09_Classifier_Happy_Or_Sad_Images.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "coursera": {
      "course_slug": "introduction-tensorflow",
      "graded_item_id": "1kAlw",
      "launcher_item_id": "PNLYD"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "09_Classifier_Happy_Or_Sad_Images.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohd-faizy/03_TensorFlow_In-Practice/blob/master/09_Classifier_Happy_Or_Sad_Images.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UncprnB0ymAE"
      },
      "source": [
        "Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad. \n",
        "Create a convolutional neural network that trains to 100% accuracy on these images,  which cancels training upon hitting training accuracy of >.999\n",
        "\n",
        "Hint -- it will work best with 3 convolutional layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQ-1JpoCilzX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4722e67c-569a-4e43-b0f2-62924d3fecf3"
      },
      "source": [
        "# Mounting the Drive to Google colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfUPZsoeimUE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -uq \"/content/drive/My Drive/Colab Notebooks/5.TensorFlow 2.0 - Python Neural Networks /2.TensorFlow Specialization/Module 1/Dataset Module 1/happy-or-sad.zip\" -d \"/content/sample_data\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ii3MQesoi0Qo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Changing the working directory\n",
        "import os\n",
        "os.chdir('/content/sample_data')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6E7oQcBWcKW6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "#import os\n",
        "#import zipfile\n",
        "#from os import path, getcwd, chdir\n",
        "\n",
        "# DO NOT CHANGE THE LINE BELOW. If you are developing in a local\n",
        "# environment, then grab happy-or-sad.zip from the Coursera Jupyter Notebook\n",
        "# and place it inside a local folder and edit the path to that location\n",
        "\n",
        "#path = f\"{getcwd()}\"\n",
        "\n",
        "#zip_ref = zipfile.ZipFile(path, 'r')\n",
        "#zip_ref.extractall(\"/tmp/h-or-s\")\n",
        "#zip_ref.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVi4vqHfcKXK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# GRADED FUNCTION: train_happy_sad_model\n",
        "def train_happy_sad_model():\n",
        "    # Please write your code only where you are indicated.\n",
        "    # please do not remove # model fitting inline comments.\n",
        "\n",
        "    DESIRED_ACCURACY = 0.999\n",
        "\n",
        "    class myCallback(tf.keras.callbacks.Callback):\n",
        "         def on_epoch_end(self, epochs, logs={}) :\n",
        "                if(logs.get('acc') >= DESIRED_ACCURACY) :\n",
        "                    print('\\nReached 99.9% accuracy so cancelling training!')\n",
        "                    self.model.stop_training = True\n",
        "\n",
        "    callbacks = myCallback()\n",
        "    \n",
        "    # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.\n",
        "    model = tf.keras.models.Sequential([\n",
        "        tf.keras.layers.Conv2D(16,(3,3), activation='relu', input_shape=(100,100,3)),\n",
        "        tf.keras.layers.MaxPooling2D(2,2),\n",
        "        tf.keras.layers.Conv2D(32,(3,3), activation='relu'),\n",
        "        tf.keras.layers.MaxPooling2D(2,2),\n",
        "        tf.keras.layers.Conv2D(64,(3,3), activation='relu'),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(512, activation='relu'),\n",
        "        tf.keras.layers.Dense(1, activation='sigmoid'),\n",
        "    ])\n",
        "\n",
        "    from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "    model.compile(loss='binary_crossentropy', optimizer='RMSprop', metrics=['acc'])\n",
        "        \n",
        "\n",
        "    # This code block should create an instance of an ImageDataGenerator called train_datagen \n",
        "    # And a train_generator by calling train_datagen.flow_from_directory\n",
        "\n",
        "    from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "    train_datagen = ImageDataGenerator(rescale = 1/255)\n",
        "\n",
        "    # Please use a target_size of 150 X 150.\n",
        "    train_generator = train_datagen.flow_from_directory(\n",
        "        '/content/sample_data', \n",
        "        target_size = (100,100),\n",
        "        batch_size = 80,\n",
        "        class_mode='binary')\n",
        "    # Expected output: 'Found 80 images belonging to 2 classes'\n",
        "\n",
        "    # This code block should call model.fit_generator and train for\n",
        "    # a number of epochs.\n",
        "    # model fitting\n",
        "    history = model.fit_generator(\n",
        "        train_generator,\n",
        "        steps_per_epoch=1,  \n",
        "        epochs=20,\n",
        "        verbose=1, \n",
        "        callbacks=[callbacks])\n",
        "    # model fitting\n",
        "    return history.history['acc'][-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6lECbA4cKXT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "outputId": "f6c7277f-64b4-444d-e84e-8f616244edb1"
      },
      "source": [
        "# The Expected output: \"Reached 99.9% accuracy so cancelling training!\"\"\n",
        "train_happy_sad_model()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 80 images belonging to 2 classes.\n",
            "WARNING:tensorflow:From <ipython-input-20-3a91d808affc>:56: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.6895 - acc: 0.5000\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 7.2695 - acc: 0.5000\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 4.3683 - acc: 0.5000\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.7514 - acc: 0.5000\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.7510 - acc: 0.5000\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.7095 - acc: 0.5000\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.6267 - acc: 0.5000\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.5122 - acc: 0.9125\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - ETA: 0s - loss: 0.3855 - acc: 1.0000\n",
            "Reached 99.9% accuracy so cancelling training!\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.3855 - acc: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    }
  ]
}