VGG_Implementation.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOh1+vcBDamjdvJXL1odBc5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashutoshsinha25/computer-vision/blob/main/VGG_Implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M9UIqJ2KGYxU"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import files\n",
        "# files.upload()"
      ],
      "metadata": {
        "id": "QiuuH75DJWLz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a kaggle folder \n",
        "!mkdir ~/.kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acDwjUxeJjow",
        "outputId": "38198a60-2705-420a-8b1a-c49ba9801da9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#copy the json file to the folder\n",
        "!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "Slwkip1dJxIF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# permission for the json to act \n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "-E5wI2ssJ6in"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# list of datasets in kaggle \n",
        "!kaggle datasets list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLweTNOlKGTv",
        "outputId": "daa62271-ea5f-4390-cabe-2a0a577c1554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                             title                                               size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------------  ------------------------------------------------  ------  -------------------  -------------  ---------  ---------------  \n",
            "akshaydattatraykhare/diabetes-dataset                           Diabetes Dataset                                     9KB  2022-10-06 08:55:25           6232        206  1.0              \n",
            "whenamancodes/covid-19-coronavirus-pandemic-dataset             COVID -19 Coronavirus Pandemic Dataset              11KB  2022-09-30 04:05:11           5058        160  1.0              \n",
            "akshaydattatraykhare/car-details-dataset                        Car Details Dataset                                 56KB  2022-10-21 06:11:56           1077         29  1.0              \n",
            "whenamancodes/students-performance-in-exams                     Students Performance in Exams                        9KB  2022-09-14 15:14:54           9179        171  1.0              \n",
            "abiodunonadeji/united-state-superstore-sales                    Superstore Sales                                  1002KB  2022-10-08 10:47:57           1498         25  0.7647059        \n",
            "thedevastator/udemy-courses-revenue-generation-and-course-anal  Udemy Courses                                      429KB  2022-10-17 00:11:53            719         33  1.0              \n",
            "vittoriogiatti/bigmacprice                                      Bigmac Prices                                       14KB  2022-10-19 21:11:14           1451         39  1.0              \n",
            "utkarshsaxenadn/vegetable-classifier-acc-9987                   Vegetable Classifier | ResNet50V2 | Acc : 99.87%    89MB  2022-10-21 01:29:02            429         26  1.0              \n",
            "thedevastator/fast-food-restaurants-in-the-united-states        Fast Food Restaurants in the United States           4MB  2022-10-08 17:30:38           1686         45  1.0              \n",
            "whenamancodes/alcohol-effects-on-study                          Alcohol Effects On Study                            18KB  2022-09-15 03:21:04           5648        103  1.0              \n",
            "whenamancodes/student-performance                               Student Performance                                104KB  2022-10-07 05:14:47           5938        137  1.0              \n",
            "narayan63/netflix-popular-movies-dataset                        Netflix popular movies dataset                       1MB  2022-09-24 08:23:22           4085         65  0.9411765        \n",
            "whenamancodes/airbnb-inc-stock-market-analysis                  Airbnb, Inc. Stock Market Analysis                  10KB  2022-10-02 05:44:12            725         30  1.0              \n",
            "eliasturk/world-happiness-based-on-cpi-20152020                 Happiness and Corruption 2015-2020                  29KB  2022-10-11 22:35:03           1106         34  1.0              \n",
            "dansbecker/melbourne-housing-snapshot                           Melbourne Housing Snapshot                         451KB  2018-06-05 12:52:24          98457       1160  0.7058824        \n",
