Chapter 2_Deep Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a966cec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are applying a deep learning model on mnist dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6ec4c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b194985",
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "356126ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32ab3b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "418c9843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19978ef9880>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gzetQb6dp5KPhk5I21f7O7fR7V+irLe8bp8sCSXAGHZAEYQeSIOxAEoQdSIKwA0kQdiAJwg4k8X+zhHFo7nUhhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "962a1962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f90b8527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a45a8f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4196f9f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7421aaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "534ebfe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eac515ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This problem is multiclass classification problem\n",
    "network = models.Sequential()\n",
    "network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))\n",
    "network.add(layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4be4a3a",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid keyword argument(s) in `compile()`: ({'metrices'},). Valid keyword arguments include \"cloning\", \"experimental_run_tf_function\", \"distribute\", \"target_tensors\", or \"sample_weight_mode\".",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnetwork\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrmsprop\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mloss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcategorical_crossentropy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mmetrices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maccuracy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mE:\\Conda\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mE:\\Conda\\lib\\site-packages\\keras\\engine\\training.py:3524\u001b[0m, in \u001b[0;36mModel._validate_compile\u001b[1;34m(self, optimizer, metrics, **kwargs)\u001b[0m\n\u001b[0;32m   3522\u001b[0m invalid_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(kwargs) \u001b[38;5;241m-\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_weight_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m   3523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m invalid_kwargs:\n\u001b[1;32m-> 3524\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   3525\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid keyword argument(s) in `compile()`: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3526\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m(invalid_kwargs,)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Valid keyword arguments include \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3527\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcloning\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexperimental_run_tf_function\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdistribute\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   3528\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_tensors\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, or \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_weight_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   3529\u001b[0m     )\n\u001b[0;32m   3531\u001b[0m \u001b[38;5;66;03m# Model must be created and compiled with the same DistStrat.\u001b[39;00m\n\u001b[0;32m   3532\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuilt \u001b[38;5;129;01mand\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mhas_strategy():\n",
      "\u001b[1;31mTypeError\u001b[0m: Invalid keyword argument(s) in `compile()`: ({'metrices'},). Valid keyword arguments include \"cloning\", \"experimental_run_tf_function\", \"distribute\", \"target_tensors\", or \"sample_weight_mode\"."
     ]
    }
   ],
   "source": [
    "network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrices=['accuracy'])\n",
    "#for multiclass classification problem:\n",
    "#last layer activation function -> softmax\n",
    "#loss function -Categorical crossentropy\n",
    "\n",
    "#Metrices\n",
    "#For classification -> accuracy\n",
    "#For Accuracy ->MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec336c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape((60000, 28 * 28))  #transforming 2 dimensions into 1 because tensorflow works only with one dimension\n",
    "train_images = train_images.astype('float32') / 255  #converting pixel between 0 and 1\n",
    "test_images = test_images.reshape((10000, 28 * 28))\n",
    "test_images = test_images.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26ef3b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization and scaling\n",
    "from keras.utils import to_categorical\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd4c9527",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "You must compile your model before training/testing. Use `model.compile(optimizer, loss)`.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[1;32mIn [17]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnetwork\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_images\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mE:\\Conda\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mE:\\Conda\\lib\\site-packages\\keras\\engine\\training.py:3618\u001b[0m, in \u001b[0;36mModel._assert_compile_was_called\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   3612\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_assert_compile_was_called\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   3613\u001b[0m     \u001b[38;5;66;03m# Checks whether `compile` has been called. If it has been called,\u001b[39;00m\n\u001b[0;32m   3614\u001b[0m     \u001b[38;5;66;03m# then the optimizer is set. This is different from whether the\u001b[39;00m\n\u001b[0;32m   3615\u001b[0m     \u001b[38;5;66;03m# model is compiled\u001b[39;00m\n\u001b[0;32m   3616\u001b[0m     \u001b[38;5;66;03m# (i.e. whether the model is built and its inputs/outputs are set).\u001b[39;00m\n\u001b[0;32m   3617\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_compiled:\n\u001b[1;32m-> 3618\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m   3619\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must compile your model before \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3620\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining/testing. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3621\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `model.compile(optimizer, loss)`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3622\u001b[0m         )\n",
      "\u001b[1;31mRuntimeError\u001b[0m: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`."
     ]
    }
   ],
   "source": [
    "network.fit(train_images, train_labels, epochs=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ad891c7",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "You must compile your model before training/testing. Use `model.compile(optimizer, loss)`.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[1;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m test_loss, test_acc \u001b[38;5;241m=\u001b[39m \u001b[43mnetwork\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_images\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_labels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_acc:\u001b[39m\u001b[38;5;124m'\u001b[39m, test_acc)\n",
      "File \u001b[1;32mE:\\Conda\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mE:\\Conda\\lib\\site-packages\\keras\\engine\\training.py:3618\u001b[0m, in \u001b[0;36mModel._assert_compile_was_called\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   3612\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_assert_compile_was_called\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   3613\u001b[0m     \u001b[38;5;66;03m# Checks whether `compile` has been called. If it has been called,\u001b[39;00m\n\u001b[0;32m   3614\u001b[0m     \u001b[38;5;66;03m# then the optimizer is set. This is different from whether the\u001b[39;00m\n\u001b[0;32m   3615\u001b[0m     \u001b[38;5;66;03m# model is compiled\u001b[39;00m\n\u001b[0;32m   3616\u001b[0m     \u001b[38;5;66;03m# (i.e. whether the model is built and its inputs/outputs are set).\u001b[39;00m\n\u001b[0;32m   3617\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_compiled:\n\u001b[1;32m-> 3618\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m   3619\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou must compile your model before \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3620\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining/testing. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3621\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `model.compile(optimizer, loss)`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3622\u001b[0m         )\n",
      "\u001b[1;31mRuntimeError\u001b[0m: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`."
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = network.evaluate(test_images, test_labels)\n",
    "print('test_acc:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5b4a3088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 234ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.1445119 , 0.14594197, 0.08177076, 0.06655755, 0.11545696,\n",
       "        0.12944637, 0.10162485, 0.08014294, 0.07218222, 0.06236447]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.predict(train_images[5].reshape((1,28*28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa9f146e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 43ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.11830978, 0.18401523, 0.07055864, 0.04902428, 0.1320442 ,\n",
       "        0.11171222, 0.10318847, 0.05317457, 0.09969777, 0.07827481]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.predict(train_images[3].reshape((1,28*28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f51bf737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 45ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.predict(train_images[3].reshape((1,28*28))).argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c781da2f",
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