Callback_implementation_on_Fashion_MNIST.ipynb

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  "metadata": {
    "colab": {
      "name": "Callback implementation on Fashion MNIST.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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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/tirthajyoti/TensorFlow_Basics/blob/master/Callback_implementation_on_Fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "N9-BCmi15L93",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "96966ab3-c23d-4d92-bde3-544c15486c1c"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('acc')>0.9):\n",
        "      print(\"\\nReached 90% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "    else:\n",
        "      print(\"\\nAccuracy not high enough. Starting another epoch...\\n\")\n",
        "\n",
        "mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "callbacks = myCallback()\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "59936/60000 [============================>.] - ETA: 0s - loss: 0.5008 - acc: 0.8248\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 105us/sample - loss: 0.5009 - acc: 0.8247\n",
            "Epoch 2/10\n",
            "59872/60000 [============================>.] - ETA: 0s - loss: 0.3784 - acc: 0.8643\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 103us/sample - loss: 0.3784 - acc: 0.8644\n",
            "Epoch 3/10\n",
            "59712/60000 [============================>.] - ETA: 0s - loss: 0.3405 - acc: 0.8758\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 104us/sample - loss: 0.3406 - acc: 0.8757\n",
            "Epoch 4/10\n",
            "59936/60000 [============================>.] - ETA: 0s - loss: 0.3167 - acc: 0.8831\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 103us/sample - loss: 0.3166 - acc: 0.8831\n",
            "Epoch 5/10\n",
            "59680/60000 [============================>.] - ETA: 0s - loss: 0.2993 - acc: 0.8899\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 103us/sample - loss: 0.2991 - acc: 0.8900\n",
            "Epoch 6/10\n",
            "59968/60000 [============================>.] - ETA: 0s - loss: 0.2834 - acc: 0.8948\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 104us/sample - loss: 0.2833 - acc: 0.8949\n",
            "Epoch 7/10\n",
            "59808/60000 [============================>.] - ETA: 0s - loss: 0.2716 - acc: 0.8987\n",
            "Accuracy not high enough. Starting another epoch...\n",
            "\n",
            "60000/60000 [==============================] - 6s 102us/sample - loss: 0.2718 - acc: 0.8987\n",
            "Epoch 8/10\n",
            "59808/60000 [============================>.] - ETA: 0s - loss: 0.2583 - acc: 0.9034\n",
            "Reached 90% accuracy so cancelling training!\n",
            "60000/60000 [==============================] - 6s 101us/sample - loss: 0.2583 - acc: 0.9034\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f302cde4358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    }
  ]
}