tf.keras.initializers.RandomUniform  |  TensorFlow v2.16.1 (original) (raw)

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 ofkeras.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

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clone()

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, the output of get_config().
Returns
An Initializer instance.

get_config

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get_config()

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

Returns
A JSON-serializable Python dict.

__call__

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__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.