Copy_of_Exercise4_Question_Happy_sad_classifier_complete.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Copy of Exercise4-Question_Happy_sad_classifier_complete.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vikash0837/-Introduction-to-TensorFlow-for-Artificial-Intelligence-Machine-Learning-and-Deep-Learning/blob/master/Copy_of_Exercise4_Question_Happy_sad_classifier_complete.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "UncprnB0ymAE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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."
      ]
    },
    {
      "metadata": {
        "id": "7Vti6p3PxmpS",
        "colab_type": "code",
        "outputId": "f5bfbdb1-8cbb-48f1-efd6-176d5e67fd66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "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",
        "  # Your Code\n",
        "  def on_epoch_end(self,epoch,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",
        "    \n",
        "\n",
        "callbacks = myCallback()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-04-03 10:32:50--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|: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",
            "2019-04-03 10:32:50 (138 MB/s) - ‘/tmp/happy-or-sad.zip’ saved [2670333/2670333]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6DLGbXXI1j_V",
        "colab_type": "code",
        "outputId": "8dec8188-1f18-419b-a52c-2908dd494513",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "cell_type": "code",
      "source": [
        "# This Code Block should Define and Compile the Model\n",
        "model = tf.keras.models.Sequential()\n",
        "# Your Code Here\n",
        "#1st layer\n",
        "model.add(tf.keras.layers.Conv2D(16,(3,3),activation='relu',input_shape=(150,150,3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D(2,2))\n",
        "#second layer\n",
        "model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(2,2))\n",
        "#third layer\n",
        "model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(2,2))\n",
        "\n",
        "#fcn\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
        "#print model summary\n",
        "print(model.summary())\n",
        "\n",
        "\n",
        "\n",
        "#compile\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=RMSprop(lr=0.001),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 148, 148, 16)      448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 74, 74, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 72, 72, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 36, 36, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 34, 34, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 17, 17, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 18496)             0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               9470464   \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 9,494,561\n",
            "Trainable params: 9,494,561\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xbdNytVucPzv",
        "colab_type": "code",
        "outputId": "531174f4-2320-46e0-bcc8-afdea0f1c342",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "#train happy\n",
        "!ls -alrt /tmp"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 2624\n",
            "-rw-r--r-- 1 root root 2670333 Feb 18 17:48 happy-or-sad.zip\n",
            "drwxr-xr-x 1 root root    4096 Apr  3 09:03 ..\n",
            "drwxr-xr-x 4 root root    4096 Apr  3 09:04 h-or-s\n",
            "drwxrwxrwt 1 root root    4096 Apr  3 10:10 .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LROGC3Zojuch",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4Ap9fUJE1vVu",
        "colab_type": "code",
        "outputId": "fb8b7942-adca-43bf-c114-2e74b2f76126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "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)# Your Code Here\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    '/tmp/h-or-s/',\n",
        "    target_size = (150,150),\n",
        "    batch_size = 10,\n",
        "    class_mode = 'binary')\n",
        "# Your Code Here)\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"
        }
      ]
    },
    {
      "metadata": {
        "id": "48dLm13U1-Le",
        "colab_type": "code",
        "outputId": "ef090079-8458-4518-9ee2-c56679570ebd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "# This code block should call model.fit_generator and train for\n",
        "# a number of epochs. \n",
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=2,  \n",
        "      epochs=15,\n",
        "      verbose=1,\n",
        "      callbacks=[callbacks])\n",
        "    \n",
        "# Expected output: \"Reached 99.9% accuracy so cancelling training!\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "8/8 [==============================] - 3s 374ms/step - loss: 1.5424 - acc: 0.6125\n",
            "Epoch 2/15\n",
            "8/8 [==============================] - 3s 372ms/step - loss: 0.3757 - acc: 0.9125\n",
            "Epoch 3/15\n",
            "8/8 [==============================] - 3s 365ms/step - loss: 0.2966 - acc: 0.8750\n",
            "Epoch 4/15\n",
            "8/8 [==============================] - 3s 372ms/step - loss: 0.0705 - acc: 0.9875\n",
            "Epoch 5/15\n",
            "8/8 [==============================] - 3s 368ms/step - loss: 0.0428 - acc: 0.9750\n",
            "Epoch 6/15\n",
            "8/8 [==============================] - 3s 368ms/step - loss: 0.0771 - acc: 0.9875\n",
            "Epoch 7/15\n",
            "7/8 [=========================>....] - ETA: 0s - loss: 0.0183 - acc: 1.0000\n",
            "Reached 99.9% accuracy so cancelling training!\n",
            "8/8 [==============================] - 3s 361ms/step - loss: 0.0160 - acc: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "B4HlkYt9saLg",
        "colab_type": "code",
        "outputId": "248370b0-f2f7-4d95-d698-60a17187ff73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "image_data = train_datagen.flow_from_directory('/tmp/h-or-s/')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 80 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xDVBnALFiU3q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "k7ICiX3ms0kX",
        "colab_type": "code",
        "outputId": "ffcb85ea-276a-4a5f-9b17-3ab612934f5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "for image_batch,label_batch in image_data:\n",
        "  print(\"Image batch shape: \", image_batch.shape)\n",
        "  print(\"Labe batch shape: \", label_batch.shape)\n",
        "  break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image batch shape:  (32, 256, 256, 3)\n",
            "Labe batch shape:  (32, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0CXYzuEDs4nM",
        "colab_type": "code",
        "outputId": "ed49e13b-4f0d-4b26-d4bf-c19c088f43b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "img = cv2.imread('/tmp/h-or-s/happy/happy2-10.png')\n",
        "img.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 150, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "Opb9mTiOtU6v",
        "colab_type": "code",
        "outputId": "a6cfcdfe-9daa-4b47-e96e-b2148157779a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "print(os.listdir('/tmp/h-or-s/happy'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['happy2-10.png', 'happy1-16.png', 'happy2-12.png', 'happy1-09.png', 'happy1-10.png', 'happy2-14.png', 'happy2-16.png', 'happy1-17.png', 'happy2-08.png', 'happy1-19.png', 'happy2-11.png', 'happy1-15.png', 'happy2-17.png', 'happy1-08.png', 'happy1-05.png', 'happy2-05.png', 'happy2-15.png', 'happy2-04.png', 'happy1-00.png', 'happy1-01.png', 'happy1-14.png', 'happy2-03.png', 'happy2-13.png', 'happy1-06.png', 'happy2-00.png', 'happy1-18.png', 'happy2-06.png', 'happy1-04.png', 'happy1-02.png', 'happy2-07.png', 'happy2-02.png', 'happy2-19.png', 'happy2-01.png', 'happy1-03.png', 'happy1-07.png', 'happy1-11.png', 'happy1-12.png', 'happy2-09.png', 'happy2-18.png', 'happy1-13.png']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "72qoPwbEtbY2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}