卷积神经网络训练MNIST

#-*-coding:utf-8-*-
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
trX = trX.reshape(-1, 28, 28, 1)
teX = teX.reshape(-1, 28, 28, 1)

X = tf.placeholder("float", [None, 28, 28, 1])
Y = tf.placeholder("float", [None, 10])

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev = 0.01))

w = init_weights([3, 3, 1, 32])
w2 = init_weights([3, 3, 32, 64])
w3 = init_weights([3, 3, 64, 128])
w4 = init_weights([128 * 4 * 4, 625])

w_o = init_weights([625, 10])

def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
    l1a = tf.nn.relu(tf.nn.conv2d(X, w, strides=[1, 1, 1, 1], padding="SAME"))
    l1 = tf.nn.max_pool(l1a, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
    l1 = tf.nn.dropout(l1, p_keep_conv)

    l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, strides=[1,1,1,1], padding='SAME'))
    l2 = tf.nn.max_pool(l2a, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    l2 = tf.nn.dropout(l2, p_keep_conv)

    l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, strides=[1,1,1,1], padding='SAME'))
    l3 = tf.nn.max_pool(l3a, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]])
    l3 = tf.nn.dropout(l3, p_keep_conv)

    l4 = tf.nn.relu(tf.matmul(l3, w4))
    l4 = tf.nn.dropout(l4, p_keep_hidden)

    pyx = tf.matmul(l4, w_o)
    return pyx

p_keep_conv = tf.placeholder("float")
p_keep_hidden = tf.placeholder("float")
py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)

batch_size = 128
test_size = 256

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(100):
        train_batch = zip(range(0, len(trX), batch_size), range(batch_size, len(trX)+1, batch_size))
        for start,end in train_batch:
            sess.run(train_op, feed_dict={X:trX[start:end], Y:trY[start:end], p_keep_conv:0.8, p_keep_hidden: 0.5})
            test_indices = np.arange(len(teX))
            np.random.shuffle(test_indices)
            test_indices = test_indices[0:test_size]
            print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
                             sess.run(predict_op, feed_dict={X: teX[test_indices], p_keep_conv:1.0, p_keep_hidden:1.0})))

训练100轮后精确率接近99.22%。（PS：大约4个小时）