Covid19.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled5.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVt3dkJbvByO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4ZLOjgtvaE7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9mxChTvvkLv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import dlib as dlib\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from keras  import utils"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzuiHjUjwrcP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Convolution2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "from keras.layers.core import Dropout, Lambda\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlunP4KM61cp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGy4FTwRws_L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrsutBjhwwB-",
        "colab_type": "code",
        "outputId": "1f6e84e6-c590-4798-d26f-d678be21d93b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "model.add(Convolution2D(64,3,3, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Convolution2D(64,3,3,activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Convolution2D(64,3,3, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Convolution2D(32,3,3,activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Convolution2D(128,3,3, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Convolution2D(128,3,3,activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Convolution2D(128,2,2, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Convolution2D(128,2,2,activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Convolution2D(256,2,2, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Convolution2D(256,2,2,activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "model.add(Convolution2D(256,3,3, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Convolution2D(256,3,3,activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Convolution2D(256,3,3, input_shape = (512,512,3), activation= 'relu'))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(output_dim=128 , activation='relu'))\n",
        "model.add(Dense(output_dim=1, activation='sigmoid'))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
        "\n"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\")`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\")`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (2, 2), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (2, 2), activation=\"relu\")`\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (2, 2), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (2, 2), activation=\"relu\")`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:24: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:26: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\")`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:30: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), input_shape=(512, 512,..., activation=\"relu\")`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:35: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=128)`\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1)`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNj8q0hexxK_",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vv2MV4R5w1hN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator( rescale = 1./255,\n",
        "                                  shear_range=0.2,\n",
        "                                  zoom_range=0.2,\n",
        "                                  horizontal_flip=True,\n",
        "                                   width_shift_range=0.2,\n",
        "                                   height_shift_range=0.2,\n",
        "                                   rotation_range=15,\n",
        "                                   vertical_flip=True,\n",
        "                                   fill_mode='reflect',\n",
        "                                   data_format='channels_last',\n",
        "                                   brightness_range=[0.5, 1.5],\n",
        "                                   featurewise_center=True,\n",
        "                                   featurewise_std_normalization=True\n",
        "                                  )\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATqc61IQynTp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "root_dir = \"drive/My Drive/\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgHP4WAmzcGm",
        "colab_type": "code",
        "outputId": "b49bed31-2ece-46e9-b30c-cca61858ceb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "training_set = train_datagen.flow_from_directory(\"/content/gdrive/My Drive/Covid19/Xray/folder1\",\n",
        "                                                 target_size = (512, 512),\n",
        "                                                 batch_size = 1,\n",
        "                                                 class_mode = 'binary')\n",
        "test_set = test_datagen.flow_from_directory(\"/content/gdrive/My Drive/Covid19/Xray/folder2\",\n",
        "                                            target_size = (512, 512),\n",
        "                                            batch_size = 1,\n",
        "                                            class_mode = 'binary')"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 347 images belonging to 2 classes.\n",
            "Found 133 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppcL12Nq0IYX",
        "colab_type": "code",
        "outputId": "d5d3f389-8a10-4548-b258-95af82448919",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "hit = model.fit_generator(training_set,\n",
        "                              samples_per_epoch = 347 ,\n",
        "                               nb_epoch =215,\n",
        "                               validation_data= test_set,\n",
        "                               nb_val_samples = 20\n",
        "                              )\n",
        "\n",
        "from keras.models import model_from_json\n",
        "model_json = model.to_json()\n",
        "with open(\"/content/gdrive/My Drive/Covid19/Xray/model.json\",'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights(\"/content/gdrive/My Drive/Covid19/Xray/model.h5\")\n",
