CholletCh4Basic1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CholletCh4Basic1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/ActionPace/DeepLearningTests/blob/master/CholletCh4Basic1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIIJU_danmkT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "240zWZSgn6_f",
        "colab_type": "text"
      },
      "source": [
        "### "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXRwrMLeNx3k",
        "colab_type": "text"
      },
      "source": [
        "## TRAINING A CONVNET FROM SCRATCH ON A SMALL DATASET\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGlnDzyuoD1_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget -q https://s3.amazonaws.com/machinelearningfiles/dogsandcats-train.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rz0kunWkoID_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -q dogsandcats-train.zip\n",
        "!rm dogsandcats-train.zip\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHGhVJQJoURZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os, shutil\n",
        "\n",
        "original_dataset_dir = './train'    \n",
        "\n",
        "base_dir = './cats_and_dogs_small'                 \n",
        "os.mkdir(base_dir)\n",
        "\n",
        "train_dir = os.path.join(base_dir, 'train')                                \n",
        "os.mkdir(train_dir)\n",
        "validation_dir = os.path.join(base_dir, 'validation')                      \n",
        "os.mkdir(validation_dir)\n",
        "test_dir = os.path.join(base_dir, 'test')                                  \n",
        "os.mkdir(test_dir)\n",
        "\n",
        "train_cats_dir = os.path.join(train_dir, 'cats')                           \n",
        "os.mkdir(train_cats_dir)                                                   \n",
        "\n",
        "train_dogs_dir = os.path.join(train_dir, 'dogs')                           \n",
        "os.mkdir(train_dogs_dir)                                                   \n",
        "\n",
        "validation_cats_dir = os.path.join(validation_dir, 'cats')                 \n",
        "os.mkdir(validation_cats_dir)                                              \n",
        "\n",
        "validation_dogs_dir = os.path.join(validation_dir, 'dogs')                 \n",
        "os.mkdir(validation_dogs_dir)                                              \n",
        "\n",
        "test_cats_dir = os.path.join(test_dir, 'cats')                             \n",
        "os.mkdir(test_cats_dir)                                                    \n",
        "\n",
        "test_dogs_dir = os.path.join(test_dir, 'dogs')                             \n",
        "os.mkdir(test_dogs_dir)                                                    \n",
        "\n",
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]                     \n",
        "for fname in fnames:                                                       \n",
        "    src = os.path.join(original_dataset_dir, fname)                       \n",
        "    dst = os.path.join(train_cats_dir, fname)                              \n",
        "    shutil.copyfile(src, dst)                                              \n",
        "\n",
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]               \n",
        "for fname in fnames:                                                       \n",
        "    src = os.path.join(original_dataset_dir, fname)                        \n",
        "    dst = os.path.join(validation_cats_dir, fname)                         \n",
        "    shutil.copyfile(src, dst)                                              \n",
        "\n",
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]               \n",
        "for fname in fnames:                                                       \n",
        "    src = os.path.join(original_dataset_dir, fname)                        \n",
        "    dst = os.path.join(test_cats_dir, fname)                               \n",
        "    shutil.copyfile(src, dst)                                              \n",
        "\n",
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]                     \n",
        "for fname in fnames:                                                       \n",
        "    src = os.path.join(original_dataset_dir, fname)                        \n",
        "    dst = os.path.join(train_dogs_dir, fname)                              \n",
        "    shutil.copyfile(src, dst)                                              \n",
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]               \n",
        "for fname in fnames:                                                       \n",
        "    src = os.path.join(original_dataset_dir, fname)                        \n",
        "    dst = os.path.join(validation_dogs_dir, fname)                         \n",
        "    shutil.copyfile(src, dst)                                              \n",
        "\n",
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]               \n",
        "for fname in fnames:                                                       \n",
        "    src = os.path.join(original_dataset_dir, fname)                        \n",
        "    dst = os.path.join(test_dogs_dir, fname)                               \n",
        "    shutil.copyfile(src, dst) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSGsK8W7ojuG",
        "colab_type": "code",
        "outputId": "c73427ca-09b2-4873-f4f9-d3c5776a4e43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "print('total training cat images:', len(os.listdir(train_cats_dir)))\n",
        "print('total training dog images:', len(os.listdir(train_dogs_dir)))\n",
        "print('total validation cat images:', len(os.listdir(validation_cats_dir)))\n",
        "print('total validation dog images:', len(os.listdir(validation_dogs_dir)))\n",
