卷积神经网络应用于MNIST数据集分类

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

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# 每个批次的大小
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size


# 初始化权值
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)  # 生成一个截断的正态分布
    return tf.Variable(initial)


# 初始化偏置
def biases_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


# 卷积层
def conv2d(x, w):
    # x input tensor of shape [batch, in_height, in_width, in_channel]
    # w filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]
    # strides[0] = strides[3] = 1, strides[1] 代表x 方向的步长， strides[2] 代表y方向的步长
    # padding: A string from:'SAME', 'VALID'
    return tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='SAME')


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


# 定义两个placeholder
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 改变x的格式转变为4D的向量[batch, in_height, in_width, in_channels]
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 初始化第一个卷积层的权值和偏置
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = biases_variable([32])

# 把x_image和权值向量进行卷积，在加上偏置值，然后应用于relu激活函数
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 初始化第二个卷积层的权值和偏置
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = biases_variable([64])

# 把h_pool1和权值向量进行卷积，在加上偏置值，然后应用于relu激活函数
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 28x28的图片第一次卷积后还是28x28，第一次池化后变为14x14
# 第二次卷积后为14x14，第二次池化后变为7x7
# 讲过上边的操作后得到64张7x7的平面

# 初始化第一个全连接层的权值
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = biases_variable([1024])
# 把池化层2的输出扁平化为1维
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
# 求第一个全连接层的输出
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# keep_drop用来表示神经元的输出概率
keep_drop = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_drop)

# 初始化第二个全连接层
W_fc2 = weight_variable([1024,10])
b_fc2 = biases_variable([10])

# 计算输出
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 交叉熵
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
# 使用AdamOptimizer优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 结果存储在一个布尔列表中
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(21):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_drop: 0.7})

        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y:mnist.test.labels, keep_drop: 1.0})
        print("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))