Initializes PWL calibration layer to represent linear function.
tfl.pwl_calibration_layer.UniformOutputInitializer(
output_min, output_max, monotonicity, keypoints=None
)
PWL calibration layer weights are one-d tensor. First element of tensor
represents bias. All remaining represent delta in y-value compare to previous
point. Aka heights of segments.
Args |
output_min
|
Minimum value of PWL calibration output after initialization.
|
output_max
|
Maximum value of PWL calibration output after initialization.
|
monotonicity
|
- if 'none' or 'increasing', the returned function will go from
(input_min, output_min) to (input_max, output_max) .
- if 'decreasing', the returned function will go from
(input_min, output_max) to (input_max, output_min) .
|
keypoints
|
- if not provided (None or []), all pieces of returned function
will have equal heights (i.e.
y[i+1] - y[i] is constant).
if provided, all pieces of returned function will have equal slopes
(i.e. (y[i+1] - y[i]) / (x[i+1] - x[i]) is constant).
|
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
View source
get_config()
Standard Keras config for serialization.
__call__
View source
__call__(
shape, dtype=None, partition_info=None
)
Returns weights of PWL calibration layer.
Args |
shape
|
Must be a collection of the form (k, units) where k >= 2 .
|
dtype
|
Standard Keras initializer param.
|
partition_info
|
Standard Keras initializer param.
|
Returns |
Weights of PWL calibration layer.
|
Raises |
ValueError
|
If requested shape is invalid for PWL calibration layer
weights.
|