        "print('total test cat images:', len(os.listdir(test_cats_dir)))\n",
        "print('total test dog images:', len(os.listdir(test_dogs_dir)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total training cat images: 1000\n",
            "total training dog images: 1000\n",
            "total validation cat images: 500\n",
            "total validation dog images: 500\n",
            "total test cat images: 500\n",
            "total test dog images: 500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHJLcgqNonFE",
        "colab_type": "code",
        "outputId": "3a5ee47b-6e39-416a-ec2c-e8438dfef250",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from keras import layers\n",
        "from keras import models"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUbcpNQrorUK",
        "colab_type": "code",
        "outputId": "f9118877-1a3b-4ecb-87ed-09ed9846efd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
        "                        input_shape=(150, 150, 3)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "subCl_zSouQa",
        "colab_type": "code",
        "outputId": "033b902b-09fb-4155-8e6e-4c18b0cc5f8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 72, 72, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 15, 15, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               3211776   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 3,453,121\n",
            "Trainable params: 3,453,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84NnkyGhowlg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import optimizers\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfFKe7wwo1_b",
        "colab_type": "code",
        "outputId": "2387c938-8c58-4951-caed-3c90172f8275",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)             \n",
        "test_datagen = ImageDataGenerator(rescale=1./255)              \n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,                                             \n",
        "        target_size=(150, 150),                                 \n",
        "        batch_size=20,\n",
        "        class_mode='binary')                                   \n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        validation_dir,\n",
        "        target_size=(150, 150),\n",
        "        batch_size=20,\n",
        "        class_mode='binary')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkKo-29ro5py",
        "colab_type": "code",
        "outputId": "440cf285-c7e0-4269-ad89-f9acdbabf1d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "for data_batch, labels_batch in train_generator:\n",
        "     print('data batch shape:', data_batch.shape)\n",
        "     print('labels batch shape:', labels_batch.shape)\n",
        "     break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data batch shape: (20, 150, 150, 3)\n",
            "labels batch shape: (20,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDlNqKiSo87S",
        "colab_type": "code",
        "outputId": "10660725-ff50-495b-aad7-1409ccbd622d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1129
        }
      },
      "source": [
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=100,\n",
        "      epochs=30,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/30\n",
            "100/100 [==============================] - 14s 136ms/step - loss: 0.6880 - acc: 0.5375 - val_loss: 0.6796 - val_acc: 0.5070\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.6579 - acc: 0.6065 - val_loss: 0.6450 - val_acc: 0.6400\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 9s 87ms/step - loss: 0.6132 - acc: 0.6800 - val_loss: 0.6808 - val_acc: 0.5790\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 9s 94ms/step - loss: 0.5788 - acc: 0.6955 - val_loss: 0.5919 - val_acc: 0.6710\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.5375 - acc: 0.7365 - val_loss: 0.5795 - val_acc: 0.6850\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.5211 - acc: 0.7415 - val_loss: 0.6056 - val_acc: 0.6740\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.4911 - acc: 0.7590 - val_loss: 0.6141 - val_acc: 0.6760\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.4664 - acc: 0.7710 - val_loss: 0.5489 - val_acc: 0.7140\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 8s 83ms/step - loss: 0.4339 - acc: 0.7995 - val_loss: 0.5391 - val_acc: 0.7300\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.4154 - acc: 0.8070 - val_loss: 0.6151 - val_acc: 0.6990\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.3963 - acc: 0.8225 - val_loss: 0.6065 - val_acc: 0.7130\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 8s 83ms/step - loss: 0.3797 - acc: 0.8265 - val_loss: 0.5595 - val_acc: 0.7320\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 9s 91ms/step - loss: 0.3437 - acc: 0.8495 - val_loss: 0.5637 - val_acc: 0.7270\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 9s 86ms/step - loss: 0.3206 - acc: 0.8595 - val_loss: 0.5919 - val_acc: 0.7310\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 8s 83ms/step - loss: 0.3115 - acc: 0.8665 - val_loss: 0.5885 - val_acc: 0.7220\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 8s 83ms/step - loss: 0.2912 - acc: 0.8790 - val_loss: 0.6303 - val_acc: 0.7170\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.2700 - acc: 0.8915 - val_loss: 0.5679 - val_acc: 0.7380\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.2365 - acc: 0.9065 - val_loss: 0.6187 - val_acc: 0.7130\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 8s 81ms/step - loss: 0.2199 - acc: 0.9160 - val_loss: 0.6664 - val_acc: 0.7140\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.2038 - acc: 0.9245 - val_loss: 0.6123 - val_acc: 0.7440\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.1907 - acc: 0.9285 - val_loss: 0.6371 - val_acc: 0.7380\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 8s 83ms/step - loss: 0.1624 - acc: 0.9390 - val_loss: 0.6687 - val_acc: 0.7300\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 9s 95ms/step - loss: 0.1463 - acc: 0.9495 - val_loss: 0.7924 - val_acc: 0.7110\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 8s 84ms/step - loss: 0.1344 - acc: 0.9560 - val_loss: 0.7336 - val_acc: 0.7310\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.1209 - acc: 0.9565 - val_loss: 0.7361 - val_acc: 0.7250\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.0988 - acc: 0.9725 - val_loss: 0.7570 - val_acc: 0.7330\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.0894 - acc: 0.9710 - val_loss: 0.7866 - val_acc: 0.7190\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 8s 81ms/step - loss: 0.0761 - acc: 0.9770 - val_loss: 0.8274 - val_acc: 0.7450\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.0688 - acc: 0.9825 - val_loss: 0.8456 - val_acc: 0.7350\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 8s 82ms/step - loss: 0.0537 - acc: 0.9850 - val_loss: 0.8441 - val_acc: 0.7400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Y2Du0Ofo_eU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('cats_and_dogs_small_1.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3Vvbxyjq0rV",
        "colab_type": "code",
        "outputId": "0b5b0674-2db0-4861-872a-330407bda45e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fhm3bqn/b27e7FAK4FMAPP7jp+/a5IPz886o33+y+pOE/kg0bumAdnvYSUe3Q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\nbYjWRsSi6QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AX3qeSwYq6fU",
        "colab_type": "code",
        "outputId": "0f19e0cf-cb31-48f0-b01e-698fe89e7126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z5IDPBlXRGFVJs7OzNScnJybnLpaZGXyO+ObN3QRl4Podt2jh5nn/6KNols4p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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pOr_uC3rF4Z",
        "colab_type": "text"
      },
      "source": [
        "### Using data augmentation\n",
        "\n",
        "Need GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cA0Rry3arU37",
        "colab_type": "text"
      },
      "source": [
        "### USING A PRETRAINED CONVNET\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL_X-XDZq-0j",
        "colab_type": "code",
        "outputId": "3eca269d-702d-43b1-9f18-48e0a958ae6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from keras.applications import VGG16\n",
        "\n",
        "conv_base = VGG16(weights='imagenet',\n",
        "                  include_top=False,\n",
        "                  input_shape=(150, 150, 3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwNnJlZFrdD5",
        "colab_type": "code",
        "outputId": "c1bfcd0b-c08c-4994-8cb5-8d88a66d07a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        }
      },
      "source": [
        "conv_base.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 150, 150, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYSDhT7-rqGO",
        "colab_type": "text"
      },
      "source": [
        "### Fast feature extraction without data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7PyOefdriZD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6QeYaFlrzL-",
        "colab_type": "code",
        "outputId": "de7a0bfa-ae9b-4b2e-9c40-02e0c0a42238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "base_dir = './cats_and_dogs_small'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'validation')\n",
        "test_dir = os.path.join(base_dir, 'test')\n",
        "\n",
        "datagen = ImageDataGenerator(rescale=1./255)\n",
        "batch_size = 20\n",
        "\n",
        "def extract_features(directory, sample_count):\n",
        "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
        "    labels = np.zeros(shape=(sample_count))\n",
        "    generator = datagen.flow_from_directory(\n",
        "        directory,\n",
        "        target_size=(150, 150),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='binary')\n",
        "    i = 0\n",
        "    for inputs_batch, labels_batch in generator:\n",
        "        features_batch = conv_base.predict(inputs_batch)\n",
        "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
        "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
        "        i += 1\n",
        "        if i * batch_size >= sample_count:\n",
        "            break                                                           \n",
        "    return features, labels\n",
        "\n",
        "train_features, train_labels = extract_features(train_dir, 2000)\n",
        "validation_features, validation_labels = extract_features(validation_dir, 1000)\n",
        "test_features, test_labels = extract_features(test_dir, 1000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZvABHQvr2Nw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\n",
        "validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\n",
        "test_features = np.reshape(test_features, (1000, 4 * 4 * 512))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2l2WOk8Lr-Pr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3CRwx0-sBdk",
        "colab_type": "code",
        "outputId": "fc1449fd-4644-4c24-a04c-899521d16234",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_vuSVhvsFiU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=optimizers.RMSprop(lr=2e-5),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWFPnJTWsJhu",
        "colab_type": "code",
        "outputId": "d7e09a0c-6a07-42fc-d745-c230cc8e61ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1095
        }
      },
      "source": [
        "history = model.fit(train_features, train_labels,\n",
        "                    epochs=30,\n",
        "                    batch_size=20,\n",
        "                    validation_data=(validation_features, validation_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2000 samples, validate on 1000 samples\n",
            "Epoch 1/30\n",
