evaluate_generator: Evaluates the model on a data generator.

In dfalbel/keras: R Interface to 'Keras'

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

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()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.