        "\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.plot(hit.history['val_loss'])\n",
        "plt.legend(['Validation'],loc='upper left')\n",
        "plt.title('validation loss vs epoch')\n",
        "plt.ylabel('validation loss')\n",
        "plt.xlabel('Epoch')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(hit.history['accuracy'])\n",
        "plt.legend(['validation'], loc='upper left')\n",
        "plt.title('validation accuracy vs epoch')\n",
        "plt.ylabel('validation accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "\n"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., validation_data=<keras.pre..., steps_per_epoch=347, epochs=215, validation_steps=20)`\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:724: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "347/347 [==============================] - 43s 124ms/step - loss: 0.7390 - accuracy: 0.7839 - val_loss: 0.2993 - val_accuracy: 0.8000\n",
            "Epoch 2/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5268 - accuracy: 0.7867 - val_loss: 0.2396 - val_accuracy: 0.8500\n",
            "Epoch 3/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5256 - accuracy: 0.7867 - val_loss: 0.2326 - val_accuracy: 0.9000\n",
            "Epoch 4/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5228 - accuracy: 0.7867 - val_loss: 0.3492 - val_accuracy: 0.9000\n",
            "Epoch 5/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5325 - accuracy: 0.7867 - val_loss: 0.2337 - val_accuracy: 0.7000\n",
            "Epoch 6/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5270 - accuracy: 0.7867 - val_loss: 0.2325 - val_accuracy: 0.8500\n",
            "Epoch 7/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5230 - accuracy: 0.7867 - val_loss: 0.2828 - val_accuracy: 0.7000\n",
            "Epoch 8/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5234 - accuracy: 0.7867 - val_loss: 0.2387 - val_accuracy: 0.9000\n",
            "Epoch 9/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5224 - accuracy: 0.7867 - val_loss: 0.2240 - val_accuracy: 0.8500\n",
            "Epoch 10/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5251 - accuracy: 0.7867 - val_loss: 1.4005 - val_accuracy: 0.7500\n",
            "Epoch 11/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5195 - accuracy: 0.7867 - val_loss: 0.3634 - val_accuracy: 0.9000\n",
            "Epoch 12/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5257 - accuracy: 0.7867 - val_loss: 0.2210 - val_accuracy: 0.8500\n",
            "Epoch 13/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 1.3715 - val_accuracy: 0.6000\n",
            "Epoch 14/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5218 - accuracy: 0.7867 - val_loss: 0.2135 - val_accuracy: 0.7000\n",
            "Epoch 15/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5286 - accuracy: 0.7867 - val_loss: 0.2513 - val_accuracy: 0.8500\n",
            "Epoch 16/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5220 - accuracy: 0.7867 - val_loss: 0.2302 - val_accuracy: 0.8000\n",
            "Epoch 17/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5226 - accuracy: 0.7867 - val_loss: 0.2607 - val_accuracy: 0.9000\n",
            "Epoch 18/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5249 - accuracy: 0.7867 - val_loss: 0.2363 - val_accuracy: 0.9000\n",
            "Epoch 19/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5165 - accuracy: 0.7867 - val_loss: 1.2563 - val_accuracy: 0.7500\n",
            "Epoch 20/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5257 - accuracy: 0.7867 - val_loss: 0.2736 - val_accuracy: 0.8000\n",
            "Epoch 21/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5250 - accuracy: 0.7867 - val_loss: 0.2799 - val_accuracy: 0.9000\n",
            "Epoch 22/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5244 - accuracy: 0.7867 - val_loss: 0.2403 - val_accuracy: 0.9500\n",
            "Epoch 23/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5238 - accuracy: 0.7867 - val_loss: 1.4848 - val_accuracy: 0.8500\n",
            "Epoch 24/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5239 - accuracy: 0.7867 - val_loss: 0.2368 - val_accuracy: 0.7000\n",
            "Epoch 25/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5217 - accuracy: 0.7867 - val_loss: 0.2418 - val_accuracy: 0.7500\n",
            "Epoch 26/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 1.5803 - val_accuracy: 0.6500\n",
            "Epoch 27/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 0.2114 - val_accuracy: 0.9500\n",
            "Epoch 28/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5172 - accuracy: 0.7867 - val_loss: 0.2747 - val_accuracy: 0.8500\n",
            "Epoch 29/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2480 - val_accuracy: 0.8000\n",
            "Epoch 30/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 1.4079 - val_accuracy: 0.7000\n",
            "Epoch 31/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5215 - accuracy: 0.7867 - val_loss: 0.2360 - val_accuracy: 0.9000\n",
            "Epoch 32/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5214 - accuracy: 0.7867 - val_loss: 1.4954 - val_accuracy: 0.8500\n",
            "Epoch 33/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 1.5555 - val_accuracy: 0.7000\n",
            "Epoch 34/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 0.2574 - val_accuracy: 0.8000\n",
            "Epoch 35/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 0.2535 - val_accuracy: 0.8000\n",
            "Epoch 36/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2162 - val_accuracy: 0.9000\n",
            "Epoch 37/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2442 - val_accuracy: 0.8000\n",
            "Epoch 38/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2817 - val_accuracy: 0.7500\n",
            "Epoch 39/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 0.2390 - val_accuracy: 0.8000\n",