            "2000/2000 [==============================] - 1s 440us/step - loss: 0.6121 - acc: 0.6620 - val_loss: 0.4502 - val_acc: 0.8080\n",
            "Epoch 2/30\n",
            "2000/2000 [==============================] - 1s 290us/step - loss: 0.4330 - acc: 0.7945 - val_loss: 0.3634 - val_acc: 0.8630\n",
            "Epoch 3/30\n",
            "2000/2000 [==============================] - 1s 288us/step - loss: 0.3642 - acc: 0.8410 - val_loss: 0.3303 - val_acc: 0.8670\n",
            "Epoch 4/30\n",
            "2000/2000 [==============================] - 1s 292us/step - loss: 0.3161 - acc: 0.8730 - val_loss: 0.3010 - val_acc: 0.8890\n",
            "Epoch 5/30\n",
            "2000/2000 [==============================] - 1s 263us/step - loss: 0.2869 - acc: 0.8910 - val_loss: 0.2889 - val_acc: 0.8900\n",
            "Epoch 6/30\n",
            "2000/2000 [==============================] - 1s 255us/step - loss: 0.2746 - acc: 0.8820 - val_loss: 0.2742 - val_acc: 0.8910\n",
            "Epoch 7/30\n",
            "2000/2000 [==============================] - 1s 261us/step - loss: 0.2530 - acc: 0.9070 - val_loss: 0.2673 - val_acc: 0.8920\n",
            "Epoch 8/30\n",
            "2000/2000 [==============================] - 1s 258us/step - loss: 0.2407 - acc: 0.9030 - val_loss: 0.2606 - val_acc: 0.8940\n",
            "Epoch 9/30\n",
            "2000/2000 [==============================] - 1s 263us/step - loss: 0.2281 - acc: 0.9175 - val_loss: 0.2579 - val_acc: 0.8930\n",
            "Epoch 10/30\n",
            "2000/2000 [==============================] - 1s 256us/step - loss: 0.2092 - acc: 0.9235 - val_loss: 0.2638 - val_acc: 0.8890\n",
            "Epoch 11/30\n",
            "2000/2000 [==============================] - 1s 264us/step - loss: 0.2022 - acc: 0.9245 - val_loss: 0.2453 - val_acc: 0.9010\n",
            "Epoch 12/30\n",
            "2000/2000 [==============================] - 1s 257us/step - loss: 0.1811 - acc: 0.9380 - val_loss: 0.2426 - val_acc: 0.9020\n",
            "Epoch 13/30\n",
            "2000/2000 [==============================] - 1s 259us/step - loss: 0.1843 - acc: 0.9335 - val_loss: 0.2400 - val_acc: 0.9020\n",
            "Epoch 14/30\n",
            "2000/2000 [==============================] - 1s 253us/step - loss: 0.1759 - acc: 0.9375 - val_loss: 0.2424 - val_acc: 0.9050\n",
            "Epoch 15/30\n",
            "2000/2000 [==============================] - 1s 263us/step - loss: 0.1593 - acc: 0.9465 - val_loss: 0.2394 - val_acc: 0.9060\n",
            "Epoch 16/30\n",
            "2000/2000 [==============================] - 1s 256us/step - loss: 0.1553 - acc: 0.9450 - val_loss: 0.2391 - val_acc: 0.9070\n",
            "Epoch 17/30\n",
            "2000/2000 [==============================] - 1s 261us/step - loss: 0.1483 - acc: 0.9490 - val_loss: 0.2365 - val_acc: 0.9080\n",
            "Epoch 18/30\n",
            "2000/2000 [==============================] - 1s 258us/step - loss: 0.1451 - acc: 0.9420 - val_loss: 0.2350 - val_acc: 0.9050\n",
            "Epoch 19/30\n",
            "2000/2000 [==============================] - 1s 260us/step - loss: 0.1375 - acc: 0.9575 - val_loss: 0.2341 - val_acc: 0.9050\n",
            "Epoch 20/30\n",
            "2000/2000 [==============================] - 1s 254us/step - loss: 0.1289 - acc: 0.9565 - val_loss: 0.2337 - val_acc: 0.9060\n",
            "Epoch 21/30\n",
            "2000/2000 [==============================] - 1s 260us/step - loss: 0.1291 - acc: 0.9510 - val_loss: 0.2336 - val_acc: 0.9060\n",
            "Epoch 22/30\n",
            "2000/2000 [==============================] - 1s 255us/step - loss: 0.1268 - acc: 0.9600 - val_loss: 0.2352 - val_acc: 0.9090\n",
            "Epoch 23/30\n",
            "2000/2000 [==============================] - 1s 271us/step - loss: 0.1195 - acc: 0.9585 - val_loss: 0.2356 - val_acc: 0.9030\n",
            "Epoch 24/30\n",
            "2000/2000 [==============================] - 1s 258us/step - loss: 0.1155 - acc: 0.9610 - val_loss: 0.2438 - val_acc: 0.9030\n",
            "Epoch 25/30\n",
            "2000/2000 [==============================] - 1s 261us/step - loss: 0.1108 - acc: 0.9660 - val_loss: 0.2371 - val_acc: 0.9020\n",
            "Epoch 26/30\n",
            "2000/2000 [==============================] - 1s 258us/step - loss: 0.1049 - acc: 0.9685 - val_loss: 0.2398 - val_acc: 0.9070\n",
            "Epoch 27/30\n",
            "2000/2000 [==============================] - 1s 264us/step - loss: 0.1031 - acc: 0.9675 - val_loss: 0.2359 - val_acc: 0.9040\n",
            "Epoch 28/30\n",
            "2000/2000 [==============================] - 1s 256us/step - loss: 0.0992 - acc: 0.9665 - val_loss: 0.2461 - val_acc: 0.9020\n",
            "Epoch 29/30\n",
            "2000/2000 [==============================] - 1s 258us/step - loss: 0.0937 - acc: 0.9735 - val_loss: 0.2373 - val_acc: 0.9030\n",
            "Epoch 30/30\n",
            "2000/2000 [==============================] - 1s 257us/step - loss: 0.0948 - acc: 0.9720 - val_loss: 0.2433 - val_acc: 0.9060\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwexi3casMbG",
        "colab_type": "code",
        "outputId": "bc7caf90-214f-48fc-f567-c96d33005bac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Da7ZGBxNRAqDGnLT8OST4XFml18egjAU3mMIRaRj0s1ZGVZVBaNHh3FsXn65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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XwpdBTSoqwo3B33gj3D82GtDLy8PrHTrArl3w7W+HYRYycOw3kYyn4J5l6mrh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\n6H8BUD9dEsT+v64AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIN4OGeasihx",
        "colab_type": "text"
      },
      "source": [
        "### Feature extraction with data augmentation\n",
        "\n",
        "Need GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTSb_UX2saMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(conv_base)\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(256, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlFTYBhltCRA",
