class neupy.layers.Upscale[source]

Upscales input over two axis (height and width).

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 name. Can be used as a reference to specific layer. Name Can be specified as:

  • String: Specified name will be used as a direct reference to the layer. For example, name=”fc”
  • Format string: Name pattern could be defined as a format string and specified field will be replaced with an index. For example, name=”fc{}” will be replaced with fc1, fc2 and so on. A bit more complex formatting methods are acceptable, for example, name=”fc-{:<03d}” will be converted to fc-001, fc-002, fc-003 and so on.
  • None: When value specified as None than name will be generated from the class name.

Defaults to None.


>>> from neupy.layers import *
>>> network = Input((10, 10, 3)) >> Upscale((2, 2))
(?, 10, 10, 3) -> [... 2 layers ...] -> (?, 20, 20, 3)
variables : dict

Variable names and their values. Dictionary can be empty in case if variables hasn’t been created yet.


variable(value, name, shape=None, trainable=True) Initializes variable with specified values.
get_output_shape(input_shape) Computes expected output shape from the layer based on the specified input shape.
output(*inputs, **kwargs) Propagates input through the layer. The kwargs variable might contain additional information that propagates through the network.
options = {'name': Option(class_name='BaseLayer', value=Property(name="name")), 'scale': Option(class_name='Upscale', value=TypedListProperty(name="scale"))}[source]
output(input_value, **kwargs)[source]
scale = None[source]