889\n",
      "Epoch 15/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1384 - accuracy: 0.4682\n",
      "Epoch 15: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1384 - accuracy: 0.4682 - val_loss: 1.5129 - val_accuracy: 0.3194\n",
      "Epoch 16/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1260 - accuracy: 0.4878\n",
      "Epoch 16: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.1260 - accuracy: 0.4878 - val_loss: 1.2641 - val_accuracy: 0.4583\n",
      "Epoch 17/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0979 - accuracy: 0.5204\n",
      "Epoch 17: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0979 - accuracy: 0.5204 - val_loss: 1.2116 - val_accuracy: 0.4444\n",
      "Epoch 18/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1226 - accuracy: 0.4698\n",
      "Epoch 18: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1226 - accuracy: 0.4698 - val_loss: 1.3355 - val_accuracy: 0.4028\n",
      "Epoch 19/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1115 - accuracy: 0.4861\n",
      "Epoch 19: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1115 - accuracy: 0.4861 - val_loss: 1.1793 - val_accuracy: 0.4722\n",
      "Epoch 20/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1165 - accuracy: 0.5024\n",
      "Epoch 20: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.1165 - accuracy: 0.5024 - val_loss: 1.4011 - val_accuracy: 0.4167\n",
      "Epoch 21/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0966 - accuracy: 0.5122\n",
      "Epoch 21: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0966 - accuracy: 0.5122 - val_loss: 1.1746 - val_accuracy: 0.4583\n",
      "Epoch 22/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0854 - accuracy: 0.5073\n",
      "Epoch 22: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0854 - accuracy: 0.5073 - val_loss: 1.1663 - val_accuracy: 0.5000\n",
      "Epoch 23/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1763 - accuracy: 0.4666\n",
      "Epoch 23: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1763 - accuracy: 0.4666 - val_loss: 1.2004 - val_accuracy: 0.4167\n",
      "Epoch 24/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1513 - accuracy: 0.4388\n",
      "Epoch 24: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1513 - accuracy: 0.4388 - val_loss: 1.1272 - val_accuracy: 0.5000\n",
      "Epoch 25/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0736 - accuracy: 0.5090\n",
      "Epoch 25: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0736 - accuracy: 0.5090 - val_loss: 1.1910 - val_accuracy: 0.4306\n",
      "Epoch 26/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0708 - accuracy: 0.5090\n",
      "Epoch 26: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0708 - accuracy: 0.5090 - val_loss: 1.2352 - val_accuracy: 0.3611\n",
      "Epoch 27/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1126 - accuracy: 0.5073\n",
      "Epoch 27: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.1126 - accuracy: 0.5073 - val_loss: 1.2746 - val_accuracy: 0.4167\n",
      "Epoch 28/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0553 - accuracy: 0.5237\n",
      "Epoch 28: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 1.0553 - accuracy: 0.5237 - val_loss: 1.1680 - val_accuracy: 0.4583\n",
      "Epoch 29/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0665 - accuracy: 0.5041\n",
      "Epoch 29: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0665 - accuracy: 0.5041 - val_loss: 1.3442 - val_accuracy: 0.4306\n",
      "Epoch 30/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0325 - accuracy: 0.5416\n",
      "Epoch 30: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0325 - accuracy: 0.5416 - val_loss: 1.3425 - val_accuracy: 0.4306\n",
      "Epoch 31/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0588 - accuracy: 0.5253\n",
      "Epoch 31: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0588 - accuracy: 0.5253 - val_loss: 1.3002 - val_accuracy: 0.4444\n",
      "Epoch 32/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0526 - accuracy: 0.5432\n",
      "Epoch 32: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0526 - accuracy: 0.5432 - val_loss: 1.1783 - val_accuracy: 0.4444\n",
      "Epoch 33/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1771 - accuracy: 0.4976\n",
      "Epoch 33: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1771 - accuracy: 0.4976 - val_loss: 1.8381 - val_accuracy: 0.2222\n",
      "Epoch 34/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1211 - accuracy: 0.4992\n",
      "Epoch 34: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 1.1211 - accuracy: 0.4992 - val_loss: 1.2606 - val_accuracy: 0.4861\n",
      "Epoch 35/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1240 - accuracy: 0.4894\n",
      "Epoch 35: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.1240 - accuracy: 0.4894 - val_loss: 1.2431 - val_accuracy: 0.3611\n",
      "Epoch 36/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0386 - accuracy: 0.5498\n",
      "Epoch 36: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0386 - accuracy: 0.5498 - val_loss: 1.3396 - val_accuracy: 0.4306\n",
      "Epoch 37/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0705 - accuracy: 0.5155\n",
      "Epoch 37: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 1.0705 - accuracy: 0.5155 - val_loss: 1.1538 - val_accuracy: 0.4861\n",
      "Epoch 38/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0461 - accuracy: 0.5302\n",
      "Epoch 38: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0461 - accuracy: 0.5302 - val_loss: 1.1293 - val_accuracy: 0.4306\n",
      "Epoch 39/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0283 - accuracy: 0.5400\n",
      "Epoch 39: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0283 - accuracy: 0.5400 - val_loss: 1.1669 - val_accuracy: 0.5000\n",
      "Epoch 40/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0704 - accuracy: 0.5155\n",
      "Epoch 40: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 1.0704 - accuracy: 0.5155 - val_loss: 1.1959 - val_accuracy: 0.4167\n",
      "Epoch 41/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0785 - accuracy: 0.5155\n",
      "Epoch 41: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0785 - accuracy: 0.5155 - val_loss: 1.2421 - val_accuracy: 0.4306\n",
      "Epoch 42/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0449 - accuracy: 0.5139\n",
      "Epoch 42: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0449 - accuracy: 0.5139 - val_loss: 1.2544 - val_accuracy: 0.3889\n",
      "Epoch 43/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0177 - accuracy: 0.5514\n",
      "Epoch 43: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0177 - accuracy: 0.5514 - val_loss: 1.3553 - val_accuracy: 0.3611\n",
      "Epoch 44/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0463 - accuracy: 0.5302\n",
      "Epoch 44: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 128ms/step - loss: 1.0463 - accuracy: 0.5302 - val_loss: 1.1517 - val_accuracy: 0.4861\n",
      "Epoch 45/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1545 - accuracy: 0.4894\n",
      "Epoch 45: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.1545 - accuracy: 0.4894 - val_loss: 1.1316 - val_accuracy: 0.4722\n",
      "Epoch 46/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0149 - accuracy: 0.5334\n",
      "Epoch 46: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0149 - accuracy: 0.5334 - val_loss: 1.4172 - val_accuracy: 0.3889\n",
      "Epoch 47/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9536 - accuracy: 0.5791\n",
      "Epoch 47: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9536 - accuracy: 0.5791 - val_loss: 1.1506 - val_accuracy: 0.4722\n",
      "Epoch 48/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9938 - accuracy: 0.5432\n",
      "Epoch 48: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9938 - accuracy: 0.5432 - val_loss: 1.2467 - val_accuracy: 0.4861\n",
      "Epoch 49/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0242 - accuracy: 0.5302\n",
      "Epoch 49: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0242 - accuracy: 0.5302 - val_loss: 1.1325 - val_accuracy: 0.4861\n",
      "Epoch 50/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0182 - accuracy: 0.5644\n",
      "Epoch 50: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0182 - accuracy: 0.5644 - val_loss: 1.1416 - val_accuracy: 0.4306\n",
      "Epoch 51/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0539 - accuracy: 0.5334\n",
      "Epoch 51: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0539 - accuracy: 0.5334 - val_loss: 1.2916 - val_accuracy: 0.4028\n",
      "Epoch 52/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9994 - accuracy: 0.5237\n",
      "Epoch 52: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9994 - accuracy: 0.5237 - val_loss: 1.1710 - val_accuracy: 0.4444\n",
      "Epoch 53/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0116 - accuracy: 0.5253\n",
      "Epoch 53: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0116 - accuracy: 0.5253 - val_loss: 1.0978 - val_accuracy: 0.5000\n",
      "Epoch 54/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0224 - accuracy: 0.5204\n",
      "Epoch 54: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0224 - accuracy: 0.5204 - val_loss: 1.2660 - val_accuracy: 0.4444\n",
      "Epoch 55/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9570 - accuracy: 0.5710\n",
      "Epoch 55: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9570 - accuracy: 0.5710 - val_loss: 1.0815 - val_accuracy: 0.4861\n",
      "Epoch 56/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9637 - accuracy: 0.5579\n",
      "Epoch 56: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9637 - accuracy: 0.5579 - val_loss: 1.0911 - val_accuracy: 0.5139\n",
      "Epoch 57/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0027 - accuracy: 0.5400\n",
      "Epoch 57: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0027 - accuracy: 0.5400 - val_loss: 1.2130 - val_accuracy: 0.4722\n",
      "Epoch 58/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9536 - accuracy: 0.5628\n",
      "Epoch 58: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9536 - accuracy: 0.5628 - val_loss: 1.2066 - val_accuracy: 0.3889\n",
      "Epoch 59/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9823 - accuracy: 0.5579\n",
      "Epoch 59: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9823 - accuracy: 0.5579 - val_loss: 1.1037 - val_accuracy: 0.4583\n",
      "Epoch 60/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9867 - accuracy: 0.5677\n",
      "Epoch 60: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9867 - accuracy: 0.5677 - val_loss: 1.2736 - val_accuracy: 0.4028\n",
      "Epoch 61/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9752 - accuracy: 0.5693\n",
      "Epoch 61: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9752 - accuracy: 0.5693 - val_loss: 1.2256 - val_accuracy: 0.4722\n",
      "Epoch 62/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9668 - accuracy: 0.5595\n",
      "Epoch 62: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9668 - accuracy: 0.5595 - val_loss: 1.2145 - val_accuracy: 0.4306\n",
      "Epoch 63/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0012 - accuracy: 0.5400\n",
      "Epoch 63: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0012 - accuracy: 0.5400 - val_loss: 1.1183 - val_accuracy: 0.4306\n",
      "Epoch 64/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9655 - accuracy: 0.5808\n",
      "Epoch 64: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9655 - accuracy: 0.5808 - val_loss: 1.0729 - val_accuracy: 0.5278\n",
      "Epoch 65/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9947 - accuracy: 0.5563\n",
      "Epoch 65: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9947 - accuracy: 0.5563 - val_loss: 1.2289 - val_accuracy: 0.3889\n",
      "Epoch 66/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1272 - accuracy: 0.4780\n",
      "Epoch 66: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.1272 - accuracy: 0.4780 - val_loss: 1.0957 - val_accuracy: 0.4306\n",
      "Epoch 67/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9924 - accuracy: 0.5612\n",
      "Epoch 67: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9924 - accuracy: 0.5612 - val_loss: 1.2297 - val_accuracy: 0.3750\n",
      "Epoch 68/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9746 - accuracy: 0.5628\n",
      "Epoch 68: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9746 - accuracy: 0.5628 - val_loss: 1.0879 - val_accuracy: 0.4861\n",
      "Epoch 69/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9416 - accuracy: 0.5922\n",
      "Epoch 69: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9416 - accuracy: 0.5922 - val_loss: 1.1443 - val_accuracy: 0.4722\n",
      "Epoch 70/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9134 - accuracy: 0.5840\n",
      "Epoch 70: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9134 - accuracy: 0.5840 - val_loss: 1.1313 - val_accuracy: 0.4583\n",
      "Epoch 71/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.9473 - accuracy: 0.5674\n",
      "Epoch 71: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9465 - accuracy: 0.5677 - val_loss: 1.1208 - val_accuracy: 0.5139\n",
      "Epoch 72/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9312 - accuracy: 0.5742\n",
      "Epoch 72: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9312 - accuracy: 0.5742 - val_loss: 1.2041 - val_accuracy: 0.4444\n",
      "Epoch 73/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9437 - accuracy: 0.5971\n",
      "Epoch 73: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9437 - accuracy: 0.5971 - val_loss: 1.1131 - val_accuracy: 0.5139\n",
      "Epoch 74/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9481 - accuracy: 0.5693\n",
      "Epoch 74: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9481 - accuracy: 0.5693 - val_loss: 1.2274 - val_accuracy: 0.4306\n",
      "Epoch 75/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.9637 - accuracy: 0.5707\n",
      "Epoch 75: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9677 - accuracy: 0.5693 - val_loss: 1.2697 - val_accuracy: 0.4444\n",
      "Epoch 76/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9896 - accuracy: 0.5498\n",
      "Epoch 76: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9896 - accuracy: 0.5498 - val_loss: 1.0235 - val_accuracy: 0.5139\n",
      "Epoch 77/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9293 - accuracy: 0.5759\n",
      "Epoch 77: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9293 - accuracy: 0.5759 - val_loss: 1.0849 - val_accuracy: 0.5139\n",
      "Epoch 78/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9874 - accuracy: 0.5465\n",
      "Epoch 78: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9874 - accuracy: 0.5465 - val_loss: 1.1893 - val_accuracy: 0.3750\n",
      "Epoch 79/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0099 - accuracy: 0.5661\n",
      "Epoch 79: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0099 - accuracy: 0.5661 - val_loss: 1.2641 - val_accuracy: 0.4722\n",
      "Epoch 80/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9411 - accuracy: 0.5889\n",
      "Epoch 80: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9411 - accuracy: 0.5889 - val_loss: 1.1683 - val_accuracy: 0.4306\n",
      "Epoch 81/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9685 - accuracy: 0.5677\n",
      "Epoch 81: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9685 - accuracy: 0.5677 - val_loss: 1.2616 - val_accuracy: 0.4583\n",
      "Epoch 82/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9333 - accuracy: 0.5579\n",
      "Epoch 82: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9333 - accuracy: 0.5579 - val_loss: 1.2032 - val_accuracy: 0.3889\n",
      "Epoch 83/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0056 - accuracy: 0.5220\n",
      "Epoch 83: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0056 - accuracy: 0.5220 - val_loss: 1.3154 - val_accuracy: 0.4167\n",
      "Epoch 84/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9839 - accuracy: 0.5579\n",
      "Epoch 84: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9839 - accuracy: 0.5579 - val_loss: 1.2419 - val_accuracy: 0.4028\n",
      "Epoch 85/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9461 - accuracy: 0.5498\n",
      "Epoch 85: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9461 - accuracy: 0.5498 - val_loss: 1.5851 - val_accuracy: 0.3889\n",
      "Epoch 86/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9507 - accuracy: 0.5612\n",
      "Epoch 86: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9507 - accuracy: 0.5612 - val_loss: 1.2501 - val_accuracy: 0.4444\n",
      "Epoch 87/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9469 - accuracy: 0.5677\n",
      "Epoch 87: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9469 - accuracy: 0.5677 - val_loss: 1.1100 - val_accuracy: 0.4028\n",
      "Epoch 88/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9376 - accuracy: 0.5759\n",
      "Epoch 88: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9376 - accuracy: 0.5759 - val_loss: 1.2247 - val_accuracy: 0.4583\n",
      "Epoch 89/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9585 - accuracy: 0.5514\n",
      "Epoch 89: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9585 - accuracy: 0.5514 - val_loss: 1.1466 - val_accuracy: 0.4444\n",
      "Epoch 90/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9042 - accuracy: 0.6036\n",
      "Epoch 90: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.9042 - accuracy: 0.6036 - val_loss: 1.1027 - val_accuracy: 0.4583\n",
      "Epoch 91/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9212 - accuracy: 0.5938\n",
      "Epoch 91: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9212 - accuracy: 0.5938 - val_loss: 1.1748 - val_accuracy: 0.4444\n",
      "Epoch 92/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9252 - accuracy: 0.5873\n",
      "Epoch 92: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9252 - accuracy: 0.5873 - val_loss: 1.1391 - val_accuracy: 0.4444\n",
      "Epoch 93/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9444 - accuracy: 0.5759\n",
      "Epoch 93: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9444 - accuracy: 0.5759 - val_loss: 1.2828 - val_accuracy: 0.5139\n",
      "Epoch 94/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0609 - accuracy: 0.5416\n",
      "Epoch 94: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0609 - accuracy: 0.5416 - val_loss: 1.3309 - val_accuracy: 0.4306\n",
      "Epoch 95/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9506 - accuracy: 0.5742\n",
      "Epoch 95: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.9506 - accuracy: 0.5742 - val_loss: 1.1486 - val_accuracy: 0.4583\n",
      "Epoch 96/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9156 - accuracy: 0.5856\n",
      "Epoch 96: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9156 - accuracy: 0.5856 - val_loss: 1.3226 - val_accuracy: 0.4306\n",
      "Epoch 97/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9606 - accuracy: 0.5514\n",
      "Epoch 97: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9606 - accuracy: 0.5514 - val_loss: 1.1741 - val_accuracy: 0.4583\n",
      "Epoch 98/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9044 - accuracy: 0.6166\n",
      "Epoch 98: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9044 - accuracy: 0.6166 - val_loss: 1.1009 - val_accuracy: 0.4722\n",
      "Epoch 99/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8963 - accuracy: 0.6134\n",
      "Epoch 99: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.8963 - accuracy: 0.6134 - val_loss: 1.1265 - val_accuracy: 0.4444\n",
      "Epoch 100/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9229 - accuracy: 0.5856\n",
      "Epoch 100: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9229 - accuracy: 0.5856 - val_loss: 1.1824 - val_accuracy: 0.4722\n",
      "Epoch 101/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9419 - accuracy: 0.5808\n",
      "Epoch 101: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9419 - accuracy: 0.5808 - val_loss: 1.0432 - val_accuracy: 0.4861\n",
      "Epoch 102/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9333 - accuracy: 0.5824\n",
      "Epoch 102: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9333 - accuracy: 0.5824 - val_loss: 1.3307 - val_accuracy: 0.4306\n",
      "Epoch 103/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8956 - accuracy: 0.5971\n",
      "Epoch 103: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.8956 - accuracy: 0.5971 - val_loss: 1.1792 - val_accuracy: 0.4722\n",
      "Epoch 104/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8807 - accuracy: 0.6085\n",
      "Epoch 104: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8807 - accuracy: 0.6085 - val_loss: 1.1551 - val_accuracy: 0.4722\n",
      "Epoch 105/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8979 - accuracy: 0.6232\n",
      "Epoch 105: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8979 - accuracy: 0.6232 - val_loss: 1.0837 - val_accuracy: 0.4722\n",
      "Epoch 106/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9624 - accuracy: 0.5595\n",
      "Epoch 106: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9624 - accuracy: 0.5595 - val_loss: 1.0772 - val_accuracy: 0.5000\n",
      "Epoch 107/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9571 - accuracy: 0.5661\n",
      "Epoch 107: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9571 - accuracy: 0.5661 - val_loss: 1.0919 - val_accuracy: 0.4861\n",
      "Epoch 108/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9162 - accuracy: 0.6003\n",
      "Epoch 108: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9162 - accuracy: 0.6003 - val_loss: 1.1497 - val_accuracy: 0.4861\n",
      "Epoch 109/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9253 - accuracy: 0.5775\n",
      "Epoch 109: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 129ms/step - loss: 0.9253 - accuracy: 0.5775 - val_loss: 1.1005 - val_accuracy: 0.4444\n",
      "Epoch 110/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8979 - accuracy: 0.6003\n",
      "Epoch 110: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8979 - accuracy: 0.6003 - val_loss: 1.0131 - val_accuracy: 0.4861\n",
      "Epoch 111/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9098 - accuracy: 0.5922\n",
