initializer_variance_scaling: Initializer capable of adapting its scale to the shape of...

In keras: R Interface to 'Keras'

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

Description

With distribution="normal", samples are drawn from a truncated normal distribution centered on zero, with 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"

Usage

initializer_variance_scaling(
  scale = 1,
  mode = c("fan_in", "fan_out", "fan_avg"),
  distribution = c("normal", "uniform", "truncated_normal", "untruncated_normal"),
  seed = NULL
)

Arguments

scale	Scaling factor (positive float).
mode	One of "fan_in", "fan_out", "fan_avg".
distribution	One of "truncated_normal", "untruncated_normal" and "uniform". For backward compatibility, "normal" will be accepted and converted to "untruncated_normal".
seed	Integer used to seed the random generator.

Details

With distribution="uniform", samples are drawn from a uniform distribution within ⁠-limit, limit⁠, with limit = sqrt(3 * scale / n).

See Also

Other initializers: initializer_constant(), initializer_glorot_normal(), initializer_glorot_uniform(), initializer_he_normal(), initializer_he_uniform(), initializer_identity(), initializer_lecun_normal(), initializer_lecun_uniform(), initializer_ones(), initializer_orthogonal(), initializer_random_normal(), initializer_random_uniform(), initializer_truncated_normal(), initializer_zeros()

keras documentation built on May 29, 2024, 3:20 a.m.