tf.keras.initializers.LecunNormal

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Lecun normal initializer.

Inherits From: VarianceScaling

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)

seed A Python integer. Used to seed the random generator.

References:

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)

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.

__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. Only floating point types are supported. If not specified, tf.keras.backend.floatx() is used, which default to float32 unless you configured it otherwise (via tf.keras.backend.set_floatx(float_dtype))