        "colab_type": "code",
        "outputId": "842f942d-963a-4a5a-eb2e-a5a90ef9daca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 256)               2097408   \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 16,812,353\n",
            "Trainable params: 16,812,353\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeWGZmCGtEnp",
        "colab_type": "code",
        "outputId": "1d297456-c164-4ec6-d7f5-22d40350c424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('This is the number of trainable weights '\n",
        "         'before freezing the conv base:', len(model.trainable_weights))\n",
        "conv_base.trainable = False\n",
        "print('This is the number of trainable weights '\n",
        "          'after freezing the conv base:', len(model.trainable_weights))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is the number of trainable weights before freezing the conv base: 30\n",
            "This is the number of trainable weights after freezing the conv base: 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13iQ4QbbtSX4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import optimizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j_OfL2-tjTI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\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')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFAkZ5dJtlmr",
        "colab_type": "code",
        "outputId": "dd72e6a8-de16-4a59-b308-59e773b4ecbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "test_datagen = ImageDataGenerator(rescale=1./255)                \n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,                                               \n",
        "        target_size=(150, 150),                                  \n",
        "        batch_size=20,\n",
        "        class_mode='binary')                                     \n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        validation_dir,\n",
        "        target_size=(150, 150),\n",
        "        batch_size=20,\n",
        "        class_mode='binary')\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=optimizers.RMSprop(lr=2e-5),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evWfVF9Jtomo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 100 epochs, 30 secs per epoch on Colab GPU\n",
        "#history = model.fit_generator(\n",
        "#      train_generator,\n",
        "#      steps_per_epoch=100,\n",
        "#      epochs=30,\n",
        "#      validation_data=validation_generator,\n",
        "#      validation_steps=50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_AvdaESt-X8",
        "colab_type": "text"
      },
      "source": [
        "### Fine-tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZHBt_lDtxbn",
        "colab_type": "code",
        "outputId": "b078d36a-7bdd-4d08-ecc7-6760a61f7e08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        }
      },
      "source": [
        "conv_base.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 150, 150, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 0\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-jrUGTYuEOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_base.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "for layer in conv_base.layers:\n",
        "    if layer.name == 'block5_conv1':\n",
        "        set_trainable = True\n",
        "    if set_trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tOipAmSuMrO",
        "colab_type": "code",
        "outputId": "ca3254e9-ef02-4edf-a737-5f33199548b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        }
      },
      "source": [
        "conv_base.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 150, 150, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 7,079,424\n",
            "Non-trainable params: 7,635,264\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPsI5WdouRNA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=optimizers.RMSprop(lr=1e-5),\n",
        "              metrics=['acc'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eq99VE6HuoKl",
        "colab_type": "code",
        "outputId": "62d19965-453f-4533-86ff-ae00a9b15377",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3504
        }
      },
      "source": [
        "# 100 epochs, 30 secs per epoch on Colab GPU\n",
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=100,\n",
        "      epochs=100,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "100/100 [==============================] - 19s 194ms/step - loss: 0.5623 - acc: 0.7125 - val_loss: 0.3504 - val_acc: 0.8640\n",
            "Epoch 2/100\n",
            "100/100 [==============================] - 18s 177ms/step - loss: 0.3755 - acc: 0.8365 - val_loss: 0.2969 - val_acc: 0.8690\n",
            "Epoch 3/100\n",
            "100/100 [==============================] - 19s 187ms/step - loss: 0.3325 - acc: 0.8450 - val_loss: 0.2262 - val_acc: 0.9030\n",
            "Epoch 4/100\n",
            "100/100 [==============================] - 18s 184ms/step - loss: 0.2844 - acc: 0.8835 - val_loss: 0.2237 - val_acc: 0.9090\n",
            "Epoch 5/100\n",
            "100/100 [==============================] - 18s 183ms/step - loss: 0.2634 - acc: 0.8785 - val_loss: 0.2303 - val_acc: 0.9080\n",
            "Epoch 6/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.2589 - acc: 0.8905 - val_loss: 0.1851 - val_acc: 0.9200\n",
            "Epoch 7/100\n",
            "100/100 [==============================] - 18s 181ms/step - loss: 0.2464 - acc: 0.9030 - val_loss: 0.1925 - val_acc: 0.9130\n",
            "Epoch 8/100\n",
            "100/100 [==============================] - 18s 184ms/step - loss: 0.2268 - acc: 0.9070 - val_loss: 0.2236 - val_acc: 0.9090\n",
            "Epoch 9/100\n",