            "Epoch 40/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5222 - accuracy: 0.7867 - val_loss: 0.2364 - val_accuracy: 0.8500\n",
            "Epoch 41/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.5087 - val_accuracy: 0.6000\n",
            "Epoch 42/215\n",
            "347/347 [==============================] - 43s 122ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2493 - val_accuracy: 0.9500\n",
            "Epoch 43/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 1.5588 - val_accuracy: 0.8000\n",
            "Epoch 44/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5221 - accuracy: 0.7867 - val_loss: 0.2607 - val_accuracy: 0.8000\n",
            "Epoch 45/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2394 - val_accuracy: 0.9000\n",
            "Epoch 46/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2423 - val_accuracy: 0.7500\n",
            "Epoch 47/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5210 - accuracy: 0.7867 - val_loss: 1.5410 - val_accuracy: 0.8000\n",
            "Epoch 48/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5216 - accuracy: 0.7867 - val_loss: 0.2636 - val_accuracy: 0.7500\n",
            "Epoch 49/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2611 - val_accuracy: 0.7500\n",
            "Epoch 50/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2578 - val_accuracy: 0.8500\n",
            "Epoch 51/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2360 - val_accuracy: 0.9000\n",
            "Epoch 52/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2384 - val_accuracy: 0.9500\n",
            "Epoch 53/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5213 - accuracy: 0.7867 - val_loss: 0.2380 - val_accuracy: 0.8500\n",
            "Epoch 54/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5218 - accuracy: 0.7867 - val_loss: 0.2350 - val_accuracy: 0.6500\n",
            "Epoch 55/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2302 - val_accuracy: 0.7500\n",
            "Epoch 56/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2472 - val_accuracy: 0.9000\n",
            "Epoch 57/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5211 - accuracy: 0.7867 - val_loss: 0.2477 - val_accuracy: 0.9000\n",
            "Epoch 58/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2642 - val_accuracy: 0.8500\n",
            "Epoch 59/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5209 - accuracy: 0.7867 - val_loss: 0.2633 - val_accuracy: 0.8500\n",
            "Epoch 60/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2306 - val_accuracy: 0.7000\n",
            "Epoch 61/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2103 - val_accuracy: 0.8000\n",
            "Epoch 62/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.4872 - val_accuracy: 0.8500\n",
            "Epoch 63/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5632 - val_accuracy: 0.7000\n",
            "Epoch 64/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2420 - val_accuracy: 0.8500\n",
            "Epoch 65/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2497 - val_accuracy: 0.8500\n",
            "Epoch 66/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2345 - val_accuracy: 0.8500\n",
            "Epoch 67/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 1.5134 - val_accuracy: 0.6000\n",
            "Epoch 68/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2430 - val_accuracy: 0.9500\n",
            "Epoch 69/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5970 - val_accuracy: 0.8000\n",
            "Epoch 70/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2466 - val_accuracy: 0.8500\n",
            "Epoch 71/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5182 - accuracy: 0.7867 - val_loss: 0.2134 - val_accuracy: 0.8500\n",
            "Epoch 72/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 1.5798 - val_accuracy: 0.9000\n",
            "Epoch 73/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5210 - accuracy: 0.7867 - val_loss: 1.5694 - val_accuracy: 0.7000\n",
            "Epoch 74/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2297 - val_accuracy: 0.7500\n",
            "Epoch 75/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2214 - val_accuracy: 0.8500\n",
            "Epoch 76/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2523 - val_accuracy: 0.7500\n",
            "Epoch 77/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 1.4870 - val_accuracy: 0.6000\n",
            "Epoch 78/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2575 - val_accuracy: 0.9500\n",
            "Epoch 79/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2508 - val_accuracy: 0.9500\n",
            "Epoch 80/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2419 - val_accuracy: 0.8500\n",
            "Epoch 81/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2553 - val_accuracy: 0.7500\n",
            "Epoch 82/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5210 - accuracy: 0.7867 - val_loss: 0.2408 - val_accuracy: 0.8500\n",
            "Epoch 83/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2236 - val_accuracy: 0.6500\n",
            "Epoch 84/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2520 - val_accuracy: 0.7000\n",
            "Epoch 85/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5209 - accuracy: 0.7867 - val_loss: 0.2475 - val_accuracy: 0.9500\n",
            "Epoch 86/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5192 - accuracy: 0.7867 - val_loss: 0.2267 - val_accuracy: 0.9000\n",
            "Epoch 87/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2143 - val_accuracy: 0.8500\n",
            "Epoch 88/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.6241 - val_accuracy: 0.7500\n",
            "Epoch 89/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2348 - val_accuracy: 0.8500\n",
            "Epoch 90/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2509 - val_accuracy: 0.8500\n",
            "Epoch 91/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2371 - val_accuracy: 0.7500\n",
            "Epoch 92/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 1.5187 - val_accuracy: 0.7500\n",
