python人工智能tensorflow函数tf.assign使用方法_python_

def assign(ref, value, validate_shape=None, use_locking=None, name=None) Update 'ref' by assigning 'value' to it. This operation outputs a Tensor that holds the new value of 'ref' after the value has been assigned. This makes it easier to chain operations that need to use the reset value. Args: ref: A mutable `Tensor`. Should be from a `Variable` node. May be uninitialized. value: A `Tensor`. Must have the same type as `ref`. The value to be assigned to the variable. validate_shape: An optional `bool`. Defaults to `True`. If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'. use_locking: An optional `bool`. Defaults to `True`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A `Tensor` that will hold the new value of 'ref' after the assignment has completed. 

import tensorflow as tf; import numpy as np; c1 = ['c1', tf.GraphKeys.GLOBAL_VARIABLES] c2 = ['c2', tf.GraphKeys.GLOBAL_VARIABLES] #常量初始化器 v1_cons = tf.get_variable('v1_cons',dtype = tf.float32,shape=[1,4], initializer=tf.constant_initializer(), collections = c1) v2_cons = tf.get_variable('v2_cons',dtype = tf.float32,shape=[1,4], initializer=tf.constant_initializer(9), collections = c1) #正太分布初始化器 v1_nor = tf.get_variable('v1_nor',dtype = tf.float32, shape=[1,4], initializer=tf.random_normal_initializer(mean=0, stddev=5), collections = c2) v2_nor = tf.get_variable('v2_nor',dtype = tf.float32, shape=[1,4], initializer=tf.random_normal_initializer(mean=0, stddev=5), collections = c2) assign1 = tf.assign(v1_cons,v2_cons) #将v2_cons赋予v1_cons c1_get = tf.get_collection('c1') #获得c1集合 c2_get = tf.get_collection('c2') #获得c2集合 assign2 = [tf.assign(cg1,cg2) for cg1,cg2 in zip(c1_get,c2_get) ] #将c2赋予c1 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print("v1_cons:",sess.run(v1_cons)) print("v2_cons:",sess.run(v2_cons)) print(sess.run(assign1)) #显示赋值后的结果 print("将v2_cons赋予v1_cons:",sess.run(v1_cons)) print("c1_get_collection:",sess.run(c1_get)) print("c2_get_collection:",sess.run(c2_get)) print(sess.run(assign2)) #显示赋值后的结果 print("将c2赋予c1:",sess.run(c1_get)) 

v1_cons: [[0. 0. 0. 0.]] v2_cons: [[9. 9. 9. 9.]] [[9. 9. 9. 9.]] 将v2_cons赋予v1_cons: [[9. 9. 9. 9.]] c1_get_collection: [array([[9., 9., 9., 9.]], dtype=float32), array([[9., 9., 9., 9.]], dtype=float32)] c2_get_collection: [array([[-3.9746916, -7.5332146, 2.4480317, -1.3282107]], dtype=float32), array([[10.687443 , 3.6653206, 1.7079141, -4.524155 ]], dtype=float32)] [array([[-3.9746916, -7.5332146, 2.4480317, -1.3282107]], dtype=float32), array([[10.687443 , 3.6653206, 1.7079141, -4.524155 ]], dtype=float32)] 将c2赋予c1: [array([[-3.9746916, -7.5332146, 2.4480317, -1.3282107]], dtype=float32), array([[10.687443 , 3.6653206, 1.7079141, -4.524155 ]], dtype=float32)]