此模型中，输入是28*28*1的图片，经过两个卷积层（卷积+池化）层之后，尺寸变为7*7*64，将最后一个卷积层展成一个以为向量，然后接两个全连接层，第一个全连接层加一个dropout，最后一个全连接层输出10个分类的预测结果，然后计算损失，进行训练。
代码如下：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data #定义一个获取卷积核的函数
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial) #定义一个获取偏置值的函数
def bias_variable(shape):
initial = tf.constant(0.1,shape=shape)
return tf.Variable(initial) #定义一个卷积函数
def conv2d(x,W):
return tf.nn.conv2d(x,W,[1,1,1,1],padding="SAME") #定义一个池化函数
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1], strides=[1,2,2,1],padding="VALID") if __name__ == "__main__":
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
x = tf.placeholder(shape=[None,28*28],dtype=tf.float32)
lable = tf.placeholder(shape=[None,10],dtype=tf.float32) x_image = tf.reshape(x,[-1,28,28,1]) #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)
#14*14*32 #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)
#7*7*64 #全连接层，输出为1024维向量
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = weight_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)
keep_prob = tf.placeholder(tf.float32)
h_fc1_dropout = tf.nn.dropout(h_fc1,keep_prob=keep_prob) #把1024维向量转换成10维，对应10个类别
W_fc2 = weight_variable([1024,10])
b_fc2 = weight_variable([10])
y_conv = tf.matmul(h_fc1,W_fc2)+b_fc2 #直接使用tf.nn.softmax_cross_entropy_with_logits直接计算交叉熵
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=lable,logits=y_conv))
#定义train_step
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #定义测试的准确率
correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(lable,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 创建Session和变量初始化
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer()) #训练20000步
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100==0:
train_accuracy = sess.run(accuracy,feed_dict={
x:batch[0],lable:batch[1],keep_prob: 1.0})
print("step %d, training accuracy %g" % (i, train_accuracy))
_ = sess.run(train_step, feed_dict={x: batch[0], lable: batch[1], keep_prob: 0.5})
print("test accuracy %g" % sess.run(accuracy, feed_dict={
x: mnist.test.images, lable: mnist.test.labels, keep_prob: 1.0}))