      "Epoch 111: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9098 - accuracy: 0.5922 - val_loss: 1.1410 - val_accuracy: 0.4722\n",
      "Epoch 112/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9317 - accuracy: 0.5726\n",
      "Epoch 112: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9317 - accuracy: 0.5726 - val_loss: 1.1439 - val_accuracy: 0.4861\n",
      "Epoch 113/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9000 - accuracy: 0.5889\n",
      "Epoch 113: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9000 - accuracy: 0.5889 - val_loss: 1.1765 - val_accuracy: 0.5694\n",
      "Epoch 114/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9137 - accuracy: 0.6085\n",
      "Epoch 114: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 129ms/step - loss: 0.9137 - accuracy: 0.6085 - val_loss: 1.1944 - val_accuracy: 0.4722\n",
      "Epoch 115/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9746 - accuracy: 0.5873\n",
      "Epoch 115: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9746 - accuracy: 0.5873 - val_loss: 1.1866 - val_accuracy: 0.4306\n",
      "Epoch 116/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9380 - accuracy: 0.5889\n",
      "Epoch 116: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9380 - accuracy: 0.5889 - val_loss: 1.2608 - val_accuracy: 0.5000\n",
      "Epoch 117/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9223 - accuracy: 0.5987\n",
      "Epoch 117: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.9223 - accuracy: 0.5987 - val_loss: 1.1537 - val_accuracy: 0.5000\n",
      "Epoch 118/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9272 - accuracy: 0.5628\n",
      "Epoch 118: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9272 - accuracy: 0.5628 - val_loss: 1.2040 - val_accuracy: 0.4583\n",
      "Epoch 119/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8722 - accuracy: 0.6101\n",
      "Epoch 119: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.8722 - accuracy: 0.6101 - val_loss: 1.1190 - val_accuracy: 0.5139\n",
      "Epoch 120/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9191 - accuracy: 0.5889\n",
      "Epoch 120: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9191 - accuracy: 0.5889 - val_loss: 1.0792 - val_accuracy: 0.4861\n",
      "Epoch 121/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9397 - accuracy: 0.5889\n",
      "Epoch 121: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9397 - accuracy: 0.5889 - val_loss: 1.1641 - val_accuracy: 0.4861\n",
      "Epoch 122/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2529 - accuracy: 0.5024\n",
      "Epoch 122: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 128ms/step - loss: 1.2529 - accuracy: 0.5024 - val_loss: 1.5164 - val_accuracy: 0.2500\n",
      "Epoch 123/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2980 - accuracy: 0.4176\n",
      "Epoch 123: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.2980 - accuracy: 0.4176 - val_loss: 1.4333 - val_accuracy: 0.4028\n",
      "Epoch 124/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2733 - accuracy: 0.3752\n",
      "Epoch 124: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.2733 - accuracy: 0.3752 - val_loss: 1.3442 - val_accuracy: 0.3194\n",
      "Epoch 125/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3024 - accuracy: 0.3785\n",
      "Epoch 125: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 1.3024 - accuracy: 0.3785 - val_loss: 1.3873 - val_accuracy: 0.3056\n",
      "Epoch 126/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 1.2761 - accuracy: 0.4178\n",
      "Epoch 126: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.2788 - accuracy: 0.4176 - val_loss: 1.3118 - val_accuracy: 0.2778\n",
      "Epoch 127/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2852 - accuracy: 0.3638\n",
      "Epoch 127: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 1.2852 - accuracy: 0.3638 - val_loss: 1.4015 - val_accuracy: 0.4028\n",
      "Epoch 128/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2541 - accuracy: 0.4405\n",
      "Epoch 128: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 1.2541 - accuracy: 0.4405 - val_loss: 1.4396 - val_accuracy: 0.2917\n",
      "Epoch 129/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2102 - accuracy: 0.4633\n",
      "Epoch 129: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.2102 - accuracy: 0.4633 - val_loss: 1.3702 - val_accuracy: 0.4583\n",
      "Epoch 130/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9796 - accuracy: 0.5856\n",
      "Epoch 130: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.9796 - accuracy: 0.5856 - val_loss: 1.2069 - val_accuracy: 0.5417\n",
      "Epoch 131/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9693 - accuracy: 0.5840\n",
      "Epoch 131: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9693 - accuracy: 0.5840 - val_loss: 1.2646 - val_accuracy: 0.4167\n",
      "Epoch 132/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9487 - accuracy: 0.5612\n",
      "Epoch 132: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9487 - accuracy: 0.5612 - val_loss: 1.0418 - val_accuracy: 0.5278\n",
      "Epoch 133/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2128 - accuracy: 0.4747\n",
      "Epoch 133: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 1.2128 - accuracy: 0.4747 - val_loss: 1.1596 - val_accuracy: 0.5139\n",
      "Epoch 134/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9529 - accuracy: 0.5677\n",
      "Epoch 134: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9529 - accuracy: 0.5677 - val_loss: 1.0727 - val_accuracy: 0.5139\n",
      "Epoch 135/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9233 - accuracy: 0.6117\n",
      "Epoch 135: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9233 - accuracy: 0.6117 - val_loss: 1.3736 - val_accuracy: 0.4444\n",
      "Epoch 136/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9167 - accuracy: 0.5987\n",
      "Epoch 136: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9167 - accuracy: 0.5987 - val_loss: 1.1386 - val_accuracy: 0.4583\n",
      "Epoch 137/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9314 - accuracy: 0.6003\n",
      "Epoch 137: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9314 - accuracy: 0.6003 - val_loss: 1.0871 - val_accuracy: 0.5000\n",
      "Epoch 138/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9690 - accuracy: 0.5971\n",
      "Epoch 138: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.9690 - accuracy: 0.5971 - val_loss: 1.2191 - val_accuracy: 0.4444\n",
      "Epoch 139/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9333 - accuracy: 0.5775\n",
      "Epoch 139: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9333 - accuracy: 0.5775 - val_loss: 1.1005 - val_accuracy: 0.5278\n",
      "Epoch 140/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9345 - accuracy: 0.5856\n",
      "Epoch 140: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9345 - accuracy: 0.5856 - val_loss: 1.0532 - val_accuracy: 0.5000\n",
      "Epoch 141/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9226 - accuracy: 0.6085\n",
      "Epoch 141: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9226 - accuracy: 0.6085 - val_loss: 1.1211 - val_accuracy: 0.5278\n",
      "Epoch 142/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9618 - accuracy: 0.5530\n",
      "Epoch 142: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9618 - accuracy: 0.5530 - val_loss: 1.0598 - val_accuracy: 0.4583\n",
      "Epoch 143/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9358 - accuracy: 0.5889\n",
      "Epoch 143: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9358 - accuracy: 0.5889 - val_loss: 1.1082 - val_accuracy: 0.5278\n",
      "Epoch 144/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9502 - accuracy: 0.5889\n",
      "Epoch 144: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9502 - accuracy: 0.5889 - val_loss: 0.9817 - val_accuracy: 0.5556\n",
      "Epoch 145/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9156 - accuracy: 0.6020\n",
      "Epoch 145: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9156 - accuracy: 0.6020 - val_loss: 1.0735 - val_accuracy: 0.5000\n",
      "Epoch 146/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9262 - accuracy: 0.6052\n",
      "Epoch 146: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9262 - accuracy: 0.6052 - val_loss: 1.0605 - val_accuracy: 0.4583\n",
      "Epoch 147/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9036 - accuracy: 0.5840\n",
      "Epoch 147: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9036 - accuracy: 0.5840 - val_loss: 1.0825 - val_accuracy: 0.5278\n",
      "Epoch 148/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9186 - accuracy: 0.5938\n",
      "Epoch 148: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9186 - accuracy: 0.5938 - val_loss: 1.0813 - val_accuracy: 0.5417\n",
      "Epoch 149/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9204 - accuracy: 0.5971\n",
      "Epoch 149: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9204 - accuracy: 0.5971 - val_loss: 1.1765 - val_accuracy: 0.5000\n",
      "Epoch 150/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8823 - accuracy: 0.6166\n",
      "Epoch 150: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8823 - accuracy: 0.6166 - val_loss: 1.1431 - val_accuracy: 0.5000\n",
      "Epoch 151/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8992 - accuracy: 0.5938\n",
      "Epoch 151: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.8992 - accuracy: 0.5938 - val_loss: 1.1750 - val_accuracy: 0.5278\n",
      "Epoch 152/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8640 - accuracy: 0.6248\n",
      "Epoch 152: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.8640 - accuracy: 0.6248 - val_loss: 1.2311 - val_accuracy: 0.4306\n",
      "Epoch 153/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9228 - accuracy: 0.5856\n",
      "Epoch 153: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9228 - accuracy: 0.5856 - val_loss: 1.1380 - val_accuracy: 0.4722\n",
      "Epoch 154/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9046 - accuracy: 0.5856\n",
      "Epoch 154: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9046 - accuracy: 0.5856 - val_loss: 1.0831 - val_accuracy: 0.5000\n",
      "Epoch 155/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8949 - accuracy: 0.6052\n",
      "Epoch 155: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.8949 - accuracy: 0.6052 - val_loss: 1.0416 - val_accuracy: 0.4583\n",
      "Epoch 156/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9117 - accuracy: 0.5938\n",
      "Epoch 156: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.9117 - accuracy: 0.5938 - val_loss: 1.0600 - val_accuracy: 0.4583\n",
      "Epoch 157/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8938 - accuracy: 0.6199\n",
      "Epoch 157: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.8938 - accuracy: 0.6199 - val_loss: 1.0175 - val_accuracy: 0.5556\n",
      "Epoch 158/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9102 - accuracy: 0.6117\n",
      "Epoch 158: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9102 - accuracy: 0.6117 - val_loss: 1.0307 - val_accuracy: 0.5417\n",
      "Epoch 159/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9182 - accuracy: 0.5791\n",
      "Epoch 159: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9182 - accuracy: 0.5791 - val_loss: 1.1146 - val_accuracy: 0.5000\n",
      "Epoch 160/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8776 - accuracy: 0.6232\n",
      "Epoch 160: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8776 - accuracy: 0.6232 - val_loss: 1.0557 - val_accuracy: 0.4861\n",
      "Epoch 161/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9214 - accuracy: 0.5791\n",
      "Epoch 161: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9214 - accuracy: 0.5791 - val_loss: 1.2806 - val_accuracy: 0.4306\n",
      "Epoch 162/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9365 - accuracy: 0.5873\n",
      "Epoch 162: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.9365 - accuracy: 0.5873 - val_loss: 1.1235 - val_accuracy: 0.4028\n",
      "Epoch 163/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9070 - accuracy: 0.6378\n",
      "Epoch 163: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.9070 - accuracy: 0.6378 - val_loss: 1.1607 - val_accuracy: 0.4722\n",
      "Epoch 164/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9970 - accuracy: 0.5595\n",
      "Epoch 164: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9970 - accuracy: 0.5595 - val_loss: 1.1050 - val_accuracy: 0.4583\n",
      "Epoch 165/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8935 - accuracy: 0.5856\n",
      "Epoch 165: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.8935 - accuracy: 0.5856 - val_loss: 1.1698 - val_accuracy: 0.4722\n",
      "Epoch 166/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9695 - accuracy: 0.5628\n",
      "Epoch 166: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9695 - accuracy: 0.5628 - val_loss: 1.2483 - val_accuracy: 0.4167\n",
      "Epoch 167/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0184 - accuracy: 0.5383\n",
      "Epoch 167: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0184 - accuracy: 0.5383 - val_loss: 1.2551 - val_accuracy: 0.3750\n",
      "Epoch 168/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9268 - accuracy: 0.5661\n",
      "Epoch 168: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9268 - accuracy: 0.5661 - val_loss: 1.0605 - val_accuracy: 0.5139\n",
      "Epoch 169/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8867 - accuracy: 0.6134\n",
      "Epoch 169: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8867 - accuracy: 0.6134 - val_loss: 1.1301 - val_accuracy: 0.4722\n",
      "Epoch 170/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8675 - accuracy: 0.6281\n",
      "Epoch 170: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8675 - accuracy: 0.6281 - val_loss: 1.1648 - val_accuracy: 0.5139\n",
      "Epoch 171/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8927 - accuracy: 0.6183\n",
      "Epoch 171: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8927 - accuracy: 0.6183 - val_loss: 1.2639 - val_accuracy: 0.4583\n",
      "Epoch 172/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8907 - accuracy: 0.5954\n",
      "Epoch 172: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.8907 - accuracy: 0.5954 - val_loss: 1.1112 - val_accuracy: 0.5278\n",
      "Epoch 173/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9028 - accuracy: 0.5954\n",
      "Epoch 173: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9028 - accuracy: 0.5954 - val_loss: 1.0988 - val_accuracy: 0.5278\n",
      "Epoch 174/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9037 - accuracy: 0.6052\n",
      "Epoch 174: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9037 - accuracy: 0.6052 - val_loss: 1.1171 - val_accuracy: 0.4583\n",
      "Epoch 175/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9199 - accuracy: 0.5661\n",
      "Epoch 175: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9199 - accuracy: 0.5661 - val_loss: 1.1294 - val_accuracy: 0.5000\n",
      "Epoch 176/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.8932 - accuracy: 0.5905\n",
      "Epoch 176: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8943 - accuracy: 0.5922 - val_loss: 1.1548 - val_accuracy: 0.4028\n",
      "Epoch 177/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8857 - accuracy: 0.6003\n",
      "Epoch 177: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8857 - accuracy: 0.6003 - val_loss: 1.1116 - val_accuracy: 0.4722\n",
      "Epoch 178/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9137 - accuracy: 0.5775\n",
      "Epoch 178: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9137 - accuracy: 0.5775 - val_loss: 1.1409 - val_accuracy: 0.4306\n",
      "Epoch 179/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9234 - accuracy: 0.5661\n",
      "Epoch 179: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9234 - accuracy: 0.5661 - val_loss: 1.0487 - val_accuracy: 0.4861\n",
      "Epoch 180/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9123 - accuracy: 0.5824\n",
      "Epoch 180: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9123 - accuracy: 0.5824 - val_loss: 1.2336 - val_accuracy: 0.4583\n",
      "Epoch 181/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9577 - accuracy: 0.5628\n",
      "Epoch 181: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9577 - accuracy: 0.5628 - val_loss: 1.0831 - val_accuracy: 0.5000\n",
      "Epoch 182/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9028 - accuracy: 0.6150\n",
      "Epoch 182: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9028 - accuracy: 0.6150 - val_loss: 1.1432 - val_accuracy: 0.4722\n",
      "Epoch 183/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8860 - accuracy: 0.5971\n",
      "Epoch 183: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8860 - accuracy: 0.5971 - val_loss: 1.1551 - val_accuracy: 0.4583\n",
      "Epoch 184/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9632 - accuracy: 0.5726\n",
      "Epoch 184: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9632 - accuracy: 0.5726 - val_loss: 1.1798 - val_accuracy: 0.4444\n",
      "Epoch 185/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8764 - accuracy: 0.6101\n",
      "Epoch 185: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8764 - accuracy: 0.6101 - val_loss: 1.1270 - val_accuracy: 0.4722\n",
      "Epoch 186/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8847 - accuracy: 0.6117\n",
      "Epoch 186: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8847 - accuracy: 0.6117 - val_loss: 1.2242 - val_accuracy: 0.3889\n",
      "Epoch 187/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9049 - accuracy: 0.6069\n",
      "Epoch 187: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9049 - accuracy: 0.6069 - val_loss: 1.1004 - val_accuracy: 0.4167\n",
      "Epoch 188/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8718 - accuracy: 0.5922\n",
      "Epoch 188: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8718 - accuracy: 0.5922 - val_loss: 1.0602 - val_accuracy: 0.4583\n",
      "Epoch 189/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9433 - accuracy: 0.5595\n",
      "Epoch 189: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9433 - accuracy: 0.5595 - val_loss: 1.0945 - val_accuracy: 0.4583\n",
      "Epoch 190/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9024 - accuracy: 0.5987\n",
      "Epoch 190: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9024 - accuracy: 0.5987 - val_loss: 1.0334 - val_accuracy: 0.5694\n",
      "Epoch 191/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9535 - accuracy: 0.5710\n",
      "Epoch 191: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9535 - accuracy: 0.5710 - val_loss: 1.1143 - val_accuracy: 0.5000\n",
      "Epoch 192/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9034 - accuracy: 0.6052\n",
      "Epoch 192: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9034 - accuracy: 0.6052 - val_loss: 1.1297 - val_accuracy: 0.5000\n",
      "Epoch 193/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8810 - accuracy: 0.6215\n",
      "Epoch 193: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8810 - accuracy: 0.6215 - val_loss: 1.1159 - val_accuracy: 0.5000\n",
      "Epoch 194/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9099 - accuracy: 0.5987\n",
      "Epoch 194: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9099 - accuracy: 0.5987 - val_loss: 1.4700 - val_accuracy: 0.3472\n",
      "Epoch 195/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9354 - accuracy: 0.5693\n",
      "Epoch 195: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.9354 - accuracy: 0.5693 - val_loss: 1.0430 - val_accuracy: 0.4167\n",
      "Epoch 196/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8981 - accuracy: 0.6020\n",
      "Epoch 196: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.8981 - accuracy: 0.6020 - val_loss: 1.0369 - val_accuracy: 0.4722\n",
      "Epoch 197/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0969 - accuracy: 0.5041\n",
      "Epoch 197: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0969 - accuracy: 0.5041 - val_loss: 1.1133 - val_accuracy: 0.4583\n",
      "Epoch 198/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0288 - accuracy: 0.5546\n",
      "Epoch 198: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0288 - accuracy: 0.5546 - val_loss: 1.1161 - val_accuracy: 0.4583\n",
      "Epoch 199/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8883 - accuracy: 0.6085\n",
      "Epoch 199: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8883 - accuracy: 0.6085 - val_loss: 1.0821 - val_accuracy: 0.5000\n",
      "Epoch 200/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8789 - accuracy: 0.6069\n",
      "Epoch 200: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8789 - accuracy: 0.6069 - val_loss: 1.1538 - val_accuracy: 0.5278\n",
      "Epoch 201/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8933 - accuracy: 0.6362\n",
      "Epoch 201: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8933 - accuracy: 0.6362 - val_loss: 1.0618 - val_accuracy: 0.5139\n",
      "Epoch 202/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8677 - accuracy: 0.6215\n",
      "Epoch 202: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8677 - accuracy: 0.6215 - val_loss: 1.1051 - val_accuracy: 0.4722\n",
      "Epoch 203/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9131 - accuracy: 0.5954\n",
      "Epoch 203: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9131 - accuracy: 0.5954 - val_loss: 1.0367 - val_accuracy: 0.5139\n",
      "Epoch 204/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8936 - accuracy: 0.6183\n",
      "Epoch 204: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8936 - accuracy: 0.6183 - val_loss: 1.1744 - val_accuracy: 0.5139\n",
      "Epoch 205/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9216 - accuracy: 0.5873\n",
      "Epoch 205: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9216 - accuracy: 0.5873 - val_loss: 0.9882 - val_accuracy: 0.5278\n",
      "Epoch 206/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9140 - accuracy: 0.5922\n",
      "Epoch 206: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9140 - accuracy: 0.5922 - val_loss: 1.1386 - val_accuracy: 0.4167\n",
