TF生成数据的方法

 转自：http://blog.youkuaiyun.com/phdat101/article/details/52442738

正常情况下，使用tf.initialize_all_variables()初始化变量，在完全构建好模型并加载之后才运行这个操作。生成数据的主要方法如下

1）如果需要利用已经初始化的参数给其他变量赋值

TF的变量有个initialized_value()属性，就是初始化的值，使用方法如下：

# 原始的变量 weights = tf . Variable ( tf . random_normal ([ 784 , 200 ], stddev = 0.35 ), name = "weights" ) # 创造相同内容的变量 w2 = tf . Variable ( weights . initialized_value (), name = "w2" ) # 也可以直接乘以比例 w_twice = tf . Variable ( weights . initialized_value () * 0.2 , name = "w_twice" )

 来自CODE的代码片

init_with_other_init.py

2）生成tensor的一些方法

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)

生成序列

tf.range(start, limit, delta=1, name='range')

tf.linspace(start, stop, num, name=None)

生成随机数

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)

效果程序：

import tensorflow as tf import numpy as np # 生成0和1矩阵 v1 = tf . Variable ( tf . zeros ([ 3 , 3 , 3 ]), name = "v1" ) v2 = tf . Variable ( tf . ones ([ 10 , 5 ]), 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 ( 10.0 , 12.0 , 30 , name = "linspace" ) #float32 or float64 v7_1 = tf . range ( 10 , 20 , 3 ) #just int32 #生成各种随机数据矩阵 v8_1 = tf . Variable ( 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 . initialize_all_variables () # 保存变量，也可以指定保存的内容 saver = tf . train . Saver () #saver = tf.train.Saver({"my_v2": v2}) #运行 with tf . Session () as sess : sess . run ( init_op ) # 输出形状和值 print tf . Variable . get_shape ( v1 ) #shape print sess . run ( v1 ) #vaule # numpy保存文件 np . save ( "v1.npy" , sess . run ( v1 )) #numpy save v1 as file test_a = np . load ( "v1.npy" ) print test_a [ 1 , 2 ] #一些输出 print sess . run ( v3 ) v5 = tf . zeros_like ( sess . run ( v1 )) print sess . run ( v6_1 ) print sess . run ( v7_1 ) print sess . run ( v8_5 ) #保存图的变量 save_path = saver . save ( sess , "/tmp/model.ckpt" ) #加载图的变量 #saver.restore(sess, "/tmp/model.ckpt") print "Model saved in file: " , save_path

 来自CODE的代码片

variables.py