predict_generator
(Deprecated) Generates predictions for the input samples from a data generator.
Description
The generator should return the same kind of data as accepted by predict_on_batch()
.
Usage
predict_generator(
object,
generator,
steps, max_queue_size = 10,
workers = 1,
verbose = 0,
callbacks = NULL
)
Arguments
Arguments | Description |
---|---|
object | Keras model object |
generator | Generator yielding batches of input samples. |
steps | Total number of steps (batches of samples) to yield from generator before stopping. |
max_queue_size | Maximum size for the generator queue. If unspecified, max_queue_size will default to 10. |
workers | Maximum number of threads to use for parallel processing. Note that parallel processing will only be performed for native Keras generators (e.g. flow_images_from_directory() ) as R based generators must run on the main thread. |
verbose | verbosity mode, 0 or 1. |
callbacks | List of callbacks to apply during prediction. |
Section
Raises
ValueError: In case the generator yields data in an invalid format.
Value
Numpy array(s) of predictions.
See Also
Other model functions: compile.keras.engine.training.Model()
, evaluate.keras.engine.training.Model()
, evaluate_generator()
, fit.keras.engine.training.Model()
, fit_generator()
, get_config()
, get_layer()
, keras_model_sequential()
, keras_model()
, multi_gpu_model()
, pop_layer()
, predict.keras.engine.training.Model()
, predict_on_batch()
, predict_proba()
, summary.keras.engine.training.Model()
, train_on_batch()