      "Epoch 207/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9213 - accuracy: 0.5905\n",
      "Epoch 207: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9213 - accuracy: 0.5905 - val_loss: 1.0944 - val_accuracy: 0.5278\n",
      "Epoch 208/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9529 - accuracy: 0.5710\n",
      "Epoch 208: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9529 - accuracy: 0.5710 - val_loss: 1.0193 - val_accuracy: 0.4861\n",
      "Epoch 209/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9201 - accuracy: 0.5938\n",
      "Epoch 209: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9201 - accuracy: 0.5938 - val_loss: 0.9643 - val_accuracy: 0.5000\n",
      "Epoch 210/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9565 - accuracy: 0.5677\n",
      "Epoch 210: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9565 - accuracy: 0.5677 - val_loss: 1.2442 - val_accuracy: 0.4861\n",
      "Epoch 211/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9228 - accuracy: 0.5840\n",
      "Epoch 211: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9228 - accuracy: 0.5840 - val_loss: 1.2911 - val_accuracy: 0.4306\n",
      "Epoch 212/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0089 - accuracy: 0.5432\n",
      "Epoch 212: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 1.0089 - accuracy: 0.5432 - val_loss: 1.1772 - val_accuracy: 0.4861\n",
      "Epoch 213/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0010 - accuracy: 0.5530\n",
      "Epoch 213: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 1.0010 - accuracy: 0.5530 - val_loss: 1.1273 - val_accuracy: 0.4861\n",
      "Epoch 214/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8919 - accuracy: 0.5938\n",
      "Epoch 214: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8919 - accuracy: 0.5938 - val_loss: 1.0808 - val_accuracy: 0.4722\n",
      "Epoch 215/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8837 - accuracy: 0.6117\n",
      "Epoch 215: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8837 - accuracy: 0.6117 - val_loss: 1.1219 - val_accuracy: 0.4861\n",
      "Epoch 216/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8591 - accuracy: 0.6346\n",
      "Epoch 216: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8591 - accuracy: 0.6346 - val_loss: 1.0819 - val_accuracy: 0.5000\n",
      "Epoch 217/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8837 - accuracy: 0.6117\n",
      "Epoch 217: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8837 - accuracy: 0.6117 - val_loss: 1.0918 - val_accuracy: 0.5417\n",
      "Epoch 218/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9185 - accuracy: 0.5954\n",
      "Epoch 218: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9185 - accuracy: 0.5954 - val_loss: 1.1993 - val_accuracy: 0.4722\n",
      "Epoch 219/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9560 - accuracy: 0.5644\n",
      "Epoch 219: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9560 - accuracy: 0.5644 - val_loss: 1.0054 - val_accuracy: 0.5417\n",
      "Epoch 220/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9045 - accuracy: 0.6052\n",
      "Epoch 220: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9045 - accuracy: 0.6052 - val_loss: 0.9353 - val_accuracy: 0.5417\n",
      "Epoch 221/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9866 - accuracy: 0.5595\n",
      "Epoch 221: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9866 - accuracy: 0.5595 - val_loss: 1.2936 - val_accuracy: 0.3750\n",
      "Epoch 222/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9431 - accuracy: 0.5971\n",
      "Epoch 222: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9431 - accuracy: 0.5971 - val_loss: 1.0874 - val_accuracy: 0.4722\n",
      "Epoch 223/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8950 - accuracy: 0.6020\n",
      "Epoch 223: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.8950 - accuracy: 0.6020 - val_loss: 1.0656 - val_accuracy: 0.4583\n",
      "Epoch 224/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9490 - accuracy: 0.5710\n",
      "Epoch 224: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9490 - accuracy: 0.5710 - val_loss: 1.1072 - val_accuracy: 0.4028\n",
      "Epoch 225/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8855 - accuracy: 0.5856\n",
      "Epoch 225: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8855 - accuracy: 0.5856 - val_loss: 1.0444 - val_accuracy: 0.4306\n",
      "Epoch 226/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9396 - accuracy: 0.5808\n",
      "Epoch 226: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 133ms/step - loss: 0.9396 - accuracy: 0.5808 - val_loss: 1.0851 - val_accuracy: 0.5000\n",
      "Epoch 227/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9785 - accuracy: 0.5612\n",
      "Epoch 227: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9785 - accuracy: 0.5612 - val_loss: 1.0245 - val_accuracy: 0.5000\n",
      "Epoch 228/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9019 - accuracy: 0.6134\n",
      "Epoch 228: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9019 - accuracy: 0.6134 - val_loss: 1.1226 - val_accuracy: 0.5139\n",
      "Epoch 229/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8665 - accuracy: 0.6101\n",
      "Epoch 229: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8665 - accuracy: 0.6101 - val_loss: 1.2202 - val_accuracy: 0.4444\n",
      "Epoch 230/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9225 - accuracy: 0.5840\n",
      "Epoch 230: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9225 - accuracy: 0.5840 - val_loss: 1.0536 - val_accuracy: 0.5000\n",
      "Epoch 231/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9029 - accuracy: 0.5971\n",
      "Epoch 231: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9029 - accuracy: 0.5971 - val_loss: 1.0509 - val_accuracy: 0.5139\n",
      "Epoch 232/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9095 - accuracy: 0.5628\n",
      "Epoch 232: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9095 - accuracy: 0.5628 - val_loss: 1.2917 - val_accuracy: 0.4167\n",
      "Epoch 233/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8903 - accuracy: 0.5840\n",
      "Epoch 233: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8903 - accuracy: 0.5840 - val_loss: 1.0789 - val_accuracy: 0.5278\n",
      "Epoch 234/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8523 - accuracy: 0.6281\n",
      "Epoch 234: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8523 - accuracy: 0.6281 - val_loss: 1.1999 - val_accuracy: 0.4167\n",
      "Epoch 235/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9301 - accuracy: 0.5742\n",
      "Epoch 235: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9301 - accuracy: 0.5742 - val_loss: 1.0516 - val_accuracy: 0.5556\n",
      "Epoch 236/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8948 - accuracy: 0.6036\n",
      "Epoch 236: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.8948 - accuracy: 0.6036 - val_loss: 1.0475 - val_accuracy: 0.5278\n",
      "Epoch 237/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8768 - accuracy: 0.5922\n",
      "Epoch 237: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.8768 - accuracy: 0.5922 - val_loss: 1.2253 - val_accuracy: 0.4722\n",
      "Epoch 238/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9046 - accuracy: 0.5791\n",
      "Epoch 238: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9046 - accuracy: 0.5791 - val_loss: 1.2405 - val_accuracy: 0.5694\n",
      "Epoch 239/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8735 - accuracy: 0.6248\n",
      "Epoch 239: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8735 - accuracy: 0.6248 - val_loss: 1.0052 - val_accuracy: 0.5000\n",
      "Epoch 240/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8868 - accuracy: 0.5922\n",
      "Epoch 240: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.8868 - accuracy: 0.5922 - val_loss: 1.0417 - val_accuracy: 0.4306\n",
      "Epoch 241/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8998 - accuracy: 0.5775\n",
      "Epoch 241: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8998 - accuracy: 0.5775 - val_loss: 1.1064 - val_accuracy: 0.4028\n",
      "Epoch 242/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8640 - accuracy: 0.5889\n",
      "Epoch 242: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8640 - accuracy: 0.5889 - val_loss: 1.1483 - val_accuracy: 0.3889\n",
      "Epoch 243/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9272 - accuracy: 0.5889\n",
      "Epoch 243: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9272 - accuracy: 0.5889 - val_loss: 1.1902 - val_accuracy: 0.4722\n",
      "Epoch 244/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9547 - accuracy: 0.5432\n",
      "Epoch 244: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9547 - accuracy: 0.5432 - val_loss: 1.0396 - val_accuracy: 0.5000\n",
      "Epoch 245/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9101 - accuracy: 0.5873\n",
      "Epoch 245: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9101 - accuracy: 0.5873 - val_loss: 1.0514 - val_accuracy: 0.4444\n",
      "Epoch 246/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8952 - accuracy: 0.5824\n",
      "Epoch 246: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.8952 - accuracy: 0.5824 - val_loss: 1.1243 - val_accuracy: 0.5278\n",
      "Epoch 247/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9014 - accuracy: 0.5840\n",
      "Epoch 247: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.9014 - accuracy: 0.5840 - val_loss: 1.1012 - val_accuracy: 0.4722\n",
      "Epoch 248/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8870 - accuracy: 0.6052\n",
      "Epoch 248: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8870 - accuracy: 0.6052 - val_loss: 1.0165 - val_accuracy: 0.5556\n",
      "Epoch 249/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8817 - accuracy: 0.6052\n",
      "Epoch 249: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 130ms/step - loss: 0.8817 - accuracy: 0.6052 - val_loss: 1.1536 - val_accuracy: 0.4444\n",
      "Epoch 250/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9510 - accuracy: 0.5677\n",
      "Epoch 250: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9510 - accuracy: 0.5677 - val_loss: 1.3330 - val_accuracy: 0.4167\n",
      "Epoch 251/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8654 - accuracy: 0.5987\n",
      "Epoch 251: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.8654 - accuracy: 0.5987 - val_loss: 1.1780 - val_accuracy: 0.4583\n",
      "Epoch 252/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9083 - accuracy: 0.5873\n",
      "Epoch 252: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9083 - accuracy: 0.5873 - val_loss: 1.3263 - val_accuracy: 0.4167\n",
      "Epoch 253/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0046 - accuracy: 0.5318\n",
      "Epoch 253: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 1.0046 - accuracy: 0.5318 - val_loss: 1.2044 - val_accuracy: 0.4167\n",
      "Epoch 254/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9564 - accuracy: 0.5856\n",
      "Epoch 254: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9564 - accuracy: 0.5856 - val_loss: 1.3285 - val_accuracy: 0.4444\n",
      "Epoch 255/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9349 - accuracy: 0.5465\n",
      "Epoch 255: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 131ms/step - loss: 0.9349 - accuracy: 0.5465 - val_loss: 1.1556 - val_accuracy: 0.4444\n",
      "Epoch 256/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9262 - accuracy: 0.5628\n",
      "Epoch 256: saving model to savedModel\\LeNet.ckpt\n",
      "39/39 [==============================] - 5s 132ms/step - loss: 0.9262 - accuracy: 0.5628 - val_loss: 1.0480 - val_accuracy: 0.4861\n"
     ]
    }
   ],
   "source": [
    "leNet.fit(train_data, validation_data, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9423 - accuracy: 0.5742\n",
      "Epoch 1: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 10s 166ms/step - loss: 0.9423 - accuracy: 0.5742 - val_loss: 1.4509 - val_accuracy: 0.3194\n",
      "Epoch 2/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6439 - accuracy: 0.7504\n",
      "Epoch 2: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.6439 - accuracy: 0.7504 - val_loss: 1.4085 - val_accuracy: 0.3194\n",
      "Epoch 3/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4189 - accuracy: 0.8467\n",
      "Epoch 3: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.4189 - accuracy: 0.8467 - val_loss: 1.3925 - val_accuracy: 0.3194\n",
      "Epoch 4/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3190 - accuracy: 0.8907\n",
      "Epoch 4: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.3190 - accuracy: 0.8907 - val_loss: 1.4799 - val_accuracy: 0.3194\n",
      "Epoch 5/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2480 - accuracy: 0.9070\n",
      "Epoch 5: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.2480 - accuracy: 0.9070 - val_loss: 1.4563 - val_accuracy: 0.3194\n",
      "Epoch 6/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.1959 - accuracy: 0.9364\n",
      "Epoch 6: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.1959 - accuracy: 0.9364 - val_loss: 1.4575 - val_accuracy: 0.2917\n",
      "Epoch 7/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.1192 - accuracy: 0.9674\n",
      "Epoch 7: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.1192 - accuracy: 0.9674 - val_loss: 1.5292 - val_accuracy: 0.3194\n",
      "Epoch 8/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.1203 - accuracy: 0.9625\n",
      "Epoch 8: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.1203 - accuracy: 0.9625 - val_loss: 1.6109 - val_accuracy: 0.1944\n",
      "Epoch 9/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0867 - accuracy: 0.9788\n",
      "Epoch 9: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.0867 - accuracy: 0.9788 - val_loss: 1.5337 - val_accuracy: 0.2361\n",
      "Epoch 10/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0686 - accuracy: 0.9755\n",
      "Epoch 10: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0686 - accuracy: 0.9755 - val_loss: 1.7838 - val_accuracy: 0.2083\n",
      "Epoch 11/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0912 - accuracy: 0.9625\n",
      "Epoch 11: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0912 - accuracy: 0.9625 - val_loss: 1.9740 - val_accuracy: 0.2083\n",
      "Epoch 12/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9723\n",
      "Epoch 12: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0924 - accuracy: 0.9723 - val_loss: 1.8216 - val_accuracy: 0.1944\n",
      "Epoch 13/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0502 - accuracy: 0.9886\n",
      "Epoch 13: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0502 - accuracy: 0.9886 - val_loss: 1.8994 - val_accuracy: 0.2083\n",
      "Epoch 14/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0910 - accuracy: 0.9690\n",
      "Epoch 14: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0910 - accuracy: 0.9690 - val_loss: 2.1108 - val_accuracy: 0.2083\n",
      "Epoch 15/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0632 - accuracy: 0.9837\n",
      "Epoch 15: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0632 - accuracy: 0.9837 - val_loss: 2.3750 - val_accuracy: 0.2222\n",
      "Epoch 16/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0412 - accuracy: 0.9869\n",
      "Epoch 16: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0412 - accuracy: 0.9869 - val_loss: 1.8906 - val_accuracy: 0.2500\n",
      "Epoch 17/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0480 - accuracy: 0.9837\n",
      "Epoch 17: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0480 - accuracy: 0.9837 - val_loss: 1.4049 - val_accuracy: 0.4028\n",
      "Epoch 18/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0312 - accuracy: 0.9918\n",
      "Epoch 18: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0312 - accuracy: 0.9918 - val_loss: 2.2162 - val_accuracy: 0.2778\n",
      "Epoch 19/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0383 - accuracy: 0.9886\n",
      "Epoch 19: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0383 - accuracy: 0.9886 - val_loss: 1.9134 - val_accuracy: 0.3611\n",
      "Epoch 20/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0271 - accuracy: 0.9918\n",
      "Epoch 20: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0271 - accuracy: 0.9918 - val_loss: 2.0820 - val_accuracy: 0.3750\n",
      "Epoch 21/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0419 - accuracy: 0.9918\n",
      "Epoch 21: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0419 - accuracy: 0.9918 - val_loss: 3.1691 - val_accuracy: 0.3194\n",
      "Epoch 22/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0557 - accuracy: 0.9804\n",
      "Epoch 22: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0557 - accuracy: 0.9804 - val_loss: 3.4061 - val_accuracy: 0.3472\n",
      "Epoch 23/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0706 - accuracy: 0.9723\n",
      "Epoch 23: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0706 - accuracy: 0.9723 - val_loss: 1.7305 - val_accuracy: 0.4583\n",
      "Epoch 24/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0605 - accuracy: 0.9804\n",
      "Epoch 24: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0605 - accuracy: 0.9804 - val_loss: 3.0049 - val_accuracy: 0.4861\n",
      "Epoch 25/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0263 - accuracy: 0.9902\n",
      "Epoch 25: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0263 - accuracy: 0.9902 - val_loss: 2.1611 - val_accuracy: 0.5556\n",
      "Epoch 26/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0744 - accuracy: 0.9706\n",
      "Epoch 26: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0744 - accuracy: 0.9706 - val_loss: 1.8192 - val_accuracy: 0.5972\n",
      "Epoch 27/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0230 - accuracy: 0.9935\n",
      "Epoch 27: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0230 - accuracy: 0.9935 - val_loss: 1.2001 - val_accuracy: 0.6944\n",
      "Epoch 28/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0527 - accuracy: 0.9821\n",
      "Epoch 28: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0527 - accuracy: 0.9821 - val_loss: 1.7825 - val_accuracy: 0.6250\n",
      "Epoch 29/256\n",
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      "Epoch 29: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0160 - accuracy: 0.9984 - val_loss: 1.1430 - val_accuracy: 0.7361\n",
      "Epoch 30/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0196 - accuracy: 0.9935\n",
      "Epoch 30: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0196 - accuracy: 0.9935 - val_loss: 0.6357 - val_accuracy: 0.8889\n",
      "Epoch 31/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0327 - accuracy: 0.9853\n",
      "Epoch 31: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0327 - accuracy: 0.9853 - val_loss: 0.6385 - val_accuracy: 0.8611\n",
      "Epoch 32/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0507 - accuracy: 0.9886\n",
      "Epoch 32: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0507 - accuracy: 0.9886 - val_loss: 0.3739 - val_accuracy: 0.9028\n",
      "Epoch 33/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0331 - accuracy: 0.9869\n",
      "Epoch 33: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0331 - accuracy: 0.9869 - val_loss: 0.4253 - val_accuracy: 0.9306\n",
      "Epoch 34/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0163 - accuracy: 0.9967\n",
      "Epoch 34: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0163 - accuracy: 0.9967 - val_loss: 0.4117 - val_accuracy: 0.9028\n",
      "Epoch 35/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0253 - accuracy: 0.9886\n",
      "Epoch 35: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0253 - accuracy: 0.9886 - val_loss: 0.5151 - val_accuracy: 0.9167\n",
      "Epoch 36/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0244 - accuracy: 0.9918\n",
      "Epoch 36: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0244 - accuracy: 0.9918 - val_loss: 0.5409 - val_accuracy: 0.9306\n",
      "Epoch 37/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0325 - accuracy: 0.9853\n",
      "Epoch 37: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0325 - accuracy: 0.9853 - val_loss: 0.5883 - val_accuracy: 0.8056\n",
      "Epoch 38/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0103 - accuracy: 0.9984\n",
      "Epoch 38: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0103 - accuracy: 0.9984 - val_loss: 0.5073 - val_accuracy: 0.9028\n",
      "Epoch 39/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0119 - accuracy: 0.9967\n",
      "Epoch 39: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0119 - accuracy: 0.9967 - val_loss: 0.5571 - val_accuracy: 0.9028\n",
      "Epoch 40/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0109 - accuracy: 0.9984\n",
      "Epoch 40: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0109 - accuracy: 0.9984 - val_loss: 0.6335 - val_accuracy: 0.9167\n",
      "Epoch 41/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0194 - accuracy: 0.9918\n",
      "Epoch 41: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0194 - accuracy: 0.9918 - val_loss: 0.5894 - val_accuracy: 0.9167\n",
      "Epoch 42/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0138 - accuracy: 0.9951\n",
      "Epoch 42: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0138 - accuracy: 0.9951 - val_loss: 0.4975 - val_accuracy: 0.9028\n",
      "Epoch 43/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0098 - accuracy: 0.9984\n",
      "Epoch 43: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0098 - accuracy: 0.9984 - val_loss: 0.3926 - val_accuracy: 0.9028\n",
      "Epoch 44/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0139 - accuracy: 0.9935\n",
      "Epoch 44: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0139 - accuracy: 0.9935 - val_loss: 0.4236 - val_accuracy: 0.8611\n",
      "Epoch 45/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0106 - accuracy: 0.9967\n",
      "Epoch 45: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0106 - accuracy: 0.9967 - val_loss: 0.3732 - val_accuracy: 0.9167\n",
      "Epoch 46/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0070 - accuracy: 0.9984\n",
