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tf.compat.v1.keras.initializers.VarianceScaling

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Class VarianceScaling

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

Inherits From: Initializer

Aliases:

  • Class tf.compat.v1.initializers.variance_scaling
  • Class tf.compat.v1.variance_scaling_initializer

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], with limit = sqrt(3 * scale / n).

Args:

  • scale: Scaling factor (positive float).
  • mode: One of "fan_in", "fan_out", "fan_avg".
  • distribution: Random distribution to use. One of "normal", "uniform".
  • seed: A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.
  • dtype: Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.

Raises:

  • ValueError: In case of an invalid value for the "scale", mode" or "distribution" arguments.

__init__

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__init__(
    scale=1.0,
    mode='fan_in',
    distribution='truncated_normal',
    seed=None,
    dtype=tf.dtypes.float32
)

DEPRECATED FUNCTION ARGUMENT VALUES (deprecated arguments)

Methods

__call__

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__call__(
    shape,
    dtype=None,
    partition_info=None
)

Returns a tensor object initialized as specified by the initializer.

Args:

  • shape: Shape of the tensor.
  • dtype: Optional dtype of the tensor. If not provided use the initializer dtype.
  • partition_info: Optional information about the possible partitioning of a tensor.

from_config

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from_config(
    cls,
    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

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get_config()

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

Returns:

A JSON-serializable Python dict.