TF.Variable

1.前言

在学习莫烦Tensorflow教学视频中有些东西不清楚，这篇是关于tf.Variable的拓展。

2.变量的创建

视频中用的tf.Variable(tf.zeros([1]))可以简写为tf.zeros([1])。
2.1生成Tensor

tf.zeros(shape, dtype=tf.float32, name=None)tf.zeros_like(tensor, dtype=None, name=None)tf.constant(value, dtype=None, shape=None, name='Const')tf.fill(dims, value, name=None)tf.ones_like(tensor, dtype=None, name=None)tf.ones(shape, dtype=tf.float32, name=None)

2.2生成序列
类似于Matlab中的用法

tf.range(start, limit, delta=1, name='range')tf.linspace(start, stop, num, name=None)

2.3生成随机矩阵

tf.random_normal(shape, mean=0.0, stddev=1.0,dtype=tf.float32, seed=None, name=None)tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)tf.random_uniform(shape, minval=0.0, maxval=1.0, dtype=tf.float32, seed=None, name=None)tf.random_shuffle(value, seed=None, name=None)

2.4例子

import tensorflow as tf# 生成0和1矩阵v1 = tf.Variable(tf.zeros([3, 3, 3]), name="v1")v2 = tf.Variable(tf.ones([2, 3]), name="v2")# 填充单值矩阵v3 = tf.Variable(tf.fill([2, 3], 9))# 常量矩阵v4_1 = tf.constant([1, 2, 3, 4, 5, 6, 7])v4_2 = tf.constant(-1.0, shape=[2, 3])# 生成等差数列v6_1 = tf.linspace(1.0, 10.0, 20, name="linspace")  # float32 or float64v7_1 = tf.range(10, 20, 3)  # just int32# 生成各种随机数据矩阵# seed 为标准差v8_1 = tf.random_uniform([2, 4], minval=0.0, maxval=2.0, dtype=tf.float32, seed=1234, name="v8_1")v8_2 = tf.Variable(tf.random_normal([2, 3], mean=0.0, stddev=1.0, dtype=tf.float32, seed=1234, name="v8_2"))v8_3 = tf.Variable(tf.truncated_normal([2, 3], mean=0.0, stddev=1.0, dtype=tf.float32, seed=1234, name="v8_3"))v8_4 = tf.Variable(tf.random_uniform([2, 3], minval=0.0, maxval=1.0, dtype=tf.float32, seed=1234, name="v8_4"))v8_5 = tf.random_shuffle([[1, 2, 3], [4, 5, 6], [6, 6, 6]], seed=134, name="v8_5")# 初始化init_op = tf.global_variables_initializer()# 运行with tf.Session() as sess:    sess.run(init_op)    print('v1')    print(v1)    print(tf.Variable.get_shape(v1))    print(sess.run(v1))    print('v2')    print(sess.run(v2))    print('v3')    print(sess.run(v3))    print('v4_1')    print(sess.run(v4_1))    print('v4_2')    print(sess.run(v4_2))    print('v6_1')    print(sess.run(v6_1))    print('v7_1')    print(sess.run(v7_1))    print('v8_1')    print(sess.run(v8_1))    print('v8_2')    print(sess.run(v8_2))    print('v8_3')    print(sess.run(v8_3))    print('v8_4')    print(sess.run(v8_4))    print('v8_5')    print(sess.run(v8_5))

3.变量的初始化

变量的初始化必须在模型的其它操作运行之前先明确地完成。
3.1最简单的方法
添加一个给所有变量初始化的操作，并在使用模型之前首先运行那个操作。

# Create two variables.weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),                      name="weights")biases = tf.Variable(tf.zeros([200]), name="biases")...# Add an op to initialize the variables.init_op = tf.global_variables_initializer()# tf.initialize_all_variables()这是旧版本的用法# Later, when launching the modelwith tf.Session() as sess:  # Run the init operation.  sess.run(init_op)  ...  # Use the model  ...

4.引用

liumw1203的简书
修雨轩陈的Blog