# 输入数据
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.InteractiveSession()

keep_prob = tf.placeholder(tf.float32)


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)  # 构建一个形为shape的数值为0.1的常量
    return tf.Variable(initial)


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


# 最大下采样操作
def max_pool_3x3(x):
    return tf.nn.max_pool(x, ksize=[1, 3, 3, 1],
                          strides=[1, 2, 2, 1], padding='SAME')


# 归一化操作
def norm(name, l_input, lsize=4):
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)


# 占位符输入
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 第一个卷积层（Alexnet采用了五个卷积层，在此我们仅用三个就足够了）
W_conv1 = weight_variable([3, 3, 1, 64])
b_conv1 = bias_variable([64])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_3x3(h_conv1)
h_norm1 = norm('norm1', h_pool1, lsize=4)

# 第二个卷积层
W_conv2 = weight_variable([3, 3, 64, 128])
b_conv2 = bias_variable([128])
h_conv2 = tf.nn.relu(conv2d(h_norm1, W_conv2) + b_conv2)
h_pool2 = max_pool_3x3(h_conv2)
h_norm2 = norm('norm2', h_pool2, lsize=4)

# 第三个卷积层
W_conv3 = weight_variable([3, 3, 128, 256])
b_conv3 = bias_variable([256])
h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
h_pool3 = max_pool_3x3(h_conv3)
h_norm3 = norm('norm3', h_pool3, lsize=4)

# 全连接层1
W_fc1 = weight_variable([4 * 4 * 256, 1024])
b_fc1 = bias_variable([1024])
h_norm3_flat = tf.reshape(h_norm3, [-1, 4 * 4 * 256])
h_fc1 = tf.nn.relu(tf.matmul(h_norm3_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 全连接层2
W_fc2 = weight_variable([1024, 1024])
b_fc2 = bias_variable([1024])
h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)

# ReadOut层
W_fc3 = weight_variable([1024, 10])
b_fc3 = bias_variable([10])
y_conv = tf.matmul(h_fc2_drop, W_fc3) + b_fc3

# 定义损失函数和训练步骤
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())
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x: batch[0], y_: batch[1], keep_prob: 1.0})
        print('step %d, training accuracy %g' % (i, train_accuracy))
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print('test accuracy %g' % accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
sess.close()