tensorflow模型的保存和重载

tensorflow模型的保存使用tf.train.Saver()
例子:

import tensorflow as tf
import numpy as np

X=tf.placeholder(dtype=tf.float32,shape=[20,1])
Y=tf.placeholder(dtype=tf.float32,shape=[20,1])

W=tf.Variable(tf.zeros([1,1]))
b=tf.Variable(tf.ones([1]))

out=tf.matmul(X,W)+b

loss=tf.reduce_mean(tf.square(out-Y))
train=tf.train.GradientDescentOptimizer(0.01).minimize(loss)

sess=tf.Session()
init=tf.global_variables_initializer()

sess.run(init)

saver=tf.train.Saver(max_to_keep=0)#创建Saver,参数max_to_keep ，这个是用来设置保存模型的个数，默认为5，即 max_to_keep=5，保存最近的5个模型。如果你想每训练一代（epoch)就想保存一次模型，则可以将 max_to_keep设置为None或者0，如果你只想保存最后一代的模型，则只需要将max_to_keep设置为1即可
for i in range(1000):
    x = np.random.uniform(0, 10000, [20, 1]) 
    x_mean=np.mean(x)
    x =x/x_mean
    y = 2 * x + 3
    sess.run(train,feed_dict={X:x,Y:y})
    print(sess.run(loss,feed_dict={X:x,Y:y}))
    print(sess.run(W),sess.run(b))
    saver.save(sess, 'ckpt/model.ckpt')# saver.save(sess, 'ckpt/model.ckpt',global_step=i)第一个参数sess,这个就不用说了。第二个参数设定保存的路径和名字，第三个参数将训练的次数作为后缀加入到模型名字中。

最后模型保存在:ckpt/中,会有四个文件:checkpoint, model.ckpt.data-00000-of-00001, model.ckpt.index, model.ckpt.meta
重载模型:

import tensorflow as tf
import numpy as np

X=tf.placeholder(dtype=tf.float32,shape=[5,1])
Y=tf.placeholder(dtype=tf.float32,shape=[5,1])

W=tf.Variable(tf.zeros([1,1]))
b=tf.Variable(tf.ones([1]))

out=tf.matmul(X,W)+b

loss=tf.reduce_mean(tf.square(out-Y))
train=tf.train.GradientDescentOptimizer(0.01).minimize(loss)

sess=tf.Session()
init=tf.global_variables_initializer()

sess.run(init)

saver=tf.train.Saver(max_to_keep=0)
for i in range(10000):
    x = np.random.uniform(0, 10000, [5, 1])  # 这个值要小一点才好,太大了要发散(梯度太大了),所以进行预处理
    x_mean=np.mean(x)
    x =x/x_mean
    y = 2 * x + 3
    sess.run(train,feed_dict={X:x,Y:y})
    #print(sess.run(loss,feed_dict={X:x,Y:y}))
    #print(sess.run(W),sess.run(b))
    saver.save(sess, 'ckpt/model.ckpt')
#test
model_file=tf.train.latest_checkpoint('ckpt/')
saver.restore(sess,model_file)
x = np.random.uniform(0, 10000, [5, 1])

y = 2 * x + 3
print(y)
print(sess.run(out,feed_dict={X:x,Y:y}))

输出:

[[ 5019.91789035]
 [ 9799.95392019]
 [ 7643.69704575]
 [10588.81737293]
 [ 8225.58717911]]
[[ 5019.9355]
 [ 9799.988 ]
 [ 7643.7236]
 [10588.854 ]
 [ 8225.615 ]]