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.

Reference: http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf

Args:

  • seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.
  • dtype: The data type. Only floating point types are supported.

__init__

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

DEPRECATED FUNCTION ARGUMENTS

SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: normal is a deprecated alias for truncated_normal

Methods

__call__

__call__(
    shape,
    dtype=None,
    partition_info=None
)

Call self as a function.

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.

get_config

get_config()

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

Returns:

A JSON-serializable Python dict.