p220_LeNet.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time

x = tf.placeholder('float', [None, 784])
y_ = tf.placeholder('float', [None, 10])

x_image = tf.reshape(x, [-1, 28, 28, 1])

filter1 = tf.Variable(tf.truncated_normal([5, 5, 1, 6]))
bias1 = tf.Variable(tf.truncated_normal([6]))
conv1 = tf.nn.conv2d(x_image, filter1, strides=[1, 1, 1, 1], padding="SAME")
h_conv1 = tf.nn.sigmoid(conv1 + bias1)

maxPool2 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

filter2 = tf.Variable(tf.truncated_normal([5, 5, 6, 16]))
bias2 = tf.Variable(tf.truncated_normal([16]))
conv2 = tf.nn.conv2d(maxPool2, filter2, strides=[1, 1, 1, 1], padding='SAME')
h_conv2 = tf.nn.sigmoid(conv2 + bias2)

maxPool3 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

filter3 = tf.Variable(tf.truncated_normal([5, 5, 16, 120]))
bias3 = tf.Variable(tf.truncated_normal([120]))
conv3 = tf.nn.conv2d(maxPool3, filter3, strides=[1, 1, 1, 1], padding='SAME')
h_conv3 = tf.nn.sigmoid(conv3 + bias3)

W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 120, 80]))
b_fc1 = tf.Variable(tf.truncated_normal([80]))

h_pool2_flat = tf.reshape(h_conv3, [-1, 7 * 7 * 120])

h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

W_fc2 = tf.Variable(tf.truncated_normal([80, 10]))
b_fc2 = tf.Variable(tf.truncated_normal([10]))
y_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

cross_entropy = tf.reduce_sum(y_ * tf.log(y_conv))

train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)

sess = tf.InteractiveSession()

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))

sess.run(tf.initialize_all_variables())

mnist_data_set = input_data.read_data_sets('data/MNIST_data', one_hot=True)

start_time = time.time()
for i in range(20000):
    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)

    if i % 2 == 0:
        train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
        print('step %d, training accuracy %g' % (i, train_accuracy))
        end_time = time.time()
        print("time: ", (end_time - start_time))

    train_step.run(feed_dict={x: batch_xs, y_: batch_ys})

sess.close()