            "Epoch 93/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2468 - val_accuracy: 0.9000\n",
            "Epoch 94/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2470 - val_accuracy: 0.9000\n",
            "Epoch 95/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2535 - val_accuracy: 0.8500\n",
            "Epoch 96/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2310 - val_accuracy: 0.7000\n",
            "Epoch 97/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2519 - val_accuracy: 0.7500\n",
            "Epoch 98/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5194 - accuracy: 0.7867 - val_loss: 0.2363 - val_accuracy: 0.7500\n",
            "Epoch 99/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2214 - val_accuracy: 0.8500\n",
            "Epoch 100/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2549 - val_accuracy: 0.8500\n",
            "Epoch 101/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5637 - val_accuracy: 0.7500\n",
            "Epoch 102/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5188 - accuracy: 0.7867 - val_loss: 0.2697 - val_accuracy: 0.8500\n",
            "Epoch 103/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2413 - val_accuracy: 0.8500\n",
            "Epoch 104/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2277 - val_accuracy: 0.7500\n",
            "Epoch 105/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2371 - val_accuracy: 0.9000\n",
            "Epoch 106/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2307 - val_accuracy: 0.7500\n",
            "Epoch 107/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5211 - accuracy: 0.7867 - val_loss: 1.5598 - val_accuracy: 0.9000\n",
            "Epoch 108/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2393 - val_accuracy: 0.8000\n",
            "Epoch 109/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 1.5640 - val_accuracy: 0.8000\n",
            "Epoch 110/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5213 - accuracy: 0.7867 - val_loss: 0.2420 - val_accuracy: 0.8000\n",
            "Epoch 111/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 1.6279 - val_accuracy: 0.7500\n",
            "Epoch 112/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2313 - val_accuracy: 0.8500\n",
            "Epoch 113/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2446 - val_accuracy: 0.9000\n",
            "Epoch 114/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2394 - val_accuracy: 0.7500\n",
            "Epoch 115/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5189 - accuracy: 0.7867 - val_loss: 0.2614 - val_accuracy: 0.7500\n",
            "Epoch 116/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.5308 - val_accuracy: 0.8500\n",
            "Epoch 117/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 1.5502 - val_accuracy: 0.7500\n",
            "Epoch 118/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2405 - val_accuracy: 0.9000\n",
            "Epoch 119/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2415 - val_accuracy: 0.8500\n",
            "Epoch 120/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5186 - accuracy: 0.7867 - val_loss: 0.2170 - val_accuracy: 0.8500\n",
            "Epoch 121/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2258 - val_accuracy: 0.8000\n",
            "Epoch 122/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2437 - val_accuracy: 0.8000\n",
            "Epoch 123/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 1.5883 - val_accuracy: 0.8000\n",
            "Epoch 124/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2404 - val_accuracy: 0.7500\n",
            "Epoch 125/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2309 - val_accuracy: 0.9000\n",
            "Epoch 126/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2406 - val_accuracy: 0.8000\n",
            "Epoch 127/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2387 - val_accuracy: 0.8500\n",
            "Epoch 128/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5209 - accuracy: 0.7867 - val_loss: 1.4765 - val_accuracy: 0.7000\n",
            "Epoch 129/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5370 - val_accuracy: 0.7500\n",
            "Epoch 130/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.5238 - val_accuracy: 0.8500\n",
            "Epoch 131/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2386 - val_accuracy: 0.8000\n",
            "Epoch 132/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2390 - val_accuracy: 0.9000\n",
            "Epoch 133/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2422 - val_accuracy: 0.8000\n",
            "Epoch 134/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2423 - val_accuracy: 0.7000\n",
            "Epoch 135/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2639 - val_accuracy: 0.9000\n",
            "Epoch 136/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5468 - val_accuracy: 0.6000\n",
            "Epoch 137/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5195 - accuracy: 0.7867 - val_loss: 0.2380 - val_accuracy: 0.8500\n",
            "Epoch 138/215\n",
            "347/347 [==============================] - 40s 117ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2375 - val_accuracy: 0.9500\n",
            "Epoch 139/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5530 - val_accuracy: 0.8000\n",
            "Epoch 140/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2466 - val_accuracy: 0.9000\n",
            "Epoch 141/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2321 - val_accuracy: 0.6000\n",
            "Epoch 142/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2341 - val_accuracy: 0.9000\n",
            "Epoch 143/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2364 - val_accuracy: 0.7000\n",
            "Epoch 144/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2536 - val_accuracy: 1.0000\n",
            "Epoch 145/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 1.5384 - val_accuracy: 0.8500\n",
            "Epoch 146/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2392 - val_accuracy: 0.8500\n",
