MNIST_Tensorflow_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Import the dataset from Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = tf.keras.utils.normalize(X_train, axis=1)\n",
    "X_test = tf.keras.utils.normalize(X_test, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create NN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create sequential model\n",
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input layer\n",
    "model.add(Flatten())\n",
    "\n",
    "# hidden layers\n",
    "model.add(Dense(units=128, activation='relu'))\n",
    "model.add(Dense(units=128, activation='relu'))\n",
    "\n",
    "# output layer\n",
    "\n",
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 7s 122us/step - loss: 0.2606 - acc: 0.9227\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1070 - acc: 0.9667\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s 92us/step - loss: 0.0731 - acc: 0.9769\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 5s 90us/step - loss: 0.0521 - acc: 0.9835\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0406 - acc: 0.9868\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x236967386a0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# train model\n",
    "model.fit(X_train, y_train, epochs=5, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 43us/step\n"
     ]
    }
   ],
   "source": [
    "# evaluate model\n",
    "loss_and_metrics = model.evaluate(X_test, y_test, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.08981593639305793, 0.9738]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_and_metrics"
   ]
  }
 ],
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