tf.initializers.random_uniform

Class random_uniform

Inherits From: Initializer

Aliases:

• Class tf.initializers.random_uniform
• Class tf.random_uniform_initializer

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

Initializer that generates tensors with a uniform distribution.

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

__init__

__init__(
    minval=0,
    maxval=None,
    seed=None,
    dtype=tf.dtypes.float32
)

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

Methods

tf.initializers.random_uniform.__call__

__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.

tf.initializers.random_uniform.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.

tf.initializers.random_uniform.get_config

get_config()

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

Returns:

A JSON-serializable Python dict.