      "Epoch 46: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0070 - accuracy: 0.9984 - val_loss: 0.4357 - val_accuracy: 0.8889\n",
      "Epoch 47/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0130 - accuracy: 0.9967\n",
      "Epoch 47: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0130 - accuracy: 0.9967 - val_loss: 0.3959 - val_accuracy: 0.9306\n",
      "Epoch 48/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0278 - accuracy: 0.9902\n",
      "Epoch 48: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0278 - accuracy: 0.9902 - val_loss: 0.3840 - val_accuracy: 0.9167\n",
      "Epoch 49/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0109 - accuracy: 0.9967\n",
      "Epoch 49: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 0.4515 - val_accuracy: 0.9167\n",
      "Epoch 50/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0104 - accuracy: 0.9967\n",
      "Epoch 50: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0104 - accuracy: 0.9967 - val_loss: 0.3831 - val_accuracy: 0.9167\n",
      "Epoch 51/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0199 - accuracy: 0.9935\n",
      "Epoch 51: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0199 - accuracy: 0.9935 - val_loss: 0.6214 - val_accuracy: 0.8056\n",
      "Epoch 52/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0258 - accuracy: 0.9918\n",
      "Epoch 52: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0258 - accuracy: 0.9918 - val_loss: 0.4915 - val_accuracy: 0.9167\n",
      "Epoch 53/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0238 - accuracy: 0.9902\n",
      "Epoch 53: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0238 - accuracy: 0.9902 - val_loss: 0.5681 - val_accuracy: 0.8889\n",
      "Epoch 54/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0127 - accuracy: 0.9967\n",
      "Epoch 54: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 0.5024 - val_accuracy: 0.9444\n",
      "Epoch 55/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0107 - accuracy: 0.9967\n",
      "Epoch 55: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0107 - accuracy: 0.9967 - val_loss: 0.4186 - val_accuracy: 0.9444\n",
      "Epoch 56/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0241 - accuracy: 0.9902\n",
      "Epoch 56: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0241 - accuracy: 0.9902 - val_loss: 0.6152 - val_accuracy: 0.8611\n",
      "Epoch 57/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 0.9902\n",
      "Epoch 57: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0257 - accuracy: 0.9902 - val_loss: 0.6223 - val_accuracy: 0.8472\n",
      "Epoch 58/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0274 - accuracy: 0.9902\n",
      "Epoch 58: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0274 - accuracy: 0.9902 - val_loss: 0.2542 - val_accuracy: 0.9306\n",
      "Epoch 59/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0130 - accuracy: 0.9935\n",
      "Epoch 59: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0130 - accuracy: 0.9935 - val_loss: 0.2236 - val_accuracy: 0.9306\n",
      "Epoch 60/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0096 - accuracy: 0.9967\n",
      "Epoch 60: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0096 - accuracy: 0.9967 - val_loss: 0.3151 - val_accuracy: 0.9167\n",
      "Epoch 61/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0160 - accuracy: 0.9918\n",
      "Epoch 61: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 134ms/step - loss: 0.0160 - accuracy: 0.9918 - val_loss: 0.3025 - val_accuracy: 0.9444\n",
      "Epoch 62/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0076 - accuracy: 0.9967\n",
      "Epoch 62: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0076 - accuracy: 0.9967 - val_loss: 0.5813 - val_accuracy: 0.9306\n",
      "Epoch 63/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.9967\n",
      "Epoch 63: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0058 - accuracy: 0.9967 - val_loss: 0.6097 - val_accuracy: 0.9306\n",
      "Epoch 64/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9984\n",
      "Epoch 64: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.4834 - val_accuracy: 0.9444\n",
      "Epoch 65/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0097 - accuracy: 0.9951\n",
      "Epoch 65: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0098 - accuracy: 0.9951 - val_loss: 0.5194 - val_accuracy: 0.9444\n",
      "Epoch 66/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9984\n",
      "Epoch 66: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.5309 - val_accuracy: 0.9306\n",
      "Epoch 67/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9984\n",
      "Epoch 67: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.0064 - accuracy: 0.9984 - val_loss: 0.4414 - val_accuracy: 0.9306\n",
      "Epoch 68/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0045 - accuracy: 1.0000\n",
      "Epoch 68: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 135ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.4150 - val_accuracy: 0.9028\n",
      "Epoch 69/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0049 - accuracy: 0.9967\n",
      "Epoch 69: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0093 - accuracy: 0.9935 - val_loss: 3.3504 - val_accuracy: 0.5694\n",
      "Epoch 70/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0476 - accuracy: 0.9804\n",
      "Epoch 70: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0476 - accuracy: 0.9804 - val_loss: 0.4685 - val_accuracy: 0.8611\n",
      "Epoch 71/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0118 - accuracy: 0.9967\n",
      "Epoch 71: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0118 - accuracy: 0.9967 - val_loss: 0.3478 - val_accuracy: 0.9306\n",
      "Epoch 72/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0104 - accuracy: 0.9967\n",
      "Epoch 72: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0104 - accuracy: 0.9967 - val_loss: 0.3658 - val_accuracy: 0.9028\n",
      "Epoch 73/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0080 - accuracy: 0.9967\n",
      "Epoch 73: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0080 - accuracy: 0.9967 - val_loss: 0.4521 - val_accuracy: 0.9306\n",
      "Epoch 74/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0092 - accuracy: 0.9984\n",
      "Epoch 74: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0092 - accuracy: 0.9984 - val_loss: 0.4012 - val_accuracy: 0.9306\n",
      "Epoch 75/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0548 - accuracy: 0.9869\n",
      "Epoch 75: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0548 - accuracy: 0.9869 - val_loss: 0.7854 - val_accuracy: 0.8194\n",
      "Epoch 76/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0181 - accuracy: 0.9951\n",
      "Epoch 76: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0181 - accuracy: 0.9951 - val_loss: 0.4412 - val_accuracy: 0.9306\n",
      "Epoch 77/256\n",
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      "Epoch 77: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0082 - accuracy: 0.9984 - val_loss: 0.3812 - val_accuracy: 0.9583\n",
      "Epoch 78/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9984\n",
      "Epoch 78: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 148ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.3609 - val_accuracy: 0.9583\n",
      "Epoch 79/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0094 - accuracy: 0.9951\n",
      "Epoch 79: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0096 - accuracy: 0.9951 - val_loss: 0.4367 - val_accuracy: 0.9444\n",
      "Epoch 80/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0081 - accuracy: 0.9967\n",
      "Epoch 80: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0081 - accuracy: 0.9967 - val_loss: 0.5852 - val_accuracy: 0.9444\n",
      "Epoch 81/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0097 - accuracy: 0.9984\n",
      "Epoch 81: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0097 - accuracy: 0.9984 - val_loss: 0.6526 - val_accuracy: 0.9306\n",
      "Epoch 82/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0108 - accuracy: 0.9951\n",
      "Epoch 82: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0108 - accuracy: 0.9951 - val_loss: 0.5962 - val_accuracy: 0.9306\n",
      "Epoch 83/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0087 - accuracy: 0.9984\n",
      "Epoch 83: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0087 - accuracy: 0.9984 - val_loss: 0.5679 - val_accuracy: 0.9306\n",
      "Epoch 84/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0112 - accuracy: 0.9951\n",
      "Epoch 84: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0112 - accuracy: 0.9951 - val_loss: 0.6615 - val_accuracy: 0.9306\n",
      "Epoch 85/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0083 - accuracy: 0.9967\n",
      "Epoch 85: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0083 - accuracy: 0.9967 - val_loss: 0.6441 - val_accuracy: 0.9306\n",
      "Epoch 86/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0246 - accuracy: 0.9853\n",
      "Epoch 86: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0246 - accuracy: 0.9853 - val_loss: 0.8986 - val_accuracy: 0.7778\n",
      "Epoch 87/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0053 - accuracy: 1.0000\n",
      "Epoch 87: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0166 - accuracy: 0.9967 - val_loss: 2.5576 - val_accuracy: 0.5278\n",
      "Epoch 88/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0450 - accuracy: 0.9853\n",
      "Epoch 88: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0450 - accuracy: 0.9853 - val_loss: 0.4112 - val_accuracy: 0.8472\n",
      "Epoch 89/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0082 - accuracy: 0.9984\n",
      "Epoch 89: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0082 - accuracy: 0.9984 - val_loss: 0.3068 - val_accuracy: 0.9306\n",
      "Epoch 90/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0171 - accuracy: 0.9951\n",
      "Epoch 90: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0171 - accuracy: 0.9951 - val_loss: 0.4007 - val_accuracy: 0.9167\n",
      "Epoch 91/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0387 - accuracy: 0.9869\n",
      "Epoch 91: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0387 - accuracy: 0.9869 - val_loss: 0.4378 - val_accuracy: 0.9028\n",
      "Epoch 92/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0109 - accuracy: 0.9967\n",
      "Epoch 92: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 0.5131 - val_accuracy: 0.8889\n",
      "Epoch 93/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0101 - accuracy: 0.9984\n",
      "Epoch 93: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0101 - accuracy: 0.9984 - val_loss: 0.5801 - val_accuracy: 0.9167\n",
      "Epoch 94/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9951\n",
      "Epoch 94: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0113 - accuracy: 0.9951 - val_loss: 0.4960 - val_accuracy: 0.9444\n",
      "Epoch 95/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0126 - accuracy: 0.9967\n",
      "Epoch 95: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0126 - accuracy: 0.9967 - val_loss: 0.7811 - val_accuracy: 0.8750\n",
      "Epoch 96/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0217 - accuracy: 0.9935\n",
      "Epoch 96: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 148ms/step - loss: 0.0217 - accuracy: 0.9935 - val_loss: 0.4843 - val_accuracy: 0.9306\n",
      "Epoch 97/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0228 - accuracy: 0.9902\n",
      "Epoch 97: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0228 - accuracy: 0.9902 - val_loss: 0.6372 - val_accuracy: 0.9306\n",
      "Epoch 98/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.9967\n",
      "Epoch 98: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0145 - accuracy: 0.9967 - val_loss: 0.5541 - val_accuracy: 0.8889\n",
      "Epoch 99/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0115 - accuracy: 0.9967\n",
      "Epoch 99: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0115 - accuracy: 0.9967 - val_loss: 0.7324 - val_accuracy: 0.8889\n",
      "Epoch 100/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0069 - accuracy: 0.9984\n",
      "Epoch 100: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0069 - accuracy: 0.9984 - val_loss: 0.6297 - val_accuracy: 0.9444\n",
      "Epoch 101/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0086 - accuracy: 0.9967\n",
      "Epoch 101: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0086 - accuracy: 0.9967 - val_loss: 0.5138 - val_accuracy: 0.9444\n",
      "Epoch 102/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 102: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 1.4195 - val_accuracy: 0.5833\n",
      "Epoch 103/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0091 - accuracy: 0.9951\n",
      "Epoch 103: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0091 - accuracy: 0.9951 - val_loss: 0.5402 - val_accuracy: 0.9306\n",
      "Epoch 104/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0146 - accuracy: 0.9967\n",
      "Epoch 104: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0146 - accuracy: 0.9967 - val_loss: 0.4304 - val_accuracy: 0.9444\n",
      "Epoch 105/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9967\n",
      "Epoch 105: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0056 - accuracy: 0.9967 - val_loss: 0.4494 - val_accuracy: 0.9444\n",
      "Epoch 106/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0075 - accuracy: 0.9967\n",
      "Epoch 106: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0075 - accuracy: 0.9967 - val_loss: 0.4578 - val_accuracy: 0.9444\n",
      "Epoch 107/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0162 - accuracy: 0.9918\n",
      "Epoch 107: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0162 - accuracy: 0.9918 - val_loss: 0.4455 - val_accuracy: 0.9444\n",
      "Epoch 108/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9967\n",
      "Epoch 108: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0068 - accuracy: 0.9967 - val_loss: 0.4770 - val_accuracy: 0.9444\n",
      "Epoch 109/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0127 - accuracy: 0.9967\n",
      "Epoch 109: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 0.3453 - val_accuracy: 0.9444\n",
      "Epoch 110/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0076 - accuracy: 0.9951\n",
      "Epoch 110: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0076 - accuracy: 0.9951 - val_loss: 0.3604 - val_accuracy: 0.9444\n",
      "Epoch 111/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 111: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.5299 - val_accuracy: 0.9444\n",
      "Epoch 112/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 1.0000\n",
      "Epoch 112: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.5447 - val_accuracy: 0.9444\n",
      "Epoch 113/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9984\n",
      "Epoch 113: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0043 - accuracy: 0.9984 - val_loss: 0.5359 - val_accuracy: 0.9444\n",
      "Epoch 114/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0201 - accuracy: 0.9935\n",
      "Epoch 114: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0201 - accuracy: 0.9935 - val_loss: 0.6752 - val_accuracy: 0.8750\n",
      "Epoch 115/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0119 - accuracy: 0.9951\n",
      "Epoch 115: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 136ms/step - loss: 0.0119 - accuracy: 0.9951 - val_loss: 0.5150 - val_accuracy: 0.9444\n",
      "Epoch 116/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9984\n",
      "Epoch 116: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0064 - accuracy: 0.9984 - val_loss: 0.4621 - val_accuracy: 0.9444\n",
      "Epoch 117/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0077 - accuracy: 0.9967\n",
      "Epoch 117: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0077 - accuracy: 0.9967 - val_loss: 0.4433 - val_accuracy: 0.9444\n",
      "Epoch 118/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9967\n",
      "Epoch 118: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0050 - accuracy: 0.9967 - val_loss: 0.4635 - val_accuracy: 0.9444\n",
      "Epoch 119/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9984\n",
      "Epoch 119: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0039 - accuracy: 0.9984 - val_loss: 0.4560 - val_accuracy: 0.9444\n",
      "Epoch 120/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9984\n",
      "Epoch 120: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.5071 - val_accuracy: 0.9444\n",
      "Epoch 121/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.9967\n",
      "Epoch 121: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0058 - accuracy: 0.9967 - val_loss: 0.4720 - val_accuracy: 0.9444\n",
      "Epoch 122/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.9984\n",
      "Epoch 122: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0040 - accuracy: 0.9984 - val_loss: 0.4739 - val_accuracy: 0.9444\n",
      "Epoch 123/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 1.0000\n",
      "Epoch 123: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.4568 - val_accuracy: 0.9444\n",
      "Epoch 124/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9967\n",
      "Epoch 124: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0038 - accuracy: 0.9967 - val_loss: 0.4524 - val_accuracy: 0.9444\n",
      "Epoch 125/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0068 - accuracy: 0.9967\n",
      "Epoch 125: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0068 - accuracy: 0.9967 - val_loss: 0.4033 - val_accuracy: 0.9306\n",
      "Epoch 126/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9984\n",
      "Epoch 126: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0038 - accuracy: 0.9984 - val_loss: 0.4319 - val_accuracy: 0.9444\n",
      "Epoch 127/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 1.0000\n",
      "Epoch 127: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.4508 - val_accuracy: 0.9444\n",
      "Epoch 128/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9967\n",
      "Epoch 128: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0049 - accuracy: 0.9967 - val_loss: 0.4811 - val_accuracy: 0.9444\n",
      "Epoch 129/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9984\n",
      "Epoch 129: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0038 - accuracy: 0.9984 - val_loss: 0.5440 - val_accuracy: 0.9444\n",
      "Epoch 130/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 130: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.5231 - val_accuracy: 0.9444\n",
      "Epoch 131/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9967\n",
      "Epoch 131: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0047 - accuracy: 0.9967 - val_loss: 0.5706 - val_accuracy: 0.9444\n",
      "Epoch 132/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 132: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.5217 - val_accuracy: 0.9444\n",
      "Epoch 133/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9984\n",
      "Epoch 133: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0033 - accuracy: 0.9984 - val_loss: 0.5272 - val_accuracy: 0.9444\n",
      "Epoch 134/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9967\n",
      "Epoch 134: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0048 - accuracy: 0.9967 - val_loss: 0.5329 - val_accuracy: 0.9444\n",
      "Epoch 135/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0035 - accuracy: 0.9984\n",
      "Epoch 135: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0035 - accuracy: 0.9984 - val_loss: 0.4509 - val_accuracy: 0.9444\n",
      "Epoch 136/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9984\n",
      "Epoch 136: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0038 - accuracy: 0.9984 - val_loss: 0.4890 - val_accuracy: 0.9444\n",
      "Epoch 137/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0021 - accuracy: 1.0000\n",
      "Epoch 137: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.5059 - val_accuracy: 0.9444\n",
      "Epoch 138/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 1.0000\n",
      "Epoch 138: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.4880 - val_accuracy: 0.9444\n",
      "Epoch 139/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 0.9967\n",
      "Epoch 139: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0030 - accuracy: 0.9967 - val_loss: 0.5134 - val_accuracy: 0.9444\n",
      "Epoch 140/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 0.9967\n",
      "Epoch 140: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0037 - accuracy: 0.9967 - val_loss: 0.5154 - val_accuracy: 0.9444\n",
      "Epoch 141/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0061 - accuracy: 0.9951\n",
      "Epoch 141: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0061 - accuracy: 0.9951 - val_loss: 0.4239 - val_accuracy: 0.9306\n",
      "Epoch 142/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0040 - accuracy: 0.9984\n",
      "Epoch 142: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0040 - accuracy: 0.9984 - val_loss: 0.4380 - val_accuracy: 0.9444\n",
      "Epoch 143/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0032 - accuracy: 0.9984  \n",
      "Epoch 143: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0032 - accuracy: 0.9984 - val_loss: 0.4884 - val_accuracy: 0.9444\n",
      "Epoch 144/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 0.9869\n",
      "Epoch 144: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0257 - accuracy: 0.9869 - val_loss: 0.3762 - val_accuracy: 0.9167\n",
      "Epoch 145/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0144 - accuracy: 0.9935\n",
      "Epoch 145: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 141ms/step - loss: 0.0144 - accuracy: 0.9935 - val_loss: 0.2812 - val_accuracy: 0.9306\n",
      "Epoch 146/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0067 - accuracy: 0.9984\n",
      "Epoch 146: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 151ms/step - loss: 0.0067 - accuracy: 0.9984 - val_loss: 0.3038 - val_accuracy: 0.9167\n",
      "Epoch 147/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0301 - accuracy: 0.9886\n",
      "Epoch 147: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 150ms/step - loss: 0.0301 - accuracy: 0.9886 - val_loss: 0.5275 - val_accuracy: 0.8194\n",
