evaluate_generator
(Deprecated) Evaluates the model on a data generator.
Description
The generator should return the same kind of data as accepted by test_on_batch().
Usage
evaluate_generator(
object,
generator,
steps,
max_queue_size = 10,
workers = 1,
callbacks = NULL
) Arguments
| Arguments | Description |
|---|---|
| object | Model object to evaluate |
| generator | Generator yielding lists (inputs, targets) or (inputs, targets, sample_weights) |
| 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. |
| callbacks | List of callbacks to apply during evaluation. |
Value
Named list of model test loss (or losses for models with multiple outputs) and model metrics.
See Also
Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), 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_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model(), train_on_batch()