            "100/100 [==============================] - 18s 177ms/step - loss: 0.2063 - acc: 0.9140 - val_loss: 0.1739 - val_acc: 0.9310\n",
            "Epoch 10/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.2155 - acc: 0.9165 - val_loss: 0.1699 - val_acc: 0.9330\n",
            "Epoch 11/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.1849 - acc: 0.9285 - val_loss: 0.1673 - val_acc: 0.9270\n",
            "Epoch 12/100\n",
            "100/100 [==============================] - 19s 187ms/step - loss: 0.1916 - acc: 0.9210 - val_loss: 0.1635 - val_acc: 0.9290\n",
            "Epoch 13/100\n",
            "100/100 [==============================] - 17s 175ms/step - loss: 0.1824 - acc: 0.9310 - val_loss: 0.1695 - val_acc: 0.9310\n",
            "Epoch 14/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.1611 - acc: 0.9375 - val_loss: 0.1770 - val_acc: 0.9300\n",
            "Epoch 15/100\n",
            "100/100 [==============================] - 18s 175ms/step - loss: 0.1623 - acc: 0.9340 - val_loss: 0.1646 - val_acc: 0.9310\n",
            "Epoch 16/100\n",
            "100/100 [==============================] - 18s 179ms/step - loss: 0.1510 - acc: 0.9360 - val_loss: 0.1706 - val_acc: 0.9320\n",
            "Epoch 17/100\n",
            "100/100 [==============================] - 18s 182ms/step - loss: 0.1512 - acc: 0.9380 - val_loss: 0.2083 - val_acc: 0.9200\n",
            "Epoch 18/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.1359 - acc: 0.9480 - val_loss: 0.1715 - val_acc: 0.9310\n",
            "Epoch 19/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.1375 - acc: 0.9440 - val_loss: 0.1859 - val_acc: 0.9300\n",
            "Epoch 20/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.1365 - acc: 0.9505 - val_loss: 0.1625 - val_acc: 0.9280\n",
            "Epoch 21/100\n",
            "100/100 [==============================] - 19s 186ms/step - loss: 0.1254 - acc: 0.9505 - val_loss: 0.2179 - val_acc: 0.9240\n",
            "Epoch 22/100\n",
            "100/100 [==============================] - 19s 190ms/step - loss: 0.1287 - acc: 0.9540 - val_loss: 0.1653 - val_acc: 0.9320\n",
            "Epoch 23/100\n",
            "100/100 [==============================] - 17s 175ms/step - loss: 0.1290 - acc: 0.9510 - val_loss: 0.1595 - val_acc: 0.9350\n",
            "Epoch 24/100\n",
            "100/100 [==============================] - 17s 175ms/step - loss: 0.1139 - acc: 0.9545 - val_loss: 0.1647 - val_acc: 0.9350\n",
            "Epoch 25/100\n",
            "100/100 [==============================] - 18s 178ms/step - loss: 0.1164 - acc: 0.9540 - val_loss: 0.1897 - val_acc: 0.9300\n",
            "Epoch 26/100\n",
            "100/100 [==============================] - 18s 183ms/step - loss: 0.1124 - acc: 0.9585 - val_loss: 0.1730 - val_acc: 0.9240\n",
            "Epoch 27/100\n",
            "100/100 [==============================] - 17s 175ms/step - loss: 0.1151 - acc: 0.9565 - val_loss: 0.1636 - val_acc: 0.9340\n",
            "Epoch 28/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.1014 - acc: 0.9635 - val_loss: 0.1724 - val_acc: 0.9390\n",
            "Epoch 29/100\n",
            "100/100 [==============================] - 18s 182ms/step - loss: 0.1036 - acc: 0.9560 - val_loss: 0.1824 - val_acc: 0.9320\n",
            "Epoch 30/100\n",
            "100/100 [==============================] - 19s 187ms/step - loss: 0.0890 - acc: 0.9640 - val_loss: 0.2332 - val_acc: 0.9170\n",
            "Epoch 31/100\n",
            "100/100 [==============================] - 18s 175ms/step - loss: 0.0936 - acc: 0.9645 - val_loss: 0.1739 - val_acc: 0.9330\n",
            "Epoch 32/100\n",
            "100/100 [==============================] - 18s 175ms/step - loss: 0.0884 - acc: 0.9655 - val_loss: 0.2271 - val_acc: 0.9220\n",
            "Epoch 33/100\n",
            "100/100 [==============================] - 18s 175ms/step - loss: 0.0886 - acc: 0.9645 - val_loss: 0.1817 - val_acc: 0.9250\n",
            "Epoch 34/100\n",
            "100/100 [==============================] - 18s 179ms/step - loss: 0.0859 - acc: 0.9665 - val_loss: 0.1826 - val_acc: 0.9350\n",
            "Epoch 35/100\n",
            "100/100 [==============================] - 18s 182ms/step - loss: 0.0788 - acc: 0.9695 - val_loss: 0.1722 - val_acc: 0.9360\n",
            "Epoch 36/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0778 - acc: 0.9725 - val_loss: 0.1781 - val_acc: 0.9350\n",
            "Epoch 37/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0768 - acc: 0.9750 - val_loss: 0.1921 - val_acc: 0.9330\n",
            "Epoch 38/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0700 - acc: 0.9710 - val_loss: 0.1766 - val_acc: 0.9280\n",
            "Epoch 39/100\n",
            "100/100 [==============================] - 19s 190ms/step - loss: 0.0602 - acc: 0.9815 - val_loss: 0.3893 - val_acc: 0.9000\n",
            "Epoch 40/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0807 - acc: 0.9695 - val_loss: 0.2232 - val_acc: 0.9280\n",
            "Epoch 41/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0623 - acc: 0.9775 - val_loss: 0.2149 - val_acc: 0.9290\n",
            "Epoch 42/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0702 - acc: 0.9735 - val_loss: 0.2195 - val_acc: 0.9280\n",
            "Epoch 43/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0678 - acc: 0.9745 - val_loss: 0.1862 - val_acc: 0.9330\n",
            "Epoch 44/100\n",
            "100/100 [==============================] - 19s 185ms/step - loss: 0.0518 - acc: 0.9815 - val_loss: 0.2230 - val_acc: 0.9370\n",
            "Epoch 45/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0546 - acc: 0.9820 - val_loss: 0.1842 - val_acc: 0.9390\n",
            "Epoch 46/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.0526 - acc: 0.9805 - val_loss: 0.2605 - val_acc: 0.9200\n",
            "Epoch 47/100\n",
            "100/100 [==============================] - 18s 179ms/step - loss: 0.0625 - acc: 0.9780 - val_loss: 0.1835 - val_acc: 0.9400\n",
