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Fits the state of the preprocessing layer to the data being passed


adapt(object, data, ..., batch_size = NULL, steps = NULL)



Preprocessing layer object


The data to train on. It can be passed either as a or as an R array.


Used for forwards and backwards compatibility. Passed on to the underlying method.


Integer or NULL. Number of asamples per state update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a TF Dataset or a generator (since they generate batches).


Integer or NULL. Total number of steps (batches of samples) When training with input tensors such as TensorFlow data tensors, the default NULL is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a, and steps is NULL, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps argument. This argument is not supported with array inputs.


Returns object, invisibly.


After calling adapt on a layer, a preprocessing layer's state will not update during training. In order to make preprocessing layers efficient in any distribution context, they are kept constant with respect to any compiled tf.Graphs that call the layer. This does not affect the layer use when adapting each layer only once, but if you adapt a layer multiple times you will need to take care to re-compile any compiled functions as follows:

  • If you are adding a preprocessing layer to a keras model, you need to call compile(model) after each subsequent call to adapt().

  • If you are calling a preprocessing layer inside tfdatasets::dataset_map(), you should call dataset_map() again on the input Dataset after each adapt().

  • If you are using a tensorflow::tf_function() directly which calls a preprocessing layer, you need to call tf_function() again on your callable after each subsequent call to adapt().

keras_model() example with multiple adapts:

layer <- layer_normalization(axis = NULL)
adapt(layer, c(0, 2))
model <- keras_model_sequential() |> layer()
predict(model, c(0, 1, 2), verbose = FALSE) # [1] -1  0  1

## [1] -1  0  1

adapt(layer, c(-1, 1))
compile(model)  # This is needed to re-compile model.predict!
predict(model, c(0, 1, 2), verbose = FALSE) # [1] 0 1 2

## [1] 0 1 2

tfdatasets example with multiple adapts:

layer <- layer_normalization(axis = NULL)
adapt(layer, c(0, 2))
input_ds <- tfdatasets::range_dataset(0, 3)
normalized_ds <- input_ds |>

## List of 3
##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([-1.], dtype=float32)>
##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([0.], dtype=float32)>
##  $ :<tf.Tensor: shape=(1), dtype=float32, numpy=array([1.], dtype=float32)>

adapt(layer, c(-1, 1))
normalized_ds <- input_ds |>
  tfdatasets::dataset_map(layer) # Re-map over the input dataset.

normalized_ds |>
  tfdatasets::as_array_iterator() |>
  tfdatasets::iterate(simplify = FALSE) |>

## List of 3
##  $ : num [1(1d)] 0
##  $ : num [1(1d)] 1
##  $ : num [1(1d)] 2