tf.compat.v2.keras.initializers.GlorotNormal

The Glorot normal initializer, also called Xavier normal initializer.

Inherits From: VarianceScaling

View aliases

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v2.initializers.GlorotNormal`, `tf.compat.v2.initializers.glorot_normal`, `tf.compat.v2.keras.initializers.glorot_normal`

tf.compat.v2.keras.initializers.GlorotNormal(
    seed=None
)

It draws samples from a truncated normal distribution centered on 0 with 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.

Args
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