            "Epoch 48/100\n",
            "100/100 [==============================] - 19s 188ms/step - loss: 0.0539 - acc: 0.9750 - val_loss: 0.1710 - val_acc: 0.9410\n",
            "Epoch 49/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0632 - acc: 0.9760 - val_loss: 0.1967 - val_acc: 0.9310\n",
            "Epoch 50/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0581 - acc: 0.9790 - val_loss: 0.1723 - val_acc: 0.9390\n",
            "Epoch 51/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0608 - acc: 0.9765 - val_loss: 0.1768 - val_acc: 0.9310\n",
            "Epoch 52/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0521 - acc: 0.9815 - val_loss: 0.2467 - val_acc: 0.9280\n",
            "Epoch 53/100\n",
            "100/100 [==============================] - 18s 185ms/step - loss: 0.0484 - acc: 0.9815 - val_loss: 0.2891 - val_acc: 0.9290\n",
            "Epoch 54/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0493 - acc: 0.9815 - val_loss: 0.2837 - val_acc: 0.9240\n",
            "Epoch 55/100\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.0496 - acc: 0.9820 - val_loss: 0.1774 - val_acc: 0.9370\n",
            "Epoch 56/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0439 - acc: 0.9825 - val_loss: 0.1747 - val_acc: 0.9420\n",
            "Epoch 57/100\n",
            "100/100 [==============================] - 20s 200ms/step - loss: 0.0470 - acc: 0.9820 - val_loss: 0.2172 - val_acc: 0.9330\n",
            "Epoch 58/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0393 - acc: 0.9875 - val_loss: 0.1826 - val_acc: 0.9410\n",
            "Epoch 59/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0439 - acc: 0.9825 - val_loss: 0.2820 - val_acc: 0.9250\n",
            "Epoch 60/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0353 - acc: 0.9865 - val_loss: 0.1899 - val_acc: 0.9410\n",
            "Epoch 61/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0407 - acc: 0.9840 - val_loss: 0.2087 - val_acc: 0.9430\n",
            "Epoch 62/100\n",
            "100/100 [==============================] - 19s 186ms/step - loss: 0.0294 - acc: 0.9900 - val_loss: 0.2536 - val_acc: 0.9380\n",
            "Epoch 63/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0404 - acc: 0.9850 - val_loss: 0.2233 - val_acc: 0.9370\n",
            "Epoch 64/100\n",
            "100/100 [==============================] - 18s 180ms/step - loss: 0.0324 - acc: 0.9885 - val_loss: 0.3037 - val_acc: 0.9300\n",
            "Epoch 65/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0326 - acc: 0.9885 - val_loss: 0.2076 - val_acc: 0.9360\n",
            "Epoch 66/100\n",
            "100/100 [==============================] - 18s 182ms/step - loss: 0.0376 - acc: 0.9875 - val_loss: 0.2929 - val_acc: 0.9290\n",
            "Epoch 67/100\n",
            "100/100 [==============================] - 18s 177ms/step - loss: 0.0468 - acc: 0.9845 - val_loss: 0.2112 - val_acc: 0.9450\n",
            "Epoch 68/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0403 - acc: 0.9880 - val_loss: 0.2077 - val_acc: 0.9390\n",
            "Epoch 69/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0399 - acc: 0.9830 - val_loss: 0.2513 - val_acc: 0.9320\n",
            "Epoch 70/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0316 - acc: 0.9885 - val_loss: 0.2241 - val_acc: 0.9370\n",
            "Epoch 71/100\n",
            "100/100 [==============================] - 19s 186ms/step - loss: 0.0356 - acc: 0.9890 - val_loss: 0.2512 - val_acc: 0.9350\n",
            "Epoch 72/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0280 - acc: 0.9905 - val_loss: 0.2368 - val_acc: 0.9380\n",
            "Epoch 73/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0268 - acc: 0.9880 - val_loss: 0.1985 - val_acc: 0.9450\n",
            "Epoch 74/100\n",
            "100/100 [==============================] - 18s 184ms/step - loss: 0.0319 - acc: 0.9885 - val_loss: 0.2523 - val_acc: 0.9360\n",
            "Epoch 75/100\n",
            "100/100 [==============================] - 18s 184ms/step - loss: 0.0423 - acc: 0.9805 - val_loss: 0.2334 - val_acc: 0.9320\n",
            "Epoch 76/100\n",
            "100/100 [==============================] - 18s 178ms/step - loss: 0.0327 - acc: 0.9895 - val_loss: 0.3006 - val_acc: 0.9210\n",
            "Epoch 77/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0292 - acc: 0.9880 - val_loss: 0.4410 - val_acc: 0.9100\n",
            "Epoch 78/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0329 - acc: 0.9885 - val_loss: 0.2214 - val_acc: 0.9360\n",
            "Epoch 79/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0228 - acc: 0.9905 - val_loss: 0.2021 - val_acc: 0.9380\n",
            "Epoch 80/100\n",
            "100/100 [==============================] - 19s 186ms/step - loss: 0.0386 - acc: 0.9885 - val_loss: 0.2080 - val_acc: 0.9390\n",
            "Epoch 81/100\n",
            "100/100 [==============================] - 18s 180ms/step - loss: 0.0225 - acc: 0.9900 - val_loss: 0.3497 - val_acc: 0.9210\n",
            "Epoch 82/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0363 - acc: 0.9870 - val_loss: 0.2118 - val_acc: 0.9350\n",
            "Epoch 83/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0348 - acc: 0.9880 - val_loss: 0.2632 - val_acc: 0.9270\n",
            "Epoch 84/100\n",
            "100/100 [==============================] - 18s 178ms/step - loss: 0.0227 - acc: 0.9900 - val_loss: 0.2249 - val_acc: 0.9380\n",
            "Epoch 85/100\n",
            "100/100 [==============================] - 18s 183ms/step - loss: 0.0272 - acc: 0.9915 - val_loss: 0.1979 - val_acc: 0.9410\n",
            "Epoch 86/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0302 - acc: 0.9880 - val_loss: 0.3292 - val_acc: 0.9260\n",
            "Epoch 87/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0275 - acc: 0.9915 - val_loss: 0.2929 - val_acc: 0.9280\n",
