TensorFlow：模型存储和调用

模型存储和调用

1.存储

import tensorflow as tf

ta = tf.Variable([1, 2], name='ta')
tb = tf.Variable(ta.initialized_value(), name="tb")
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
print(sess.run(ta), sess.run(tb), ta, tb)

saver = tf.train.Saver()
saver.save(sess, "./model")

2.调用

import tensorflow as tf

with tf.Session() as sess:
    saver = tf.train.import_meta_graph("./model.meta")
    saver.restore(sess, tf.train.latest_checkpoint("./"))
    # 取用最近保存的模型
    sess.run("ta:0")
    # 用name输出结果