Method evaluate_generator

tensorflow/python/keras/engine/training.py:1298–1363

Evaluates the model on a data generator. The generator should return the same kind of data as accepted by `test_on_batch`. Arguments: generator: Generator yielding tuples (inputs, targets) or (inputs, targets, sample_weights) or an instance of `keras.uti

(self,
                         generator,
                         steps=None,
                         callbacks=None,
                         max_queue_size=10,
                         workers=1,
                         use_multiprocessing=False,
                         verbose=0)

        steps_name='steps_per_epoch')

  def evaluate_generator(self,
                         generator,
                         steps=None,
                         callbacks=None,
                         max_queue_size=10,
                         workers=1,
                         use_multiprocessing=False,
                         verbose=0):
    """Evaluates the model on a data generator.

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

    Arguments:
        generator: Generator yielding tuples (inputs, targets)
            or (inputs, targets, sample_weights)
            or an instance of `keras.utils.Sequence`
            object in order to avoid duplicate data
            when using multiprocessing.
        steps: Total number of steps (batches of samples)
            to yield from `generator` before stopping.
            Optional for `Sequence`: if unspecified, will use
            the `len(generator)` as a number of steps.
        callbacks: List of `keras.callbacks.Callback` instances.
            List of callbacks to apply during evaluation.
            See [callbacks](/api_docs/python/tf/keras/callbacks).
        max_queue_size: maximum size for the generator queue
        workers: Integer. Maximum number of processes to spin up
            when using process-based threading.
            If unspecified, `workers` will default to 1. If 0, will
            execute the generator on the main thread.
        use_multiprocessing: Boolean.
            If `True`, use process-based threading.
            If unspecified, `use_multiprocessing` will default to `False`.
            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.
        verbose: Verbosity mode, 0 or 1.

    Returns:
        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.

    Raises:
        ValueError: in case of invalid arguments.

    Raises:
        ValueError: In case the generator yields data in an invalid format.
    """
    if self._distribution_strategy:
      raise NotImplementedError('`evaluate_generator` is not supported for '
                                'models compiled with tf.distribute.Strategy.')
    _keras_api_gauge.get_cell('evaluate_generator').set(True)
    self._check_call_args('evaluate_generator')

    return training_generator.evaluate_generator(