[Deep Learning] TensorFlow模型、参数的保存与读取

大部分情况，我们会把训练的网络保存下来，用于后面的使用。或者，在当前网络下对参数进行一定程度的微调。

TensorFlow中的tensorflow.train.Saver()提供了保存tf.Variable的方法。

存储变量

import tensorflow as tf

# The file path to save the data
save_file = './model.ckpt'

# Two Tensor Variables: weights and bias
weights = tf.Variable(tf.truncated_normal([2, 3]))
bias = tf.Variable(tf.truncated_normal([3]))

# Class used to save and/or restore Tensor Variables
saver = tf.train.Saver()

with tf.Session() as sess:
    # Initialize all the Variables
    sess.run(tf.global_variables_initializer())

    # Show the values of weights and bias
    print('Weights:')
    print(sess.run(weights))
    print('Bias:')
    print(sess.run(bias))

    # Save the model
    saver.save(sess, save_file)

Weights:

[[-0.97990924 1.03016174 0.74119264]

[-0.82581609 -0.07361362 -0.86653847]]

Bias:

[ 1.62978125 -0.37812829 0.64723819]

读取变量

# Remove the previous weights and bias
tf.reset_default_graph()

# Two Variables: weights and bias
weights = tf.Variable(tf.truncated_normal([2, 3]))
bias = tf.Variable(tf.truncated_normal([3]))

# Class used to save and/or restore Tensor Variables
saver = tf.train.Saver()

with tf.Session() as sess:
    # Load the weights and bias
    saver.restore(sess, save_file)

    # Show the values of weights and bias
    print('Weight:')
    print(sess.run(weights))
    print('Bias:')
    print(sess.run(bias))