TensorFlow学习笔记----TF生成数据的方法

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")

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 tfimport 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 float64v7_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