import tensorflow as tf

sess = tf.InteractiveSession()

### 生成符合正态分布的随机值

# tf.random_normal(shape, mean, stddev, dtype, seed, name)

a = tf.random_normal([2, 3], name='a')

print(a.eval())

# [[-1.2077953  -0.69333565 -0.10252991]

#  [ 0.51914424  0.7754795  -0.02618051]]

### 生成截断的正态分布的随机值

## 只保留[mean - 2stddev, mean + 2stddev]内的随机数

# tf.truncated_normal(shape, mean, stddev, dtype, seed, name)

b = tf.truncated_normal([2, 3], name='b')

print(b.eval())

# [[-1.8038174   1.521785    0.33182728]

#  [ 1.0274183  -0.39916983 -0.50485927]]

### 生成均匀分布的随机数

# tf.random_uniform(shape, minval, maxval, dtype, seed, name)

c = tf.random_uniform([2, 3], name='c')

print(c.eval())

# [[0.6636964  0.20990396 0.44687605]

#  [0.64548564 0.22155988 0.19247997]]

### 按行乱序

# tf.random_shuffle(value, dtype, name)

d = tf.random_shuffle([[1, 2], [3, 2]], name='d')

print(d.eval())