Course_2_Week_3_Transfer_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Transfer Learning.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1xJZ5glPPCRz",
        "outputId": "27e9141e-270b-4f91-ee06-c534f26241d5",
        "colab": {
          "height": 259
        }
      },
      "source": [
        "import os\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \\\n",
        "    -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
        "  \n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "\n",
        "local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
        "\n",
        "pre_trained_model = InceptionV3(input_shape = (150, 150, 3), \n",
        "                                include_top = False, \n",
        "                                weights = None)\n",
        "\n",
        "pre_trained_model.load_weights(local_weights_file)\n",
        "\n",
        "for layer in pre_trained_model.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "# pre_trained_model.summary()\n",
        "\n",
        "last_layer = pre_trained_model.get_layer('mixed7')\n",
        "print('last layer output shape: ', last_layer.output_shape)\n",
        "last_output = last_layer.output"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-13 14:04:55--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\r\n",
            "Resolving storage.googleapis.com... 2607:f8b0:4003:c05::80, 64.233.168.128\r\n",
            "Connecting to storage.googleapis.com|2607:f8b0:4003:c05::80|:443... connected.\r\n",
            "WARNING: cannot verify storage.googleapis.com's certificate, issued by 'CN=Google Internet Authority G3,O=Google Trust Services,C=US':\r\n",
            "  Unable to locally verify the issuer's authority.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 87910968 (84M) [application/x-hdf]\n",
            "Saving to: '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
            "\n",
            "/tmp/inception_v3_w 100%[=====================>]  83.84M  75.6MB/s   in 1.1s   \n",
            "\n",
            "2019-02-13 14:04:56 (75.6 MB/s) - '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5' saved [87910968/87910968]\n",
            "\n",
            "('last layer output shape: ', (None, 7, 7, 768))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BMXb913pbvFg",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "# Flatten the output layer to 1 dimension\n",
        "x = layers.Flatten()(last_output)\n",
        "# Add a fully connected layer with 1,024 hidden units and ReLU activation\n",
        "x = layers.Dense(1024, activation='relu')(x)\n",
        "# Add a dropout rate of 0.2\n",
        "x = layers.Dropout(0.2)(x)                  \n",
        "# Add a final sigmoid layer for classification\n",
        "x = layers.Dense  (1, activation='sigmoid')(x)           \n",
        "\n",
        "model = Model( pre_trained_model.input, x) \n",
        "\n",
        "model.compile(optimizer = RMSprop(lr=0.0001), \n",
        "              loss = 'binary_crossentropy', \n",
        "              metrics = ['acc'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O4s8HckqGlnb",
        "outputId": "76893e93-b44b-41f2-a4f2-428ad05c537d",
        "colab": {
          "height": 276
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "        https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \\\n",
        "       -O /tmp/cats_and_dogs_filtered.zip\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '//tmp/cats_and_dogs_filtered.zip'\n",
        "\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "# Define our example directories and files\n",
        "base_dir = '/tmp/cats_and_dogs_filtered'\n",
        "\n",
        "train_dir = os.path.join( base_dir, 'train')\n",
        "validation_dir = os.path.join( base_dir, 'validation')\n",
        "\n",
        "\n",
        "train_cats_dir = os.path.join(train_dir, 'cats') # Directory with our training cat pictures\n",
        "train_dogs_dir = os.path.join(train_dir, 'dogs') # Directory with our training dog pictures\n",
        "validation_cats_dir = os.path.join(validation_dir, 'cats') # Directory with our validation cat pictures\n",
        "validation_dogs_dir = os.path.join(validation_dir, 'dogs')# Directory with our validation dog pictures\n",
        "\n",
        "train_cat_fnames = os.listdir(train_cats_dir)\n",
        "train_dog_fnames = os.listdir(train_dogs_dir)\n",
        "\n",
        "# Add our data-augmentation parameters to ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(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",
        "\n",
        "# Note that the validation data should not be augmented!\n",
        "test_datagen = ImageDataGenerator( rescale = 1.0/255. )\n",
        "\n",
        "# Flow training images in batches of 20 using train_datagen generator\n",
        "train_generator = train_datagen.flow_from_directory(train_dir,\n",
        "                                                    batch_size = 20,\n",
        "                                                    class_mode = 'binary', \n",
        "                                                    target_size = (150, 150))     \n",
        "\n",
        "# Flow validation images in batches of 20 using test_datagen generator\n",
        "validation_generator =  test_datagen.flow_from_directory( validation_dir,\n",
        "                                                          batch_size  = 20,\n",
        "                                                          class_mode  = 'binary', \n",
        "                                                          target_size = (150, 150))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-13 14:05:24--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip\r\n",
            "Resolving storage.googleapis.com... 2607:f8b0:4003:c0a::80, 173.194.223.128\r\n",
            "Connecting to storage.googleapis.com|2607:f8b0:4003:c0a::80|:443... connected.\r\n",
            "WARNING: cannot verify storage.googleapis.com's certificate, issued by 'CN=Google Internet Authority G3,O=Google Trust Services,C=US':\r\n",
            "  Unable to locally verify the issuer's authority.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 68606236 (65M) [application/zip]\n",
            "Saving to: '/tmp/cats_and_dogs_filtered.zip'\n",
            "\n",
            "/tmp/cats_and_dogs_ 100%[=====================>]  65.43M   168MB/s   in 0.4s   \n",
            "\n",
            "2019-02-13 14:05:24 (168 MB/s) - '/tmp/cats_and_dogs_filtered.zip' saved [68606236/68606236]\n",
            "\n",
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Blhq2MAUeyGA",
        "outputId": "a1659af1-32c1-4503-8972-d47b77b5035d",
        "colab": {
          "height": 708
        }
      },
      "source": [
        "history = model.fit_generator(\n",
        "            train_generator,\n",
        "            validation_data = validation_generator,\n",
        "            steps_per_epoch = 100,\n",
        "            epochs = 20,\n",
        "            validation_steps = 50,\n",
        "            verbose = 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "100/100 - 17s - loss: 0.5283 - acc: 0.7525 - val_loss: 0.3843 - val_acc: 0.8940\n",
            "Epoch 2/20\n",
            "100/100 - 14s - loss: 0.3678 - acc: 0.8340 - val_loss: 0.2040 - val_acc: 0.9480\n",
            "Epoch 3/20\n",
            "100/100 - 15s - loss: 0.3352 - acc: 0.8535 - val_loss: 0.3987 - val_acc: 0.9270\n",
            "Epoch 4/20\n",
            "100/100 - 15s - loss: 0.3432 - acc: 0.8550 - val_loss: 0.2987 - val_acc: 0.9440\n",
            "Epoch 5/20\n",
            "100/100 - 15s - loss: 0.3391 - acc: 0.8640 - val_loss: 0.3390 - val_acc: 0.9450\n",
            "Epoch 6/20\n",
            "100/100 - 14s - loss: 0.3135 - acc: 0.8680 - val_loss: 0.3465 - val_acc: 0.9480\n",
            "Epoch 7/20\n",
            "100/100 - 14s - loss: 0.3113 - acc: 0.8700 - val_loss: 0.3115 - val_acc: 0.9530\n",
            "Epoch 8/20\n",
            "100/100 - 15s - loss: 0.2901 - acc: 0.8820 - val_loss: 0.5042 - val_acc: 0.9370\n",
            "Epoch 9/20\n",
            "100/100 - 15s - loss: 0.2912 - acc: 0.8865 - val_loss: 0.3065 - val_acc: 0.9620\n",
            "Epoch 10/20\n",
            "100/100 - 15s - loss: 0.2944 - acc: 0.8760 - val_loss: 0.2641 - val_acc: 0.9640\n",
            "Epoch 11/20\n",
            "100/100 - 14s - loss: 0.2831 - acc: 0.8810 - val_loss: 0.4515 - val_acc: 0.9450\n",
            "Epoch 12/20\n",
            "100/100 - 15s - loss: 0.2682 - acc: 0.8895 - val_loss: 0.3231 - val_acc: 0.9580\n",
            "Epoch 13/20\n",
            "100/100 - 15s - loss: 0.2748 - acc: 0.8840 - val_loss: 0.2427 - val_acc: 0.9680\n",
            "Epoch 14/20\n",
            "100/100 - 15s - loss: 0.2669 - acc: 0.8945 - val_loss: 0.3075 - val_acc: 0.9630\n",
            "Epoch 15/20\n",
            "100/100 - 15s - loss: 0.2732 - acc: 0.8910 - val_loss: 0.2629 - val_acc: 0.9620\n",
            "Epoch 16/20\n",
            "100/100 - 14s - loss: 0.2634 - acc: 0.8940 - val_loss: 0.3864 - val_acc: 0.9570\n",
            "Epoch 17/20\n",
            "100/100 - 14s - loss: 0.2473 - acc: 0.9040 - val_loss: 0.2648 - val_acc: 0.9670\n",
            "Epoch 18/20\n",
            "100/100 - 15s - loss: 0.2767 - acc: 0.8890 - val_loss: 0.2519 - val_acc: 0.9620\n",
            "Epoch 19/20\n",
            "100/100 - 17s - loss: 0.2660 - acc: 0.8990 - val_loss: 0.2495 - val_acc: 0.9680\n",
            "Epoch 20/20\n",
            "100/100 - 15s - loss: 0.2535 - acc: 0.9020 - val_loss: 0.2682 - val_acc: 0.9670\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2Fp6Se9rKuL",
        "colab_type": "code",
        "outputId": "498822a1-4a8d-4840-cdb9-26366f754c41",
        "colab": {
          "height": 306
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\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(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zEmOrbdtp7u0KV9M52l/OcEWL06ITw4GTeZgtCv3jLldnqNUq/jy5O698/iv/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\nfatTe1ZWVmhtbR2wzYULffHNN2Voa2sDYwwlJcV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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff264d46dd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff19c530b90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}