tf.keras.initializers.GlorotUniform

The Glorot uniform initializer, also called Xavier uniform initializer.

Inherits From: VarianceScaling, Initializer

View aliases

Main aliases

tf.keras.initializers.glorot_uniform

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

Used in the notebooks

Used in the tutorials
Scalable model compression

Draws samples from a uniform distribution within [-limit, limit], where limit = sqrt(6 / (fan_in + fan_out)) (fan_in is the number of input units in the weight tensor and fan_out is the number of output units).

Examples:

# Standalone usage:
initializer = GlorotUniform()
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = GlorotUniform()
layer = Dense(3, kernel_initializer=initializer)

Args
seed	A Python integer or instance of keras.backend.SeedGenerator. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of keras.backend.SeedGenerator.

Reference:

• Glorot et al., 2010

Methods

clone

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clone()

from_config

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@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, the output of get_config().

Returns
An Initializer instance.

get_config

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get_config()

Returns the initializer's configuration as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__call__

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__call__(
    shape, dtype=None
)

Returns a tensor object initialized as specified by the initializer.

Args
shape	Shape of the tensor.
dtype	Optional dtype of the tensor.