03.Using Convolutions to improve Accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Convolution Layers in the Mnist and Fashion_Mnist dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.fashion_mnist\n",
    "(training_images,training_labels),(testing_images,testing_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_images.shape,testing_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14ef2ef50c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.set_printoptions(linewidth = 200)\n",
    "print(training_images[0])\n",
    "plt.imshow(training_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 11, 11, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 128)               204928    \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 243,786\n",
      "Trainable params: 243,786\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples\n",
      "Epoch 1/15\n",
      "60000/60000 [==============================] - 35s 587us/sample - loss: 0.4364 - accuracy: 0.8413\n",
      "Epoch 2/15\n",
      "60000/60000 [==============================] - 34s 563us/sample - loss: 0.2888 - accuracy: 0.8931\n",
      "Epoch 3/15\n",
      "60000/60000 [==============================] - 33s 547us/sample - loss: 0.2452 - accuracy: 0.9079\n",
      "Epoch 4/15\n",
      "60000/60000 [==============================] - 33s 557us/sample - loss: 0.2118 - accuracy: 0.9208\n",
      "Epoch 5/15\n",
      "60000/60000 [==============================] - 33s 551us/sample - loss: 0.1862 - accuracy: 0.9295\n",
      "Epoch 6/15\n",
      "60000/60000 [==============================] - 33s 545us/sample - loss: 0.1632 - accuracy: 0.9385\n",
      "Epoch 7/15\n",
      "60000/60000 [==============================] - 33s 549us/sample - loss: 0.1435 - accuracy: 0.9466\n",
      "Epoch 8/15\n",
      "60000/60000 [==============================] - 33s 555us/sample - loss: 0.1240 - accuracy: 0.9528\n",
      "Epoch 9/15\n",
      "60000/60000 [==============================] - 33s 555us/sample - loss: 0.1084 - accuracy: 0.9582\n",
      "Epoch 10/15\n",
      "60000/60000 [==============================] - 33s 543us/sample - loss: 0.0958 - accuracy: 0.9635\n",
      "Epoch 11/15\n",
      "60000/60000 [==============================] - 33s 551us/sample - loss: 0.0836 - accuracy: 0.9674\n",
      "Epoch 12/15\n",
      "60000/60000 [==============================] - 33s 548us/sample - loss: 0.0746 - accuracy: 0.9714\n",
      "Epoch 13/15\n",
      "60000/60000 [==============================] - 34s 563us/sample - loss: 0.0677 - accuracy: 0.9744- loss: 0\n",
      "Epoch 14/15\n",
      "60000/60000 [==============================] - 37s 611us/sample - loss: 0.0594 - accuracy: 0.9773\n",
      "Epoch 15/15\n",
      "60000/60000 [==============================] - 37s 609us/sample - loss: 0.0519 - accuracy: 0.9796\n",
      "10000/10000 [==============================] - 3s 280us/sample - loss: 0.4372 - accuracy: 0.9093\n"
     ]
    }
   ],
   "source": [
    "training_images = training_images.reshape(60000,28,28,1)#Bcoz the 1st convolution expects a single tensor containing everything ie..4D array\n",
    "testing_images = testing_images.reshape(10000,28,28,1)\n",
    "training_images = training_images/255.0\n",
    "testing_images = testing_images/255.0\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.Conv2D(64,(3,3),activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(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',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.fit(training_images,training_labels,epochs=15)\n",
    "test_loss = model.evaluate(testing_images,testing_labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_10 (Conv2D)           (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 11, 11, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 1, 1, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 84,106\n",
      "Trainable params: 84,106\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples\n",
      "Epoch 1/15\n",
      " 7712/60000 [==>...........................] - ETA: 55s - loss: 2.3027 - accuracy: 0.1036"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-3aaaf47cd5ec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     31\u001b[0m              metrics=['accuracy'])\n\u001b[0;32m     32\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_images\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtraining_labels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmy_callback\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[0mtest_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtesting_images\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtesting_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    126\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    127\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     96\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 98\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    570\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36m_non_none_constant_value\u001b[1;34m(v)\u001b[0m\n\u001b[0;32m    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_non_none_constant_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m   \u001b[0mconstant_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstant_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    131\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mconstant_value\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconstant_value\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\framework\\tensor_util.py\u001b[0m in \u001b[0;36mconstant_value\u001b[1;34m(tensor, partial)\u001b[0m\n\u001b[0;32m    820\u001b[0m   \"\"\"\n\u001b[0;32m    821\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    940\u001b[0m     \"\"\"\n\u001b[0;32m    941\u001b[0m     \u001b[1;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 942\u001b[1;33m     \u001b[0mmaybe_arr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    943\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anacondafiles\\envs\\deeplearning\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    906\u001b[0m     \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 908\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    909\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    910\u001b[0m       \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "class my_callback(tf.keras.callbacks.Callback) :\n",
    "    def on_epoch_end(self,epoch,logs={}):\n",
    "        if(logs.get('accuracy') >= 0.90):\n",
    "            print('\\n \\n Accuracy reached 90% ...---> Stopping the training!! \\n \\n')\n",
    "            self.model.stop_training=True\n",
    "\n",
    "my_callback = my_callback()\n",
    "\n",
    "training_images = training_images.reshape(60000,28,28,1)#Bcoz the 1st convolution expects a single tensor containing everything ie..4D array\n",
    "testing_images = testing_images.reshape(10000,28,28,1)\n",
    "training_images = training_images/255.0\n",
    "testing_images = testing_images/255.0\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",
    "    \n",
    "    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(2,2),\n",
    "    \n",
    "    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(2,2),\n",
    "    \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',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.fit(training_images,training_labels,epochs=15,callbacks=[my_callback])\n",
    "test_loss = model.evaluate(testing_images,testing_labels)\n",
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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