tf.keras.initializers.GlorotNormal

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The Glorot normal initializer, also called Xavier normal initializer.

Inherits From: VarianceScaling

View aliases

Main aliases

tf.initializers.GlorotNormal, tf.initializers.glorot_normal, tf.keras.initializers.glorot_normal

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

tf.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

`call`

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__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

`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`

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get_config()

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

Returns:

A JSON-serializable Python dict.