A preprocessing layer which rescales input values to a new range.
Inherits From: Layer, Operation
tf.keras.layers.Rescaling(
scale, offset=0.0, **kwargs
)
| Used in the guide | Used in the tutorials |
|---|---|
This layer rescales every value of an input (often an image) by multiplying by scale and adding offset.
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.
Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).
| 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. |
from_config@classmethod
from_config(
config
)
Creates a layer from its config.
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. |
| Returns | |
|---|---|
| A layer instance. |
symbolic_callsymbolic_call(
*args, **kwargs
)
© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling