Fashion_Mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN-FASHION-MNIST.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPXtlfYvShihHSTCYNXBmAv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mohamed-derbeli/Convolutional-Neural-Network-CNN/blob/main/CNN_FASHION_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0p3DMQXgl9c",
        "outputId": "ec7f61eb-50b3-463d-fcbb-ec2ecf397ac8"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('loss')<0.03):\n",
        "      print(\"\\nReached high accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()  \n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
        "\n",
        "training_images=training_images.reshape(60000, 28, 28, 1)\n",
        "training_images=training_images / 255.0\n",
        "\n",
        "test_images = test_images.reshape(10000, 28, 28, 1)\n",
        "test_images=test_images/255.0\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(32, (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",
        "\n",
        "model.fit(training_images, training_labels, epochs=10, callbacks=[callbacks])\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "\n",
        "print(test_acc)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.6.0\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 37s 19ms/step - loss: 0.1567 - accuracy: 0.9535\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 36s 19ms/step - loss: 0.0523 - accuracy: 0.9845\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 36s 19ms/step - loss: 0.0333 - accuracy: 0.9899\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 36s 19ms/step - loss: 0.0210 - accuracy: 0.9936\n",
            "\n",
            "Reached high accuracy so cancelling training!\n",
            "313/313 [==============================] - 2s 7ms/step - loss: 0.0502 - accuracy: 0.9832\n",
            "0.9832000136375427\n"
          ]
        }
      ]
    }
  ]
}