Happy_vs_Sad.ipynb

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  "metadata": {
    "colab": {
      "name": "Happy_vs_Sad.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ivaibhavjindal/DIC/blob/master/Happy_vs_Sad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": {
        "colab_type": "code",
        "id": "7Vti6p3PxmpS",
        "outputId": "d8226c40-7366-46f8-ed7b-03f7c1c903ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "\n",
        "DESIRED_ACCURACY = 0.999\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    \"https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip\" \\\n",
        "    -O \"/tmp/happy-or-sad.zip\"\n",
        "\n",
        "zip_ref = zipfile.ZipFile(\"/tmp/happy-or-sad.zip\", 'r')\n",
        "zip_ref.extractall(\"/tmp/h-or-s\")\n",
        "zip_ref.close()\n",
        "\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epochs, logs={}):\n",
        "        if(logs['acc'] > DESIRED_ACCURACY):\n",
        "            print(\"\\nReached 99.9% accuracy so cancelling training!\")\n",
        "            self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-06-14 10:18:47--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.69.128, 2607:f8b0:4001:c18::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.69.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2670333 (2.5M) [application/zip]\n",
            "Saving to: ‘/tmp/happy-or-sad.zip’\n",
            "\n",
            "\r/tmp/happy-or-sad.z   0%[                    ]       0  --.-KB/s               \r/tmp/happy-or-sad.z 100%[===================>]   2.55M  --.-KB/s    in 0.02s   \n",
            "\n",
            "2020-06-14 10:18:47 (107 MB/s) - ‘/tmp/happy-or-sad.zip’ saved [2670333/2670333]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6DLGbXXI1j_V",
        "colab": {}
      },
      "source": [
        "# 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), input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\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(lr=0.001), metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Ap9fUJE1vVu",
        "outputId": "79ad9996-2cdf-4657-d54f-4e96a175dde8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# 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",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    '/tmp/h-or-s/',\n",
        "    target_size=(150, 150),\n",
        "    batch_size = 2,\n",
        "    class_mode = 'binary'\n",
        ")\n",
        "\n",
        "# Expected output: 'Found 80 images belonging to 2 classes'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 80 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "48dLm13U1-Le",
        "outputId": "2012c87e-ad5c-43b6-8cb1-1dc2a92fca49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "# This code block should call model.fit and train for\n",
        "# a number of epochs. \n",
        "history = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch=20,\n",
        "    epochs = 15,\n",
        "    verbose=1,\n",
        "    callbacks = [callbacks]\n",
        ")\n",
        "    \n",
        "# Expected output: \"Reached 99.9% accuracy so cancelling training!\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-5-6ace3ed42907>:8: 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 1/15\n",
            "20/20 [==============================] - 4s 179ms/step - loss: 8.6638 - acc: 0.5750\n",
            "Epoch 2/15\n",
            "20/20 [==============================] - 4s 177ms/step - loss: 0.3116 - acc: 0.9250\n",
            "Epoch 3/15\n",
            "20/20 [==============================] - 4s 179ms/step - loss: 1.0356 - acc: 0.8000\n",
            "Epoch 4/15\n",
            "20/20 [==============================] - 4s 176ms/step - loss: 0.6160 - acc: 0.8250\n",
            "Epoch 5/15\n",
            "20/20 [==============================] - 4s 179ms/step - loss: 0.4834 - acc: 0.8000\n",
            "Epoch 6/15\n",
            "20/20 [==============================] - 4s 179ms/step - loss: 0.1503 - acc: 0.9250\n",
            "Epoch 7/15\n",
            "20/20 [==============================] - 4s 181ms/step - loss: 0.1862 - acc: 0.9500\n",
            "Epoch 8/15\n",
            "20/20 [==============================] - 4s 180ms/step - loss: 0.1123 - acc: 0.9500\n",
            "Epoch 9/15\n",
            "20/20 [==============================] - 4s 180ms/step - loss: 0.7426 - acc: 0.8500\n",
            "Epoch 10/15\n",
            "20/20 [==============================] - ETA: 0s - loss: 0.0173 - acc: 1.0000\n",
            "Reached 99.9% accuracy so cancelling training!\n",
            "20/20 [==============================] - 4s 179ms/step - loss: 0.0173 - acc: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsqYWVz9V3-6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}