The generator should return the same kind of data as accepted by test_on_batch().

evaluate_generator(object, generator, steps, max_queue_size = 10)

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

Value

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.

See also

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