TensorFlow中权重的随机初始化的方法

一开始没看懂stddev是什么参数，找了一下，在tensorflow/python/ops里有random_ops，其中是这么写的：

def random_normal(shape, mean=0.0, stddev=1.0, dtype=types.float32,
seed=None, name=None):
"""Outputs random values from a normal distribution.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""

也就是按照正态分布初始化权重，mean是正态分布的平均值，stddev是正态分布的标准差（standard deviation），seed是作为分布的random seed（随机种子，我百度了一下，跟什么伪随机数发生器还有关，就是产生随机数的），在mnist/concolutional中seed赋值为66478，挺有意思，不知道是什么原理。

后面还有truncated_normal的定义：

def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=types.float32,
seed=None, name=None):
"""Outputs random values from a truncated normal distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the truncated normal distribution.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random truncated normal values.
"""

截断正态分布，以前都没听说过。

TensorFlow还提供了平均分布等。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。

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