Tensorflow实现CNN、Saver保存与读取

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

mnist=input_data.read_data_sets(‘MNIST_data‘,one_hot=True)

def weight_variable(shape): 
    initial=tf.truncated_normal(shape,stddev=0.1) #tf.truncted_normal产生随机变量来进行初始化,类似normal  
    return tf.Variable(initial)

def bias_variable(shape): 
    initial=tf.constant(0.1,shape=shape) 
    return tf.Variable(initial)

def conv2d(x,W):
    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‘)

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

xs=tf.placeholder(tf.float32,[None,784])
ys=tf.placeholder(tf.float32,[None,10])

keep_prob=tf.placeholder(tf.float32)
x_image=tf.reshape(xs,[-1,28,28,1])#-1表示暂时不管,自动计算 [n_samples,28,28,-1]

# 建立卷积层conv1,conv2
W_conv1=weight_variable([5,5,1,32])#patch/kernel 5*5,in size 1是image的厚度,out size 32
b_conv1=bias_variable([32]) #一个kernel一个bias
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output size 28*28*32
h_pool1=max_pool_2x2(h_conv1) #output size 14*14*32  

W_conv2=weight_variable([5,5,32,64])#kernel 5*5,in size 32,out size 64
b_conv2=bias_variable([64])#output size 14*14*64
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)#output size 7*7*64
h_pool2=max_pool_2x2(h_conv2)

#建立全连接层fc1,fc2
#[n_samples,7,7,64]->>[n_samples,7*7*64]
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64]) 
W_fc1=weight_variable([7*7*64,1024]) 
b_fc1=bias_variable([1024])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

W_fc2=weight_variable([1024,10]) 
b_fc2=bias_variable([10])
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess=tf.Session()
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:1.0})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images[:2000],mnist.test.labels[:2000]))

import tensorflow as tf
import numpy as np

## Save to file
# remember to define the same dtype and shape when restore
W = tf.Variable([[1,2,3],[3,4,5]], dtype=tf.float32, name=‘weights‘)
b = tf.Variable([[1,2,3]], dtype=tf.float32, name=‘biases‘)

# init= tf.initialize_all_variables() # tf 马上就要废弃这种写法
# 替换成下面的写法:
init = tf.global_variables_initializer()　

saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init)
    save_path = saver.save(sess, "nxf_net/save_net.ckpt")
    print("Save to path: ", save_path)

"""    
Save to path:  nxf_net/save_net.ckpt
"""

# restore variables
# reduce the same shape and same type for your variables
# 先建立W,b的容器
W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights")
b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases")

# 这里不需要初始化步骤 init= tf.initialize_all_variables()

saver = tf.train.Saver()
with tf.Session() as sess:
    # 提取变量
    saver.restore(sess, "my_net/save_net.ckpt")
    print("weights:", sess.run(W))
    print("biases:", sess.run(b))

"""
weights: [[ 1.  2.  3.]
          [ 3.  4.  5.]]
biases: [[ 1.  2.  3.]]
"""