{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 16s 263us/step - loss: 14.5055\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 14s 241us/step - loss: 14.5063\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 15s 253us/step - loss: 14.5063\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 14s 237us/step - loss: 14.5063\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 14s 226us/step - loss: 14.5063\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7f2ca0628cf8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if(logs.get('loss')<0.4):\n",
    "            print(\"\\n Reached 60% accuracy so no cancelling traning!\")\n",
    "            self.model.stop_training= True\n",
    "            \n",
    "callbacks = myCallback()            \n",
    "mnist=tf.keras.datasets.fashion_mnist\n",
    "(training_images, training_labels),(test_images, test_labels)= mnist.load_data()\n",
    "test_images=test_images/255.0\n",
    "model=tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
    "    tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n",
    "model.fit(training_images, training_labels, epochs=5, callbacks=[callbacks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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