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)

Args
scale	Scaling factor (positive float).
mode	One of "fan_in", "fan_out", "fan_avg".
distribution	Random distribution to use. One of "truncated_normal", "untruncated_normal" and "uniform".
seed	A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.

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, **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.