keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras is a popular deep learning framework and it's easy to get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "NUM_CLASSES = 10\n",
    "NUM_EPOCHS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we read the mnist data and preprocess them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download and load the data (split them between train and test sets)\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# expand the channel dimension\n",
    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
    "input_shape = (28, 28, 1)\n",
    "\n",
    "# make the value of pixels from [0, 255] to [0, 1] for further process\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "\n",
    "# convert class vectors to binary class matrics\n",
    "y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
    "y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we define the model, object function and optimizer that we use to classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the model\n",
    "num_classes = 10\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(NUM_CLASSES, activation='softmax'))\n",
    "\n",
    "\n",
    "# define the object function, optimizer and metrics\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can start to train and evaluate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "469/469 - 13s - loss: 0.0372 - accuracy: 0.9885 - val_loss: 0.0388 - val_accuracy: 0.9865\n",
      "Epoch 2/10\n",
      "469/469 - 12s - loss: 0.0294 - accuracy: 0.9906 - val_loss: 0.0373 - val_accuracy: 0.9877\n",
      "Epoch 3/10\n",
      "469/469 - 12s - loss: 0.0239 - accuracy: 0.9921 - val_loss: 0.0350 - val_accuracy: 0.9881\n",
      "Epoch 4/10\n",
      "469/469 - 12s - loss: 0.0186 - accuracy: 0.9942 - val_loss: 0.0314 - val_accuracy: 0.9892\n",
      "Epoch 5/10\n",
      "469/469 - 12s - loss: 0.0150 - accuracy: 0.9953 - val_loss: 0.0323 - val_accuracy: 0.9896\n",
      "Epoch 6/10\n",
      "469/469 - 12s - loss: 0.0148 - accuracy: 0.9950 - val_loss: 0.0361 - val_accuracy: 0.9880\n",
      "Epoch 7/10\n",
      "469/469 - 12s - loss: 0.0112 - accuracy: 0.9964 - val_loss: 0.0366 - val_accuracy: 0.9892\n",
      "Epoch 8/10\n",
      "469/469 - 12s - loss: 0.0097 - accuracy: 0.9969 - val_loss: 0.0358 - val_accuracy: 0.9899\n",
      "Epoch 9/10\n",
      "469/469 - 12s - loss: 0.0090 - accuracy: 0.9972 - val_loss: 0.0400 - val_accuracy: 0.9891\n",
      "Epoch 10/10\n",
      "469/469 - 12s - loss: 0.0088 - accuracy: 0.9968 - val_loss: 0.0437 - val_accuracy: 0.9890\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1f96218a7b8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train\n",
    "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=NUM_EPOCHS, batch_size=BATCH_SIZE,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0055, Training accuracy: 1.00%\n",
      "Testing loss: 0.0437, Testing accuracy: 0.99%\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "score_train = model.evaluate(x_train, y_train, verbose=0)\n",
    "print('Training loss: %.4f, Training accuracy: %.2f%%' % (score_train[0], score_train[1]))\n",
    "score_test = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Testing loss: %.4f, Testing accuracy: %.2f%%' % (score_test[0], score_test[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q6:\n",
    "Please print the training and testing accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0055, Training accuracy: 1.00%\n",
      "Testing loss: 0.0437, Testing accuracy: 0.99%\n"
     ]
    }
   ],
   "source": [
    "print('Training loss: %.4f, Training accuracy: %.2f%%' % (score_train[0], score_train[1]))\n",
    "print('Testing loss: %.4f, Testing accuracy: %.2f%%' % (score_test[0], score_test[1]))"
   ]
  }
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