Pneumonia_Diagnosis_using_VGG19_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pneumonia-Diagnosis-using-VGG19 .ipynb",
      "provenance": [],
      "mount_file_id": "1EN1DPcHAVN_u-In0wVkVgyzn7CFGkqNi",
      "authorship_tag": "ABX9TyOFC8l7xVK+mEVhcdZcwpZw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nasiksami/Deep-Learning/blob/main/Pneumonia_Diagnosis_using_VGG19_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxNEtSGT-RGV"
      },
      "source": [
        "**Dataset Link: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOyK7dkMz5Mz"
      },
      "source": [
        "# Install Kaggle API\r\n",
        "!pip install --quiet kaggle"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "XbT5ze-z0OVP",
        "outputId": "9adef8c4-97f4-47bf-fd03-82ea11c5ceb1"
      },
      "source": [
        "# Choose kaggle.json that created for new API token in your account\r\n",
        "from google.colab import files\r\n",
        "files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-79138f71-4e0a-4d0f-83c9-f28d7fef6128\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-79138f71-4e0a-4d0f-83c9-f28d7fef6128\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"nasiksami\",\"key\":\"91bbb2edb4273365531b811ec9c0f46c\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC3y4PbY07Lj"
      },
      "source": [
        "!mkdir -p ~/.kaggle\r\n",
        "!cp kaggle.json ~/.kaggle/\r\n",
        "!chmod 600 ~/.kaggle/kaggle.json\r\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qM_19T-a1ZPL",
        "outputId": "500a2962-3b34-4da7-bf16-a04e87cbf726"
      },
      "source": [
        "!kaggle datasets download -d paultimothymooney/chest-xray-pneumonia"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading chest-xray-pneumonia.zip to /content\n",
            "100% 2.29G/2.29G [00:28<00:00, 36.6MB/s]\n",
            "100% 2.29G/2.29G [00:28<00:00, 85.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etiwdFw31cYJ",
        "outputId": "2b4beec9-4ff6-473f-ed77-013624c42e1f"
      },
      "source": [
        "!kaggle datasets list"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.4)\n",
            "ref                                                          title                                           size  lastUpdated          downloadCount  \n",
            "-----------------------------------------------------------  ---------------------------------------------  -----  -------------------  -------------  \n",
            "alexgude/california-traffic-collision-data-from-switrs       California Traffic Collision Data from SWITRS    1GB  2020-11-22 16:51:55           1548  \n",
            "babyoda/women-entrepreneurship-and-labor-force               Women Entrepreneurship and Labor Force           1KB  2020-11-21 08:38:51           3649  \n",
            "szymonjanowski/internet-articles-data-with-users-engagement  Internet news data with readers engagement       3MB  2020-11-21 17:09:57           1748  \n",
            "sakshigoyal7/credit-card-customers                           Credit Card customers                          379KB  2020-11-19 07:38:44           6942  \n",
            "imoore/2020-us-general-election-turnout-rates                2020 US General Election Turnout rates           4KB  2020-11-26 00:21:15           2350  \n",
            "afrniomelo/3w-dataset                                        3W Dataset - Undesirable events in oil wells   658MB  2020-11-21 21:22:49            388  \n",
