20171130_tensorflow_tf.Variable

tf.Variable

转自：TensorFlow图变量tf.Variable的用法解析

1.在TensorFlow的世界里，变量的定义和初始化是分开的，所有关于图变量的赋值和计算都要通过tf.Session的run来进行。想要将所有图变量进行集体初始化时应该使用tf.global_variables_initializer。
2.
tf.Variable

tf.Variable.init(initial_value, trainable=True, collections=None, validate_shape=True, name=None)

In [1]: import tensorflow as tfIn [2]: a = tf.Variable(3,name='a')In [3]: a2 = a.assign(5)In [4]: sess = tf.Session()In [5]: sess.run(a.initializer)   #必须先定义a的值，否则会报错In [6]: sess.run(a)Out[6]: 13In [7]: sess.run(a2)Out[7]: 24

#!/usr/bin/env python# -*- coding: utf-8 -*-# @Date    : 2017-11-30 14:57:27# @Author  : [e-mail]# @Link    : https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/2-2-example2/# @Version : Tensorflow 例子2import tensorflow as tfimport numpy as np#creat datax_data = np.random.rand(100).astype(np.float32)y_data = x_data*0.1 + 0.3### creat tensorflow strucure start ###Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0))biases = tf.Variable(tf.zeros([1]))y = Weights*x_data + biasesloss = tf.reduce_mean(tf.square(y-y_data))optimizer = tf.train.GradientDescentOptimizer(0.5)train = optimizer.minimize(loss)init = tf.initialize_all_variables()### creat tensorflow strucure end ###sess = tf.Session()sess.run(init)    #Importantfor step in range(201):    sess.run(train)    if step % 10 == 0:        print(step,sess.run(Weights),sess.run(biases))

#!/usr/bin/env python# -*- coding: utf-8 -*-# @Date    : 2017-11-30 19:51:50# @Author  : [e-mail]# @Link    : https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/2-4-variable/# @Version : Variable 变量import tensorflow as tfstate = tf.Variable(0,name='counter')print(state.name)one = tf.constant(1)new_value = tf.add(state,one)    #addupdate = tf.assign(state,new_value)    #赋值，new_value赋值stateinit = tf.initialize_all_variables()   #must have if define variablewith tf.Session() as sess:    sess.run(init)    for _ in range(3):        sess.run(update)        print(sess.run(state))