      "Epoch 148/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0087 - accuracy: 0.9967\n",
      "Epoch 148: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.0087 - accuracy: 0.9967 - val_loss: 0.4019 - val_accuracy: 0.9444\n",
      "Epoch 149/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0053 - accuracy: 0.9984\n",
      "Epoch 149: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.4501 - val_accuracy: 0.9444\n",
      "Epoch 150/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9984\n",
      "Epoch 150: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 148ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.4879 - val_accuracy: 0.9444\n",
      "Epoch 151/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9984\n",
      "Epoch 151: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.4914 - val_accuracy: 0.9444\n",
      "Epoch 152/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0073 - accuracy: 0.9984\n",
      "Epoch 152: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0073 - accuracy: 0.9984 - val_loss: 0.3108 - val_accuracy: 0.9306\n",
      "Epoch 153/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9984\n",
      "Epoch 153: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0051 - accuracy: 0.9984 - val_loss: 0.4220 - val_accuracy: 0.9444\n",
      "Epoch 154/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9967\n",
      "Epoch 154: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0064 - accuracy: 0.9967 - val_loss: 0.4086 - val_accuracy: 0.9444\n",
      "Epoch 155/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9984\n",
      "Epoch 155: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0051 - accuracy: 0.9984 - val_loss: 0.4706 - val_accuracy: 0.9306\n",
      "Epoch 156/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9984\n",
      "Epoch 156: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.4759 - val_accuracy: 0.9444\n",
      "Epoch 157/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 1.0000\n",
      "Epoch 157: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.5129 - val_accuracy: 0.9444\n",
      "Epoch 158/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9984\n",
      "Epoch 158: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0044 - accuracy: 0.9984 - val_loss: 0.5437 - val_accuracy: 0.9444\n",
      "Epoch 159/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0020 - accuracy: 1.0000\n",
      "Epoch 159: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.5410 - val_accuracy: 0.9444\n",
      "Epoch 160/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0021 - accuracy: 1.0000\n",
      "Epoch 160: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.5335 - val_accuracy: 0.9444\n",
      "Epoch 161/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9984\n",
      "Epoch 161: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.5810 - val_accuracy: 0.9444\n",
      "Epoch 162/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0031 - accuracy: 0.9984\n",
      "Epoch 162: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0031 - accuracy: 0.9984 - val_loss: 0.5540 - val_accuracy: 0.9444\n",
      "Epoch 163/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 1.0000\n",
      "Epoch 163: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.5417 - val_accuracy: 0.9444\n",
      "Epoch 164/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0028 - accuracy: 0.9984\n",
      "Epoch 164: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0028 - accuracy: 0.9984 - val_loss: 0.5243 - val_accuracy: 0.9444\n",
      "Epoch 165/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9984\n",
      "Epoch 165: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.4693 - val_accuracy: 0.9306\n",
      "Epoch 166/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 0.9984\n",
      "Epoch 166: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0038 - accuracy: 0.9984 - val_loss: 0.5120 - val_accuracy: 0.9444\n",
      "Epoch 167/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9984\n",
      "Epoch 167: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0033 - accuracy: 0.9984 - val_loss: 0.5248 - val_accuracy: 0.9444\n",
      "Epoch 168/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.9984\n",
      "Epoch 168: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0058 - accuracy: 0.9984 - val_loss: 0.4582 - val_accuracy: 0.9306\n",
      "Epoch 169/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9984\n",
      "Epoch 169: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.5283 - val_accuracy: 0.9306\n",
      "Epoch 170/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0139 - accuracy: 0.9951\n",
      "Epoch 170: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0139 - accuracy: 0.9951 - val_loss: 0.7771 - val_accuracy: 0.9306\n",
      "Epoch 171/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0125 - accuracy: 0.9935\n",
      "Epoch 171: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0125 - accuracy: 0.9935 - val_loss: 0.5974 - val_accuracy: 0.8889\n",
      "Epoch 172/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 1.0000\n",
      "Epoch 172: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4401 - val_accuracy: 0.9167\n",
      "Epoch 173/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9967\n",
      "Epoch 173: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0054 - accuracy: 0.9967 - val_loss: 0.5581 - val_accuracy: 0.9306\n",
      "Epoch 174/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0080 - accuracy: 0.9967\n",
      "Epoch 174: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0090 - accuracy: 0.9967 - val_loss: 0.8227 - val_accuracy: 0.8194\n",
      "Epoch 175/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 0.9967\n",
      "Epoch 175: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0037 - accuracy: 0.9967 - val_loss: 0.7437 - val_accuracy: 0.9444\n",
      "Epoch 176/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0144 - accuracy: 0.9951\n",
      "Epoch 176: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0144 - accuracy: 0.9951 - val_loss: 0.8496 - val_accuracy: 0.9444\n",
      "Epoch 177/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0193 - accuracy: 0.9902\n",
      "Epoch 177: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0193 - accuracy: 0.9902 - val_loss: 1.7225 - val_accuracy: 0.6528\n",
      "Epoch 178/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9967\n",
      "Epoch 178: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0044 - accuracy: 0.9967 - val_loss: 0.8336 - val_accuracy: 0.9167\n",
      "Epoch 179/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0122 - accuracy: 0.9935\n",
      "Epoch 179: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0122 - accuracy: 0.9935 - val_loss: 0.8025 - val_accuracy: 0.8889\n",
      "Epoch 180/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0155 - accuracy: 0.9935\n",
      "Epoch 180: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0155 - accuracy: 0.9935 - val_loss: 0.8599 - val_accuracy: 0.8889\n",
      "Epoch 181/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0142 - accuracy: 0.9935\n",
      "Epoch 181: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0142 - accuracy: 0.9935 - val_loss: 0.7814 - val_accuracy: 0.9306\n",
      "Epoch 182/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0090 - accuracy: 0.9967\n",
      "Epoch 182: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0090 - accuracy: 0.9967 - val_loss: 0.7959 - val_accuracy: 0.9167\n",
      "Epoch 183/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 183: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.7541 - val_accuracy: 0.9444\n",
      "Epoch 184/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 0.9967\n",
      "Epoch 184: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0058 - accuracy: 0.9967 - val_loss: 0.7774 - val_accuracy: 0.9444\n",
      "Epoch 185/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0091 - accuracy: 0.9967\n",
      "Epoch 185: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0091 - accuracy: 0.9967 - val_loss: 0.7703 - val_accuracy: 0.9444\n",
      "Epoch 186/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9967\n",
      "Epoch 186: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0056 - accuracy: 0.9967 - val_loss: 0.7538 - val_accuracy: 0.9306\n",
      "Epoch 187/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0443 - accuracy: 0.9935\n",
      "Epoch 187: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0443 - accuracy: 0.9935 - val_loss: 1.4006 - val_accuracy: 0.8194\n",
      "Epoch 188/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0101 - accuracy: 0.9951\n",
      "Epoch 188: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0101 - accuracy: 0.9951 - val_loss: 0.7688 - val_accuracy: 0.9167\n",
      "Epoch 189/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0103 - accuracy: 0.9951\n",
      "Epoch 189: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0103 - accuracy: 0.9951 - val_loss: 0.9036 - val_accuracy: 0.8750\n",
      "Epoch 190/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0083 - accuracy: 0.9967\n",
      "Epoch 190: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0083 - accuracy: 0.9967 - val_loss: 0.7900 - val_accuracy: 0.8889\n",
      "Epoch 191/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9967\n",
      "Epoch 191: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0041 - accuracy: 0.9967 - val_loss: 0.6783 - val_accuracy: 0.9167\n",
      "Epoch 192/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0211 - accuracy: 0.9951\n",
      "Epoch 192: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0211 - accuracy: 0.9951 - val_loss: 0.7890 - val_accuracy: 0.8056\n",
      "Epoch 193/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0055 - accuracy: 0.9984\n",
      "Epoch 193: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.5522 - val_accuracy: 0.9028\n",
      "Epoch 194/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0063 - accuracy: 0.9951\n",
      "Epoch 194: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0063 - accuracy: 0.9951 - val_loss: 0.4524 - val_accuracy: 0.9306\n",
      "Epoch 195/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9984\n",
      "Epoch 195: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0048 - accuracy: 0.9984 - val_loss: 0.5520 - val_accuracy: 0.9306\n",
      "Epoch 196/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0067 - accuracy: 0.9967\n",
      "Epoch 196: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0067 - accuracy: 0.9967 - val_loss: 0.5587 - val_accuracy: 0.9306\n",
      "Epoch 197/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0028 - accuracy: 0.9984\n",
      "Epoch 197: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0028 - accuracy: 0.9984 - val_loss: 0.5875 - val_accuracy: 0.9306\n",
      "Epoch 198/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0027 - accuracy: 0.9984\n",
      "Epoch 198: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0027 - accuracy: 0.9984 - val_loss: 0.5901 - val_accuracy: 0.9306\n",
      "Epoch 199/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 1.0000\n",
      "Epoch 199: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.5819 - val_accuracy: 0.9444\n",
      "Epoch 200/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9967\n",
      "Epoch 200: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0048 - accuracy: 0.9967 - val_loss: 0.5646 - val_accuracy: 0.9444\n",
      "Epoch 201/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 0.9984\n",
      "Epoch 201: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0037 - accuracy: 0.9984 - val_loss: 0.5542 - val_accuracy: 0.9444\n",
      "Epoch 202/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 202: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.5532 - val_accuracy: 0.9444\n",
      "Epoch 203/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0032 - accuracy: 0.9967\n",
      "Epoch 203: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0032 - accuracy: 0.9967 - val_loss: 0.5761 - val_accuracy: 0.9444\n",
      "Epoch 204/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 0.9967\n",
      "Epoch 204: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 148ms/step - loss: 0.0036 - accuracy: 0.9967 - val_loss: 0.5858 - val_accuracy: 0.9444\n",
      "Epoch 205/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9935\n",
      "Epoch 205: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0113 - accuracy: 0.9935 - val_loss: 0.4136 - val_accuracy: 0.8333\n",
      "Epoch 206/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0050 - accuracy: 0.9967\n",
      "Epoch 206: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0050 - accuracy: 0.9967 - val_loss: 0.4070 - val_accuracy: 0.9444\n",
      "Epoch 207/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9967\n",
      "Epoch 207: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0044 - accuracy: 0.9967 - val_loss: 0.4326 - val_accuracy: 0.9444\n",
      "Epoch 208/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0035 - accuracy: 0.9967\n",
      "Epoch 208: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0035 - accuracy: 0.9967 - val_loss: 0.4462 - val_accuracy: 0.9444\n",
      "Epoch 209/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9967\n",
      "Epoch 209: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 147ms/step - loss: 0.0044 - accuracy: 0.9967 - val_loss: 0.4466 - val_accuracy: 0.9444\n",
      "Epoch 210/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9967\n",
      "Epoch 210: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0041 - accuracy: 0.9967 - val_loss: 0.4753 - val_accuracy: 0.9444\n",
      "Epoch 211/256\n",
      "38/39 [============================>.] - ETA: 0s - loss: 0.0090 - accuracy: 0.9951\n",
      "Epoch 211: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0090 - accuracy: 0.9951 - val_loss: 0.5679 - val_accuracy: 0.9167\n",
      "Epoch 212/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9967\n",
      "Epoch 212: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0053 - accuracy: 0.9967 - val_loss: 0.4698 - val_accuracy: 0.9444\n",
      "Epoch 213/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0049 - accuracy: 0.9967\n",
      "Epoch 213: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 149ms/step - loss: 0.0049 - accuracy: 0.9967 - val_loss: 0.4823 - val_accuracy: 0.9444\n",
      "Epoch 214/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 1.0000\n",
      "Epoch 214: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 149ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4595 - val_accuracy: 0.9306\n",
      "Epoch 215/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0029 - accuracy: 0.9984\n",
      "Epoch 215: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 151ms/step - loss: 0.0029 - accuracy: 0.9984 - val_loss: 0.4525 - val_accuracy: 0.9306\n",
      "Epoch 216/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9967\n",
      "Epoch 216: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0044 - accuracy: 0.9967 - val_loss: 0.4513 - val_accuracy: 0.9444\n",
      "Epoch 217/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0026 - accuracy: 1.0000\n",
      "Epoch 217: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4672 - val_accuracy: 0.9306\n",
      "Epoch 218/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9967\n",
      "Epoch 218: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0048 - accuracy: 0.9967 - val_loss: 0.4835 - val_accuracy: 0.9306\n",
      "Epoch 219/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9984\n",
      "Epoch 219: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0033 - accuracy: 0.9984 - val_loss: 0.4947 - val_accuracy: 0.9306\n",
      "Epoch 220/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9967\n",
      "Epoch 220: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0057 - accuracy: 0.9967 - val_loss: 0.4491 - val_accuracy: 0.9444\n",
      "Epoch 221/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9967\n",
      "Epoch 221: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0033 - accuracy: 0.9967 - val_loss: 0.4771 - val_accuracy: 0.9444\n",
      "Epoch 222/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 0.9984\n",
      "Epoch 222: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 148ms/step - loss: 0.0034 - accuracy: 0.9984 - val_loss: 0.5097 - val_accuracy: 0.9306\n",
      "Epoch 223/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0048 - accuracy: 0.9967\n",
      "Epoch 223: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0048 - accuracy: 0.9967 - val_loss: 0.5669 - val_accuracy: 0.9444\n",
      "Epoch 224/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0029 - accuracy: 0.9984\n",
      "Epoch 224: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0029 - accuracy: 0.9984 - val_loss: 0.5683 - val_accuracy: 0.9444\n",
      "Epoch 225/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 0.9984\n",
      "Epoch 225: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0030 - accuracy: 0.9984 - val_loss: 0.5488 - val_accuracy: 0.9444\n",
      "Epoch 226/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0027 - accuracy: 1.0000\n",
      "Epoch 226: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4763 - val_accuracy: 0.9444\n",
      "Epoch 227/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 0.9967\n",
      "Epoch 227: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0030 - accuracy: 0.9967 - val_loss: 0.4808 - val_accuracy: 0.9444\n",
      "Epoch 228/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9967\n",
      "Epoch 228: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0039 - accuracy: 0.9967 - val_loss: 0.5062 - val_accuracy: 0.9444\n",
      "Epoch 229/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0029 - accuracy: 1.0000\n",
      "Epoch 229: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 141ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.5248 - val_accuracy: 0.9444\n",
      "Epoch 230/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 0.9967\n",
      "Epoch 230: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0044 - accuracy: 0.9967 - val_loss: 0.4388 - val_accuracy: 0.9444\n",
      "Epoch 231/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9967\n",
      "Epoch 231: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0039 - accuracy: 0.9967 - val_loss: 0.4918 - val_accuracy: 0.9306\n",
      "Epoch 232/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0030 - accuracy: 0.9984\n",
      "Epoch 232: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0030 - accuracy: 0.9984 - val_loss: 0.4900 - val_accuracy: 0.9306\n",
      "Epoch 233/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9967\n",
      "Epoch 233: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0043 - accuracy: 0.9967 - val_loss: 0.4950 - val_accuracy: 0.9444\n",
      "Epoch 234/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 1.0000\n",
      "Epoch 234: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.5042 - val_accuracy: 0.9444\n",
      "Epoch 235/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 0.9984\n",
      "Epoch 235: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0036 - accuracy: 0.9984 - val_loss: 0.4982 - val_accuracy: 0.9444\n",
      "Epoch 236/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9967\n",
      "Epoch 236: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0047 - accuracy: 0.9967 - val_loss: 0.4764 - val_accuracy: 0.9444\n",
      "Epoch 237/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9984\n",
      "Epoch 237: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0041 - accuracy: 0.9984 - val_loss: 0.4848 - val_accuracy: 0.9444\n",
      "Epoch 238/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9984\n",
      "Epoch 238: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 144ms/step - loss: 0.0033 - accuracy: 0.9984 - val_loss: 0.4953 - val_accuracy: 0.9444\n",
      "Epoch 239/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0024 - accuracy: 1.0000\n",
      "Epoch 239: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 143ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.5442 - val_accuracy: 0.9444\n",
      "Epoch 240/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0035 - accuracy: 0.9967\n",
      "Epoch 240: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 142ms/step - loss: 0.0035 - accuracy: 0.9967 - val_loss: 0.5493 - val_accuracy: 0.9444\n",
      "Epoch 241/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0023 - accuracy: 0.9984\n",
      "Epoch 241: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0023 - accuracy: 0.9984 - val_loss: 0.5324 - val_accuracy: 0.9444\n",
      "Epoch 242/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0051 - accuracy: 0.9967\n",
      "Epoch 242: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 137ms/step - loss: 0.0051 - accuracy: 0.9967 - val_loss: 0.5178 - val_accuracy: 0.9444\n",
      "Epoch 243/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0042 - accuracy: 0.9984\n",
      "Epoch 243: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 140ms/step - loss: 0.0042 - accuracy: 0.9984 - val_loss: 0.5291 - val_accuracy: 0.9444\n",
      "Epoch 244/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0031 - accuracy: 0.9984\n",
      "Epoch 244: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0031 - accuracy: 0.9984 - val_loss: 0.5402 - val_accuracy: 0.9444\n",
      "Epoch 245/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0024 - accuracy: 0.9984\n",
      "Epoch 245: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 146ms/step - loss: 0.0024 - accuracy: 0.9984 - val_loss: 0.5510 - val_accuracy: 0.9444\n",
      "Epoch 246/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0026 - accuracy: 0.9984\n",
      "Epoch 246: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 145ms/step - loss: 0.0026 - accuracy: 0.9984 - val_loss: 0.5497 - val_accuracy: 0.9444\n",
      "Epoch 247/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0101 - accuracy: 0.9951\n",
      "Epoch 247: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0101 - accuracy: 0.9951 - val_loss: 0.6313 - val_accuracy: 0.9306\n",
      "Epoch 248/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0039 - accuracy: 0.9967\n",
      "Epoch 248: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0039 - accuracy: 0.9967 - val_loss: 0.5575 - val_accuracy: 0.9444\n",
      "Epoch 249/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0045 - accuracy: 0.9967\n",
