experiment2.py

import sys, os
sys.path.append(os.pardir)

from dataset.mnist import load_mnist

import tensorflow as tf
import numpy as np

(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def main(_):
    sess = tf.InteractiveSession()
    
    x = tf.placeholder(tf.float32, [None, 784])
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7*7*64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    y_ = tf.placeholder(tf.float32, [None, 10])
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    sess.run(tf.global_variables_initializer())

    saver = tf.train.Saver()

    train_size = x_train.shape[0]
    batch_size = 100
    for i in range(20000):
        batch_mask = np.random.choice(train_size, batch_size)
        batch_xs = x_train[batch_mask]
        batch_ys = t_train[batch_mask]

        if i%100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x:batch_xs, y_: batch_ys, keep_prob: 1.0})
            print("step %d, training accuracy %g"%(i, train_accuracy))
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})

    saver.save(sess, "exp2_model.ckpt")

    print("test accuracy %g"%accuracy.eval(feed_dict={x: x_test, y_: t_test, keep_prob: 1.0}))

if __name__ == '__main__':
    tf.app.run(main=main)