tf.compat.v1.random_uniform_initializer

Initializer that generates tensors with a uniform distribution.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.initializers.random_uniform

tf.compat.v1.random_uniform_initializer(
    minval=0.0,
    maxval=None,
    seed=None,
    dtype=tf.dtypes.float32
)

Migrate to TF2

Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code.

Although it is a legacy compat.v1 API, this symbol is compatible with eager execution and tf.function.

To switch to TF2, switch to using either tf.initializers.RandomUniform or tf.keras.initializers.RandomUniform (neither from compat.v1) and pass the dtype when calling the initializer. Keep in mind that the default minval, maxval and the behavior of fixed seeds have changed.

Structural Mapping to TF2

Before:

initializer = tf.compat.v1.random_uniform_initializer(
  minval=minval,
  maxval=maxval,
  seed=seed,
  dtype=dtype)

weight_one = tf.Variable(initializer(shape_one))
weight_two = tf.Variable(initializer(shape_two))

After:

initializer = tf.initializers.RandomUniform(
  minval=minval,
  maxval=maxval,
  seed=seed)

weight_one = tf.Variable(initializer(shape_one, dtype=dtype))
weight_two = tf.Variable(initializer(shape_two, dtype=dtype))

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
minval	minval	Default changes from 0 to -0.05
maxval	maxval	Default changes from 1.0 to 0.05
seed	seed
dtype	dtype	The TF2 native api only takes it as a __call__ arg, not a constructor arg.
partition_info	-	(__call__ arg in TF1) Not supported

Description

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.
dtype	Default data type, used if no dtype argument is provided when calling the initializer.

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, partition_info=None
)

Returns a tensor object initialized as specified by the initializer.

Args
shape	Shape of the tensor.
dtype	Optional dtype of the tensor. If not provided use the initializer dtype.
partition_info	Optional information about the possible partitioning of a tensor.