      "Epoch 249: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0045 - accuracy: 0.9967 - val_loss: 0.5144 - val_accuracy: 0.9444\n",
      "Epoch 250/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9967\n",
      "Epoch 250: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 140ms/step - loss: 0.0041 - accuracy: 0.9967 - val_loss: 0.5917 - val_accuracy: 0.9444\n",
      "Epoch 251/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 251: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.6100 - val_accuracy: 0.9444\n",
      "Epoch 252/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0032 - accuracy: 0.9984\n",
      "Epoch 252: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0032 - accuracy: 0.9984 - val_loss: 0.6020 - val_accuracy: 0.9444\n",
      "Epoch 253/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0034 - accuracy: 0.9984\n",
      "Epoch 253: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0034 - accuracy: 0.9984 - val_loss: 0.5991 - val_accuracy: 0.9444\n",
      "Epoch 254/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0032 - accuracy: 0.9984\n",
      "Epoch 254: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 138ms/step - loss: 0.0032 - accuracy: 0.9984 - val_loss: 0.6055 - val_accuracy: 0.9444\n",
      "Epoch 255/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0040 - accuracy: 0.9984\n",
      "Epoch 255: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 6s 141ms/step - loss: 0.0040 - accuracy: 0.9984 - val_loss: 0.6784 - val_accuracy: 0.9444\n",
      "Epoch 256/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.0029 - accuracy: 0.9984\n",
      "Epoch 256: saving model to savedModel\\CNNModel.ckpt\n",
      "39/39 [==============================] - 5s 139ms/step - loss: 0.0029 - accuracy: 0.9984 - val_loss: 0.6184 - val_accuracy: 0.9444\n"
     ]
    }
   ],
   "source": [
    "resNet.fit(train_data, validation_data, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3600 - accuracy: 0.3067\n",
      "Epoch 1: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 9s 184ms/step - loss: 1.3600 - accuracy: 0.3067 - val_loss: 1.4271 - val_accuracy: 0.1806\n",
      "Epoch 2/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3383 - accuracy: 0.3148\n",
      "Epoch 2: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.3383 - accuracy: 0.3148 - val_loss: 1.3987 - val_accuracy: 0.2361\n",
      "Epoch 3/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3239 - accuracy: 0.3426\n",
      "Epoch 3: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.3239 - accuracy: 0.3426 - val_loss: 1.3803 - val_accuracy: 0.3889\n",
      "Epoch 4/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.3086 - accuracy: 0.3850\n",
      "Epoch 4: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 1.3086 - accuracy: 0.3850 - val_loss: 1.3600 - val_accuracy: 0.3333\n",
      "Epoch 5/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2668 - accuracy: 0.4225\n",
      "Epoch 5: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.2668 - accuracy: 0.4225 - val_loss: 1.4821 - val_accuracy: 0.2083\n",
      "Epoch 6/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2649 - accuracy: 0.4649\n",
      "Epoch 6: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 1.2649 - accuracy: 0.4649 - val_loss: 1.3089 - val_accuracy: 0.5000\n",
      "Epoch 7/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2450 - accuracy: 0.4633\n",
      "Epoch 7: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 1.2450 - accuracy: 0.4633 - val_loss: 1.3912 - val_accuracy: 0.3889\n",
      "Epoch 8/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2128 - accuracy: 0.4372\n",
      "Epoch 8: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 1.2128 - accuracy: 0.4372 - val_loss: 1.3230 - val_accuracy: 0.2917\n",
      "Epoch 9/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1682 - accuracy: 0.5024\n",
      "Epoch 9: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 1.1682 - accuracy: 0.5024 - val_loss: 1.1761 - val_accuracy: 0.4861\n",
      "Epoch 10/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1999 - accuracy: 0.4715\n",
      "Epoch 10: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 151ms/step - loss: 1.1999 - accuracy: 0.4715 - val_loss: 1.5281 - val_accuracy: 0.2639\n",
      "Epoch 11/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1521 - accuracy: 0.4910\n",
      "Epoch 11: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 1.1521 - accuracy: 0.4910 - val_loss: 1.4273 - val_accuracy: 0.2083\n",
      "Epoch 12/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.2493 - accuracy: 0.4225\n",
      "Epoch 12: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.2493 - accuracy: 0.4225 - val_loss: 1.3021 - val_accuracy: 0.1944\n",
      "Epoch 13/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1666 - accuracy: 0.4910\n",
      "Epoch 13: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 1.1666 - accuracy: 0.4910 - val_loss: 1.2041 - val_accuracy: 0.3750\n",
      "Epoch 14/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1855 - accuracy: 0.4878\n",
      "Epoch 14: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 1.1855 - accuracy: 0.4878 - val_loss: 1.1978 - val_accuracy: 0.4167\n",
      "Epoch 15/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1377 - accuracy: 0.5318\n",
      "Epoch 15: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.1377 - accuracy: 0.5318 - val_loss: 1.1262 - val_accuracy: 0.3750\n",
      "Epoch 16/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1154 - accuracy: 0.4747\n",
      "Epoch 16: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.1154 - accuracy: 0.4747 - val_loss: 1.1293 - val_accuracy: 0.4167\n",
      "Epoch 17/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1202 - accuracy: 0.5008\n",
      "Epoch 17: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 151ms/step - loss: 1.1202 - accuracy: 0.5008 - val_loss: 1.1587 - val_accuracy: 0.4861\n",
      "Epoch 18/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0949 - accuracy: 0.5073\n",
      "Epoch 18: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 1.0949 - accuracy: 0.5073 - val_loss: 1.0806 - val_accuracy: 0.4444\n",
      "Epoch 19/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1530 - accuracy: 0.4976\n",
      "Epoch 19: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.1530 - accuracy: 0.4976 - val_loss: 1.1734 - val_accuracy: 0.3750\n",
      "Epoch 20/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1431 - accuracy: 0.4747\n",
      "Epoch 20: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.1431 - accuracy: 0.4747 - val_loss: 1.1022 - val_accuracy: 0.4722\n",
      "Epoch 21/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0554 - accuracy: 0.5449\n",
      "Epoch 21: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.0554 - accuracy: 0.5449 - val_loss: 1.3022 - val_accuracy: 0.1944\n",
      "Epoch 22/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0151 - accuracy: 0.5432\n",
      "Epoch 22: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 151ms/step - loss: 1.0151 - accuracy: 0.5432 - val_loss: 1.1636 - val_accuracy: 0.4306\n",
      "Epoch 23/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.1071 - accuracy: 0.4976\n",
      "Epoch 23: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 1.1071 - accuracy: 0.4976 - val_loss: 1.1593 - val_accuracy: 0.4583\n",
      "Epoch 24/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0209 - accuracy: 0.5465\n",
      "Epoch 24: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.0209 - accuracy: 0.5465 - val_loss: 1.0822 - val_accuracy: 0.4861\n",
      "Epoch 25/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9737 - accuracy: 0.5628\n",
      "Epoch 25: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.9737 - accuracy: 0.5628 - val_loss: 1.0830 - val_accuracy: 0.4861\n",
      "Epoch 26/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0254 - accuracy: 0.5237\n",
      "Epoch 26: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 1.0254 - accuracy: 0.5237 - val_loss: 1.2481 - val_accuracy: 0.3611\n",
      "Epoch 27/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0588 - accuracy: 0.5285\n",
      "Epoch 27: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 1.0588 - accuracy: 0.5285 - val_loss: 1.0725 - val_accuracy: 0.4861\n",
      "Epoch 28/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9884 - accuracy: 0.5432\n",
      "Epoch 28: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.9884 - accuracy: 0.5432 - val_loss: 1.0594 - val_accuracy: 0.4861\n",
      "Epoch 29/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9731 - accuracy: 0.5530\n",
      "Epoch 29: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 0.9731 - accuracy: 0.5530 - val_loss: 1.1103 - val_accuracy: 0.4028\n",
      "Epoch 30/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9766 - accuracy: 0.5449\n",
      "Epoch 30: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.9766 - accuracy: 0.5449 - val_loss: 1.0971 - val_accuracy: 0.4861\n",
      "Epoch 31/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 1.0164 - accuracy: 0.5351\n",
      "Epoch 31: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 1.0164 - accuracy: 0.5351 - val_loss: 1.0451 - val_accuracy: 0.4722\n",
      "Epoch 32/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9585 - accuracy: 0.5530\n",
      "Epoch 32: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.9585 - accuracy: 0.5530 - val_loss: 1.0418 - val_accuracy: 0.5139\n",
      "Epoch 33/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9481 - accuracy: 0.5563\n",
      "Epoch 33: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.9481 - accuracy: 0.5563 - val_loss: 1.1439 - val_accuracy: 0.4444\n",
      "Epoch 34/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9321 - accuracy: 0.5677\n",
      "Epoch 34: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.9321 - accuracy: 0.5677 - val_loss: 1.1010 - val_accuracy: 0.4444\n",
      "Epoch 35/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9871 - accuracy: 0.5595\n",
      "Epoch 35: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.9871 - accuracy: 0.5595 - val_loss: 1.0815 - val_accuracy: 0.4167\n",
      "Epoch 36/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9423 - accuracy: 0.5775\n",
      "Epoch 36: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.9423 - accuracy: 0.5775 - val_loss: 1.0803 - val_accuracy: 0.4583\n",
      "Epoch 37/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9696 - accuracy: 0.5595\n",
      "Epoch 37: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.9696 - accuracy: 0.5595 - val_loss: 1.0775 - val_accuracy: 0.4722\n",
      "Epoch 38/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9536 - accuracy: 0.5710\n",
      "Epoch 38: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.9536 - accuracy: 0.5710 - val_loss: 1.0740 - val_accuracy: 0.4583\n",
      "Epoch 39/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9205 - accuracy: 0.5677\n",
      "Epoch 39: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.9205 - accuracy: 0.5677 - val_loss: 0.9967 - val_accuracy: 0.4583\n",
      "Epoch 40/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9132 - accuracy: 0.5759\n",
      "Epoch 40: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.9132 - accuracy: 0.5759 - val_loss: 1.0729 - val_accuracy: 0.4722\n",
      "Epoch 41/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9116 - accuracy: 0.5808\n",
      "Epoch 41: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 165ms/step - loss: 0.9116 - accuracy: 0.5808 - val_loss: 1.1181 - val_accuracy: 0.4722\n",
      "Epoch 42/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9255 - accuracy: 0.5856\n",
      "Epoch 42: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 172ms/step - loss: 0.9255 - accuracy: 0.5856 - val_loss: 1.0635 - val_accuracy: 0.4583\n",
      "Epoch 43/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9055 - accuracy: 0.5889\n",
      "Epoch 43: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.9055 - accuracy: 0.5889 - val_loss: 1.1593 - val_accuracy: 0.4444\n",
      "Epoch 44/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9059 - accuracy: 0.5661\n",
      "Epoch 44: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.9059 - accuracy: 0.5661 - val_loss: 1.1821 - val_accuracy: 0.3611\n",
      "Epoch 45/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9380 - accuracy: 0.5367\n",
      "Epoch 45: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.9380 - accuracy: 0.5367 - val_loss: 1.2030 - val_accuracy: 0.4722\n",
      "Epoch 46/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9248 - accuracy: 0.5546\n",
      "Epoch 46: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.9248 - accuracy: 0.5546 - val_loss: 1.0795 - val_accuracy: 0.4722\n",
      "Epoch 47/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9091 - accuracy: 0.5971\n",
      "Epoch 47: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 168ms/step - loss: 0.9091 - accuracy: 0.5971 - val_loss: 1.0093 - val_accuracy: 0.4722\n",
      "Epoch 48/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9001 - accuracy: 0.5612\n",
      "Epoch 48: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 177ms/step - loss: 0.9001 - accuracy: 0.5612 - val_loss: 1.0517 - val_accuracy: 0.5000\n",
      "Epoch 49/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9146 - accuracy: 0.5677\n",
      "Epoch 49: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 181ms/step - loss: 0.9146 - accuracy: 0.5677 - val_loss: 1.0319 - val_accuracy: 0.4306\n",
      "Epoch 50/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8896 - accuracy: 0.5628\n",
      "Epoch 50: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.8896 - accuracy: 0.5628 - val_loss: 1.0309 - val_accuracy: 0.4444\n",
      "Epoch 51/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9108 - accuracy: 0.5677\n",
      "Epoch 51: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 160ms/step - loss: 0.9108 - accuracy: 0.5677 - val_loss: 1.0194 - val_accuracy: 0.4583\n",
      "Epoch 52/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8963 - accuracy: 0.5612\n",
      "Epoch 52: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.8963 - accuracy: 0.5612 - val_loss: 1.0272 - val_accuracy: 0.4444\n",
      "Epoch 53/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8886 - accuracy: 0.5742\n",
      "Epoch 53: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 168ms/step - loss: 0.8886 - accuracy: 0.5742 - val_loss: 1.1318 - val_accuracy: 0.4028\n",
      "Epoch 54/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8888 - accuracy: 0.5775\n",
      "Epoch 54: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.8888 - accuracy: 0.5775 - val_loss: 1.1885 - val_accuracy: 0.4722\n",
      "Epoch 55/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8740 - accuracy: 0.5742\n",
      "Epoch 55: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 179ms/step - loss: 0.8740 - accuracy: 0.5742 - val_loss: 1.0948 - val_accuracy: 0.5000\n",
      "Epoch 56/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8788 - accuracy: 0.5922\n",
      "Epoch 56: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.8788 - accuracy: 0.5922 - val_loss: 1.0904 - val_accuracy: 0.4167\n",
      "Epoch 57/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.9167 - accuracy: 0.5595\n",
      "Epoch 57: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.9167 - accuracy: 0.5595 - val_loss: 1.0449 - val_accuracy: 0.4306\n",
      "Epoch 58/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8843 - accuracy: 0.5922\n",
      "Epoch 58: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.8843 - accuracy: 0.5922 - val_loss: 1.1034 - val_accuracy: 0.4583\n",
      "Epoch 59/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8687 - accuracy: 0.5742\n",
      "Epoch 59: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.8687 - accuracy: 0.5742 - val_loss: 1.1458 - val_accuracy: 0.5000\n",
      "Epoch 60/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8654 - accuracy: 0.6150\n",
      "Epoch 60: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.8654 - accuracy: 0.6150 - val_loss: 1.0006 - val_accuracy: 0.4583\n",
      "Epoch 61/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8802 - accuracy: 0.5922\n",
      "Epoch 61: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.8802 - accuracy: 0.5922 - val_loss: 1.0147 - val_accuracy: 0.4444\n",
      "Epoch 62/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8707 - accuracy: 0.5824\n",
      "Epoch 62: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 160ms/step - loss: 0.8707 - accuracy: 0.5824 - val_loss: 1.0554 - val_accuracy: 0.4444\n",
      "Epoch 63/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8756 - accuracy: 0.5889\n",
      "Epoch 63: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.8756 - accuracy: 0.5889 - val_loss: 1.0489 - val_accuracy: 0.4444\n",
      "Epoch 64/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8451 - accuracy: 0.6052\n",
      "Epoch 64: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 161ms/step - loss: 0.8451 - accuracy: 0.6052 - val_loss: 0.9927 - val_accuracy: 0.4861\n",
      "Epoch 65/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8436 - accuracy: 0.6069\n",
      "Epoch 65: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.8436 - accuracy: 0.6069 - val_loss: 1.0516 - val_accuracy: 0.4722\n",
      "Epoch 66/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8534 - accuracy: 0.6117\n",
      "Epoch 66: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 166ms/step - loss: 0.8534 - accuracy: 0.6117 - val_loss: 1.2711 - val_accuracy: 0.4722\n",
      "Epoch 67/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8609 - accuracy: 0.5954\n",
      "Epoch 67: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.8609 - accuracy: 0.5954 - val_loss: 1.1020 - val_accuracy: 0.4861\n",
      "Epoch 68/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8233 - accuracy: 0.6085\n",
      "Epoch 68: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 167ms/step - loss: 0.8233 - accuracy: 0.6085 - val_loss: 1.0880 - val_accuracy: 0.4583\n",
      "Epoch 69/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8468 - accuracy: 0.6117\n",
      "Epoch 69: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.8468 - accuracy: 0.6117 - val_loss: 1.1346 - val_accuracy: 0.4444\n",
      "Epoch 70/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8550 - accuracy: 0.6069\n",
      "Epoch 70: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 169ms/step - loss: 0.8550 - accuracy: 0.6069 - val_loss: 1.1099 - val_accuracy: 0.3611\n",
      "Epoch 71/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8456 - accuracy: 0.5922\n",
      "Epoch 71: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 175ms/step - loss: 0.8456 - accuracy: 0.5922 - val_loss: 1.0622 - val_accuracy: 0.5278\n",
      "Epoch 72/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8381 - accuracy: 0.6313\n",
      "Epoch 72: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.8381 - accuracy: 0.6313 - val_loss: 0.9653 - val_accuracy: 0.4861\n",
      "Epoch 73/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8436 - accuracy: 0.6199\n",
      "Epoch 73: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 181ms/step - loss: 0.8436 - accuracy: 0.6199 - val_loss: 1.1183 - val_accuracy: 0.4306\n",
      "Epoch 74/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8292 - accuracy: 0.6069\n",
      "Epoch 74: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.8292 - accuracy: 0.6069 - val_loss: 1.0032 - val_accuracy: 0.4583\n",
      "Epoch 75/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8233 - accuracy: 0.6411\n",
      "Epoch 75: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.8233 - accuracy: 0.6411 - val_loss: 1.1549 - val_accuracy: 0.4583\n",
      "Epoch 76/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8475 - accuracy: 0.5889\n",
      "Epoch 76: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 165ms/step - loss: 0.8475 - accuracy: 0.5889 - val_loss: 1.0404 - val_accuracy: 0.4722\n",
      "Epoch 77/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8315 - accuracy: 0.6117\n",
      "Epoch 77: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 171ms/step - loss: 0.8315 - accuracy: 0.6117 - val_loss: 1.0827 - val_accuracy: 0.4583\n",
      "Epoch 78/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8368 - accuracy: 0.6003\n",
      "Epoch 78: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.8368 - accuracy: 0.6003 - val_loss: 1.1294 - val_accuracy: 0.4722\n",
      "Epoch 79/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8340 - accuracy: 0.6117\n",
      "Epoch 79: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 178ms/step - loss: 0.8340 - accuracy: 0.6117 - val_loss: 1.0798 - val_accuracy: 0.4306\n",
      "Epoch 80/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8617 - accuracy: 0.6117\n",
      "Epoch 80: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.8617 - accuracy: 0.6117 - val_loss: 1.0165 - val_accuracy: 0.4444\n",
      "Epoch 81/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8644 - accuracy: 0.5938\n",
      "Epoch 81: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 167ms/step - loss: 0.8644 - accuracy: 0.5938 - val_loss: 1.1711 - val_accuracy: 0.4861\n",
      "Epoch 82/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8372 - accuracy: 0.6101\n",
      "Epoch 82: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 164ms/step - loss: 0.8372 - accuracy: 0.6101 - val_loss: 1.0467 - val_accuracy: 0.4722\n",
      "Epoch 83/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8264 - accuracy: 0.6264\n",
      "Epoch 83: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.8264 - accuracy: 0.6264 - val_loss: 0.9787 - val_accuracy: 0.4583\n",
      "Epoch 84/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8228 - accuracy: 0.6036\n",
      "Epoch 84: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 175ms/step - loss: 0.8228 - accuracy: 0.6036 - val_loss: 1.0750 - val_accuracy: 0.4583\n",
      "Epoch 85/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8204 - accuracy: 0.6183\n",
      "Epoch 85: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 180ms/step - loss: 0.8204 - accuracy: 0.6183 - val_loss: 1.0391 - val_accuracy: 0.4861\n",
      "Epoch 86/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7745 - accuracy: 0.6542\n",
      "Epoch 86: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.7745 - accuracy: 0.6542 - val_loss: 1.0057 - val_accuracy: 0.4583\n",
