def fit_generator(wrapper, *args: Any, **kwargs: Any) -> None:
if not self.compile_args:
raise errors.InvalidExperimentException(
"Must call .compile before calling .fit_generator().")
fit_generator_args = inspect.signature(
model.fit_generator).bind(*args, **kwargs)
fit_generator_args.apply_defaults()
training_data = keras.SequenceAdapter(
fit_generator_args.arguments["generator"],
use_multiprocessing=fit_generator_args.
arguments["use_multiprocessing"],
workers=fit_generator_args.arguments["workers"],
)
validation_data = keras.SequenceAdapter(
fit_generator_args.arguments["validation_data"],
use_multiprocessing=fit_generator_args.
arguments["use_multiprocessing"],
workers=fit_generator_args.arguments["workers"],
)
self.train_config = TFKerasTrainConfig(
training_data=training_data,
validation_data=validation_data,
callbacks=fit_generator_args.arguments["callbacks"],
)
if train_fn:
train_fn()
def test_sequence_adapter(workers: int, use_multiprocessing: bool,
seq: Sequence) -> None:
data = keras.SequenceAdapter(seq,
workers=workers,
use_multiprocessing=use_multiprocessing)
assert len(data) == len(seq)
data.start()
iterator = data.get_iterator()
assert iterator is not None
for i in range(len(seq)):
a = seq[i]
b = next(iterator)
assert len(a) == len(b)
for i in range(len(a)):
assert np.equal(a[i], b[i]).all()
data.stop()
def build_validation_data_loader(self) -> keras.InputData:
_, test = make_xor_data_sequences(batch_size=4)
return keras.SequenceAdapter(test, workers=0)
def build_training_data_loader(self) -> keras.InputData:
train, _ = make_xor_data_sequences(batch_size=4)
return keras.SequenceAdapter(train, workers=0)
