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)

Arguments

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.

Value

Named list of model test loss (or losses for models with multiple outputs) and model metrics.

See also

Other model functions: compile, evaluate.keras.engine.training.Model, fit_generator, fit, 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