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Initializer that adapts its scale to the shape of its input tensors.
Inherits From: Initializer
tf.keras.initializers.VarianceScaling(
scale=1.0,
mode='fan_in',
distribution='truncated_normal',
seed=None
)
Also available via the shortcut function
tf.keras.initializers.variance_scaling
.
With distribution="truncated_normal" or "untruncated_normal"
, samples are
drawn from a truncated/untruncated normal distribution with a mean of zero
and a standard deviation (after truncation, if used) stddev = sqrt(scale /
n)
, where n
is:
- number of input units in the weight tensor, if
mode="fan_in"
- number of output units, if
mode="fan_out"
- average of the numbers of input and output units, if
mode="fan_avg"
With distribution="uniform"
, samples are drawn from a uniform distribution
within [-limit, limit]
, where limit = sqrt(3 * scale / n)
.
Examples:
# Standalone usage:
initializer = tf.keras.initializers.VarianceScaling(
scale=0.1, mode='fan_in', distribution='uniform')
values = initializer(shape=(2, 2))
# Usage in a Keras layer:
initializer = tf.keras.initializers.VarianceScaling(
scale=0.1, mode='fan_in', distribution='uniform')
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
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, the output of get_config() .
|
Returns | |
---|---|
An Initializer instance.
|
get_config
get_config()
Returns the initializer's configuration 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. |