tf.keras.initializers.GlorotNormal

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The Glorot normal initializer, also called Xavier normal initializer.

Inherits From: VarianceScaling, Initializer

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Main aliases

tf.keras.initializers.glorot_normal

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

Used in the notebooks

Used in the tutorials
Building Your Own Federated Learning Algorithm Composing Learning Algorithms

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.

Examples:

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

# Usage in a Keras layer:
initializer = GlorotNormal()
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.