mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# handwritten digits classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from keras import layers\n",
    "from keras import models\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    #load MNIST data which is already divided into train & test\n",
    "    (train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
    "\n",
    "    #reshape into the shape that the network expects && scaling it so that all values are in the [0, 1] interval \n",
    "    train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "    train_images = train_images.astype('float32') / 255\n",
    "\n",
    "    test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "    test_images = test_images.astype('float32') / 255\n",
    "\n",
    "    #categorically encode the labels\n",
    "    train_labels = to_categorical(train_labels)\n",
    "    test_labels = to_categorical(test_labels)\n",
    "\n",
    "    return train_images, train_labels, test_images, test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(64, activation='relu'))\n",
    "    model.add(layers.Dense(10, activation='softmax'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_accuray(history):\n",
    "    # list all data in history\n",
    "    print(history.history.keys())\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'val'], loc='upper left')\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'val'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets connect the puzzles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#get Model\n",
    "model = create_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get Data\n",
    "train_images, train_labels, test_images, test_labels = get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile and train model\n",
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy',\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 6s 96us/step - loss: 0.1755 - acc: 0.9447 - val_loss: 0.0471 - val_acc: 0.9842\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0454 - acc: 0.9858 - val_loss: 0.0520 - val_acc: 0.9823\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 3s 49us/step - loss: 0.0317 - acc: 0.9905 - val_loss: 0.0308 - val_acc: 0.9908\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0244 - acc: 0.9923 - val_loss: 0.0256 - val_acc: 0.9926\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 6s 103us/step - loss: 0.0190 - acc: 0.9941 - val_loss: 0.0311 - val_acc: 0.9913\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=5, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#let us plot \n",
    "\"\"\"\n",
    "Just 5 Epochs and we already achieved 99% accuracy\n",
    "\"\"\"\n",
    "\n",
    "draw_accuray(history)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}