实现

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

# 加载MNIST数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSession()


# 初始化函数
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)


# 卷积层-2维卷积函数
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


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


x = tf.placeholder(tf.float32, [None, 784])  # 特征
y_ = tf.placeholder(tf.float32, [None, 10])  # 真实label
x_image = tf.reshape(x, [-1, 28, 28, 1])  # 1*784 -> 28*28

# 定义第一个卷积层
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 定义第二个卷积层
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)

# 全连接层
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)

# 减轻过拟合，使用Dropout层
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 将Dropout层输出连接一个Softmax层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 定义损失函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))

# 定义优化器
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 对模型准确率评测
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 开始训练
tf.global_variables_initializer().run()

for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval({x: batch[0], y_: batch[1], keep_prob: 1.0})
        print("step %d training accuracy %g" % (i, train_accuracy))
    train_step.run({x: batch[0], y_: batch[1], keep_prob: 0.5})

# 测试集上测试
print("test accuracy %g" % accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

结果

image.png