            "mrmorj/us-politicians-twitter-dataset                        US Politicians Twitter Dataset                  68KB  2020-11-23 09:54:05            626  \n",
            "patrickb1912/ipl-complete-dataset-20082020                   IPL Complete Dataset (2008-2020)                 1MB  2020-11-23 06:53:37           1617  \n",
            "arioboo/clumps-in-vela-galaxy-images                         Clumps in VELA galaxy images                     8MB  2020-11-23 13:42:09            197  \n",
            "shivamb/netflix-shows                                        Netflix Movies and TV Shows                    971KB  2020-01-20 07:33:56          71887  \n",
            "manchunhui/us-election-2020-tweets                           US Election 2020 Tweets                        353MB  2020-11-09 18:51:59           4674  \n",
            "sootersaalu/amazon-top-50-bestselling-books-2009-2019        Amazon Top 50 Bestselling Books 2009 - 2019     15KB  2020-10-13 09:39:21           8623  \n",
            "unanimad/us-election-2020                                    US Election 2020                               430KB  2020-12-10 13:09:21          11170  \n",
            "terenceshin/covid19s-impact-on-airport-traffic               COVID-19's Impact on Airport Traffic           106KB  2020-10-19 12:40:17           7544  \n",
            "nehaprabhavalkar/indian-food-101                             Indian Food 101                                  7KB  2020-09-30 06:23:43           9505  \n",
            "datasnaek/youtube-new                                        Trending YouTube Video Statistics              201MB  2019-06-03 00:56:47         120113  \n",
            "zynicide/wine-reviews                                        Wine Reviews                                    51MB  2017-11-27 17:08:04         122497  \n",
            "google/tinyquickdraw                                         QuickDraw Sketches                              11GB  2018-04-18 19:38:04           2605  \n",
            "karangadiya/fifa19                                           FIFA 19 complete player dataset                  2MB  2018-12-21 03:52:59         108319  \n",
            "datasnaek/chess                                              Chess Game Dataset (Lichess)                     3MB  2017-09-04 03:09:09          12117  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZ7A-kua16pZ",
        "outputId": "0f359869-4f99-4001-a582-7428979a68f6"
      },
      "source": [
        "!unzip *.zip"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: chest_xray/train/NORMAL/IM-0435-0001-0001.jpeg  \n",
            "  inflating: chest_xray/train/NORMAL/IM-0435-0001.jpeg  \n",
            "  inflating: chest_xray/train/NORMAL/IM-0437-0001-0001.jpeg  \n",
            "  inflating: chest_xray/train/NORMAL/IM-0437-0001-0002.jpeg  \n",
            "  inflating: chest_xray/train/NORMAL/IM-0437-0001.jpeg  \n",
            "  inflating: chest_xray/train/NORMAL/IM-0438-0001.jpeg  \n",
            "  inflating: chest_xray/train/NORMAL/IM-0439-0001-0001.jpeg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fahnYwWx2ASH"
      },
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\r\n",
        "from keras.models import Model\r\n",
        "from keras.applications.vgg16 import VGG16\r\n",
        "from keras.applications.vgg16 import preprocess_input\r\n",
        "from keras.preprocessing import image\r\n",
        "from keras.preprocessing.image import ImageDataset\r\n",
