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tf.compat.v1.random_uniform_initializer

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Initializer that generates tensors with a uniform distribution.

Inherits From: Initializer

tf.compat.v1.random_uniform_initializer(
    minval=0, maxval=None, seed=None, dtype=tf.dtypes.float32
)

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.
  • dtype: Default data type, used if no dtype argument is provided when calling the initializer.

Methods

__call__

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__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.

from_config

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@classmethod
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.