            "arslanali4343/covid19-data-from-world                           COVID-19 data from World                            11MB  2022-10-02 05:56:12           1588         95  0.9411765        \n",
            "whenamancodes/flight-delay-prediction                           Flight Delay Prediction                             31MB  2022-10-07 05:26:20           1751         41  0.88235295       \n",
            "thedevastator/popularity-of-spotify-top-tracks-by-genre         Popularity of Spotify Top Tracks by Genre            9MB  2022-10-18 17:55:53            573         22  0.7647059        \n",
            "anushabellam/cars-cars-2                                        Cars_India_dataset                                   4KB  2022-10-12 06:34:20            975         32  1.0              \n",
            "thedevastator/disney-character-success-a-comprehensive-analysi  Disney Character Success                            39KB  2022-10-08 17:07:22           1027         33  1.0              \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using api command from kaggle to download dataset\n",
        "!kaggle datasets download -d fanconic/skin-cancer-malignant-vs-benign"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKs0gAYcKN1k",
        "outputId": "93fdccc4-654f-45b8-8362-e9b47a506977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading skin-cancer-malignant-vs-benign.zip to /content\n",
            " 96% 311M/325M [00:02<00:00, 135MB/s]\n",
            "100% 325M/325M [00:02<00:00, 144MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip skin-cancer-malignant-vs-benign.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awEUcG7iKvPI",
        "outputId": "6722c17a-68fa-4658-82bd-86b72f7c243c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing libraries \n",
        "from keras.layers import Input , Lambda , Dense, Flatten \n",
        "from keras.models import Model \n",
        "from keras.applications.vgg16 import VGG16 \n",
        "from keras.applications.vgg16 import preprocess_input \n",
        "from keras.preprocessing import image \n",
        "from keras.preprocessing.image import ImageDataGenerator \n",
        "from keras.models import Sequential \n",
        "\n",
        "import numpy as np \n",
        "from glob import glob \n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore' , category = FutureWarning)"
      ],
      "metadata": {
        "id": "eNBICPVZLAnF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IMAGE_SIZE = [224,224]"
      ],
      "metadata": {
        "id": "P0vo526ULq3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset path \n",
        "train_path = '/content/train'\n",
        "test_path = '/content/test'"
      ],
      "metadata": {
        "id": "b_dHGXP0L3u3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg = VGG16(input_shape = IMAGE_SIZE + [3] ,\n",
        "            weights = 'imagenet' ,\n",
        "            include_top=False)"
      ],
      "metadata": {
        "id": "R1SG_-aLL_qe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# not training existing weights \n",
        "for layer in vgg.layers:\n",
        "  layer.trainable = False"
      ],
      "metadata": {
        "id": "bXHaW7KUMIP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# getting number of classes \n",
        "folders = glob(train_path + '/*')\n",
        "len(folders)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2135hXQMPdO",
        "outputId": "a20a5cff-b93e-4d45-ffed-5777b0905191"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating the model \n",
        "\n",
        "x = Flatten()(vgg.output)\n",
        "outputs = Dense(len(folders) , activation = 'softmax')(x)\n",
        "\n",
        "\n",
        "model = Model(inputs = vgg.input , outputs = outputs)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5f4a3hlMeTc",
        "outputId": "06deb0d5-b778-4f74-9e53-fad92060c9ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 50178     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,764,866\n",
            "Trainable params: 50,178\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import optimizers \n",
        "\n",
        "sgd = optimizers.SGD(lr = 0.1 ,\n",
        "                     decay = 1e-6,\n",
        "                     momentum = 0.9)\n",
        "adam = optimizers.Adam()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JyfVogTDM1dG",
        "outputId": "c155853f-9d8e-42df-900e-f26f85905d8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'binary_crossentropy' ,\n",
        "              optimizer = adam ,\n",
        "              metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "SFgo1Ef_NHd8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data augmentation \n",