            "Epoch 147/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2401 - val_accuracy: 0.8000\n",
            "Epoch 148/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 1.6160 - val_accuracy: 0.8000\n",
            "Epoch 149/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2284 - val_accuracy: 0.8000\n",
            "Epoch 150/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 1.5760 - val_accuracy: 0.7500\n",
            "Epoch 151/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2412 - val_accuracy: 0.7500\n",
            "Epoch 152/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2330 - val_accuracy: 0.8500\n",
            "Epoch 153/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2452 - val_accuracy: 0.9000\n",
            "Epoch 154/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5195 - accuracy: 0.7867 - val_loss: 0.2643 - val_accuracy: 0.7500\n",
            "Epoch 155/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2504 - val_accuracy: 1.0000\n",
            "Epoch 156/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2386 - val_accuracy: 0.9000\n",
            "Epoch 157/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 1.5440 - val_accuracy: 0.6500\n",
            "Epoch 158/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2492 - val_accuracy: 0.8000\n",
            "Epoch 159/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 1.5593 - val_accuracy: 0.6500\n",
            "Epoch 160/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2413 - val_accuracy: 0.9500\n",
            "Epoch 161/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 1.5783 - val_accuracy: 0.8500\n",
            "Epoch 162/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2296 - val_accuracy: 0.7500\n",
            "Epoch 163/215\n",
            "347/347 [==============================] - 46s 132ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2337 - val_accuracy: 0.9000\n",
            "Epoch 164/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2501 - val_accuracy: 0.6500\n",
            "Epoch 165/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2369 - val_accuracy: 0.8500\n",
            "Epoch 166/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2288 - val_accuracy: 0.8000\n",
            "Epoch 167/215\n",
            "296/347 [========================>.....] - ETA: 5s - loss: 0.5374 - accuracy: 0.7736Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRi2SbJL3gQv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d5d3f389-8a10-4548-b258-95af82448919",
        "id": "kjUENYtuH5hR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., validation_data=<keras.pre..., steps_per_epoch=347, epochs=215, validation_steps=20)`\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/215\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:724: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "347/347 [==============================] - 43s 124ms/step - loss: 0.7390 - accuracy: 0.7839 - val_loss: 0.2993 - val_accuracy: 0.8000\n",
            "Epoch 2/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5268 - accuracy: 0.7867 - val_loss: 0.2396 - val_accuracy: 0.8500\n",
            "Epoch 3/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5256 - accuracy: 0.7867 - val_loss: 0.2326 - val_accuracy: 0.9000\n",
            "Epoch 4/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5228 - accuracy: 0.7867 - val_loss: 0.3492 - val_accuracy: 0.9000\n",
            "Epoch 5/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5325 - accuracy: 0.7867 - val_loss: 0.2337 - val_accuracy: 0.7000\n",
            "Epoch 6/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5270 - accuracy: 0.7867 - val_loss: 0.2325 - val_accuracy: 0.8500\n",
            "Epoch 7/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5230 - accuracy: 0.7867 - val_loss: 0.2828 - val_accuracy: 0.7000\n",
            "Epoch 8/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5234 - accuracy: 0.7867 - val_loss: 0.2387 - val_accuracy: 0.9000\n",
            "Epoch 9/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5224 - accuracy: 0.7867 - val_loss: 0.2240 - val_accuracy: 0.8500\n",
            "Epoch 10/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5251 - accuracy: 0.7867 - val_loss: 1.4005 - val_accuracy: 0.7500\n",
            "Epoch 11/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5195 - accuracy: 0.7867 - val_loss: 0.3634 - val_accuracy: 0.9000\n",
            "Epoch 12/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5257 - accuracy: 0.7867 - val_loss: 0.2210 - val_accuracy: 0.8500\n",
            "Epoch 13/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 1.3715 - val_accuracy: 0.6000\n",
            "Epoch 14/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5218 - accuracy: 0.7867 - val_loss: 0.2135 - val_accuracy: 0.7000\n",
            "Epoch 15/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5286 - accuracy: 0.7867 - val_loss: 0.2513 - val_accuracy: 0.8500\n",
            "Epoch 16/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5220 - accuracy: 0.7867 - val_loss: 0.2302 - val_accuracy: 0.8000\n",
            "Epoch 17/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5226 - accuracy: 0.7867 - val_loss: 0.2607 - val_accuracy: 0.9000\n",
            "Epoch 18/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5249 - accuracy: 0.7867 - val_loss: 0.2363 - val_accuracy: 0.9000\n",
            "Epoch 19/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5165 - accuracy: 0.7867 - val_loss: 1.2563 - val_accuracy: 0.7500\n",
            "Epoch 20/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5257 - accuracy: 0.7867 - val_loss: 0.2736 - val_accuracy: 0.8000\n",
            "Epoch 21/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5250 - accuracy: 0.7867 - val_loss: 0.2799 - val_accuracy: 0.9000\n",
            "Epoch 22/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5244 - accuracy: 0.7867 - val_loss: 0.2403 - val_accuracy: 0.9500\n",
            "Epoch 23/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5238 - accuracy: 0.7867 - val_loss: 1.4848 - val_accuracy: 0.8500\n",