            "Epoch 88/100\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.0322 - acc: 0.9895 - val_loss: 0.4167 - val_acc: 0.9200\n",
            "Epoch 89/100\n",
            "100/100 [==============================] - 19s 186ms/step - loss: 0.0273 - acc: 0.9885 - val_loss: 0.2658 - val_acc: 0.9300\n",
            "Epoch 90/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0244 - acc: 0.9950 - val_loss: 0.2280 - val_acc: 0.9450\n",
            "Epoch 91/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0204 - acc: 0.9905 - val_loss: 0.2414 - val_acc: 0.9430\n",
            "Epoch 92/100\n",
            "100/100 [==============================] - 19s 189ms/step - loss: 0.0218 - acc: 0.9905 - val_loss: 0.2257 - val_acc: 0.9430\n",
            "Epoch 93/100\n",
            "100/100 [==============================] - 18s 177ms/step - loss: 0.0245 - acc: 0.9910 - val_loss: 0.2555 - val_acc: 0.9400\n",
            "Epoch 94/100\n",
            "100/100 [==============================] - 18s 183ms/step - loss: 0.0303 - acc: 0.9880 - val_loss: 0.2165 - val_acc: 0.9390\n",
            "Epoch 95/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0257 - acc: 0.9930 - val_loss: 0.2381 - val_acc: 0.9340\n",
            "Epoch 96/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0202 - acc: 0.9925 - val_loss: 0.3611 - val_acc: 0.9300\n",
            "Epoch 97/100\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.0241 - acc: 0.9910 - val_loss: 0.2681 - val_acc: 0.9380\n",
            "Epoch 98/100\n",
            "100/100 [==============================] - 19s 186ms/step - loss: 0.0254 - acc: 0.9925 - val_loss: 0.2676 - val_acc: 0.9360\n",
            "Epoch 99/100\n",
            "100/100 [==============================] - 17s 171ms/step - loss: 0.0204 - acc: 0.9925 - val_loss: 0.4855 - val_acc: 0.9120\n",
            "Epoch 100/100\n",
            "100/100 [==============================] - 17s 172ms/step - loss: 0.0224 - acc: 0.9910 - val_loss: 0.2943 - val_acc: 0.9330\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWow9h9OuwgO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('cats_and_dogs_small_3.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsyaPl541pog",
        "colab_type": "code",
        "outputId": "a29fce77-ba0c-4c55-ba58-373ebdd7d191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S8UN10bpbRqKgwIWOmjSpHqM8y8OePU5sr7ii9DZf5KKFk/bscSJ9/fXOox89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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5A5gQOn2WoamtBfbuBb7+OtElsaiuljJ16+a/vXlzb9Ey2dmW1R+ua8bu6x8x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\nUZQ0RMVdURQlDVFxVxRFSUP+P3CdTgZOyK4QAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWSt2OJ41vSq",
        "colab_type": "code",
        "outputId": "34c9f5a5-358e-443d-9cf9-59f0669d59c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "def smooth_curve(points, factor=0.8):\n",
        "  smoothed_points = []\n",
        "  for point in points:\n",
        "    if smoothed_points:\n",
        "      previous = smoothed_points[-1]\n",
        "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
        "    else:\n",
        "      smoothed_points.append(point)\n",
        "  return smoothed_points\n",
        "\n",
        "plt.plot(epochs,\n",
        "         smooth_curve(acc), 'bo', label='Smoothed training acc')\n",
        "plt.plot(epochs,\n",
        "         smooth_curve(val_acc), 'b', label='Smoothed validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs,\n",
        "         smooth_curve(loss), 'bo', label='Smoothed training loss')\n",
        "plt.plot(epochs,\n",
        "         smooth_curve(val_loss), 'b', label='Smoothed validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MMfEmYvOOiCQBjwPnA7nAEhGZr6org1a7E3hFVZ8UkU7AAiDdW7ZOVTOjW+zq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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lXbo09LplWbnS/QK4+mq48043Mm/8eJg+HVavTszM1BKqmpDHqaeeqhVp507V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\nILynTeGCsNUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arah287011YK",
        "colab_type": "code",
        "outputId": "006e2cd8-8ae9-4883-f913-84473fe18525",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "test_generator = test_datagen.flow_from_directory(\n",
        "        test_dir,\n",
        "        target_size=(150, 150),\n",
        "        batch_size=20,\n",
        "        class_mode='binary')\n",
        "\n",
        "test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\n",
        "print('test acc:', test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1000 images belonging to 2 classes.\n",
            "test acc: 0.9329999899864196\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiWmH9o116ab",
        "colab_type": "code",
        "outputId": "b458731e-84af-4cd4-8766-074e6e48ed19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnj_AJl53j8V",
        "colab_type": "code",
        "outputId": "a6316c4b-b361-4e7f-dfa5-aa2bdf49180e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "!ls /content/gdrive/My\\ Drive/DeepLearningFiles"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cats_and_dogs_small_1.h5  cats_and_dogs_small_3.h5  foo2.txt  foo3.txt\tfoo.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNVNvaBE4YFy",
        "colab_type": "code",
        "outputId": "926b64fe-345f-4540-aa42-0aad73c10503",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "!ls "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cats_and_dogs_small\t  cats_and_dogs_small_3.h5  sample_data\n",
            "cats_and_dogs_small_1.h5  gdrive\t\t    train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0iKeUB44bcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp *.h5 /content/gdrive/My\\ Drive/DeepLearningFiles"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybs4zqts44g0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}