python人工智能tensorflow函數tf.assign使用方法
更新時間:2022年05月05日 14:44:31 作者:Bubbliiiing
這篇文章主要為大家介紹了python人工智能tensorflow函數tf.assign使用方法,有需要的朋友可以借鑒參考下,希望能夠有所幫助,祝大家多多進步,早日升職加薪
參數數量及其作用
該函數共有五個參數,分別是:
- 被賦值的變量 ref
- 要分配給變量的值 value、
- 是否驗證形狀 validate_shape
- 是否進行鎖定保護 use_locking
- 名稱 name
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.
該函數的作用是將一個要分配給變量的值value賦予被賦值的變量ref,用于tensorflow各個參數的變量賦值。
例子
該例子將舉例如何進行變量之間的數據賦值和如何進行集合間的數據賦值。
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) #將v2_cons賦予v1_cons c1_get = tf.get_collection('c1') #獲得c1集合 c2_get = tf.get_collection('c2') #獲得c2集合 assign2 = [tf.assign(cg1,cg2) for cg1,cg2 in zip(c1_get,c2_get) ] #將c2賦予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)) #顯示賦值后的結果 print("將v2_cons賦予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)) #顯示賦值后的結果 print("將c2賦予c1:",sess.run(c1_get))
其輸出為:
v1_cons: [[0. 0. 0. 0.]] v2_cons: [[9. 9. 9. 9.]] [[9. 9. 9. 9.]] 將v2_cons賦予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)] 將c2賦予c1: [array([[-3.9746916, -7.5332146, 2.4480317, -1.3282107]], dtype=float32), array([[10.687443 , 3.6653206, 1.7079141, -4.524155 ]], dtype=float32)]
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