Generates output predictions for the input samples.

Usage

# S3 method for class 'keras.src.models.model.Model'
predict(
  object,
  x,
  ...,
  batch_size = NULL,
  verbose = getOption("keras.verbose", default = "auto"),
  steps = NULL,
  callbacks = NULL
)

Arguments

object

Keras model object

x

Input samples. It can be:

A array (or array-like), or a list of arrays (in case the model has multiple inputs).
A backend-native tensor, or a list of tensors (in case the model has multiple inputs).
A TF Dataset.
A Python generator function.

...

For forward/backward compatability.

batch_size

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

verbose

"auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. "auto" becomes 1 for most cases, 2 if in a knitr render or running on a distributed training server. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g., in a production environment). Defaults to "auto".

steps

Total number of steps (batches of samples) to draw before declaring the prediction round finished. If steps is NULL, predict() will run until x is exhausted. In the case of an infinitely repeating dataset, predict() will run indefinitely.

callbacks

List of Callback instances. List of callbacks to apply during prediction.

Value

R array(s) of predictions.

Details

Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.

For small numbers of inputs that fit in one batch, directly call the model model$call for faster execution, e.g., model(x), or model(x, training = FALSE) if you have layers such as BatchNormalization that behave differently during inference.

Note

See this FAQ entry for more details about the difference between Model methods predict() and call().