        "from keras.models import Sequential\r\n",
        "import numpy as np\r\n",
        "from glob import glob\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glqtVvmw2xPV"
      },
      "source": [
        "# re-size all the images to this\r\n",
        "IMAGE_SIZE = [224, 224]\r\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwwPxzS82zGu"
      },
      "source": [
        "train_path = '/content/chest_xray/train'\r\n",
        "valid_path = '/content/chest_xray/test'"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ly5LuV4253k",
        "outputId": "3cf71fc8-6e57-460a-aacc-59af2e497c4d"
      },
      "source": [
        "# add preprocessing layer to the front of VGG\r\n",
        "vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfXOnI3L27iM"
      },
      "source": [
        "# don't train existing weights\r\n",
        "for layer in vgg.layers:\r\n",
        "  layer.trainable = False"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SwOX0Tv2-8Z"
      },
      "source": [
        "  # useful for getting number of classes\r\n",
        "folders = glob('/content/chest_xray/train/*')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7byQUDb3DAJ"
      },
      "source": [
        "# our layers - you can add more if you want\r\n",
        "x = Flatten()(vgg.output)\r\n",
        "# x = Dense(1000, activation='relu')(x)\r\n",
        "prediction = Dense(len(folders), activation='softmax')(x)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VJAxDrD3E_e"
      },
      "source": [
        "# create a model object\r\n",
        "model = Model(inputs=vgg.input, outputs=prediction)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coaCuXWe3GQ1",
        "outputId": "6b7e6d01-970f-4178-9450-131c49c98929"
      },
      "source": [
        "# view the structure of the model\r\n",
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\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",
            "Total params: 14,764,866\n",
            "Trainable params: 50,178\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gs3T9Bsd3H1N"
      },
      "source": [
        "# tell the model what cost and optimization method to use\r\n",
        "model.compile(\r\n",
        "  loss='categorical_crossentropy',\r\n",
        "  optimizer='adam',\r\n",
        "  metrics=['accuracy']\r\n",
        ")\r\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3llt8bi03KGB"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataset\r\n",
        "\r\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\r\n",
        "                                   shear_range = 0.2,\r\n",
        "                                   zoom_range = 0.2,\r\n",
        "                                   horizontal_flip = True)\r\n",
        "\r\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOX7TQ7t3Mta",
        "outputId": "024f3734-4aa0-456e-fe1b-7c061b56c63e"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory('/content/chest_xray/train',\r\n",
        "                                                 target_size = (224, 224),\r\n",
        "                                                 batch_size = 32,\r\n",
        "                                                 class_mode = 'categorical')\r\n",
        "\r\n",
        "test_set = test_datagen.flow_from_directory('/content/chest_xray/test',\r\n",
        "                                            target_size = (224, 224),\r\n",
        "                                            batch_size = 32,\r\n",
