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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
@classmethodfrom_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. |