scale and adds offsetMultiply inputs by scale and adds offset
layer_rescaling(object, scale, offset = 0, ...)What to call the new Layer instance with. Typically a keras
Model, another Layer, or a tf.Tensor/KerasTensor. If object is
missing, the Layer instance is returned, otherwise, layer(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_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()