tf.keras.initializers.RandomUniform

Random uniform initializer.

Inherits From: Initializer

View aliases

Main aliases

tf.keras.initializers.random_uniform

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

Used in the notebooks

Used in the tutorials
Train a Deep Q Network with TF-Agents Networks

Draws samples from a uniform distribution for given parameters.

Examples:

# Standalone usage:
initializer = RandomUniform(minval=0.0, maxval=1.0)
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = RandomUniform(minval=0.0, maxval=1.0)
layer = Dense(3, kernel_initializer=initializer)

Args
minval	A python scalar or a scalar keras tensor. Lower bound of the range of random values to generate (inclusive).
maxval	A python scalar or a scalar keras tensor. Upper bound of the range of random values to generate (exclusive).
seed	A Python integer or instance of keras.backend.SeedGenerator. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of keras.backend.SeedGenerator.

Methods

clone

View source

clone()

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, the output of get_config().

Returns
An Initializer instance.

get_config

View source

get_config()

Returns the initializer's configuration as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__call__

View source

__call__(
    shape, dtype=None
)

Returns a tensor object initialized as specified by the initializer.

Args
shape	Shape of the tensor.
dtype	Optional dtype of the tensor.