        "train_datagen = ImageDataGenerator(\n",
        "    preprocessing_function = preprocess_input ,\n",
        "    rotation_range = 40 ,\n",
        "    width_shift_range = 0.2 ,\n",
        "    height_shift_range = 0.2 ,\n",
        "    shear_range = 0.2 ,\n",
        "    zoom_range = 0.2 ,\n",
        "    horizontal_flip =True,\n",
        "    fill_mode = 'nearest'\n",
        ")"
      ],
      "metadata": {
        "id": "VoHCYhq8NO7l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_datagen = ImageDataGenerator(\n",
        "    preprocessing_function = preprocess_input ,\n",
        "    rotation_range = 40 ,\n",
        "    width_shift_range = 0.2 ,\n",
        "    height_shift_range = 0.2 ,\n",
        "    shear_range = 0.2 ,\n",
        "    zoom_range = 0.2 ,\n",
        "    horizontal_flip =True,\n",
        "    fill_mode = 'nearest'\n",
        ")"
      ],
      "metadata": {
        "id": "sgA6IVPyNk44"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data \n",
        "train = train_datagen.flow_from_directory(\n",
        "    train_path ,\n",
        "    target_size = (224,224),\n",
        "    batch_size = 32 ,\n",
        "    class_mode = 'categorical'\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "worg--3vNs04",
        "outputId": "4ad0789c-03f4-4bc2-fe25-e9007853fd12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2637 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = test_datagen.flow_from_directory(\n",
        "    test_path ,\n",
        "    target_size = (224,224),\n",
        "    batch_size = 32 ,\n",
        "    class_mode = 'categorical'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kC9gOXsxO4LD",
        "outputId": "196b846a-084b-42b4-b977-c1bd06e41c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 660 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint , LearningRateScheduler \n",
        "from keras.callbacks import ReduceLROnPlateau "
      ],
      "metadata": {
        "id": "ByLY2dBMN9HG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reducer = ReduceLROnPlateau(factor = np.sqrt(0.1) ,\n",
        "                            cooldown = 0 ,\n",
        "                            patience = 5,\n",
        "                            min_lr = 0.5e-6)\n",
        "\n",
        "checkpoint = ModelCheckpoint(filepath = 'model.h5' ,\n",
        "                             verbose = 1 ,\n",
        "                             save_best_only = True)"
      ],
      "metadata": {
        "id": "WMyu1xoBOK9C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EPOCHS = 1000 \n",
        "# BATCH_SIZE = 32"
      ],
      "metadata": {
        "id": "G1hKLqT9Of9M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train ,\n",
        "          validation_data = test ,\n",
        "          epochs = 100 ,\n",
        "          validation_steps = 5,\n",
        "          steps_per_epoch = 32,\n",
        "          callbacks = [checkpoint],\n",
        "          verbose = 1\n",
        "          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXBFeBE3OkAk",
        "outputId": "f93823cb-f0f5-436f-9592-64e9ca8b1c5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 2.3991 - accuracy: 0.7432\n",
            "Epoch 1: val_loss improved from inf to 1.83008, saving model to model.h5\n",
            "32/32 [==============================] - 22s 504ms/step - loss: 2.3991 - accuracy: 0.7432 - val_loss: 1.8301 - val_accuracy: 0.7937\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6747 - accuracy: 0.7832\n",
            "Epoch 2: val_loss did not improve from 1.83008\n",
            "32/32 [==============================] - 16s 490ms/step - loss: 1.6747 - accuracy: 0.7832 - val_loss: 1.9997 - val_accuracy: 0.7812\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.9262 - accuracy: 0.7682\n",
            "Epoch 3: val_loss did not improve from 1.83008\n",
            "32/32 [==============================] - 17s 518ms/step - loss: 1.9262 - accuracy: 0.7682 - val_loss: 3.0913 - val_accuracy: 0.7625\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6497 - accuracy: 0.8154\n",
            "Epoch 4: val_loss improved from 1.83008 to 1.28426, saving model to model.h5\n",
            "32/32 [==============================] - 17s 517ms/step - loss: 1.6497 - accuracy: 0.8154 - val_loss: 1.2843 - val_accuracy: 0.8813\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7038 - accuracy: 0.8184\n",
            "Epoch 5: val_loss improved from 1.28426 to 1.27156, saving model to model.h5\n",