        "                                            class_mode = 'categorical')\r\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5216 images belonging to 2 classes.\n",
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_S5erlN3Tee",
        "outputId": "cafb5ba9-10ab-47f5-98e8-f0891c87ee96"
      },
      "source": [
        "# fit the model\r\n",
        "r = model.fit_generator(\r\n",
        "  training_set,\r\n",
        "  validation_data=test_set,\r\n",
        "  epochs=5,\r\n",
        "  steps_per_epoch=len(training_set),\r\n",
        "  validation_steps=len(test_set)\r\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-20-39a22121036e>:7: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/5\n",
            "163/163 [==============================] - 108s 660ms/step - loss: 0.1900 - accuracy: 0.9258 - val_loss: 0.2496 - val_accuracy: 0.9135\n",
            "Epoch 2/5\n",
            "163/163 [==============================] - 108s 663ms/step - loss: 0.1094 - accuracy: 0.9576 - val_loss: 0.3229 - val_accuracy: 0.9151\n",
            "Epoch 3/5\n",
            "163/163 [==============================] - 107s 659ms/step - loss: 0.1029 - accuracy: 0.9617 - val_loss: 0.2820 - val_accuracy: 0.9135\n",
            "Epoch 4/5\n",
            "163/163 [==============================] - 107s 659ms/step - loss: 0.0910 - accuracy: 0.9653 - val_loss: 0.5641 - val_accuracy: 0.8622\n",
            "Epoch 5/5\n",
            "163/163 [==============================] - 107s 659ms/step - loss: 0.0847 - accuracy: 0.9707 - val_loss: 0.3271 - val_accuracy: 0.9151\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kdEYLKPs3WI9",
        "outputId": "b331b361-36b0-406b-abfc-6d53643cc9c3"
      },
      "source": [
        "# loss\r\n",
        "plt.plot(r.history['loss'], label='train loss')\r\n",
        "plt.plot(r.history['val_loss'], label='val loss')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "plt.savefig('LossVal_loss')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "K_15Cc0w3ZAK",
        "outputId": "888e92f3-513b-499a-b3c9-5c9029e35cf8"
      },
      "source": [
        "# accuracies\r\n",
        "plt.plot(r.history['accuracy'], label='train accuracy')\r\n",
        "plt.plot(r.history['val_accuracy'], label='val accuracy')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "plt.savefig('AccVal_acc')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4epAlmTH3aXT"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "from keras.models import load_model\r\n",
        "model.save('/content/drive/MyDrive/Chest_xray_model_vgg19.h5')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDd5zG0Y3iVV"
      },
      "source": [
        "from keras.models import load_model\r\n",
        "from keras.preprocessing import image\r\n",
        "from keras.applications.vgg16 import preprocess_input\r\n",
        "import numpy as np\r\n",
        "model = load_model('/content/drive/MyDrive/Chest_xray_model_vgg19.h5')\r\n",
        "img = image.load_img('/content/chest_xray/val/NORMAL/NORMAL2-IM-1427-0001.jpeg', target_size=(224, 224))\r\n",
        "x = image.img_to_array(img)\r\n",
        "x = np.expand_dims(x, axis=0)\r\n",
        "img_data = preprocess_input(x)\r\n",
        "classes = model.predict(img_data)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZFDJVca6ITt",
        "outputId": "33356e7d-11fd-4e8c-80f4-c534482bbfe3"
      },
      "source": [
