代码语言：javascript复制
def xavier_initializer(uniform=True, seed=None, dtype=dtypes.float32):
"""Returns an initializer performing "Xavier" initialization for weights.
This function implements the weight initialization from:
Xavier Glorot and Yoshua Bengio (2010):
[Understanding the difficulty of training deep feedforward neural
networks. International conference on artificial intelligence and
statistics.](
http://www.jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)
This initializer is designed to keep the scale of the gradients roughly the
same in all layers. In uniform distribution this ends up being the range:
`x = sqrt(6. / (in out)); [-x, x]` and for normal distribution a standard
deviation of `sqrt(2. / (in out))` is used.
Args:
uniform: Whether to use uniform or normal distributed random initialization.
seed: A Python integer. Used to create random seeds. See
`tf.set_random_seed` for behavior.
dtype: The data type. Only floating point types are supported.
Returns:
An initializer for a weight matrix.
Raises:
ValueError: if `dtype` is not a floating point type.
TypeError: if `mode` is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].
"""
if not dtype.is_floating:
raise TypeError('Cannot create initializer for non-floating point type.')
if mode not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG']:
raise TypeError('Unknown mode %s [FAN_IN, FAN_OUT, FAN_AVG]', mode)返回对权重执行“Xavier”初始化的初始化器。此函数实现权重初始化，从:
Xavier Glorot和yobengio(2010):[了解深度前馈神经网络训练的难点]。(http://www.jmlr.org/programedings/papers/v9/glorot10a/glorot10a.pdf)
这个初始化器的设计目的是在所有层中保持梯度的比例大致相同。在均匀分布中，这个范围是' x = sqrt(6。/ (in out);正态分布的标准差为√2。/ (in out))’。
参数：
- factor:浮动。一个乘法因素
- mode:字符串。“FAN_IN”、“FAN_OUT’,‘FAN_AVG’
- uniform:是否使用均匀或正态分布随机初始化
- seed:一个Python整数。用于创建随机种子。看到“特遣部队。set_random_seed”行为
- dtype:数据类型。只支持浮点类型
返回值：
- 生成单位方差张量的初始化器
可能产生的异常：
- ValueError: if `dtype` is not a floating point type. TypeError: if `mode` is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].
