TensorFlow入门使用 tf.train.Saver()保存模型

import tensorflow as tf

config = tf.ConfigProto()

config.gpu_options.allow_growth = True

sess = tf.Session(config=config)

# Create some variables.

v1 = tf.Variable([11.0, 16.3], name="v1")

v2 = tf.Variable(33.5, name="v2")

# Add ops to save and restore all the variables.

saver = tf.train.Saver()

# Later, launch the model, use the saver to restore variables from disk, and

# do some work with the model.

# Restore variables from disk.

ckpt_path = './ckpt/test-model.ckpt'

saver.restore(sess, ckpt_path + '-'+ str(1))

print("Model restored.")

print sess.run(v1)

print sess.run(v2)

import tensorflow as tf

config = tf.ConfigProto()

config.gpu_options.allow_growth = True

sess = tf.Session(config=config)

# Create some variables.

v1 = tf.Variable([11.0, 16.3], name="v1")

# Add ops to save and restore all the variables.

saver = tf.train.Saver()

# Later, launch the model, use the saver to restore variables from disk, and

# do some work with the model.

# Restore variables from disk.

ckpt_path = './ckpt/test-model.ckpt'

saver.restore(sess, ckpt_path + '-'+ str(1))

print("Model restored.")

print sess.run(v1)