tf.compat.v1.random_uniform_initializer

Initializer that generates tensors with a uniform distribution.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.initializers.random_uniform

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

from_config

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@classmethod
from_config(
    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.

__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.