This layer rescales every value of an input (often an image) by multiplying
by scale and adding offset.
For instance:
To rescale an input in the [0, 255] range
to be in the [0, 1] range, you would pass scale=1./255.
To rescale an input in the [0, 255] range to be in the [-1, 1] range,
you would pass scale=1./127.5, offset=-1.
The rescaling is applied both during training and inference. Inputs can be
of integer or floating point dtype, and by default the layer will output
floats.
Args
scale
Float, the scale to apply to the inputs.
offset
Float, the offset to apply to the inputs.
**kwargs
Base layer keyword arguments, such as name and dtype.
Attributes
input
Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the first time
the operation was called.
output
Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the first time
the operation was called.
This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).
Args
config
A Python dictionary, typically the
output of get_config.