Transfer_Learning.ipynb

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  "metadata": {
    "colab": {
      "name": "Transfer_Learning.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "metadata": {
        "id": "9XGhEYxHkbRq",
        "colab_type": "code",
        "outputId": "ae6926e6-fdf8-4c4d-8f2b-02e671643296",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dense,GlobalAveragePooling2D\n",
        "from keras.applications import MobileNet\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.mobilenet import preprocess_input\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Model\n",
        "from keras.optimizers import Adam"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "-G17kVoXkhLi",
        "colab_type": "code",
        "outputId": "baab2612-cee0-46fa-d001-ff0502501bea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.\n",
        "\n",
        "x=base_model.output\n",
        "x=GlobalAveragePooling2D()(x)\n",
        "x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.\n",
        "x=Dense(1024,activation='relu')(x) #dense layer 2\n",
        "x=Dense(512,activation='relu')(x) #dense layer 3\n",
        "preds=Dense(3,activation='softmax')(x) #final layer with softmax activation"
      ],
      "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"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_applications/mobilenet.py:208: UserWarning: MobileNet shape is undefined. Weights for input shape (224, 224) will be loaded.\n",
            "  warnings.warn('MobileNet shape is undefined.'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf_no_top.h5\n",
            "17227776/17225924 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R9INumO_kqLZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model=Model(inputs=base_model.input,outputs=preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4c9FnfM3ksr5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for layer in model.layers[:20]:\n",
        "    layer.trainable=False\n",
        "for layer in model.layers[20:]:\n",
        "    layer.trainable=True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SN02f_Ztk2PR",
        "colab_type": "code",
        "outputId": "358eff3e-1b01-44f7-a542-9b0e2b55c435",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q kaggle\n",
        "!mkdir -p ~/.kaggle\n",
        "\n",
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a3d0b042-5189-4351-a1df-b3cbf2fb2203\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-a3d0b042-5189-4351-a1df-b3cbf2fb2203\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"sirajr\",\"key\":\"df02ffd99acc284bf9472c08e61dec0a\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "lcB8ITYMoVhx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "!kaggle datasets download -d paultimothymooney/chest-xray-pneumonia"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PdoSkpMkopAR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "R99OTrc_oYuR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import formatter as fm\n",
        "df = pd.read_csv(BERT_MODEL)\n",
        "preprocess_input, test_data = fm(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nNvVDZTfkx8B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) #included in our dependencies\n",
        "\n",
        "train_generator=train_datagen.flow_from_directory('./train/', # this is where you specify the path to the main data folder\n",
        "                                                 target_size=(224,224),\n",
        "                                                 color_mode='rgb',\n",
        "                                                 batch_size=32,\n",
        "                                                 class_mode='categorical',\n",
        "                                                 shuffle=True)\n",
        "\n",
        "model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "# Adam optimizer\n",
        "# loss function will be categorical cross entropy\n",
        "# evaluation metric will be accuracy\n",
        "\n",
        "step_size_train=train_generator.n//train_generator.batch_size\n",
        "model.fit_generator(generator=train_generator,\n",
        "                   steps_per_epoch=step_size_train,\n",
        "                   epochs=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AvO6yOp-o7uQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "tnJHEADQqgEB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download the weights! "
      ]
    },
    {
      "metadata": {
        "id": "0dQjZ5vqqfOZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# 2. Save Keras Model or weights on google drive\n",
        "\n",
        "# create on Colab directory\n",
        "model.save('model.h5')    \n",
        "model_file = drive.CreateFile({'title' : 'model.h5'})\n",
        "model_file.SetContentFile('model.h5')\n",
        "model_file.Upload()\n",
        "\n",
        "# download to google drive\n",
        "drive.CreateFile({'id': model_file.get('id')})"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}