The generator should return the same kind of data as accepted by
test_on_batch().
evaluate_generator(object, generator, steps, max_q_size = 10, workers = 1,
pickle_safe = FALSE)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
maximum number of processes to spin up when using process based threading
if TRUE, use process based threading. Note that because
this implementation relies on multiprocessing, you should not pass non
picklable arguments to the generator as they can't be passed easily to
children processes.
Scalar test loss (if the model has a single output and no metrics) or
list of scalars (if the model has multiple outputs and/or metrics). The
attribute model$metrics_names will give you the display labels for the
scalar outputs.
Other model functions: compile,
evaluate, fit_generator,
fit, get_config,
get_layer,
keras_model_sequential,
keras_model, pop_layer,
predict.keras.engine.training.Model,
predict_generator,
predict_on_batch,
predict_proba,
summary.keras.engine.training.Model,
train_on_batch