Watch keynotes, product sessions, workshops, and more from Google I/O See playlist


The Glorot normal initializer, also called Xavier normal initializer.

Inherits From: VarianceScaling

It draws samples from a truncated normal distribution centered on 0 with standard deviation (after truncation) given by stddev = sqrt(2 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.

seed A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.
dtype Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.


Glorot et al., 2010 (pdf)



View source

Instantiates an initializer from a configuration dictionary.


initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

config A Python dictionary. It will typically be the output of get_config.

An Initializer instance.


View source

Returns the configuration of the initializer as a JSON-serializable dict.

A JSON-serializable Python dict.


View source

Returns a tensor object initialized as specified by the initializer.

shape Shape of the tensor.
dtype Optional dtype of the tensor. If not provided use the initializer dtype.
partition_info Optional information about the possible partitioning of a tensor.