        "print(classes)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.198211e-12 1.000000e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "fg2RRgwZ6v-V",
        "outputId": "4dbec374-426a-4fb1-894f-160ebb5b36de"
      },
      "source": [
        "\r\n",
        "# list all data in history\r\n",
        "print(r.history.keys())\r\n",
        "# summarize history for accuracy\r\n",
        "plt.plot(r.history['accuracy'])\r\n",
        "plt.plot(r.history['val_accuracy'])\r\n",
        "plt.title('model accuracy')\r\n",
        "plt.ylabel('accuracy')\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.legend(['train', 'Validation'], loc='upper left')\r\n",
        "plt.show()\r\n",
        "# summarize history for loss\r\n",
        "plt.plot(r.history['loss'])\r\n",
        "plt.plot(r.history['val_loss'])\r\n",
        "plt.title('model loss')\r\n",
        "plt.ylabel('loss')\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.legend(['train', 'validation'], loc='upper left')\r\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oaqqm4xkw5HZY+JJfD0kOSEEgIhVzDibf5C8yc96DtfMb9cm/ho5n+euUrJ4bdDURS0EgIhWb9yxkfQpn/QkatAm6msMTFwcXPgUNWsMrV8OuzUFXFJEUBCKyv+3Z8N4fIOMUv/5/bZbaxA953b0FJl0DRYVBVxRxFAQiUpZz8Pb/giuCYQ/Xzi6h8lr3hHP/ASs/gel/DrqaiKMgEJGyFr4My96D0++ExulBV1N9el/hWzczHoSv3wm6moiiIBCRfXZs8Kt4tjseBlwXdDXV7+y/+aUoNNmsDAWBiOzz7q1QkAvDHvUnWqNNyWSzeHj5CtizK+iKIkIU/p8WkcOy+A1YOhmG3AbNjw26mvBp1B4ufho2LoW3bvHnRGKcgkBEYPdW3xpo3QtOvCnoasLvmNP9dZa/mghz/x10NYFTEIgI/Pd2yP0Bhj8K8UGvTl9DBv/Kz5j+7+1+xdIYpiAQiXXfvueXYRj8K2jVPehqak5cnF+DqGFbmHg17Izd65koCERiWd52ePsWaN4FBt8adDU1r05juPQ5yN0a05PNFAQisez9P8KOdTD8MUhICrqaYLTu4ddSyvoUPrwn6GoCoSAQiVUrPvbrCZ1wA6T1DbqaYPW6DPpe46/CtvStoKupcQoCkVi0Z5e/tm+To/3oGYGz/wpt+sDr18Pm5UFXU6MUBCKx6MM/w7ZVMOwRSKwTdDWRISHZTzaLT4y5yWYKApFY8/0cmP0E9L8W0gcFXU1kadQOLnkaNn3tr8UQI5PNFAQisaQgDybfCA3T4Iw7g64mMh19Gpx2ByyaBJ+PDbqaGqEgEIklH/8VNn8L5/8TkusHXU3kOumXcOzZMPV38P3soKsJOwWBSKxYu8CPiul1hV9iQQ6sZLJZO3hlNOzcGHRFYaUgEIkFRQXw5o1Qtzn8SBdmqZQ6jWDkc5C7DV6J7slmCgKRWDDjIdjwFZz3gJ9NK5XTqjuc/xCsmgEf3BV0NWGjIBCJdhuXwid/g24XQ+dzg66m9uk5Cvr9BD57BJa8GXQ1YaEgEIlmxUW+Syi5vr86lxyeoX+Btv3gjRtg07dBV1PtFAQi0Wz2E7Am04dA3WZBV1N7JSTDpeP9ekwvXwH5O4OuqFopCESi1Zbv4MM/QadzfLeQHJmGaXDJONiyzC/PEUWTzRQEItGouNjPjI1PhnMfALOgK4oORw2B0/4Ai1+DOU8GXU21URCIRKN54/xIlx/dCw1aB11NdDnpf6HTufDeHbBqVtDVVAsFgUi02bYa3r8TjjoVel8RdDXRxwwufAIatfeTzXZsCLqiI6YgEIkmzsFbN/v/nv9PdQmFS0pDGPkff4W3Sdf4CXu1mIJAJJp8+SJ89wGceTc07hB0NdGtZVcY9jCsmgnT7gq6miMS1iAws6Fm9o2ZLTez2w6y38Vm5sysXzjrEYlqO9bDf2+D9if6CVASfj0u9ct5z3oUFr8edDWHLWxBYGbxwGPA2cBxwI/N7LgK9qsP3AzMCVctIlHPOXjnV1CY7y82E6fGfo350f9BWn8/cW/TN0FXc1jC+a9lALDcObfCObcHeAkYXsF+fwL+CuSFsRaR6Lb4dfj6bTj1d9DsmKCriS0JSTBiPCSkhCab7Qi6oioLZxC0BVaXup0duq+EmfUB2jnn3jnYE5nZdWaWaWaZmzZtqv5KRWqzXVvg3V9Dm94w8Iagq4lNDdvCiGdgy3LfMqhlk80Caz+aWRzwAPCrQ+3rnBvrnOvnnOvXvHnz8BcnUpv897d+9MrwxyA+IehqYlfGyXD6nbDkDZj9eNDVVEk4g2AN0K7U7bTQfXvVB7oBH5lZFjAQmKwTxiJV8M0U+OoVOPlWP4pFgjXoZuh8Hrz3B8iaGXQ1lRbOIJgLdDSzDDNLAkYBk/dudM5td841c86lO+fSgdnAMOdcZhhrEokeudvg7f+FFl39pRUleGZwwePQJMPPL9ixPuiKKiVsQeCcKwRuBKYCS4GJzrnFZnaPmQ0L13FFYsb7f4CdG2D4o/6EpUSGvZPN8nf4mce1YLJZWM8ROOfedc4d65w72jl3b+i+PzrnJlew7xC1BkQq6bvpMH8CnHgTtO0TdDVSXosufhjv97Pg/T8GXc0habCxSG2TvxPeugmaHgNDDjhPU4LW/RI4/uf+xPGi14Ku5qAUBCK1zQf3+IXlhj8GiXWCrkYO5sw/Qbvj/ZDSjV8HXc0BKQhEapNVs+DzsTDgOmg/MOhq5FASkmDEs5CU6ieb5eUEXVGFFAQitUVBLky+ERq1g9Mjv99ZQhq0gUuega0r4M0bInKymYJApLb46D4/c/X8hyG5XtDVSFVkDIYz7oKlk/0CdRFGQSBSG6yZD589An2ugqNPDboaORwn/g90GeYvGpQ1I+hqylAQiES6wj3+ZGO9FnDWn4OuRg6XmT/B3+QoeOUayFkXdEUlFAQikW7Gg7BxMZz3kJ+sJLVXSgM/2WzPLnjlah/yEUBBIBLJNiyBT/4O3UdAp6FBVyPVoUVnGP4IrJ7jZ4dHAAWBSKQqKvSjTFIawtC/Bl2NVKduF8PAX8CcJ+GrSUFXoyAQiVizH4O18+Gcv0PdpkFXI9XtzHug/Qkw+X9g49JAS1EQiESizcth+v/5JY27Xhh0NRIO8YmhyWb1Ap9spiAQiTTFxX7iWEIynPsPP9pEolP9Vj4Mtq6EN38R2GQzBYFIpMl82q9aOfQ+/0Eh0S19kO8mWvoWfPZwICUoCEQiyQ+r/ISjY86Anj8OuhqpKSfcAMcNh2l3wcpPavzwCgKRSOEcvHWz7wo67yF1CcWSvZPNmh4Dk8ZAztoaPbyCQCRSfPEfWDEdzrzbLywnsSW5vp9sVpALE2t2spmCQCQS5KyDqb+HDidB3zFBVyNBad7JX3o0+3N47/c1dlgFgUjQnIN3fglFe2DYwxCnt2VM63ohnHCjv+7Ewok1cshK/Yszs5vNrIF5T5vZfDM7K9zFicSERa/CN+/CaXdA06ODrkYiwRl3QfsT/TmjDYvDfrjKfvUY45zLAc4CGgNXAveFrSqRWLFrM0z5DbTtBwOvD7oaiRTxiTDiGX/e4OUrIG97WA9X2SDYO3zhHOA559ziUveJyOGa8hvI3+H7hePig65GIkn9VjBiPGz7Ht4I72SzygbBPDN7Dx8EU82sPlActqpEYsHX7/huoZN/Ay26BF2NRKIOJ8CZf4Kv34aZD4XtMAmV3O8nQC9ghXNut5k1Aa4JW1Ui0S73B3j7l9CyO5x0S9DVSCQbeD1kz4UP7oE2veGoIdV+iMoGwQnAAufcLjO7AugD/LPaq5HIUlysESzhMvUO2LUJLnvZ9weLHIgZDHsEdm4I2yEqGwRPAD3NrCfwK+DfwATglHAVJgEpyIUlk2H+eL/eTfMu0K4/pA2AtP5+5qPC4cgs/wAW/AdO+iW06RV0NVIbJNeD0e+EbbZ5ZYOg0DnnzGw48Khz7mkz+0lYKpJgbFwK88bDly9C3jZonAEDfgabv4FFr8O8Z/1+KY18IKT19wHRtq8un1gV+Tv8kMBmx8Ipvw26GqlNwrjkSGWDYIeZ3Y4fNjrYzOIAtWdruz27YfHr/tv/6jkQlwhdzoe+oyF98L5v/sXFsPlbP9sxey6sngvLpwEOMGjeuWyrodmxajUcyLS7YXs2/OQ9SEwJuhoRoPJBMBK4DD+fYL2ZtQf+Hr6yJKzWf+W//S+cCPnboWlHOOvPfrXLus323z8uzl9ntUVn6HOVvy9vO6yZ50Mh+3NY8ibMn+C3pTT04+JLWg39oE6jmvv7IlXWTJj7L3+JwnYDgq5GpIS5So5NNbOWQP/Qzc+dcxvDVtVB9OvXz2VmZgZx6Notf6cfqjh/vP8Aj0/2y972HQ0dTjzyZmdxMWxZXrbVsHEJvtUANOtUttXQvHNstRoKcuGJQeCK4PrPIKlu0BVJjDGzec65fhVtq1SLwMwuxbcAPsJPJHvEzH7tnAv+qstycGu/8P37X02CPTv9B/DQ+6DHSEhtUn3HiYuD5sf6n95X+Pvycvw1d/e2Gr5+x6+wCZDcwJ9fSOvvvx237Vu99USa6f8HW7+DqyYrBCTiVLZr6PdA/72tADNrDkwDFASRKC8HvnrFf/tf9yUk1IFuF0Gfq/2Hbk2tc5/SwI95PmqIv+0cbPmubKvh0/vBheYmNu3o69t7MrpFl+iYbbtmHsx61Le+jtJAO4k8lQ2CuHJdQVuoxKxkMxuKn28QD/zbOXdfue0/B24AioCdwHXOuSWVrElKc85/4Mx7Bha9BgW7oWU3OOd+6D4iMvrozaDZMf6n12X+vvwdvtWyOhQO3/4XFjzvtyXVh7Z9SrUa+kHdpsHVfzgK8+GNG6B+a385QpEIVNkg+K+ZTQVeDN0eCbx7sAeYWTzwGHAmkA3MNbPJ5T7oX3DOPRnafxjwADC0CvVL7jZ/0nf+eNiwCBLrQvdLoM9o/yEa6Ve5Sq4PGSf7H/CBtnWFD4XsuT4gZjzo+9YBmhxdrtVwHMRX9p9xAD79B2xaCpe9omG2ErEq9Q5yzv3azC4GBoXuGuuce/0QDxsALHfOrQAws5eA4UBJEIRWNN2rLiVnFuWgnPPDPec9C4vfgMJcaN3LX96w28W+S6a2MvNLMTc9GnqO8vft2VW21bDsfT/fAXzwlW41pPWveORTENYv8kHQYxQcq1XbJXJV+quUc+5V4NUqPHdbYHWp29nA8eV3MrMbgF8CScBpFT2RmV0HXAfQvn37KpQQZXZvhS9f8t/+N33tu056/dj3/UfzDNWkupB+kv8BH4Q/ZJVtNcz8575WQ+OMsq2Glt1qvtVQVAhv3gB1GsPQv9TssUWq6KDvDjPbQcXf0g1wzrkj/urpnHsMeMzMLgPuAK6uYJ+xwFjww0eP9Ji1inOwaqb/9r9kMhTl+77yYY/6Kxkl1wu6wppnBk0y/E+PS/19e3bDugX7Wg3fTYeFL/ttianQpg+k9dsXEPVahLfGWY/4ei6dEN2joSQqHDQInHP1j+C51wClr8CdFrrvQF7Cr2kk4C9YsuAF/+1/y3JIbgh9r/bf/lt1C7q6yJOU6udDdDjR33bOr+NeutUw61GYWei3N+pQttXQqnv1Lf62eRlM/wt0GebnaohEuHC2l+cCHc0sAx8Ao/Czk0uYWUfn3LLQzXOBZcSy4mLI+sR/+1/6NhQXQLuBMPhW/4GSlBp0hbWHGTTu4H+6X+LvK8j1w2lXf+6HsK781A+zBUhI8Uv8lj7XUL9V1Y9bXOS7hJJS/YgtkVogbEHgnCs0sxuBqfjho+Occ4vN7B4g0zk3GbjRzM4ACoAfqKBbKCbs2OCHTM4f7/u+UxrBgGv9t/8WnYOuLnok1oH2A/0P+FbD9uzQvIZMHxCzn4DPHvbbG7YPzYYOzYhu1R0Skg5+jM//5U/kX/gU1G8Z3r9HpJpUeomJSBE1S0wUF8OKD/23/2+mQHEhdDjJTzrqcr4WJAtKQR6sX7iv1ZCdCTmhHs34ZH9SvnSroUGbfY/9IQseP8Gf1L5sYuQP3ZWYcsRLTEg1ylkLXzzvF2jb/j2kNvVXIOpzNTTrGHR1kpjiP+RLLwq3fU3ZVsPnY/35BoAGaftaDV+/CxYP5z2oEJBaRUFQE4qL/Nj3+eP9zFlX7JddOPNu6HwuJCQHXaEcTMO20PBCP0oL/Gzh9V+VbTUsDk2rOe9BaJgWXK0ih0FBEE7bVvtF1r54zncv1G0Bg272Szk3OSro6uRwJST7oahp/YBf+Pty1sG2VdBuv6kyIhFPQVDdigrg26n+2/+y9/19x5zuV/zsdLauTxutGrT2PyK1kIKguvyQBfOf8y2Anev9ImMn3wq9r/RDGEVEIpSC4EgU7oFv3vXf/r+b7k8QdjzLn/jteFZkL4YmIhKiT6rDseU7/+G/4AXYtcmPHBlyu78gS8O2QVcnIlIlCoLKKsyHpW/5cf9Zn/phgp3O9t/+jzk9Oi6gIiIxSUFwKJu+3fftP3crNGoPp/0Bel2uk4MiEhUUBBUpyPUrfc57Fr7/DOIS/Hj/PlfDUafG1kXXRSTqKQhK27