tf.variance_scaling_initializer

Class variance_scaling_initializer

Inherits From: Initializer

Aliases:

• Class tf.initializers.variance_scaling
• Class tf.keras.initializers.VarianceScaling
• Class tf.variance_scaling_initializer

Defined in tensorflow/python/ops/init_ops.py.

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

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"

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.set_random_seed for behavior.
• dtype: The data type. Only floating point types are supported.

Raises:

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

Methods

__init__

__init__(
    scale=1.0,
    mode='fan_in',
    distribution='normal',
    seed=None,
    dtype=tf.float32
)

Initialize self. See help(type(self)) for accurate signature.

__call__

__call__(
    shape,
    dtype=None,
    partition_info=None
)

Call self as a function.

from_config

from_config(
    cls,
    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

get_config()

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

Returns:

A JSON-serializable Python dict.