tensorflow--代码学习5(CNN)

#cnn
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#number 1-10 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})
    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):
    init = tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(init)
    
def bias_variable(shape):
    init = tf.constant(0.1,shape=shape)
    return tf.Variable(init)
    
def conv2d(x,W):
    #stride[1,x_movement,y_movement,1]
    #must have stride[0] = stride[3] = 1
    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 inputs 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_samples,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()
#import step
sess.run(tf.global_variables_initializer())

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:1})
    if i%50==0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))

此代码学习之前需自行学习cnn神经网络理论架构，并且，运行时cpu会占用很大
另外注意代码注释