tensorflow训练代码

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/",one_hot = True)
sess = tf.InteractiveSession()

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(input,filter):
    return tf.nn.conv2d(input,filter,strides = [1,1,1,1],padding = 'SAME')

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

x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])
x_image = tf.reshape(x,[-1,28,28,1])

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)

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)

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) 

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

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)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y*tf.log(y_conv),reduction_indices = [1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

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

print('test accuary %g'%accuracy.eval(feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}))