tf.keras.initializers.VarianceScaling

Initializer that adapts its scale to the shape of its input tensors.

Inherits From: Initializer

View aliases

Main aliases

tf.keras.initializers.variance_scaling

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

Used in the notebooks

Used in the tutorials
Train a Deep Q Network with TF-Agents Networks

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 = VarianceScaling(
    scale=0.1, mode='fan_in', distribution='uniform')
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = VarianceScaling(
    scale=0.1, mode='fan_in', distribution='uniform')
layer = 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", or "uniform".
seed	A Python integer or instance of keras.backend.SeedGenerator. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of keras.backend.SeedGenerator.

Methods

clone

View source

clone()

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, the output of get_config().

Returns
An Initializer instance.

get_config

View source

get_config()

Returns the initializer's configuration as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__call__

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__call__(
    shape, dtype=None
)

Returns a tensor object initialized as specified by the initializer.

Args
shape	Shape of the tensor.
dtype	Optional dtype of the tensor.