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()