def predict_generator(self,
generator,
steps,
max_q_size=10,
workers=1,
pickle_safe=False,
verbose=0):
"""Generates predictions for the input samples from a data generator.
The generator should return the same kind of data as accepted by
`predict_on_batch`.
Arguments:
generator: generator yielding batches of input samples.
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.
verbose: verbosity mode, 0 or 1.
Returns:
A Numpy array of predictions.
"""
if self.model is None:
self.build()
return self.model.predict_generator(
generator,
steps,
max_q_size=max_q_size,
workers=workers,
pickle_safe=pickle_safe,
verbose=verbose)
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