tf.keras.initializers.VarianceScaling

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

Inherits From: Initializer

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

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 "truncated_normal", "untruncated_normal" and "uniform".
• seed: A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.

Raises:

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

Methods

__call__

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__call__(
    shape, dtype=tf.dtypes.float32
)

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.

Raises:

• ValueError: If the dtype is not floating point

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. 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.