tf.keras.initializers.HeNormal

He normal initializer.

Inherits From: VarianceScaling, Initializer

View aliases

Main aliases

tf.keras.initializers.he_normal

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

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

It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.

Examples:

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

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

Args
seed	A Python integer. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will not produce the same random values across multiple calls, but multiple initializers will produce the same sequence when constructed with the same seed value.

References:

• He et al., 2015

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, the output of get_config.

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