tf.compat.v2.keras.initializers.RandomUniform

Initializer that generates tensors with a uniform distribution.

Inherits From: Initializer

View aliases

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v2.initializers.RandomUniform`, `tf.compat.v2.random_uniform_initializer`

tf.compat.v2.keras.initializers.RandomUniform(
    minval=-0.05, maxval=0.05, seed=None
)

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.

Methods

from_config

View source

@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

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__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.