tf.keras.initializers.RandomUniform

Initializer that generates tensors with a uniform distribution.

Inherits From: random_uniform_initializer, Initializer

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

Also available via the shortcut function tf.keras.initializers.random_uniform.

Examples:

# Standalone usage:
initializer = tf.keras.initializers.RandomUniform(minval=0., maxval=1.)
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = tf.keras.initializers.RandomUniform(minval=0., maxval=1.)
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

minval A python scalar or a scalar tensor. Lower bound of the range of random values to generate (inclusive).
maxval A python scalar or a scalar tensor. Upper bound of the range of random values to generate (exclusive).
seed A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype.

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=None, **kwargs
)

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. If not specified, tf.keras.backend.floatx() is used, which default to float32 unless you configured it otherwise (via tf.keras.backend.set_floatx(float_dtype)).
**kwargs Additional keyword arguments.