tensorflow中创建一个CNN网络识别mnist

程序模板来自莫烦python
https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/5-05-CNN3/
本程序使用了一个输入层，两个卷积层（卷积层，池化），两个全连接层，一个输出层

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

#number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs: v_xs,keep_prob:1})#为什么用1;
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})
    return result

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)

def conv2d(x,w):
    #stride[1,x_movement,y_movement,1]
    #must have strides[0] = strides[3] = 1,pooling层的核函数也是一样的
    #卷积层有两种卷积方式，SAME还有VALID
    return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME')

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

# define placeholder for innputs to network
xs = tf.placeholder(tf.float32,[None,784])#28*28
ys = tf.placeholder(tf.float32,[None,10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs,[-1,28,28,1])
# print(x_image.shape) #[n_samples,28,28,1]


## conv1 layer ##
W_conv1 = weight_variable([5,5,1,32])#patch 5x5 ,in size 1,out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output size 28x28x32
h_pool1  = max_pool_2x2(h_conv1)                     #output size 14x14x32




## conv2 layer ##
W_conv2 = weight_variable([5,5,32,64])#patch 5x5 ,in size 32,out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)#output size  14x14x64
h_pool2  = max_pool_2x2(h_conv2)                     #output size 7x7x64

## func1 layer ##
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])

#[n_samples,7,7,64]->>[n_sample,7*7*64]
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)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)



## func2 layer ##
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])

prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)


# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                              reduction_indices=[1]))       # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()
# important step
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()
sess.run(init)

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
    if i % 50 == 0:
        print(compute_accuracy(
            mnist.test.images[:1000], mnist.test.labels[:1000]))

我训练一万次以后精准度在0.994