            "Epoch 24/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5239 - accuracy: 0.7867 - val_loss: 0.2368 - val_accuracy: 0.7000\n",
            "Epoch 25/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5217 - accuracy: 0.7867 - val_loss: 0.2418 - val_accuracy: 0.7500\n",
            "Epoch 26/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 1.5803 - val_accuracy: 0.6500\n",
            "Epoch 27/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 0.2114 - val_accuracy: 0.9500\n",
            "Epoch 28/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5172 - accuracy: 0.7867 - val_loss: 0.2747 - val_accuracy: 0.8500\n",
            "Epoch 29/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2480 - val_accuracy: 0.8000\n",
            "Epoch 30/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 1.4079 - val_accuracy: 0.7000\n",
            "Epoch 31/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5215 - accuracy: 0.7867 - val_loss: 0.2360 - val_accuracy: 0.9000\n",
            "Epoch 32/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5214 - accuracy: 0.7867 - val_loss: 1.4954 - val_accuracy: 0.8500\n",
            "Epoch 33/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 1.5555 - val_accuracy: 0.7000\n",
            "Epoch 34/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 0.2574 - val_accuracy: 0.8000\n",
            "Epoch 35/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 0.2535 - val_accuracy: 0.8000\n",
            "Epoch 36/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2162 - val_accuracy: 0.9000\n",
            "Epoch 37/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2442 - val_accuracy: 0.8000\n",
            "Epoch 38/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2817 - val_accuracy: 0.7500\n",
            "Epoch 39/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5219 - accuracy: 0.7867 - val_loss: 0.2390 - val_accuracy: 0.8000\n",
            "Epoch 40/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5222 - accuracy: 0.7867 - val_loss: 0.2364 - val_accuracy: 0.8500\n",
            "Epoch 41/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.5087 - val_accuracy: 0.6000\n",
            "Epoch 42/215\n",
            "347/347 [==============================] - 43s 122ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2493 - val_accuracy: 0.9500\n",
            "Epoch 43/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 1.5588 - val_accuracy: 0.8000\n",
            "Epoch 44/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5221 - accuracy: 0.7867 - val_loss: 0.2607 - val_accuracy: 0.8000\n",
            "Epoch 45/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2394 - val_accuracy: 0.9000\n",
            "Epoch 46/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2423 - val_accuracy: 0.7500\n",
            "Epoch 47/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5210 - accuracy: 0.7867 - val_loss: 1.5410 - val_accuracy: 0.8000\n",
            "Epoch 48/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5216 - accuracy: 0.7867 - val_loss: 0.2636 - val_accuracy: 0.7500\n",
            "Epoch 49/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2611 - val_accuracy: 0.7500\n",
            "Epoch 50/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2578 - val_accuracy: 0.8500\n",
            "Epoch 51/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2360 - val_accuracy: 0.9000\n",
            "Epoch 52/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2384 - val_accuracy: 0.9500\n",
            "Epoch 53/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5213 - accuracy: 0.7867 - val_loss: 0.2380 - val_accuracy: 0.8500\n",
            "Epoch 54/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5218 - accuracy: 0.7867 - val_loss: 0.2350 - val_accuracy: 0.6500\n",
            "Epoch 55/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2302 - val_accuracy: 0.7500\n",
            "Epoch 56/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2472 - val_accuracy: 0.9000\n",
            "Epoch 57/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5211 - accuracy: 0.7867 - val_loss: 0.2477 - val_accuracy: 0.9000\n",
            "Epoch 58/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2642 - val_accuracy: 0.8500\n",
            "Epoch 59/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5209 - accuracy: 0.7867 - val_loss: 0.2633 - val_accuracy: 0.8500\n",
            "Epoch 60/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2306 - val_accuracy: 0.7000\n",
            "Epoch 61/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2103 - val_accuracy: 0.8000\n",
            "Epoch 62/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.4872 - val_accuracy: 0.8500\n",
            "Epoch 63/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5632 - val_accuracy: 0.7000\n",
            "Epoch 64/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2420 - val_accuracy: 0.8500\n",
            "Epoch 65/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2497 - val_accuracy: 0.8500\n",
            "Epoch 66/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2345 - val_accuracy: 0.8500\n",
            "Epoch 67/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 1.5134 - val_accuracy: 0.6000\n",
            "Epoch 68/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2430 - val_accuracy: 0.9500\n",
            "Epoch 69/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5970 - val_accuracy: 0.8000\n",
            "Epoch 70/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2466 - val_accuracy: 0.8500\n",
            "Epoch 71/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5182 - accuracy: 0.7867 - val_loss: 0.2134 - val_accuracy: 0.8500\n",
            "Epoch 72/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5223 - accuracy: 0.7867 - val_loss: 1.5798 - val_accuracy: 0.9000\n",
            "Epoch 73/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5210 - accuracy: 0.7867 - val_loss: 1.5694 - val_accuracy: 0.7000\n",