            "32/32 [==============================] - 16s 502ms/step - loss: 1.7038 - accuracy: 0.8184 - val_loss: 1.2716 - val_accuracy: 0.8000\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5275 - accuracy: 0.8174\n",
            "Epoch 6: val_loss did not improve from 1.27156\n",
            "32/32 [==============================] - 16s 485ms/step - loss: 1.5275 - accuracy: 0.8174 - val_loss: 1.3187 - val_accuracy: 0.8313\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4925 - accuracy: 0.8350\n",
            "Epoch 7: val_loss did not improve from 1.27156\n",
            "32/32 [==============================] - 16s 488ms/step - loss: 1.4925 - accuracy: 0.8350 - val_loss: 1.5878 - val_accuracy: 0.7937\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4436 - accuracy: 0.8328\n",
            "Epoch 8: val_loss did not improve from 1.27156\n",
            "32/32 [==============================] - 15s 482ms/step - loss: 1.4436 - accuracy: 0.8328 - val_loss: 1.5956 - val_accuracy: 0.8188\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4922 - accuracy: 0.8477\n",
            "Epoch 9: val_loss improved from 1.27156 to 0.88512, saving model to model.h5\n",
            "32/32 [==============================] - 16s 489ms/step - loss: 1.4922 - accuracy: 0.8477 - val_loss: 0.8851 - val_accuracy: 0.8938\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4300 - accuracy: 0.8525\n",
            "Epoch 10: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 486ms/step - loss: 1.4300 - accuracy: 0.8525 - val_loss: 2.8706 - val_accuracy: 0.7437\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6343 - accuracy: 0.8369\n",
            "Epoch 11: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.6343 - accuracy: 0.8369 - val_loss: 1.9905 - val_accuracy: 0.8438\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7188 - accuracy: 0.8291\n",
            "Epoch 12: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.7188 - accuracy: 0.8291 - val_loss: 1.9327 - val_accuracy: 0.8375\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7937 - accuracy: 0.8209\n",
            "Epoch 13: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 475ms/step - loss: 1.7937 - accuracy: 0.8209 - val_loss: 2.3056 - val_accuracy: 0.7688\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7130 - accuracy: 0.8320\n",
            "Epoch 14: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 489ms/step - loss: 1.7130 - accuracy: 0.8320 - val_loss: 1.8015 - val_accuracy: 0.8250\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3795 - accuracy: 0.8535\n",
            "Epoch 15: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 491ms/step - loss: 1.3795 - accuracy: 0.8535 - val_loss: 3.5616 - val_accuracy: 0.7688\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4105 - accuracy: 0.8584\n",
            "Epoch 16: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.4105 - accuracy: 0.8584 - val_loss: 2.3875 - val_accuracy: 0.7937\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7123 - accuracy: 0.8279\n",
            "Epoch 17: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 477ms/step - loss: 1.7123 - accuracy: 0.8279 - val_loss: 1.6584 - val_accuracy: 0.8375\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3772 - accuracy: 0.8535\n",
            "Epoch 18: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 486ms/step - loss: 1.3772 - accuracy: 0.8535 - val_loss: 1.1740 - val_accuracy: 0.8750\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.2991 - accuracy: 0.8594\n",
            "Epoch 19: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 485ms/step - loss: 1.2991 - accuracy: 0.8594 - val_loss: 2.2014 - val_accuracy: 0.7937\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5056 - accuracy: 0.8604\n",
            "Epoch 20: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 480ms/step - loss: 1.5056 - accuracy: 0.8604 - val_loss: 2.0606 - val_accuracy: 0.8188\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5662 - accuracy: 0.8269\n",
            "Epoch 21: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 474ms/step - loss: 1.5662 - accuracy: 0.8269 - val_loss: 2.3918 - val_accuracy: 0.7937\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3850 - accuracy: 0.8587\n",
            "Epoch 22: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 477ms/step - loss: 1.3850 - accuracy: 0.8587 - val_loss: 1.7604 - val_accuracy: 0.8375\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5767 - accuracy: 0.8623\n",
            "Epoch 23: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 481ms/step - loss: 1.5767 - accuracy: 0.8623 - val_loss: 1.7955 - val_accuracy: 0.8250\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5545 - accuracy: 0.8627\n",
