scale and adds offsetMultiply inputs by scale and adds offset
layer_rescaling(object, scale, offset = 0, ...)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
Float, the scale to apply to the inputs.
Float, the offset to apply to the inputs.
standard layer arguments.
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.
Input shape: Arbitrary.
Output shape: Same as input.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling
https://keras.io/api/layers/preprocessing_layers/image_preprocessing/rescaling
Other image preprocessing layers:
layer_center_crop(),
layer_resizing()
Other preprocessing layers:
layer_category_encoding(),
layer_center_crop(),
layer_discretization(),
layer_hashing(),
layer_integer_lookup(),
layer_normalization(),
layer_random_brightness(),
layer_random_contrast(),
layer_random_crop(),
layer_random_flip(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
layer_random_zoom(),
layer_resizing(),
layer_string_lookup(),
layer_text_vectorization()