The Glorot uniform initializer, also called Xavier uniform initializer.
Inherits From: VarianceScaling
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v2.initializers.GlorotUniform`, `tf.compat.v2.initializers.glorot_uniform`, `tf.compat.v2.keras.initializers.glorot_uniform`
tf.compat.v2.keras.initializers.GlorotUniform(
seed=None
)
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.
References:
Glorot et al., 2010
(pdf)
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=tf.dtypes.float32
)
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 types are
supported.
|
Raises |
ValueError
|
If the dtype is not floating point
|