tf.keras.initializers.VarianceScaling

View source on GitHub

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

Inherits From: Initializer

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

Used in the notebooks

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)

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

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)

config A Python dictionary, the output of get_config().

get_config

View source

get_config()

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

__call__

View source

__call__(
    shape, dtype=None
)

Returns a tensor object initialized as specified by the initializer.

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