tf.keras.initializers.GlorotUniform

View source on GitHub

The Glorot uniform initializer, also called Xavier uniform initializer.

Inherits From: VarianceScaling

tf.keras.initializers.GlorotUniform(
    seed=None
)

It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (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.

References:

Glorot et al., 2010 (pdf)

Methods

from_config

View source

@classmethod
from_config(
    config
)

Instantiates an initializer from a configuration dictionary.

Example:

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

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

Returns
An Initializer instance.

get_config

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__call__

View source

__call__(
    shape, dtype=tf.dtypes.float32
)

Returns a tensor object initialized as specified by the initializer.

Args
shape Shape of the tensor.
dtype Optional dtype of the tensor. Only floating point types are supported.

Raises
ValueError If the dtype is not floating point