DEf/v/8iXI2+bH+p9xt7+sYriXLRYRCYiCYM9uPyt03rN+lmh8kl/rp+9ov/aPvrLAtiIAABFMSURBVP2LSJSL3SBYt9B/+184EfJzoGlHOOte6Pnj2neBdBGRIxBbQZC/Exa96r/9r53vV5PseoH/9t/+BC0UJiIxKXaCIPMZeO8O2LMTmneBoX/1lznUZQRFJMbFThA0yfBX+eo72i8ZrG//IiJALAXBUUP8j4iIlKEhMSIiMU5BICIS4xQEIiIxTkEgIhLjFAQiIjFOQSAiEuMUBCIiMU5BICIS4xQEIiIxTkEgIhLjFAQiIjFOQSAiEuMUBCIiMU5BICIS4xQEIiIxLqxBYGZDzewbM1tuZrdVsP2XZrbEzBaa2Qdm1iGc9YiIyP7CFgRmFg88BpwNHAf82MyOK7fbF0A/51wPYBLwt3DVIyIiFQtni2AAsNw5t8I5twd4CRheegfn3HTn3O7QzdlAWhjrERGRCoQzCNoCq0vdzg7ddyA/AaZUtMHMrjOzTDPL3LRpUzWWKCIiEXGy2MyuAPoBf69ou3NurHOun3OuX/PmzWu2OBGRKBfOi9evAdqVup0Wuq8MMzsD+D1winMuP4z1iIhIBcLZIpgLdDSzDDNLAkYBk0vvYGa9gaeAYc65jWGshY05eWRmbcU5F87DiIjUOmFrETjnCs3sRmAqEA+Mc84tNrN7gEzn3GR8V1A94BUzA/jeOTcsHPU8N3sVj3y4nB5pDRkzKINzurcmKSEiesZERAJlte0bcr9+/VxmZmaVH7d7TyGvzV/DuJkrWbFpFy3qJ3PlwA5cdnx7mtZLDkOlIiKRw8zmOef6VbgtVoJgr+JixyfLNjFuZhaffLuJpIQ4LujVhmsGZdCldYNqrFREJHIcLAjCebI4IsXFGUM6tWBIpxYs37iDZ2Zm8er8bCZmZnPi0U0ZMyiD0zq3IC7Ogi5VRKRGxFyLoCLbdu/hxc9XM2FWFuu255HeNJWrT0xnRL921EuOuawUkSikrqFKKigqZuri9YybsZL532+jfnICl/Zvx+gT02nXJDUsxxQRqQkKgsPwxfc/8MzMLN79ah3FznFGl5aMOSmD4zOaEBrhJCJSaygIjsD67Xk8NzuLF+Z8zw+7CziudQPGnJTB+T1bk5wQX2N1iIgcCQVBNcgrKOKNL/zw02837KRZvSQuP74Dlw9sT4v6KTVej4hIVSgIqpFzjpnLtzBu5ko+/HojSfFxnN+zDdcMSqdb24aB1SUicjAaPlqNzIyTOjbjpI7NWLFpJ+M/y+KVedm8Oj+bARlNGDMogzOPa0m8hp+KSC2hFkE12J5bwMS5q3n2syzWbMslrXEdRp+YzqX929EgJTHo8kRE1DVUUwqLinl/yQaemZnF51lbqZsUz4h+7bj6xHQymtUNujwRiWEKggB8lb2dZ2au5K2FayksdpzWqQVjTsrgxKObavipiNQ4BUGANubk8Z853/P87FVs2bWHTi3rM+akdIb3aktKooafikjNUBBEgLyCIiZ/uZZxM1by9fodNKmbxGUD2nPlCR1o2UDDT0UkvBQEEcQ5x+wVWxk3cyXTlm4g3ozzerTmmkEZ9GzXKOjyRCRKafhoBDEzTji6KScc3ZRVW3bx7GdZvJKZzRsL1tK3Q2PGDMrgR11bkhCvi+aISM1QiyAC7Mgr4JXMbJ79LIvvt+6mTcMUrjoxnR/3b0/DVA0/FZEjp66hWqKo2PHBUj/8dNaKLdRJjOfivm0ZfWIGx7SoF3R5IlKLKQhqoSVrc3hm5kreXLCWPUXFnHJsc8aclMHJHZtp+KmIVJmCoBbbvDOfF+Z8z4RZq9i8M59jWtTjmkHpXNQ7jTpJGn4qIpWjIIgC+YVFvLNwHeNmrmTRmhwa1knkxwPac9UJHWjTqE7Q5YlIhFMQRBHnHJmrfmDcjJVMXbweM+Psbq0Yc1IGfdo3Dro8EYlQGj4aRcyM/ulN6J/ehNVbdzNhVhYvzV3N2wvX0bNdI8YMSuec7q1J1PBTEakktQiiwK78Ql6dn80zM7NYuXkXrRqkcOUJHbhsQHsa100KujwRiQDqGooRxcWOj77dyLgZWcxYvpnkhDgu6tOWawZlcGzL+kGXJyIBUtdQjIiLM07r3JLTOrfkm/U7ePazlbw2fw0vfr6awR2bMWZQBqcc25w4XTRHREpRiyDKbd21hxc//54Js7LYkJPPUc3qMnpQOhf3SaNusr4HiMQKdQ0JBUXFvPvVOsbNzOLL1duon5LAqP7tuOqEdNo1SQ26PBEJMwWBlDH/ez/8dMqi9Tjn+FFXP/y0X4fGmrUsEqV0jkDK6NO+MX0ua8zabblMmLWKFz//nimL1tOtbQPGDMrgvB5tSErQ8FORWKEWgbB7TyGvf7GGcTNW8t2mXTSvn8yVAzswakA7mtdLVitBJAqoa0gqpbjY8enyzYybsZKPv90EgBkkJ8SRkhhPSkI8KYn+9+TEeFJC99dJ3He/3xYX2rf0/XHUKXlc2f1TSu2fnBCnUU0iYRBY15CZDQX+CcQD/3bO3Vdu+8nAQ0APYJRzblI465GDi4szTjm2Oacc25zlG3cybekGducXkldYTF5BEbl7ikp+zysoIr+gmB9272FdQRF5BfvuzyssZk9h8WHXURI8iWUDKHlvcCSUDZeSYDpAAKUkxJOSdOAAUvBIrAtbEJhZPPAYcCaQDcw1s8nOuSWldvseGA3cGq465PAc06LeEV0DoajYkV9YLiAKiskr3BciPjT27ZMb2ie/gv337rM9t4CNoe25pe7PP4LgSYqP862Y8q2bhPiS+8sH0L4w2RcodZLiaZCSSIM6CTRISaRhnUTqpyToanMS8cLZIhgALHfOrQAws5eA4UBJEDjnskLbDv9dLBEpPs5ITUogtYZWuHDOkV/SWtk/ZMqEUamWS16ZMCm7PbegiB15hWzakV/qufcFVGV7VeslJ9AgJYEGdRJpUMcHxN6gKB0aJdvqJJTsk5oUr3M0EnbhDIK2wOpSt7OB48N4PIlhZlbyDb0mOOfYU1RcqgVTzO6CQnbkFbJ9dwE5eQVszy0gJ7fQ/7fkdgHZP+SSk5tDTm4BO/ILD3qchDgrFR77wqR0kJQNlrL7avFBqYxaMXzUzK4DrgNo3759wNWI+OBJTognOSEe6hz+daULi4rZmV94wNDYd7uQnNDva7blkhO6vafo4I3p1FB3VfnQaFBBaJQESqq/r15yglojMSKcQbAGaFfqdlrovipzzo0FxoIfNXTkpYlEhoT4OBqlJtHoMPrQ9naH7R8apUKl1P05uYWs3ZbH13k72J5bwI68g7dG4oxDdmU1CAVJwwq6vTQXpfYIZxDMBTqaWQY+AEYBl4XxeCIxpXR3WMsGKVV+fFGxY2de4QFaIBW3UNbn5JX8fqgT9CmJcRV2WTUs1b3lR3PFlQwdLn0CPjlh/xP3yQlxaqWEQdiCwDlXaGY3AlPxw0fHOecWm9k9QKZzbrKZ9QdeBxoD55vZ3c65ruGqSUT2iY8zGqb6rqB2h959P3kFRaGWRtmuqzKhUipMNu7IY9lGf19OXkGlT7aXl7xfcJS9vTdAygRJaGRX8n7BUm4OTEIF+8fAEGNNKBORGldc7Ni5p7DsUOKC4rIjtwpD2wrLDhMuP7w4v9ww4zL77P29sJii4sP/rNs7xLh8uJTcLjUhMrmiIcgJ+4dNcmIFLaG9j0+Iq/Zhx1prSEQiSlyc+ZPWKYd/or2qCor2hUleQQXhUlhBGJW7nV9BOOXuKWLb7oIy++/dp6Do8MMnIc72C45bzjiWYT3bVOOrEjpWtT+jiEgESoyPIzE+jpq8Vl9RsSsXPuXCpbCI/AOEU0Wtnsap4QlOBYGISJjExxl1kxOomxx0JQen8V0iIjFOQSAiEuMUBCIiMU5BICIS4xQEIiIxTkEgIhLjFAQiIjFOQSAiEuNq3VpDZrYJWHWYD28GbK7GcqqL6qoa1VV1kVqb6qqaI6mrg3OueUUbal0QHAkzyzzQoktBUl1Vo7qqLlJrU11VE6661DUkIhLjFAQiIjEu1oJgbNAFHIDqqhrVVXWRWpvqqpqw1BVT5whERGR/sdYiEBGRchQEIiIxLiqDwMyGmtk3ZrbczG6rYHuymb0c2j7HzNIjpK7RZrbJzBaEfn5aQ3WNM7ONZrboANvNzB4O1b3QzPpESF1DzGx7qdfrjzVQUzszm25mS8xssZndXME+Nf56VbKuIF6vFDP73My+DNV1dwX71Pj7sZJ1BfJ+DB073sy+MLO3K9hW/a+Xcy6qfoB44DvgKCAJ+BI4rtw+vwCeDP0+Cng5QuoaDTwawGt2MtAHWHSA7ecAUwADBgJzIqSuIcDbNfxatQb6hH6vD3xbwf/HGn+9KllXEK+XAfVCvycCc4CB5fYJ4v1YmboCeT+Gjv1L4IWK/n+F4/WKxhbBAGC5c26Fc24P8BIwvNw+w4Hxod8nAaebmUVAXYFwzn0CbD3ILsOBCc6bDTQys9YRUFeNc86tc87ND/2+A1gKtC23W42/XpWsq8aFXoOdoZuJoZ/yI1Rq/P1YyboCYWZpwLnAvw+wS7W/XtEYBG2B1aVuZ7P/G6JkH+dcIbAdaBoBdQFcHOpOmGRm7cJcU2VVtvYgnBBq3k8xs641eeBQk7w3/ttkaYG+XgepCwJ4vULdHAuAjcD7zrkDvl41+H6sTF0QzPvxIeA3QPEBtlf76xWNQVCbvQWkO+d6AO+zL/WlYvPx66f0BB4B3qipA5tZPeBV4BbnXE5NHfdQDlFXIK+Xc67IOdcLSAMGmFm3mjjuoVSirhp/P5rZecBG59y8cB+rtGgMgjVA6eROC91X4T5mlgA0BLYEXZdzbotzLj90899A3zDXVFmVeU1rnHMuZ2/z3jn3LpBoZs3CfVwzS8R/2D7vnHutgl0Ceb0OVVdQr1ep428DpgNDy20K4v14yLoCej8OAoaZWRa++/g0M/tPuX2q/fWKxiCYC3Q0swwzS8KfTJlcbp/JwNWh3y8BPnShMy9B1lWuH3kYvp83EkwGrgqNhhkIbHfOrQu6KDNrtbdv1MwG4P89h/UDJHS8p4GlzrkHDrBbjb9elakroNeruZk1Cv1eBzgT+LrcbjX+fqxMXUG8H51ztzvn0pxz6fjPiA+dc1eU263aX6+EI3lwJHLOFZrZjcBU/Eidcc65xWZ2D5DpnJuMf8M8Z2bL8ScjR0VIXTeZ2TCgMFTX6HDXBWBmL+JHlDQzs2zgTvzJM5xzTwLv4kfCLAd2A9dESF2XANebWSGQC4yqgUAfBFwJfBXqXwb4HdC+VF1BvF6VqSuI16s1MN7M4vHBM9E593bQ78dK1hXI+7Ei4X69tMSEiEiMi8auIRERqQIFgYhIjFMQiIjEOAWBiEiMUxCIiMQ4BYFIDTK/Auh+K0qKBElBICIS4xQEIhUwsytC69UvMLOnQguU7TSzB0Pr139gZs1D+/Yys9mhxcleN7PGofuPMbNpoUXe5pvZ0aGnrxdaxOxrM3u+Bla+FTkoBYFIOWbWBRgJDAotSlYEXA7Uxc/u7Ap8jJ/pDDAB+G1ocbKvSt3/PPBYaJG3E4G9y0z0Bm4BjsNfn2JQ2P8okYOIuiUmRKrB6fgFxuaGvqzXwS9VXAy8HNrnP8BrZtYQaOSc+zh0/3jgFTOrD7R1zr0O4JzLAwg93+fOuezQ7QVAOjAj/H+WSMUUBCL7M2C8c+72Mnea/aHcfoe7Pkt+qd+L0PtQAqauIZH9fQBcYmYtAMysiZl1wL9fLgntcxkwwzm3HfjBzAaH7r8S+Dh0lbBsM7sg9BzJZpZao3+FSCXpm4hIOc65JWZ2B/CemcUBBcANwC78BUzuwHcVjQw95GrgydAH/Qr2rTZ6JfBUaOXIAmBEDf4ZIpWm1UdFKsnMdjrn6gVdh0h1U9eQiEiMU4tARCTGqUUgIhLjFAQiIjFOQSAiEuMUBCIiMU5BICIS4/4fkfOoKabAwhkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0A-LqeU7zoZ",
        "outputId": "84a13587-5c91-4044-fd02-4997c4326696"
      },
      "source": [
        "training_set.reset()\r\n",
        "score = model.evaluate_generator(training_set, (training_set.samples + (32-1)) // 32)\r\n",
        "print(\"For training data set; Loss: \",score[0],\" Accuracy: \", score[1])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For training data set; Loss:  0.056330159306526184  Accuracy:  0.9798696041107178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJ6OI5pb9j7Q",
        "outputId": "dda2e000-074d-47eb-8ec9-4ac6e3ca6991"
      },
      "source": [
        "test_set.reset()\r\n",
        "score = model.evaluate_generator(test_set, (test_set.samples + (32-1)) // 32)\r\n",
        "print(\"For test data set; Loss: \",score[0],\" Accuracy: \", score[1])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For test data set; Loss:  0.32709503173828125  Accuracy:  0.9150640964508057\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}