      "Epoch 87/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8038 - accuracy: 0.6166\n",
      "Epoch 87: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 163ms/step - loss: 0.8038 - accuracy: 0.6166 - val_loss: 1.0046 - val_accuracy: 0.5000\n",
      "Epoch 88/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7981 - accuracy: 0.6330\n",
      "Epoch 88: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 166ms/step - loss: 0.7981 - accuracy: 0.6330 - val_loss: 1.1915 - val_accuracy: 0.4583\n",
      "Epoch 89/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7988 - accuracy: 0.6313\n",
      "Epoch 89: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.7988 - accuracy: 0.6313 - val_loss: 1.2342 - val_accuracy: 0.4444\n",
      "Epoch 90/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.8071 - accuracy: 0.6395\n",
      "Epoch 90: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 175ms/step - loss: 0.8071 - accuracy: 0.6395 - val_loss: 1.1738 - val_accuracy: 0.4306\n",
      "Epoch 91/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7930 - accuracy: 0.6215\n",
      "Epoch 91: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.7930 - accuracy: 0.6215 - val_loss: 1.1782 - val_accuracy: 0.4583\n",
      "Epoch 92/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7980 - accuracy: 0.6444\n",
      "Epoch 92: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.7980 - accuracy: 0.6444 - val_loss: 1.0113 - val_accuracy: 0.4583\n",
      "Epoch 93/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7673 - accuracy: 0.6444\n",
      "Epoch 93: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.7673 - accuracy: 0.6444 - val_loss: 1.1060 - val_accuracy: 0.4028\n",
      "Epoch 94/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7827 - accuracy: 0.6281\n",
      "Epoch 94: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.7827 - accuracy: 0.6281 - val_loss: 0.9881 - val_accuracy: 0.4583\n",
      "Epoch 95/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7754 - accuracy: 0.6444\n",
      "Epoch 95: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 178ms/step - loss: 0.7754 - accuracy: 0.6444 - val_loss: 0.9861 - val_accuracy: 0.4722\n",
      "Epoch 96/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7474 - accuracy: 0.6623\n",
      "Epoch 96: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 182ms/step - loss: 0.7474 - accuracy: 0.6623 - val_loss: 1.1804 - val_accuracy: 0.4583\n",
      "Epoch 97/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7724 - accuracy: 0.6656\n",
      "Epoch 97: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.7724 - accuracy: 0.6656 - val_loss: 0.9194 - val_accuracy: 0.5278\n",
      "Epoch 98/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7768 - accuracy: 0.6427\n",
      "Epoch 98: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.7768 - accuracy: 0.6427 - val_loss: 1.3859 - val_accuracy: 0.4583\n",
      "Epoch 99/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7815 - accuracy: 0.6672\n",
      "Epoch 99: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.7815 - accuracy: 0.6672 - val_loss: 1.1859 - val_accuracy: 0.4306\n",
      "Epoch 100/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7695 - accuracy: 0.6754\n",
      "Epoch 100: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.7695 - accuracy: 0.6754 - val_loss: 1.1391 - val_accuracy: 0.5000\n",
      "Epoch 101/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7726 - accuracy: 0.6558\n",
      "Epoch 101: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 172ms/step - loss: 0.7726 - accuracy: 0.6558 - val_loss: 0.8798 - val_accuracy: 0.6111\n",
      "Epoch 102/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7805 - accuracy: 0.6411\n",
      "Epoch 102: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.7805 - accuracy: 0.6411 - val_loss: 0.9885 - val_accuracy: 0.5139\n",
      "Epoch 103/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7763 - accuracy: 0.6558\n",
      "Epoch 103: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 171ms/step - loss: 0.7763 - accuracy: 0.6558 - val_loss: 0.8926 - val_accuracy: 0.5694\n",
      "Epoch 104/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7320 - accuracy: 0.6607\n",
      "Epoch 104: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 167ms/step - loss: 0.7320 - accuracy: 0.6607 - val_loss: 1.0507 - val_accuracy: 0.4861\n",
      "Epoch 105/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7482 - accuracy: 0.6835\n",
      "Epoch 105: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 161ms/step - loss: 0.7482 - accuracy: 0.6835 - val_loss: 0.8785 - val_accuracy: 0.5556\n",
      "Epoch 106/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7102 - accuracy: 0.7129\n",
      "Epoch 106: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.7102 - accuracy: 0.7129 - val_loss: 0.9614 - val_accuracy: 0.5694\n",
      "Epoch 107/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7493 - accuracy: 0.6852\n",
      "Epoch 107: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 169ms/step - loss: 0.7493 - accuracy: 0.6852 - val_loss: 0.9072 - val_accuracy: 0.5972\n",
      "Epoch 108/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7370 - accuracy: 0.6868\n",
      "Epoch 108: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 175ms/step - loss: 0.7370 - accuracy: 0.6868 - val_loss: 0.8659 - val_accuracy: 0.6250\n",
      "Epoch 109/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7235 - accuracy: 0.6868\n",
      "Epoch 109: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.7235 - accuracy: 0.6868 - val_loss: 1.1472 - val_accuracy: 0.4583\n",
      "Epoch 110/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7057 - accuracy: 0.6900\n",
      "Epoch 110: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 164ms/step - loss: 0.7057 - accuracy: 0.6900 - val_loss: 1.0980 - val_accuracy: 0.5000\n",
      "Epoch 111/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7229 - accuracy: 0.6933\n",
      "Epoch 111: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.7229 - accuracy: 0.6933 - val_loss: 1.0328 - val_accuracy: 0.4861\n",
      "Epoch 112/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7309 - accuracy: 0.6639\n",
      "Epoch 112: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 167ms/step - loss: 0.7309 - accuracy: 0.6639 - val_loss: 0.8826 - val_accuracy: 0.5833\n",
      "Epoch 113/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7233 - accuracy: 0.6721\n",
      "Epoch 113: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 168ms/step - loss: 0.7233 - accuracy: 0.6721 - val_loss: 1.0431 - val_accuracy: 0.5972\n",
      "Epoch 114/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7314 - accuracy: 0.6852\n",
      "Epoch 114: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 175ms/step - loss: 0.7314 - accuracy: 0.6852 - val_loss: 0.9547 - val_accuracy: 0.5556\n",
      "Epoch 115/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6875 - accuracy: 0.7064\n",
      "Epoch 115: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 172ms/step - loss: 0.6875 - accuracy: 0.7064 - val_loss: 0.8919 - val_accuracy: 0.5556\n",
      "Epoch 116/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.7013 - accuracy: 0.6982\n",
      "Epoch 116: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.7013 - accuracy: 0.6982 - val_loss: 0.8473 - val_accuracy: 0.6250\n",
      "Epoch 117/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6984 - accuracy: 0.6884\n",
      "Epoch 117: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.6984 - accuracy: 0.6884 - val_loss: 0.8938 - val_accuracy: 0.5556\n",
      "Epoch 118/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6799 - accuracy: 0.6884\n",
      "Epoch 118: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.6799 - accuracy: 0.6884 - val_loss: 0.9001 - val_accuracy: 0.5278\n",
      "Epoch 119/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6753 - accuracy: 0.7145\n",
      "Epoch 119: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 169ms/step - loss: 0.6753 - accuracy: 0.7145 - val_loss: 1.1194 - val_accuracy: 0.4861\n",
      "Epoch 120/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6834 - accuracy: 0.7080\n",
      "Epoch 120: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 166ms/step - loss: 0.6834 - accuracy: 0.7080 - val_loss: 0.8794 - val_accuracy: 0.6250\n",
      "Epoch 121/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6876 - accuracy: 0.6917\n",
      "Epoch 121: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 177ms/step - loss: 0.6876 - accuracy: 0.6917 - val_loss: 1.0573 - val_accuracy: 0.4861\n",
      "Epoch 122/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6714 - accuracy: 0.7080\n",
      "Epoch 122: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.6714 - accuracy: 0.7080 - val_loss: 1.0278 - val_accuracy: 0.5833\n",
      "Epoch 123/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6546 - accuracy: 0.7080\n",
      "Epoch 123: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.6546 - accuracy: 0.7080 - val_loss: 0.9644 - val_accuracy: 0.5833\n",
      "Epoch 124/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6481 - accuracy: 0.7292\n",
      "Epoch 124: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.6481 - accuracy: 0.7292 - val_loss: 0.9240 - val_accuracy: 0.5556\n",
      "Epoch 125/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6882 - accuracy: 0.7113\n",
      "Epoch 125: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 167ms/step - loss: 0.6882 - accuracy: 0.7113 - val_loss: 0.7903 - val_accuracy: 0.6528\n",
      "Epoch 126/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6760 - accuracy: 0.6933\n",
      "Epoch 126: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.6760 - accuracy: 0.6933 - val_loss: 0.7984 - val_accuracy: 0.6806\n",
      "Epoch 127/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6699 - accuracy: 0.7243\n",
      "Epoch 127: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 171ms/step - loss: 0.6699 - accuracy: 0.7243 - val_loss: 0.8134 - val_accuracy: 0.6250\n",
      "Epoch 128/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6770 - accuracy: 0.7162\n",
      "Epoch 128: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.6770 - accuracy: 0.7162 - val_loss: 0.8444 - val_accuracy: 0.6111\n",
      "Epoch 129/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6896 - accuracy: 0.7047\n",
      "Epoch 129: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.6896 - accuracy: 0.7047 - val_loss: 0.9939 - val_accuracy: 0.5417\n",
      "Epoch 130/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6397 - accuracy: 0.7374\n",
      "Epoch 130: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.6397 - accuracy: 0.7374 - val_loss: 0.8349 - val_accuracy: 0.5972\n",
      "Epoch 131/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6305 - accuracy: 0.7325\n",
      "Epoch 131: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 171ms/step - loss: 0.6305 - accuracy: 0.7325 - val_loss: 1.1456 - val_accuracy: 0.5694\n",
      "Epoch 132/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6439 - accuracy: 0.7308\n",
      "Epoch 132: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 172ms/step - loss: 0.6439 - accuracy: 0.7308 - val_loss: 0.7625 - val_accuracy: 0.6806\n",
      "Epoch 133/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6478 - accuracy: 0.7243\n",
      "Epoch 133: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.6478 - accuracy: 0.7243 - val_loss: 0.7560 - val_accuracy: 0.6250\n",
      "Epoch 134/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6748 - accuracy: 0.7227\n",
      "Epoch 134: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.6748 - accuracy: 0.7227 - val_loss: 1.0026 - val_accuracy: 0.5417\n",
      "Epoch 135/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6568 - accuracy: 0.7341\n",
      "Epoch 135: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.6568 - accuracy: 0.7341 - val_loss: 0.7633 - val_accuracy: 0.5833\n",
      "Epoch 136/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6364 - accuracy: 0.7210\n",
      "Epoch 136: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.6364 - accuracy: 0.7210 - val_loss: 0.7803 - val_accuracy: 0.6111\n",
      "Epoch 137/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6194 - accuracy: 0.7308\n",
      "Epoch 137: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 166ms/step - loss: 0.6194 - accuracy: 0.7308 - val_loss: 0.7188 - val_accuracy: 0.6389\n",
      "Epoch 138/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6394 - accuracy: 0.7227\n",
      "Epoch 138: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 176ms/step - loss: 0.6394 - accuracy: 0.7227 - val_loss: 0.8072 - val_accuracy: 0.6111\n",
      "Epoch 139/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6504 - accuracy: 0.7080\n",
      "Epoch 139: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.6504 - accuracy: 0.7080 - val_loss: 0.7978 - val_accuracy: 0.6528\n",
      "Epoch 140/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6006 - accuracy: 0.7374\n",
      "Epoch 140: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.6006 - accuracy: 0.7374 - val_loss: 0.8295 - val_accuracy: 0.6667\n",
      "Epoch 141/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6292 - accuracy: 0.7374\n",
      "Epoch 141: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.6292 - accuracy: 0.7374 - val_loss: 0.7577 - val_accuracy: 0.6389\n",
      "Epoch 142/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5710 - accuracy: 0.7716\n",
      "Epoch 142: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.5710 - accuracy: 0.7716 - val_loss: 0.8390 - val_accuracy: 0.5972\n",
      "Epoch 143/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6360 - accuracy: 0.7308\n",
      "Epoch 143: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 166ms/step - loss: 0.6360 - accuracy: 0.7308 - val_loss: 0.8230 - val_accuracy: 0.6250\n",
      "Epoch 144/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6014 - accuracy: 0.7488\n",
      "Epoch 144: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 169ms/step - loss: 0.6014 - accuracy: 0.7488 - val_loss: 0.7389 - val_accuracy: 0.6667\n",
      "Epoch 145/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5668 - accuracy: 0.7471\n",
      "Epoch 145: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.5668 - accuracy: 0.7471 - val_loss: 0.7327 - val_accuracy: 0.6389\n",
      "Epoch 146/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6386 - accuracy: 0.7227\n",
      "Epoch 146: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.6386 - accuracy: 0.7227 - val_loss: 0.6803 - val_accuracy: 0.6806\n",
      "Epoch 147/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6298 - accuracy: 0.7227\n",
      "Epoch 147: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.6298 - accuracy: 0.7227 - val_loss: 0.9339 - val_accuracy: 0.5000\n",
      "Epoch 148/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6086 - accuracy: 0.7553\n",
      "Epoch 148: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.6086 - accuracy: 0.7553 - val_loss: 0.6739 - val_accuracy: 0.6667\n",
      "Epoch 149/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5725 - accuracy: 0.7374\n",
      "Epoch 149: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 162ms/step - loss: 0.5725 - accuracy: 0.7374 - val_loss: 0.8450 - val_accuracy: 0.6250\n",
      "Epoch 150/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5901 - accuracy: 0.7488\n",
      "Epoch 150: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 171ms/step - loss: 0.5901 - accuracy: 0.7488 - val_loss: 0.9357 - val_accuracy: 0.5972\n",
      "Epoch 151/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5590 - accuracy: 0.7667\n",
      "Epoch 151: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.5590 - accuracy: 0.7667 - val_loss: 0.7949 - val_accuracy: 0.6111\n",
      "Epoch 152/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6173 - accuracy: 0.7325\n",
      "Epoch 152: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 161ms/step - loss: 0.6173 - accuracy: 0.7325 - val_loss: 0.7032 - val_accuracy: 0.6667\n",
      "Epoch 153/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6058 - accuracy: 0.7406\n",
      "Epoch 153: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 160ms/step - loss: 0.6058 - accuracy: 0.7406 - val_loss: 0.6901 - val_accuracy: 0.6528\n",
      "Epoch 154/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6225 - accuracy: 0.7439\n",
      "Epoch 154: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.6225 - accuracy: 0.7439 - val_loss: 0.8152 - val_accuracy: 0.6250\n",
      "Epoch 155/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5647 - accuracy: 0.7602\n",
      "Epoch 155: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 169ms/step - loss: 0.5647 - accuracy: 0.7602 - val_loss: 0.8006 - val_accuracy: 0.6250\n",
      "Epoch 156/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5687 - accuracy: 0.7765\n",
      "Epoch 156: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.5687 - accuracy: 0.7765 - val_loss: 0.8247 - val_accuracy: 0.5972\n",
      "Epoch 157/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5834 - accuracy: 0.7455\n",
      "Epoch 157: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 172ms/step - loss: 0.5834 - accuracy: 0.7455 - val_loss: 0.9781 - val_accuracy: 0.5139\n",
      "Epoch 158/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5847 - accuracy: 0.7423\n",
      "Epoch 158: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.5847 - accuracy: 0.7423 - val_loss: 0.6713 - val_accuracy: 0.6389\n",
      "Epoch 159/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5743 - accuracy: 0.7602\n",
      "Epoch 159: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.5743 - accuracy: 0.7602 - val_loss: 0.7217 - val_accuracy: 0.6667\n",
      "Epoch 160/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5778 - accuracy: 0.7732\n",
      "Epoch 160: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.5778 - accuracy: 0.7732 - val_loss: 0.6510 - val_accuracy: 0.6667\n",
      "Epoch 161/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5590 - accuracy: 0.7504\n",
      "Epoch 161: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.5590 - accuracy: 0.7504 - val_loss: 0.8511 - val_accuracy: 0.5694\n",
      "Epoch 162/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5468 - accuracy: 0.7537\n",
      "Epoch 162: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 166ms/step - loss: 0.5468 - accuracy: 0.7537 - val_loss: 0.8407 - val_accuracy: 0.5972\n",
      "Epoch 163/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5756 - accuracy: 0.7553\n",
      "Epoch 163: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 167ms/step - loss: 0.5756 - accuracy: 0.7553 - val_loss: 0.7317 - val_accuracy: 0.6389\n",
      "Epoch 164/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5395 - accuracy: 0.7716\n",
      "Epoch 164: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.5395 - accuracy: 0.7716 - val_loss: 0.7342 - val_accuracy: 0.6667\n",
      "Epoch 165/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5468 - accuracy: 0.7667\n",
      "Epoch 165: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.5468 - accuracy: 0.7667 - val_loss: 0.8113 - val_accuracy: 0.6389\n",
      "Epoch 166/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5295 - accuracy: 0.7830\n",
      "Epoch 166: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.5295 - accuracy: 0.7830 - val_loss: 0.6990 - val_accuracy: 0.6667\n",
      "Epoch 167/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5651 - accuracy: 0.7618\n",
      "Epoch 167: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 164ms/step - loss: 0.5651 - accuracy: 0.7618 - val_loss: 0.6778 - val_accuracy: 0.6944\n",
      "Epoch 168/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5159 - accuracy: 0.7847\n",
      "Epoch 168: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 169ms/step - loss: 0.5159 - accuracy: 0.7847 - val_loss: 0.7061 - val_accuracy: 0.6806\n",
      "Epoch 169/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5146 - accuracy: 0.7863\n",
      "Epoch 169: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 164ms/step - loss: 0.5146 - accuracy: 0.7863 - val_loss: 0.7137 - val_accuracy: 0.6806\n",
      "Epoch 170/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5132 - accuracy: 0.7847\n",
      "Epoch 170: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.5132 - accuracy: 0.7847 - val_loss: 0.5906 - val_accuracy: 0.7361\n",
      "Epoch 171/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5085 - accuracy: 0.7879\n",
      "Epoch 171: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.5085 - accuracy: 0.7879 - val_loss: 0.7930 - val_accuracy: 0.6111\n",
      "Epoch 172/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5380 - accuracy: 0.7896\n",
      "Epoch 172: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.5380 - accuracy: 0.7896 - val_loss: 0.6979 - val_accuracy: 0.6806\n",
      "Epoch 173/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5065 - accuracy: 0.7993\n",
      "Epoch 173: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 165ms/step - loss: 0.5065 - accuracy: 0.7993 - val_loss: 0.7698 - val_accuracy: 0.5972\n",
      "Epoch 174/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5090 - accuracy: 0.7896\n",
      "Epoch 174: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 165ms/step - loss: 0.5090 - accuracy: 0.7896 - val_loss: 0.7549 - val_accuracy: 0.6389\n",
      "Epoch 175/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4918 - accuracy: 0.7945\n",
      "Epoch 175: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 165ms/step - loss: 0.4918 - accuracy: 0.7945 - val_loss: 0.8927 - val_accuracy: 0.6250\n",
      "Epoch 176/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5371 - accuracy: 0.7749\n",
      "Epoch 176: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.5371 - accuracy: 0.7749 - val_loss: 0.6582 - val_accuracy: 0.6806\n",
      "Epoch 177/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5241 - accuracy: 0.7732\n",
      "Epoch 177: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 157ms/step - loss: 0.5241 - accuracy: 0.7732 - val_loss: 0.6029 - val_accuracy: 0.7361\n",
      "Epoch 178/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4896 - accuracy: 0.8042\n",
      "Epoch 178: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.4896 - accuracy: 0.8042 - val_loss: 0.7231 - val_accuracy: 0.6250\n",
