max pooling '''
return tf.nn.max\_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def cnn...)))
y\_p = tf.matmul(net\_fc2,weight([84,10])) + biases(10)
return y\_p
# 3.搭建CNN...tf.float32,[None,32,32,1], name='x')
y\_t = tf.placeholder(tf.int32,[None,10],name='y\_t')
y\_p = cnn...tf.train.AdamOptimizer(1e-4).minimize(cross\_entropy)
# tf.argmax()返回的是某一维度上其数据最大所在的索引值,在这里即代表预测值和真实值...# 判断预测值y和真实值y\_中最大数的索引是否一致,y的值为1-10概率
correct\_prediction = tf.equal(tf.argmax(y\_conv,1), tf.argmax