tf.keras.initializers.HeUniform

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He uniform variance scaling initializer.

Inherits From: VarianceScaling, Initializer

View aliases

Main aliases

tf.initializers.HeUniform, tf.initializers.he_uniform, tf.keras.initializers.he_uniform

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

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

Draws samples from a uniform distribution within [-limit, limit], where limit = sqrt(6 / fan_in) (fan_in is the number of input units in the weight tensor).

Examples:

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

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

Args
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:

He et al., 2015 (pdf)

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, the output of get_config.

Returns
A tf.keras.initializers.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, **kwargs
)

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))
**kwargs	Additional keyword arguments.