tf.keras.initializers.VarianceScaling

Initializer capable of adapting its scale to the shape of weights tensors.

Inherits From: Initializer

View aliases

Main aliases

tf.initializers.VarianceScaling, tf.initializers.variance_scaling, tf.keras.initializers.variance_scaling

tf.keras.initializers.VarianceScaling(
    scale=1.0, mode='fan_in', distribution='truncated_normal',
    seed=None
)

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

With distribution="truncated_normal" or "untruncated_normal", samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n), where n is:

• number of input units in the weight tensor, if mode="fan_in"
• number of output units, if mode="fan_out"
• average of the numbers of input and output units, if mode="fan_avg"

With distribution="uniform", samples are drawn from a uniform distribution within [-limit, limit], where limit = sqrt(3 * scale / n).

Examples:

# Standalone usage:
initializer = tf.keras.initializers.VarianceScaling(
scale=0.1, mode='fan_in', distribution='uniform')
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = tf.keras.initializers.VarianceScaling(
scale=0.1, mode='fan_in', distribution='uniform')
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

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)

get_config

View source

get_config()

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

__call__

View source

__call__(
    shape, dtype=None, **kwargs
)

Returns a tensor object initialized as specified by the initializer.