tf.glorot_uniform_initializer

Class glorot_uniform_initializer

Inherits From: VarianceScaling

Aliases:

  • Class tf.glorot_uniform_initializer
  • Class tf.initializers.glorot_uniform
  • Class tf.keras.initializers.glorot_uniform

Defined in tensorflow/python/ops/init_ops.py.

The Glorot uniform initializer, also called Xavier uniform initializer.

It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.

Args:

  • seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.
  • dtype: Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.

References: Glorot et al., 2010 (pdf)

__init__

__init__(
    seed=None,
    dtype=tf.dtypes.float32
)

DEPRECATED FUNCTION ARGUMENT VALUES

Methods

tf.glorot_uniform_initializer.__call__

__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.

tf.glorot_uniform_initializer.from_config

from_config(
    cls,
    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.

tf.glorot_uniform_initializer.get_config

get_config()

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

Returns:

A JSON-serializable Python dict.