The generator should return the same kind of data as accepted by
test_on_batch().
evaluate_generator(
object,
generator,
steps,
max_queue_size = 10,
workers = 1,
callbacks = NULL
)Named list of model test loss (or losses for models with multiple outputs) and model metrics.
Model object to evaluate
Generator yielding lists (inputs, targets) or (inputs, targets, sample_weights)
Total number of steps (batches of samples) to yield from
generator before stopping.
Maximum size for the generator queue. If unspecified,
max_queue_size will default to 10.
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.
List of callbacks to apply during evaluation.
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()