Description of commonly used functions in Tensorflow (random related functions)

tf commonly used functions

tf.cast(tensor name, dtype=data type)

Force tensor to be converted to a data type of dtype type

tf.reduce_min(tensor name)

Calculate the minimum value of elements in the tensor dimension

tf.reduce_max(tensor name)

Calculate the maximum value of the elements in the tensor dimension

The axis parameter controls the operation dimension

tf.random function

tf.random_uniform random uniform distribution

At present, I use this more, mainly for generating some parameters, such as w matrix
format : tf.random_uniform(shape, minval=0.0, maxval=1.0, dtype=tf.float32, seed=None, name=None)
parameters Description :

shape defines dimensions
minval minimum interval
maxval maximum value of interval
dtype defines the type, such as float, int
seed defines the seed
name defines the name

Example

import tensorflow as tf
# 定义 W 的shape[5,3]，最小值-1.0，最大值1.0
w1 = tf.random_uniform([5,3], -1.0, 1.0)
# 推荐w2写法，提高代码可读性
w2 = tf.random_uniform(shape=[5,3], minval=-1.0, maxval=1.0)
with tf.Session() as sess:
    print(sess.run(w1))
    print(sess.run(w2))

rf.random_normal random normal distribution

Data generated in accordance with the normal distribution, its name is intended
format : tf.random_normal (shape, mean = 0.0 , stddev = 1.0, dtype = tf.float32, seed = None, name = None)
Parameters :

shape defines the dimension (same as above)
mean defines the mean
stddev defines variance
dtype defines the type (same as above)
seed defines the seed
name defines the name

Example

import tensorflow as tf
# 定义 W 的shape[5,3]，均值-1.0，方差 4.0
w1 = tf.random_normal([5,3], -1.0, 4.0)
# 推荐w2写法，提高代码可读性
w2 = tf.random_normal(shape=[5,3], mean=-1.0, stddev=4.0)
with tf.Session() as sess:
    print(sess.run(w1))
    print(sess.run(w2))

tf.truncated_normal standard normal distribution

Compared with random_normal random normal distribution, truncated_normal truncated normal distribution random numbers, and only keep random numbers in the range of [mean-2stddev,mean+2stddev]
Format : tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype= tf.float32, seed=None, name=None)
parameter description :

shape defines the dimension (same as above)
mean defines the mean
stddev defines variance
dtype defines the type (same as above)
seed defines the seed
name defines the name

Example

import tensorflow as tf
# 定义 W 的shape[5,3]，均值-1.0，方差 4.0
w1 = tf.truncated_normal([5,3], 0.0, 1.0)
# 推荐w2写法，提高代码可读性
w2 = tf.truncated_normal(shape=[5,3], mean=0.0, stddev=1.0)
with tf.Session() as sess:
    print(sess.run(w1))
    print(sess.run(w2))

tf.random_shuffle randomly swap positions (shuffle)

This is a rarely used
format : tf.random_shuffle(value, seed=None, name=None)
parameter description :

value is a given tensor
seed defined seed
name defines the name

Example

import tensorflow as tf
#定义一个张量
vec = tf.constant([[1,2],[3,4],[5,6],[7,8],[9,10],[11,12]])
#定一随机交换位置（打乱）计算
shuff = tf.random_shuffle(value=vec,seed=1,name="shuff")
with tf.Session() as sess:
    print (sess.run(shuff))

Reference: https://cloud.tencent.com/developer/article/1392320 with a little modification