tf.keras.initializers.LecunNormal

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Lecun normal initializer.

Inherits From: VarianceScaling, Initializer

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

Also available via the shortcut function tf.keras.initializers.lecun_normal.

Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized.

Draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor.

Examples:

# Standalone usage:
initializer = tf.keras.initializers.LecunNormal()
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = tf.keras.initializers.LecunNormal()
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

References:

• Self-Normalizing Neural Networks, Klambauer et al., 2017 (pdf)
• Efficient Backprop, Lecun et al., 1998

Methods

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)

get_config

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get_config()

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

__call__

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__call__(
    shape, dtype=None, **kwargs
)

Returns a tensor object initialized as specified by the initializer.