tf.compat.v2.keras.initializers.RandomUniform

Initializer that generates tensors with a uniform distribution.

Inherits From: Initializer

Compat aliases for migration

`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

@classmethod
from_config(
    config
)

Instantiates an initializer from a configuration dictionary.

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()

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

Returns
A JSON-serializable Python dict.

__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.