tf.compat.v1.keras.initializers.VarianceScaling

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

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.initializers.variance_scaling, tf.compat.v1.variance_scaling_initializer

tf.compat.v1.keras.initializers.VarianceScaling(
    scale=1.0,
    mode='fan_in',
    distribution='truncated_normal',
    seed=None,
    dtype=tf.dtypes.float32
)

Migrate to TF2

Although it is a legacy compat.v1 API, this symbol is compatible with eager execution and tf.function.

To switch to TF2 APIs, move to using either tf.initializers.variance_scaling or tf.keras.initializers.VarianceScaling (neither from compat.v1) and pass the dtype when calling the initializer.

Structural Mapping to TF2

Before:

initializer = tf.compat.v1.variance_scaling_initializer(
  scale=scale,
  mode=mode,
  distribution=distribution
  seed=seed,
  dtype=dtype)

weight_one = tf.Variable(initializer(shape_one))
weight_two = tf.Variable(initializer(shape_two))

After:

initializer = tf.keras.initializers.VarianceScaling(
  scale=scale,
  mode=mode,
  distribution=distribution
  seed=seed)

weight_one = tf.Variable(initializer(shape_one, dtype=dtype))
weight_two = tf.Variable(initializer(shape_two, dtype=dtype))

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
scale	scale	No change to defaults
mode	mode	No change to defaults
distribution	distribution	No change to defaults. 'normal' maps to 'truncated_normal'
seed	seed
dtype	dtype	The TF2 api only takes it as a __call__ arg, not a constructor arg.
partition_info	-	(__call__ arg in TF1) Not supported

Description

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.

Methods

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)

Args
config	A Python dictionary. It will typically be the output of get_config.

Returns
An Initializer instance.

get_config

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__call__

View source

__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.