            "Epoch 74/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2297 - val_accuracy: 0.7500\n",
            "Epoch 75/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2214 - val_accuracy: 0.8500\n",
            "Epoch 76/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2523 - val_accuracy: 0.7500\n",
            "Epoch 77/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 1.4870 - val_accuracy: 0.6000\n",
            "Epoch 78/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2575 - val_accuracy: 0.9500\n",
            "Epoch 79/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2508 - val_accuracy: 0.9500\n",
            "Epoch 80/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2419 - val_accuracy: 0.8500\n",
            "Epoch 81/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2553 - val_accuracy: 0.7500\n",
            "Epoch 82/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5210 - accuracy: 0.7867 - val_loss: 0.2408 - val_accuracy: 0.8500\n",
            "Epoch 83/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2236 - val_accuracy: 0.6500\n",
            "Epoch 84/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2520 - val_accuracy: 0.7000\n",
            "Epoch 85/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5209 - accuracy: 0.7867 - val_loss: 0.2475 - val_accuracy: 0.9500\n",
            "Epoch 86/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5192 - accuracy: 0.7867 - val_loss: 0.2267 - val_accuracy: 0.9000\n",
            "Epoch 87/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2143 - val_accuracy: 0.8500\n",
            "Epoch 88/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.6241 - val_accuracy: 0.7500\n",
            "Epoch 89/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2348 - val_accuracy: 0.8500\n",
            "Epoch 90/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2509 - val_accuracy: 0.8500\n",
            "Epoch 91/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2371 - val_accuracy: 0.7500\n",
            "Epoch 92/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 1.5187 - val_accuracy: 0.7500\n",
            "Epoch 93/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5196 - accuracy: 0.7867 - val_loss: 0.2468 - val_accuracy: 0.9000\n",
            "Epoch 94/215\n",
            "347/347 [==============================] - 43s 124ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2470 - val_accuracy: 0.9000\n",
            "Epoch 95/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2535 - val_accuracy: 0.8500\n",
            "Epoch 96/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2310 - val_accuracy: 0.7000\n",
            "Epoch 97/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2519 - val_accuracy: 0.7500\n",
            "Epoch 98/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5194 - accuracy: 0.7867 - val_loss: 0.2363 - val_accuracy: 0.7500\n",
            "Epoch 99/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2214 - val_accuracy: 0.8500\n",
            "Epoch 100/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2549 - val_accuracy: 0.8500\n",
            "Epoch 101/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5637 - val_accuracy: 0.7500\n",
            "Epoch 102/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5188 - accuracy: 0.7867 - val_loss: 0.2697 - val_accuracy: 0.8500\n",
            "Epoch 103/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2413 - val_accuracy: 0.8500\n",
            "Epoch 104/215\n",
            "347/347 [==============================] - 43s 123ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2277 - val_accuracy: 0.7500\n",
            "Epoch 105/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2371 - val_accuracy: 0.9000\n",
            "Epoch 106/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2307 - val_accuracy: 0.7500\n",
            "Epoch 107/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5211 - accuracy: 0.7867 - val_loss: 1.5598 - val_accuracy: 0.9000\n",
            "Epoch 108/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2393 - val_accuracy: 0.8000\n",
            "Epoch 109/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 1.5640 - val_accuracy: 0.8000\n",
            "Epoch 110/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5213 - accuracy: 0.7867 - val_loss: 0.2420 - val_accuracy: 0.8000\n",
            "Epoch 111/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 1.6279 - val_accuracy: 0.7500\n",
            "Epoch 112/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2313 - val_accuracy: 0.8500\n",
            "Epoch 113/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2446 - val_accuracy: 0.9000\n",
            "Epoch 114/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2394 - val_accuracy: 0.7500\n",
            "Epoch 115/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5189 - accuracy: 0.7867 - val_loss: 0.2614 - val_accuracy: 0.7500\n",
            "Epoch 116/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.5308 - val_accuracy: 0.8500\n",
            "Epoch 117/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 1.5502 - val_accuracy: 0.7500\n",
            "Epoch 118/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2405 - val_accuracy: 0.9000\n",
            "Epoch 119/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5208 - accuracy: 0.7867 - val_loss: 0.2415 - val_accuracy: 0.8500\n",
            "Epoch 120/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5186 - accuracy: 0.7867 - val_loss: 0.2170 - val_accuracy: 0.8500\n",
            "Epoch 121/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2258 - val_accuracy: 0.8000\n",
            "Epoch 122/215\n",
            "347/347 [==============================] - 41s 120ms/step - loss: 0.5205 - accuracy: 0.7867 - val_loss: 0.2437 - val_accuracy: 0.8000\n",
            "Epoch 123/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 1.5883 - val_accuracy: 0.8000\n",
            "Epoch 124/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2404 - val_accuracy: 0.7500\n",
