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tf.initializers.truncated_normal

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Class truncated_normal

Initializer that generates a truncated normal distribution.

Inherits From: Initializer

Aliases:

  • Class tf.compat.v1.initializers.truncated_normal
  • Class tf.compat.v1.truncated_normal_initializer
  • Class tf.truncated_normal_initializer

These values are similar to values from a random_normal_initializer except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

Args:

  • mean: a python scalar or a scalar tensor. Mean of the random values to generate.
  • stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate.
  • 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.

__init__

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__init__(
    mean=0.0,
    stddev=1.0,
    seed=None,
    dtype=tf.dtypes.float32
)

DEPRECATED FUNCTION ARGUMENTS

Methods

__call__

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

from_config

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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

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get_config()