            "Epoch 24: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 480ms/step - loss: 1.5545 - accuracy: 0.8627 - val_loss: 1.2600 - val_accuracy: 0.8500\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.2019 - accuracy: 0.8756\n",
            "Epoch 25: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 482ms/step - loss: 1.2019 - accuracy: 0.8756 - val_loss: 2.4434 - val_accuracy: 0.8313\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8444 - accuracy: 0.8350\n",
            "Epoch 26: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.8444 - accuracy: 0.8350 - val_loss: 2.9711 - val_accuracy: 0.7937\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.1969 - accuracy: 0.8706\n",
            "Epoch 27: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 477ms/step - loss: 1.1969 - accuracy: 0.8706 - val_loss: 1.7116 - val_accuracy: 0.8750\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4337 - accuracy: 0.8677\n",
            "Epoch 28: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 478ms/step - loss: 1.4337 - accuracy: 0.8677 - val_loss: 2.1282 - val_accuracy: 0.8125\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8320 - accuracy: 0.8525\n",
            "Epoch 29: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 486ms/step - loss: 1.8320 - accuracy: 0.8525 - val_loss: 1.8776 - val_accuracy: 0.8438\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.1978 - accuracy: 0.8789\n",
            "Epoch 30: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 481ms/step - loss: 1.1978 - accuracy: 0.8789 - val_loss: 1.9818 - val_accuracy: 0.8375\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4795 - accuracy: 0.8652\n",
            "Epoch 31: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 506ms/step - loss: 1.4795 - accuracy: 0.8652 - val_loss: 2.6796 - val_accuracy: 0.8000\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7198 - accuracy: 0.8594\n",
            "Epoch 32: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.7198 - accuracy: 0.8594 - val_loss: 1.6756 - val_accuracy: 0.8500\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4053 - accuracy: 0.8677\n",
            "Epoch 33: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 476ms/step - loss: 1.4053 - accuracy: 0.8677 - val_loss: 2.0747 - val_accuracy: 0.8188\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4741 - accuracy: 0.8657\n",
            "Epoch 34: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 480ms/step - loss: 1.4741 - accuracy: 0.8657 - val_loss: 1.7472 - val_accuracy: 0.8188\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.2682 - accuracy: 0.8691\n",
            "Epoch 35: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.2682 - accuracy: 0.8691 - val_loss: 1.3991 - val_accuracy: 0.8500\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6965 - accuracy: 0.8637\n",
            "Epoch 36: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 477ms/step - loss: 1.6965 - accuracy: 0.8637 - val_loss: 1.8603 - val_accuracy: 0.8438\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6649 - accuracy: 0.8613\n",
            "Epoch 37: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 483ms/step - loss: 1.6649 - accuracy: 0.8613 - val_loss: 2.6645 - val_accuracy: 0.8125\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8661 - accuracy: 0.8604\n",
            "Epoch 38: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.8661 - accuracy: 0.8604 - val_loss: 2.2966 - val_accuracy: 0.8750\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7398 - accuracy: 0.8535\n",
            "Epoch 39: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.7398 - accuracy: 0.8535 - val_loss: 1.5736 - val_accuracy: 0.8250\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6077 - accuracy: 0.8587\n",
            "Epoch 40: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 473ms/step - loss: 1.6077 - accuracy: 0.8587 - val_loss: 2.8213 - val_accuracy: 0.8375\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4138 - accuracy: 0.8857\n",
            "Epoch 41: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 478ms/step - loss: 1.4138 - accuracy: 0.8857 - val_loss: 2.3552 - val_accuracy: 0.8250\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6466 - accuracy: 0.8613\n",
            "Epoch 42: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 483ms/step - loss: 1.6466 - accuracy: 0.8613 - val_loss: 1.9875 - val_accuracy: 0.8125\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3786 - accuracy: 0.8789\n",
            "Epoch 43: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 482ms/step - loss: 1.3786 - accuracy: 0.8789 - val_loss: 2.3400 - val_accuracy: 0.8313\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3100 - accuracy: 0.8776\n",