            "Epoch 125/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2309 - val_accuracy: 0.9000\n",
            "Epoch 126/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2406 - val_accuracy: 0.8000\n",
            "Epoch 127/215\n",
            "347/347 [==============================] - 42s 121ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2387 - val_accuracy: 0.8500\n",
            "Epoch 128/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5209 - accuracy: 0.7867 - val_loss: 1.4765 - val_accuracy: 0.7000\n",
            "Epoch 129/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5370 - val_accuracy: 0.7500\n",
            "Epoch 130/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 1.5238 - val_accuracy: 0.8500\n",
            "Epoch 131/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2386 - val_accuracy: 0.8000\n",
            "Epoch 132/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2390 - val_accuracy: 0.9000\n",
            "Epoch 133/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2422 - val_accuracy: 0.8000\n",
            "Epoch 134/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2423 - val_accuracy: 0.7000\n",
            "Epoch 135/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2639 - val_accuracy: 0.9000\n",
            "Epoch 136/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5468 - val_accuracy: 0.6000\n",
            "Epoch 137/215\n",
            "347/347 [==============================] - 42s 122ms/step - loss: 0.5195 - accuracy: 0.7867 - val_loss: 0.2380 - val_accuracy: 0.8500\n",
            "Epoch 138/215\n",
            "347/347 [==============================] - 40s 117ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2375 - val_accuracy: 0.9500\n",
            "Epoch 139/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 1.5530 - val_accuracy: 0.8000\n",
            "Epoch 140/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2466 - val_accuracy: 0.9000\n",
            "Epoch 141/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5203 - accuracy: 0.7867 - val_loss: 0.2321 - val_accuracy: 0.6000\n",
            "Epoch 142/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2341 - val_accuracy: 0.9000\n",
            "Epoch 143/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5206 - accuracy: 0.7867 - val_loss: 0.2364 - val_accuracy: 0.7000\n",
            "Epoch 144/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2536 - val_accuracy: 1.0000\n",
            "Epoch 145/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 1.5384 - val_accuracy: 0.8500\n",
            "Epoch 146/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2392 - val_accuracy: 0.8500\n",
            "Epoch 147/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2401 - val_accuracy: 0.8000\n",
            "Epoch 148/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 1.6160 - val_accuracy: 0.8000\n",
            "Epoch 149/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2284 - val_accuracy: 0.8000\n",
            "Epoch 150/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 1.5760 - val_accuracy: 0.7500\n",
            "Epoch 151/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2412 - val_accuracy: 0.7500\n",
            "Epoch 152/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5207 - accuracy: 0.7867 - val_loss: 0.2330 - val_accuracy: 0.8500\n",
            "Epoch 153/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5200 - accuracy: 0.7867 - val_loss: 0.2452 - val_accuracy: 0.9000\n",
            "Epoch 154/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5195 - accuracy: 0.7867 - val_loss: 0.2643 - val_accuracy: 0.7500\n",
            "Epoch 155/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5204 - accuracy: 0.7867 - val_loss: 0.2504 - val_accuracy: 1.0000\n",
            "Epoch 156/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2386 - val_accuracy: 0.9000\n",
            "Epoch 157/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 1.5440 - val_accuracy: 0.6500\n",
            "Epoch 158/215\n",
            "347/347 [==============================] - 41s 117ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2492 - val_accuracy: 0.8000\n",
            "Epoch 159/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 1.5593 - val_accuracy: 0.6500\n",
            "Epoch 160/215\n",
            "347/347 [==============================] - 42s 120ms/step - loss: 0.5199 - accuracy: 0.7867 - val_loss: 0.2413 - val_accuracy: 0.9500\n",
            "Epoch 161/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 1.5783 - val_accuracy: 0.8500\n",
            "Epoch 162/215\n",
            "347/347 [==============================] - 40s 116ms/step - loss: 0.5198 - accuracy: 0.7867 - val_loss: 0.2296 - val_accuracy: 0.7500\n",
            "Epoch 163/215\n",
            "347/347 [==============================] - 46s 132ms/step - loss: 0.5202 - accuracy: 0.7867 - val_loss: 0.2337 - val_accuracy: 0.9000\n",
            "Epoch 164/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5212 - accuracy: 0.7867 - val_loss: 0.2501 - val_accuracy: 0.6500\n",
            "Epoch 165/215\n",
            "347/347 [==============================] - 41s 118ms/step - loss: 0.5201 - accuracy: 0.7867 - val_loss: 0.2369 - val_accuracy: 0.8500\n",
            "Epoch 166/215\n",
            "347/347 [==============================] - 41s 119ms/step - loss: 0.5197 - accuracy: 0.7867 - val_loss: 0.2288 - val_accuracy: 0.8000\n",
            "Epoch 167/215\n",
            "296/347 [========================>.....] - ETA: 5s - loss: 0.5374 - accuracy: 0.7736Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbcZ9PKW0Juv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtUPx8oW08X0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2RLBL2RzfL4",
        "colab_type": "code",
        "outputId": "9c5619fd-e2f2-4a62-c550-2d7d2bff60f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 347 images belonging to 2 classes.\n",
            "Found 133 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIuCypBMzjhS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6xDrxBm5BRG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}