莫烦Python--Tensorflow Day5

'''Save 保存模型'''
'''remember to define the same dtype and shape when restore'''
import tensorflow as tf

# save to file
W = tf.Variable([[1, 2, 3], [4, 5, 6]], dtype=tf.float32, name='weight')
b = tf.Variable([[1, 2, 3]], dtype=tf.float32, name='biases')

init = tf.initialize_all_variables()

saver = tf.train.Saver()

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

保存和读取文件

'''Save 保存模型'''
'''remember to define the same dtype and shape when restore'''
import tensorflow as tf
import numpy as np

# save to file
W = tf.Variable([[1, 2, 3], [4, 5, 6]], dtype=tf.float32, name='weight')
b = tf.Variable([[1, 2, 3]], dtype=tf.float32, name='biases')

init = tf.initialize_all_variables()

saver = tf.train.Saver()

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


# restore variable
# redefine the same shape and same type for your variable

# 这只是一个空的框架，需要把他们放入我们的文件中
# 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')

# not need init step
saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess, "my_net/save_net.ckpt")
    print("weights:", sess.run(W))
    print("biases:", sess.run(b))

'''
运行结果：
weights: [[1. 2. 3.]
 [4. 5. 6.]]
biases: [[1. 2. 3.]]
'''