tf.random_normal_initializer

TensorFlow 1 version View source on GitHub

Class random_normal_initializer

Initializer that generates tensors with a normal distribution.

Inherits From: Initializer

Aliases: tf.initializers.RandomNormal, tf.keras.initializers.RandomNormal

Used in the guide:

Used in the tutorials:

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.

__init__

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__init__(
    mean=0.0,
    stddev=0.05,
    seed=None
)

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

Methods

__call__

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__call__(
    shape,
    dtype=tf.dtypes.float32
)

Returns a tensor object initialized as specified by the initializer.

Args:

  • shape: Shape of the tensor.
  • dtype: Optional dtype of the tensor. Only floating point types are supported.

Raises:

  • ValueError: If the dtype is not floating point

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()

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

Returns:

A JSON-serializable Python dict.

Compat aliases