|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/preprocessing/rescaling.py#L7-L78) | A preprocessing layer which rescales input values to a new range. Inherits From: [`Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/Layer), [`Operation`](https://www.tensorflow.org/api_docs/python/tf/keras/Operation) tf.keras.layers.Rescaling( scale, offset=0.0, **kwargs ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) | - [Image classification](https://www.tensorflow.org/tutorials/images/classification) - [Load and preprocess images](https://www.tensorflow.org/tutorials/load_data/images) - [Data augmentation](https://www.tensorflow.org/tutorials/images/data_augmentation) - [Transfer learning and fine-tuning](https://www.tensorflow.org/tutorials/images/transfer_learning) - [Transfer learning with TensorFlow Hub](https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub) | This layer rescales every value of an input (often an image) by multiplying by `scale` and adding `offset`. #### For instance: 1. To rescale an input in the `[0, 255]` range to be in the `[0, 1]` range, you would pass `scale=1./255`. 2. 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] > **Note:** This layer is safe to use inside a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) or `grain` 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. |
## Methods ### `convert_weight` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/preprocessing/data_layer.py#L153-L159) convert_weight( weight ) Convert the weight if it is from the a different backend. ### `from_config` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/preprocessing/rescaling.py#L69-L78) @classmethod from_config( config, custom_objects=None ) Creates an operation from its config. This method is the reverse of `get_config`, capable of instantiating the same operation from the config dictionary. > [!NOTE] > **Note:** If you override this method, you might receive a serialized dtype config, which is a `dict`. You can deserialize it as follows: if "dtype" in config and isinstance(config["dtype"], dict): policy = dtype_policies.deserialize(config["dtype"])


| Args || |---|---| | `config` | A Python dictionary, typically the output of `get_config`. |



| Returns || |---|---| | An operation instance. ||