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The Glorot uniform initializer, also called Xavier uniform initializer.
Inherits From: VarianceScaling
, Initializer
tf.keras.initializers.GlorotUniform(
seed=None
)
Also available via the shortcut function
tf.keras.initializers.glorot_uniform
.
Draws samples from a uniform distribution within [-limit, limit]
, where
limit = sqrt(6 / (fan_in + fan_out))
(fan_in
is the number of input units
in the weight tensor and fan_out
is the number of output units).
Examples:
# Standalone usage:
initializer = tf.keras.initializers.GlorotUniform()
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.GlorotUniform()
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
Args | |
---|---|
seed
|
A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. |
References:
Methods
from_config
@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
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
Returns | |
---|---|
A JSON-serializable Python dict. |
__call__
__call__(
shape, dtype=None, **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 types are
supported. If not specified, tf.keras.backend.floatx() is used, which
default to float32 unless you configured it otherwise (via
tf.keras.backend.set_floatx(float_dtype) )
|
**kwargs
|
Additional keyword arguments. |