            "Epoch 44: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 482ms/step - loss: 1.3100 - accuracy: 0.8776 - val_loss: 3.0053 - val_accuracy: 0.8125\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5132 - accuracy: 0.8652\n",
            "Epoch 45: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 483ms/step - loss: 1.5132 - accuracy: 0.8652 - val_loss: 2.6261 - val_accuracy: 0.8500\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7378 - accuracy: 0.8623\n",
            "Epoch 46: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.7378 - accuracy: 0.8623 - val_loss: 4.1035 - val_accuracy: 0.7875\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6428 - accuracy: 0.8477\n",
            "Epoch 47: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 490ms/step - loss: 1.6428 - accuracy: 0.8477 - val_loss: 1.2390 - val_accuracy: 0.8687\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6960 - accuracy: 0.8617\n",
            "Epoch 48: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 491ms/step - loss: 1.6960 - accuracy: 0.8617 - val_loss: 3.5200 - val_accuracy: 0.7937\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 2.0388 - accuracy: 0.8657\n",
            "Epoch 49: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 492ms/step - loss: 2.0388 - accuracy: 0.8657 - val_loss: 2.2636 - val_accuracy: 0.8500\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3355 - accuracy: 0.8789\n",
            "Epoch 50: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 493ms/step - loss: 1.3355 - accuracy: 0.8789 - val_loss: 2.2405 - val_accuracy: 0.8562\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5718 - accuracy: 0.8613\n",
            "Epoch 51: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 491ms/step - loss: 1.5718 - accuracy: 0.8613 - val_loss: 3.0136 - val_accuracy: 0.7937\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6970 - accuracy: 0.8672\n",
            "Epoch 52: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 489ms/step - loss: 1.6970 - accuracy: 0.8672 - val_loss: 2.3079 - val_accuracy: 0.8562\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.9457 - accuracy: 0.8604\n",
            "Epoch 53: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 489ms/step - loss: 1.9457 - accuracy: 0.8604 - val_loss: 2.7893 - val_accuracy: 0.8375\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5777 - accuracy: 0.8647\n",
            "Epoch 54: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.5777 - accuracy: 0.8647 - val_loss: 3.1740 - val_accuracy: 0.8500\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7333 - accuracy: 0.8555\n",
            "Epoch 55: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.7333 - accuracy: 0.8555 - val_loss: 2.4857 - val_accuracy: 0.8188\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6811 - accuracy: 0.8706\n",
            "Epoch 56: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 506ms/step - loss: 1.6811 - accuracy: 0.8706 - val_loss: 2.3092 - val_accuracy: 0.8438\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4506 - accuracy: 0.8975\n",
            "Epoch 57: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 492ms/step - loss: 1.4506 - accuracy: 0.8975 - val_loss: 2.3099 - val_accuracy: 0.8313\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6738 - accuracy: 0.8816\n",
            "Epoch 58: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 477ms/step - loss: 1.6738 - accuracy: 0.8816 - val_loss: 2.8381 - val_accuracy: 0.8188\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6888 - accuracy: 0.8726\n",
            "Epoch 59: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 481ms/step - loss: 1.6888 - accuracy: 0.8726 - val_loss: 2.4830 - val_accuracy: 0.8500\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6006 - accuracy: 0.8818\n",
            "Epoch 60: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 486ms/step - loss: 1.6006 - accuracy: 0.8818 - val_loss: 2.6589 - val_accuracy: 0.8250\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6323 - accuracy: 0.8691\n",
            "Epoch 61: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 483ms/step - loss: 1.6323 - accuracy: 0.8691 - val_loss: 2.7328 - val_accuracy: 0.7937\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4936 - accuracy: 0.8691\n",
            "Epoch 62: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 485ms/step - loss: 1.4936 - accuracy: 0.8691 - val_loss: 3.0333 - val_accuracy: 0.8250\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5740 - accuracy: 0.8836\n",
