tf.keras.initializers.LecunNormal

Lecun normal initializer.

Inherits From: VarianceScaling, Initializer

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

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 = LecunNormal()
values = initializer(shape=(2, 2))
 
# Usage in a Keras layer:
initializer = LecunNormal()
layer = Dense(3, kernel_initializer=initializer)

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:

Methods

clone

View source

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

View source

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.