Conv2d_Maxpooling_with_Callback.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Conv2d_Maxpooling_with_Callback",
      "provenance": [],
      "authorship_tag": "ABX9TyNmF+Q7Y4p7eovreqJIpQY1",
      "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/FuadKhalit/CNN_With_TensorFlow/blob/master/Conv2d_Maxpooling_with_Callback.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yddVkNKte8WO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFzpUz27fQbP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# GRADED FUNCTION: train_mnist_conv\n",
        "def train_mnist_conv():\n",
        "    class myCallback(tf.keras.callbacks.Callback):\n",
        "        def on_epoch_end(self, epoch, logs={}):\n",
        "            if(logs.get('accuracy')>0.998):\n",
        "                print(\"\\nReached 99.8% accuracy so cancelling training!\")\n",
        "                self.model.stop_training = True\n",
        "\n",
        "    mnist = tf.keras.datasets.mnist\n",
        "    (training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
        "    training_images=training_images.reshape(60000, 28, 28, 1)\n",
        "    training_images=training_images / 255.0\n",
        "    test_images = test_images.reshape(10000, 28, 28, 1)\n",
        "    test_images=test_images/255.0\n",
        "    \n",
        "    callbacks = myCallback()\n",
        "\n",
        "    model = tf.keras.models.Sequential([\n",
        "            tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),\n",
        "            tf.keras.layers.MaxPooling2D(2, 2),\n",
        "            tf.keras.layers.Flatten(),\n",
        "            tf.keras.layers.Dense(128, activation='relu'),\n",
        "            tf.keras.layers.Dense(10, activation='softmax')\n",
        "    ])\n",
        "\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    # model fitting\n",
        "    history = model.fit(\n",
        "        training_images, training_labels, epochs=10, callbacks=[callbacks]\n",
        "    )\n",
        "    # model fitting\n",
        "    return history.epoch, history.history['acc'][-1]\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHtswcxefUXG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "1ada2370-56b4-4f35-95bd-05fb14991804"
      },
      "source": [
        "test_loss, test_acc = train_mnist_conv()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 53s 28ms/step - loss: 0.1343 - accuracy: 0.9604\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 53s 28ms/step - loss: 0.0459 - accuracy: 0.9861\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 52s 28ms/step - loss: 0.0276 - accuracy: 0.9908\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 52s 28ms/step - loss: 0.0177 - accuracy: 0.9943\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 52s 28ms/step - loss: 0.0121 - accuracy: 0.9961\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 52s 28ms/step - loss: 0.0090 - accuracy: 0.9971\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9980\n",
            "Reached 99.8% accuracy so cancelling training!\n",
            "1875/1875 [==============================] - 52s 28ms/step - loss: 0.0065 - accuracy: 0.9980\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-52ee330190d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_mnist_conv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-5-0140ae359540>\u001b[0m in \u001b[0;36mtrain_mnist_conv\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m     )\n\u001b[1;32m     31\u001b[0m     \u001b[0;31m# model fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'acc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'acc'"
          ]
        }
      ]
    }
  ]
}