            "Epoch 63: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 478ms/step - loss: 1.5740 - accuracy: 0.8836 - val_loss: 2.5606 - val_accuracy: 0.8250\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8299 - accuracy: 0.8643\n",
            "Epoch 64: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 483ms/step - loss: 1.8299 - accuracy: 0.8643 - val_loss: 1.8762 - val_accuracy: 0.8625\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8675 - accuracy: 0.8604\n",
            "Epoch 65: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.8675 - accuracy: 0.8604 - val_loss: 2.0780 - val_accuracy: 0.8813\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4456 - accuracy: 0.8945\n",
            "Epoch 66: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 482ms/step - loss: 1.4456 - accuracy: 0.8945 - val_loss: 1.8813 - val_accuracy: 0.8750\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8090 - accuracy: 0.8535\n",
            "Epoch 67: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 488ms/step - loss: 1.8090 - accuracy: 0.8535 - val_loss: 2.5102 - val_accuracy: 0.8562\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3988 - accuracy: 0.8915\n",
            "Epoch 68: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 483ms/step - loss: 1.3988 - accuracy: 0.8915 - val_loss: 1.6889 - val_accuracy: 0.8625\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.2512 - accuracy: 0.9033\n",
            "Epoch 69: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 512ms/step - loss: 1.2512 - accuracy: 0.9033 - val_loss: 3.3475 - val_accuracy: 0.8062\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.3420 - accuracy: 0.8828\n",
            "Epoch 70: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 491ms/step - loss: 1.3420 - accuracy: 0.8828 - val_loss: 2.3674 - val_accuracy: 0.8250\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5289 - accuracy: 0.8838\n",
            "Epoch 71: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 486ms/step - loss: 1.5289 - accuracy: 0.8838 - val_loss: 2.2907 - val_accuracy: 0.8500\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.5274 - accuracy: 0.8789\n",
            "Epoch 72: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 492ms/step - loss: 1.5274 - accuracy: 0.8789 - val_loss: 3.9881 - val_accuracy: 0.7812\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.9511 - accuracy: 0.8633\n",
            "Epoch 73: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 481ms/step - loss: 1.9511 - accuracy: 0.8633 - val_loss: 3.2213 - val_accuracy: 0.8188\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.9248 - accuracy: 0.8617\n",
            "Epoch 74: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 478ms/step - loss: 1.9248 - accuracy: 0.8617 - val_loss: 3.3794 - val_accuracy: 0.8188\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4946 - accuracy: 0.8701\n",
            "Epoch 75: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 486ms/step - loss: 1.4946 - accuracy: 0.8701 - val_loss: 2.4951 - val_accuracy: 0.8500\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4349 - accuracy: 0.8776\n",
            "Epoch 76: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 479ms/step - loss: 1.4349 - accuracy: 0.8776 - val_loss: 2.0853 - val_accuracy: 0.8062\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4420 - accuracy: 0.8896\n",
            "Epoch 77: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 472ms/step - loss: 1.4420 - accuracy: 0.8896 - val_loss: 2.7529 - val_accuracy: 0.8562\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7765 - accuracy: 0.8706\n",
            "Epoch 78: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 475ms/step - loss: 1.7765 - accuracy: 0.8706 - val_loss: 2.9726 - val_accuracy: 0.8125\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 2.0492 - accuracy: 0.8594\n",
            "Epoch 79: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 480ms/step - loss: 2.0492 - accuracy: 0.8594 - val_loss: 3.2119 - val_accuracy: 0.8375\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7615 - accuracy: 0.8779\n",
            "Epoch 80: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 482ms/step - loss: 1.7615 - accuracy: 0.8779 - val_loss: 1.9531 - val_accuracy: 0.8813\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8503 - accuracy: 0.8816\n",
            "Epoch 81: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 475ms/step - loss: 1.8503 - accuracy: 0.8816 - val_loss: 3.0482 - val_accuracy: 0.8062\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.2653 - accuracy: 0.9015\n",
