def evaluate_generator(self,
generator,
steps,
max_q_size=10,
workers=1,
pickle_safe=False):
"""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)
steps: Total number of steps (batches of samples)
to yield from `generator` before stopping.
max_q_size: maximum size for the generator queue
workers: maximum number of processes to spin up
pickle_safe: 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.
Returns:
Scalar test loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
Raises:
RuntimeError: if the model was never compiled.
"""
if self.model is None:
raise RuntimeError('The model needs to be compiled ' 'before being used.')
return self.model.evaluate_generator(
generator,
steps,
max_q_size=max_q_size,
workers=workers,
pickle_safe=pickle_safe)
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