tf.random_uniform_initializer  |  TensorFlow v2.16.1 (original) (raw)

Initializer that generates tensors with a uniform distribution.

tf.random_uniform_initializer(
    minval=-0.05, maxval=0.05, seed=None
)

Used in the notebooks

Used in the tutorials
Parametrized Quantum Circuits for Reinforcement Learning

Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized.

Examples:

def make_variables(k, initializer): return (tf.Variable(initializer(shape=[k], dtype=tf.float32)), tf.Variable(initializer(shape=[k, k], dtype=tf.float32))) v1, v2 = make_variables(3, tf.ones_initializer()) v1 <tf.Variable ... shape=(3,) ... numpy=array([1., 1., 1.], dtype=float32)> v2 <tf.Variable ... shape=(3, 3) ... numpy= array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], dtype=float32)> make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.)) (<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...

Args
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. Used to create random seeds. Seetf.random.set_seed for behavior.

Methods

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. It will typically be the output of get_config.
Returns
An Initializer instance.

get_config

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get_config()

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

Returns
A JSON-serializable Python dict.

__call__

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__call__(
    shape,
    dtype=tf.dtypes.float32,
    **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.
**kwargs Additional keyword arguments.
Raises
ValueError If the dtype is not numeric.