            "Epoch 82: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 496ms/step - loss: 1.2653 - accuracy: 0.9015 - val_loss: 2.5433 - val_accuracy: 0.8500\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7077 - accuracy: 0.8711\n",
            "Epoch 83: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.7077 - accuracy: 0.8711 - val_loss: 2.4910 - val_accuracy: 0.8625\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6131 - accuracy: 0.8746\n",
            "Epoch 84: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 481ms/step - loss: 1.6131 - accuracy: 0.8746 - val_loss: 3.7176 - val_accuracy: 0.8188\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4480 - accuracy: 0.8721\n",
            "Epoch 85: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 487ms/step - loss: 1.4480 - accuracy: 0.8721 - val_loss: 2.7536 - val_accuracy: 0.8500\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7832 - accuracy: 0.8672\n",
            "Epoch 86: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 478ms/step - loss: 1.7832 - accuracy: 0.8672 - val_loss: 2.9878 - val_accuracy: 0.8125\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6284 - accuracy: 0.8799\n",
            "Epoch 87: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 481ms/step - loss: 1.6284 - accuracy: 0.8799 - val_loss: 2.8680 - val_accuracy: 0.8250\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4790 - accuracy: 0.8867\n",
            "Epoch 88: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 483ms/step - loss: 1.4790 - accuracy: 0.8867 - val_loss: 2.9375 - val_accuracy: 0.8313\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6168 - accuracy: 0.8779\n",
            "Epoch 89: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 484ms/step - loss: 1.6168 - accuracy: 0.8779 - val_loss: 3.4959 - val_accuracy: 0.8188\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7409 - accuracy: 0.8766\n",
            "Epoch 90: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 475ms/step - loss: 1.7409 - accuracy: 0.8766 - val_loss: 1.8660 - val_accuracy: 0.8875\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7565 - accuracy: 0.8756\n",
            "Epoch 91: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 476ms/step - loss: 1.7565 - accuracy: 0.8756 - val_loss: 2.9039 - val_accuracy: 0.8313\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.7131 - accuracy: 0.8915\n",
            "Epoch 92: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 477ms/step - loss: 1.7131 - accuracy: 0.8915 - val_loss: 2.6546 - val_accuracy: 0.8500\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 2.2165 - accuracy: 0.8516\n",
            "Epoch 93: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 479ms/step - loss: 2.2165 - accuracy: 0.8516 - val_loss: 2.5028 - val_accuracy: 0.8438\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.6364 - accuracy: 0.8740\n",
            "Epoch 94: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 511ms/step - loss: 1.6364 - accuracy: 0.8740 - val_loss: 2.7521 - val_accuracy: 0.8375\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 2.1558 - accuracy: 0.8633\n",
            "Epoch 95: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 483ms/step - loss: 2.1558 - accuracy: 0.8633 - val_loss: 2.4312 - val_accuracy: 0.8062\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.8058 - accuracy: 0.8770\n",
            "Epoch 96: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 485ms/step - loss: 1.8058 - accuracy: 0.8770 - val_loss: 2.7688 - val_accuracy: 0.8250\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.9011 - accuracy: 0.8633\n",
            "Epoch 97: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 16s 483ms/step - loss: 1.9011 - accuracy: 0.8633 - val_loss: 3.6243 - val_accuracy: 0.8250\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 2.1928 - accuracy: 0.8525\n",
            "Epoch 98: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 480ms/step - loss: 2.1928 - accuracy: 0.8525 - val_loss: 2.2305 - val_accuracy: 0.8500\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4210 - accuracy: 0.8994\n",
            "Epoch 99: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 483ms/step - loss: 1.4210 - accuracy: 0.8994 - val_loss: 3.1420 - val_accuracy: 0.8250\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - ETA: 0s - loss: 1.4982 - accuracy: 0.8915\n",
            "Epoch 100: val_loss did not improve from 0.88512\n",
            "32/32 [==============================] - 15s 473ms/step - loss: 1.4982 - accuracy: 0.8915 - val_loss: 1.4350 - val_accuracy: 0.8938\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7f203d91d0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IYCe8IloPZoQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}