tf.compat.v2.keras.initializers.RandomUniform

View source on GitHub

Class RandomUniform

Initializer that generates tensors with a uniform distribution.

Inherits From: Initializer

Aliases:

Args:

  • minval: A python scalar or a scalar tensor. Lower bound of the range of random values to generate.
  • maxval: A python scalar or a scalar tensor. Upper bound of the range of random values to generate. Defaults to 1 for float types.
  • seed: A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.

__init__

View source

__init__(
    minval=-0.05,
    maxval=0.05,
    seed=None
)

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

Methods

tf.compat.v2.keras.initializers.RandomUniform.__call__

View source

__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 and integer types are supported.

Raises:

  • ValueError: If the dtype is not numeric.

tf.compat.v2.keras.initializers.RandomUniform.from_config

View source

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.

tf.compat.v2.keras.initializers.RandomUniform.get_config

View source

get_config()

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

Returns:

A JSON-serializable Python dict.