tf.keras.initializers.LecunUniform

Lecun uniform initializer.

Inherits From: VarianceScaling

View aliases

Main aliases

tf.initializers.LecunUniform, tf.initializers.lecun_uniform, tf.keras.initializers.lecun_uniform

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

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

Draws samples from a uniform distribution within [-limit, limit], where limit = sqrt(3 / fan_in) (fan_in is the number of input units in the weight tensor).

Examples:

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

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

Arguments
seed	A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.

References:

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

Methods

from_config

View source

@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

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__call__

View source

__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))