      "Epoch 179/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5178 - accuracy: 0.7896\n",
      "Epoch 179: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 173ms/step - loss: 0.5178 - accuracy: 0.7896 - val_loss: 0.5944 - val_accuracy: 0.7222\n",
      "Epoch 180/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4998 - accuracy: 0.7732\n",
      "Epoch 180: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 174ms/step - loss: 0.4998 - accuracy: 0.7732 - val_loss: 0.5558 - val_accuracy: 0.7778\n",
      "Epoch 181/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4701 - accuracy: 0.8271\n",
      "Epoch 181: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 170ms/step - loss: 0.4701 - accuracy: 0.8271 - val_loss: 0.9162 - val_accuracy: 0.6528\n",
      "Epoch 182/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5150 - accuracy: 0.7896\n",
      "Epoch 182: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.5150 - accuracy: 0.7896 - val_loss: 0.5895 - val_accuracy: 0.6806\n",
      "Epoch 183/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4491 - accuracy: 0.8124\n",
      "Epoch 183: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.4491 - accuracy: 0.8124 - val_loss: 0.7387 - val_accuracy: 0.6944\n",
      "Epoch 184/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5146 - accuracy: 0.7928\n",
      "Epoch 184: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.5146 - accuracy: 0.7928 - val_loss: 0.9414 - val_accuracy: 0.5417\n",
      "Epoch 185/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4681 - accuracy: 0.8108\n",
      "Epoch 185: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 164ms/step - loss: 0.4681 - accuracy: 0.8108 - val_loss: 0.7188 - val_accuracy: 0.6667\n",
      "Epoch 186/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4240 - accuracy: 0.8271\n",
      "Epoch 186: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 178ms/step - loss: 0.4240 - accuracy: 0.8271 - val_loss: 0.6117 - val_accuracy: 0.7222\n",
      "Epoch 187/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5118 - accuracy: 0.7977\n",
      "Epoch 187: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 172ms/step - loss: 0.5118 - accuracy: 0.7977 - val_loss: 0.5137 - val_accuracy: 0.7778\n",
      "Epoch 188/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5131 - accuracy: 0.7830\n",
      "Epoch 188: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 161ms/step - loss: 0.5131 - accuracy: 0.7830 - val_loss: 0.9203 - val_accuracy: 0.5972\n",
      "Epoch 189/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4892 - accuracy: 0.8091\n",
      "Epoch 189: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 161ms/step - loss: 0.4892 - accuracy: 0.8091 - val_loss: 1.0089 - val_accuracy: 0.5556\n",
      "Epoch 190/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4596 - accuracy: 0.8026\n",
      "Epoch 190: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 159ms/step - loss: 0.4596 - accuracy: 0.8026 - val_loss: 0.8093 - val_accuracy: 0.6528\n",
      "Epoch 191/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4144 - accuracy: 0.8385\n",
      "Epoch 191: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 7s 171ms/step - loss: 0.4144 - accuracy: 0.8385 - val_loss: 0.6071 - val_accuracy: 0.7500\n",
      "Epoch 192/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4601 - accuracy: 0.8271\n",
      "Epoch 192: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4601 - accuracy: 0.8271 - val_loss: 0.8135 - val_accuracy: 0.7083\n",
      "Epoch 193/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6115 - accuracy: 0.7618\n",
      "Epoch 193: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 0.6115 - accuracy: 0.7618 - val_loss: 0.5966 - val_accuracy: 0.7361\n",
      "Epoch 194/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.5053 - accuracy: 0.8042\n",
      "Epoch 194: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.5053 - accuracy: 0.8042 - val_loss: 0.7471 - val_accuracy: 0.6389\n",
      "Epoch 195/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4648 - accuracy: 0.7928\n",
      "Epoch 195: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4648 - accuracy: 0.7928 - val_loss: 1.0067 - val_accuracy: 0.5417\n",
      "Epoch 196/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4628 - accuracy: 0.8206\n",
      "Epoch 196: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4628 - accuracy: 0.8206 - val_loss: 0.7449 - val_accuracy: 0.6667\n",
      "Epoch 197/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4586 - accuracy: 0.8124\n",
      "Epoch 197: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4586 - accuracy: 0.8124 - val_loss: 0.6520 - val_accuracy: 0.7083\n",
      "Epoch 198/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4424 - accuracy: 0.8075\n",
      "Epoch 198: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4424 - accuracy: 0.8075 - val_loss: 0.7272 - val_accuracy: 0.6389\n",
      "Epoch 199/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4400 - accuracy: 0.8140\n",
      "Epoch 199: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 0.4400 - accuracy: 0.8140 - val_loss: 0.5706 - val_accuracy: 0.7361\n",
      "Epoch 200/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4503 - accuracy: 0.8075\n",
      "Epoch 200: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4503 - accuracy: 0.8075 - val_loss: 0.5456 - val_accuracy: 0.7778\n",
      "Epoch 201/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4293 - accuracy: 0.8271\n",
      "Epoch 201: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4293 - accuracy: 0.8271 - val_loss: 0.6735 - val_accuracy: 0.6528\n",
      "Epoch 202/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4567 - accuracy: 0.8222\n",
      "Epoch 202: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4567 - accuracy: 0.8222 - val_loss: 0.5818 - val_accuracy: 0.7361\n",
      "Epoch 203/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3948 - accuracy: 0.8369\n",
      "Epoch 203: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3948 - accuracy: 0.8369 - val_loss: 0.9853 - val_accuracy: 0.6528\n",
      "Epoch 204/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4364 - accuracy: 0.8042\n",
      "Epoch 204: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4364 - accuracy: 0.8042 - val_loss: 0.7435 - val_accuracy: 0.6667\n",
      "Epoch 205/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.6317 - accuracy: 0.7781\n",
      "Epoch 205: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.6317 - accuracy: 0.7781 - val_loss: 0.6815 - val_accuracy: 0.7500\n",
      "Epoch 206/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4542 - accuracy: 0.8189\n",
      "Epoch 206: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4542 - accuracy: 0.8189 - val_loss: 0.7444 - val_accuracy: 0.6528\n",
      "Epoch 207/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4184 - accuracy: 0.8369\n",
      "Epoch 207: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4184 - accuracy: 0.8369 - val_loss: 0.7118 - val_accuracy: 0.6667\n",
      "Epoch 208/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.8189\n",
      "Epoch 208: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4153 - accuracy: 0.8189 - val_loss: 0.7324 - val_accuracy: 0.6806\n",
      "Epoch 209/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4083 - accuracy: 0.8450\n",
      "Epoch 209: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4083 - accuracy: 0.8450 - val_loss: 0.5172 - val_accuracy: 0.7500\n",
      "Epoch 210/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4414 - accuracy: 0.8140\n",
      "Epoch 210: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.4414 - accuracy: 0.8140 - val_loss: 0.7309 - val_accuracy: 0.6667\n",
      "Epoch 211/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4502 - accuracy: 0.8173\n",
      "Epoch 211: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.4502 - accuracy: 0.8173 - val_loss: 0.6960 - val_accuracy: 0.6806\n",
      "Epoch 212/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4147 - accuracy: 0.8320\n",
      "Epoch 212: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4147 - accuracy: 0.8320 - val_loss: 0.6904 - val_accuracy: 0.6944\n",
      "Epoch 213/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4282 - accuracy: 0.8254\n",
      "Epoch 213: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4282 - accuracy: 0.8254 - val_loss: 0.6725 - val_accuracy: 0.6667\n",
      "Epoch 214/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3907 - accuracy: 0.8303\n",
      "Epoch 214: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3907 - accuracy: 0.8303 - val_loss: 0.5616 - val_accuracy: 0.7500\n",
      "Epoch 215/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3705 - accuracy: 0.8613\n",
      "Epoch 215: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3705 - accuracy: 0.8613 - val_loss: 0.5874 - val_accuracy: 0.7778\n",
      "Epoch 216/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3719 - accuracy: 0.8564\n",
      "Epoch 216: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.3719 - accuracy: 0.8564 - val_loss: 0.4664 - val_accuracy: 0.7778\n",
      "Epoch 217/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4418 - accuracy: 0.8385\n",
      "Epoch 217: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4418 - accuracy: 0.8385 - val_loss: 1.2383 - val_accuracy: 0.5417\n",
      "Epoch 218/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3853 - accuracy: 0.8467\n",
      "Epoch 218: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3853 - accuracy: 0.8467 - val_loss: 0.6748 - val_accuracy: 0.7500\n",
      "Epoch 219/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3773 - accuracy: 0.8385\n",
      "Epoch 219: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3773 - accuracy: 0.8385 - val_loss: 0.7265 - val_accuracy: 0.7500\n",
      "Epoch 220/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4040 - accuracy: 0.8401\n",
      "Epoch 220: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 152ms/step - loss: 0.4040 - accuracy: 0.8401 - val_loss: 0.7239 - val_accuracy: 0.6528\n",
      "Epoch 221/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.4005 - accuracy: 0.8450\n",
      "Epoch 221: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.4005 - accuracy: 0.8450 - val_loss: 0.6832 - val_accuracy: 0.7222\n",
      "Epoch 222/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3617 - accuracy: 0.8499\n",
      "Epoch 222: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3617 - accuracy: 0.8499 - val_loss: 0.7761 - val_accuracy: 0.6806\n",
      "Epoch 223/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3434 - accuracy: 0.8613\n",
      "Epoch 223: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3434 - accuracy: 0.8613 - val_loss: 0.7735 - val_accuracy: 0.7222\n",
      "Epoch 224/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3683 - accuracy: 0.8467\n",
      "Epoch 224: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3683 - accuracy: 0.8467 - val_loss: 0.7831 - val_accuracy: 0.6944\n",
      "Epoch 225/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3841 - accuracy: 0.8418\n",
      "Epoch 225: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3841 - accuracy: 0.8418 - val_loss: 0.6428 - val_accuracy: 0.7500\n",
      "Epoch 226/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3558 - accuracy: 0.8613\n",
      "Epoch 226: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3558 - accuracy: 0.8613 - val_loss: 0.5693 - val_accuracy: 0.7500\n",
      "Epoch 227/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3734 - accuracy: 0.8597\n",
      "Epoch 227: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3734 - accuracy: 0.8597 - val_loss: 0.6753 - val_accuracy: 0.6806\n",
      "Epoch 228/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3344 - accuracy: 0.8662\n",
      "Epoch 228: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3344 - accuracy: 0.8662 - val_loss: 0.6408 - val_accuracy: 0.7639\n",
      "Epoch 229/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3602 - accuracy: 0.8564\n",
      "Epoch 229: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3602 - accuracy: 0.8564 - val_loss: 0.8411 - val_accuracy: 0.6806\n",
      "Epoch 230/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8662\n",
      "Epoch 230: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3670 - accuracy: 0.8662 - val_loss: 0.6892 - val_accuracy: 0.6806\n",
      "Epoch 231/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3617 - accuracy: 0.8548\n",
      "Epoch 231: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.3617 - accuracy: 0.8548 - val_loss: 0.7145 - val_accuracy: 0.7222\n",
      "Epoch 232/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2962 - accuracy: 0.8777\n",
      "Epoch 232: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.2962 - accuracy: 0.8777 - val_loss: 0.5481 - val_accuracy: 0.7500\n",
      "Epoch 233/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3875 - accuracy: 0.8515\n",
      "Epoch 233: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3875 - accuracy: 0.8515 - val_loss: 0.4879 - val_accuracy: 0.7778\n",
      "Epoch 234/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3164 - accuracy: 0.8711\n",
      "Epoch 234: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3164 - accuracy: 0.8711 - val_loss: 0.4396 - val_accuracy: 0.8056\n",
      "Epoch 235/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8662\n",
      "Epoch 235: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3401 - accuracy: 0.8662 - val_loss: 0.6169 - val_accuracy: 0.7361\n",
      "Epoch 236/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3446 - accuracy: 0.8695\n",
      "Epoch 236: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3446 - accuracy: 0.8695 - val_loss: 0.4569 - val_accuracy: 0.8472\n",
      "Epoch 237/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8760\n",
      "Epoch 237: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3314 - accuracy: 0.8760 - val_loss: 0.5625 - val_accuracy: 0.7917\n",
      "Epoch 238/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3422 - accuracy: 0.8646\n",
      "Epoch 238: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3422 - accuracy: 0.8646 - val_loss: 0.4709 - val_accuracy: 0.7639\n",
      "Epoch 239/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3667 - accuracy: 0.8630\n",
      "Epoch 239: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3667 - accuracy: 0.8630 - val_loss: 0.6756 - val_accuracy: 0.7639\n",
      "Epoch 240/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3803 - accuracy: 0.8450\n",
      "Epoch 240: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3803 - accuracy: 0.8450 - val_loss: 0.4669 - val_accuracy: 0.8056\n",
      "Epoch 241/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3255 - accuracy: 0.8613\n",
      "Epoch 241: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3255 - accuracy: 0.8613 - val_loss: 0.4461 - val_accuracy: 0.7917\n",
      "Epoch 242/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3415 - accuracy: 0.8515\n",
      "Epoch 242: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.3415 - accuracy: 0.8515 - val_loss: 0.5202 - val_accuracy: 0.8194\n",
      "Epoch 243/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3322 - accuracy: 0.8597\n",
      "Epoch 243: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.3322 - accuracy: 0.8597 - val_loss: 0.4837 - val_accuracy: 0.8056\n",
      "Epoch 244/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3387 - accuracy: 0.8793\n",
      "Epoch 244: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 153ms/step - loss: 0.3387 - accuracy: 0.8793 - val_loss: 0.4674 - val_accuracy: 0.8333\n",
      "Epoch 245/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8760\n",
      "Epoch 245: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.3670 - accuracy: 0.8760 - val_loss: 0.5069 - val_accuracy: 0.7639\n",
      "Epoch 246/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3639 - accuracy: 0.8646\n",
      "Epoch 246: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.3639 - accuracy: 0.8646 - val_loss: 0.6358 - val_accuracy: 0.7917\n",
      "Epoch 247/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2854 - accuracy: 0.8858\n",
      "Epoch 247: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.2854 - accuracy: 0.8858 - val_loss: 0.3961 - val_accuracy: 0.8056\n",
      "Epoch 248/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3302 - accuracy: 0.8760\n",
      "Epoch 248: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 161ms/step - loss: 0.3302 - accuracy: 0.8760 - val_loss: 0.7709 - val_accuracy: 0.7222\n",
      "Epoch 249/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8744\n",
      "Epoch 249: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.3519 - accuracy: 0.8744 - val_loss: 0.5241 - val_accuracy: 0.7778\n",
      "Epoch 250/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3414 - accuracy: 0.8744\n",
      "Epoch 250: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.3414 - accuracy: 0.8744 - val_loss: 0.4132 - val_accuracy: 0.7917\n",
      "Epoch 251/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2827 - accuracy: 0.8956\n",
      "Epoch 251: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.2827 - accuracy: 0.8956 - val_loss: 0.5056 - val_accuracy: 0.7500\n",
      "Epoch 252/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3148 - accuracy: 0.8728\n",
      "Epoch 252: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 155ms/step - loss: 0.3148 - accuracy: 0.8728 - val_loss: 0.5870 - val_accuracy: 0.7361\n",
      "Epoch 253/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2927 - accuracy: 0.8760\n",
      "Epoch 253: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 158ms/step - loss: 0.2927 - accuracy: 0.8760 - val_loss: 0.5026 - val_accuracy: 0.7778\n",
      "Epoch 254/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8646\n",
      "Epoch 254: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.3443 - accuracy: 0.8646 - val_loss: 0.5702 - val_accuracy: 0.7778\n",
      "Epoch 255/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2634 - accuracy: 0.9021\n",
      "Epoch 255: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 156ms/step - loss: 0.2634 - accuracy: 0.9021 - val_loss: 0.6526 - val_accuracy: 0.7083\n",
      "Epoch 256/256\n",
      "39/39 [==============================] - ETA: 0s - loss: 0.2944 - accuracy: 0.8891\n",
      "Epoch 256: saving model to savedModel\\WaveletAlexNet.ckpt\n",
      "39/39 [==============================] - 6s 154ms/step - loss: 0.2944 - accuracy: 0.8891 - val_loss: 0.4128 - val_accuracy: 0.8333\n"
     ]
    }
   ],
   "source": [
    "waveletAlexNet.fit(train_data, validation_data, 256)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Comparing Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "CNNModel.compareAccuracy([waveletAlexNet,resNet,leNet])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Comparing Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "CNNModel.compareLoss([waveletAlexNet,resNet,leNet])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Make Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 13ms/step\n",
      "Squamous cell carcinoma\n"
     ]
    }
   ],
   "source": [
    "alexNet.predict(\"Data/test/adenocarcinoma/000114.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n",
      "Squamous cell carcinoma\n"
     ]
    }
   ],
   "source": [
    "waveletAlexNet.predict(\"Data/test/adenocarcinoma/000114.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 15ms/step\n",
      "Adenocarcinoma\n"
     ]
    }
   ],
   "source": [
    "resNet.predict(\"Data/test/adenocarcinoma/000114.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Models History Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(resNet.history_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.27895596623420715, 0.27569329738616943, 0.3425774872303009, 0.404567688703537, 0.42577487230300903, 0.43066883087158203, 0.4600326120853424, 0.5073409676551819, 0.4730831980705261, 0.48939642310142517, 0.5073409676551819, 0.4698205590248108, 0.37357258796691895, 0.3980424106121063, 0.43066883087158203, 0.3931484520435333, 0.4877651035785675, 0.5367047190666199, 0.49592170119285583, 0.5089722871780396, 0.5252854824066162, 0.5318107604980469, 0.5334420800209045, 0.49429038166999817, 0.5350733995437622, 0.5236541628837585, 0.5432299971580505, 0.5530179738998413, 0.5513865947723389, 0.5464926362037659, 0.559543251991272, 0.5530179738998413]\n"
     ]
    }
   ],
   "source": [
    "print(waveletAlexNet.history_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2724306583404541, 0.2528548240661621, 0.26590538024902344, 0.2724306583404541, 0.2969004809856415, 0.28874388337135315, 0.3001631200313568, 0.3099510669708252, 0.3050571084022522, 0.2854812443256378, 0.28384992480278015, 0.2920065224170685, 0.29853180050849915, 0.2903752028942108, 0.3017944395542145, 0.30668842792510986, 0.3017944395542145, 0.30668842792510986, 0.2903752028942108, 0.29363784193992615, 0.31158238649368286, 0.2969004809856415, 0.29853180050849915, 0.3148450255393982, 0.2969004809856415, 0.3230016231536865, 0.3132137060165405, 0.31647634506225586, 0.28874388337135315, 0.3181076645851135, 0.3050571084022522, 0.30342578887939453]\n"
     ]
    }
   ],
   "source": [
    "print(leNet.history_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Models History Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(resNet.history_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Save to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('accuracy.csv', 'w') as f:\n",
    "\n",
    "    write = csv.writer(f)\n",
    "\n",
    "    write.writerow([\"ResNet accuracy\", \"LeNet accuracy\", \"AlexNet accuracy\"])\n",
    "    \n",
    "    for x in range(256):\n",
    "        row = [resNet.history_accuracy[x],leNet.history_accuracy[x],waveletAlexNet.history_accuracy[x]]\n",
    "        write.writerow(row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('loss.csv', 'w') as f:\n",
    "\n",
    "    write = csv.writer(f)\n",
    "\n",
    "    write.writerow([\"ResNet loss\", \"LeNet loss\", \"AlexNet loss\"])\n",
    "    \n",
    "    for x in range(256):\n",
    "        row = [resNet.history_loss[x],leNet.history_loss[x],waveletAlexNet.history_loss[x]]\n",
    "        write.writerow(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(alexNet.history_accuracy)"
   ]
  }
 ],
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