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