neupy.layers.Upscale

class neupy.layers.Upscale[source]

Upscales input over two axis (height and width).

Parameters:
scale : int or tuple with two int

Scaling factor for the input value. In the tuple first parameter identifies scale of the height and the second one of the width.

name : str or None

Layer’s identifier. If name is equal to None than name will be generated automatically. Defaults to None.

Examples

>>> from neupy.layers import *
>>> network = Input((10, 10, 3)) > Upscale((2, 2))
>>> network.output_shape
(3, 20, 20)
Attributes:
input_shape : tuple

Returns layer’s input shape in the form of a tuple. Shape will not include batch size dimension.

output_shape : tuple

Returns layer’s output shape in the form of a tuple. Shape will not include batch size dimension.

training_state : bool

Defines whether layer in training state or not. Training state will enable some operations inside of the layers that won’t work otherwise.

parameters : dict

Parameters that networks uses during propagation. It might include trainable and non-trainable parameters.

graph : LayerGraph instance

Graphs that stores all relations between layers.

Methods

disable_training_state() Context manager that switches off trainig state.
initialize() Set up important configurations related to the layer.
options = {'name': Option(class_name='BaseLayer', value=Property(name="name")), 'scale': Option(class_name='Upscale', value=ScaleFactorProperty(name="scale"))}[source]
output(input_value)[source]

Return output base on the input value.

Parameters:
input_value
output_shape[source]
scale = None[source]
validate(input_shape)[source]

Validate input shape value before assigning it.

Parameters:
input_shape : tuple with int