Binary_classification_between_happy_and_sad_faces.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Binary classification between happy and sad faces.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM/dw5F/5rIm1biA9bzr3As",
      "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/udaysrinu/Tensorflow-classification-models/blob/master/Binary_classification_between_happy_and_sad_faces.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uF72xxygaWiI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "727167eb-aa6d-4366-9ba4-242728b4ff4e"
      },
      "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, 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",
        "callbacks = myCallback()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-28 00:14:52--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.216.128, 173.194.217.128, 172.217.193.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.216.128|: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",
            "2020-08-28 00:14:52 (104 MB/s) - ‘/tmp/happy-or-sad.zip’ saved [2670333/2670333]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCWxiLbfaprs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 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(32, (3,3), activation='relu'),\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',\n",
        "              optimizer=RMSprop(lr=0.001),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ByQdSzbar_e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e3b4b90-bd1b-406a-fdcb-6c925b9ccc99"
      },
      "source": [
        "\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=10,\n",
        "        class_mode='binary')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 80 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jc1ukonqauVn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "b5f12892-0598-4126-8990-e26815553661"
      },
      "source": [
        "history = model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=2,  \n",
        "      epochs=15,\n",
        "      verbose=1,\n",
        "      callbacks=[callbacks])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "2/2 [==============================] - 0s 136ms/step - loss: 9.9189 - acc: 0.5000\n",
            "Epoch 2/15\n",
            "2/2 [==============================] - 0s 122ms/step - loss: 0.6383 - acc: 0.6500\n",
            "Epoch 3/15\n",
            "2/2 [==============================] - 0s 124ms/step - loss: 0.7295 - acc: 0.4500\n",
            "Epoch 4/15\n",
            "2/2 [==============================] - 0s 123ms/step - loss: 0.9102 - acc: 0.3000\n",
            "Epoch 5/15\n",
            "2/2 [==============================] - 0s 125ms/step - loss: 0.6354 - acc: 0.5000\n",
            "Epoch 6/15\n",
            "2/2 [==============================] - 0s 124ms/step - loss: 0.5661 - acc: 0.7000\n",
            "Epoch 7/15\n",
            "2/2 [==============================] - 0s 120ms/step - loss: 0.5371 - acc: 0.7000\n",
            "Epoch 8/15\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.3911 - acc: 0.9000\n",
            "Epoch 9/15\n",
            "2/2 [==============================] - 0s 122ms/step - loss: 0.6057 - acc: 0.5000\n",
            "Epoch 10/15\n",
            "2/2 [==============================] - 0s 128ms/step - loss: 0.3763 - acc: 0.9500\n",
            "Epoch 11/15\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2006 - acc: 1.0000\n",
            "Reached 99.9% accuracy so cancelling training!\n",
            "2/2 [==============================] - 0s 123ms/step - loss: 0.2006 - acc: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3wkGDfXaw77",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}