def fit(self, xs, epochs=1, batch_size=None, max_steps=10**6):
"""Fits to sequences given as [N x length] token array."""
if batch_size is None:
batch_size = self._batch_size
if hasattr(xs, 'as_numpy_iterator'):
# TF Dataset
ds = xs.repeat(epochs)
num_train_steps = max_steps
elif hasattr(xs, 'element_spec'):
# Dataset iterator.
if epochs != 1:
raise ValueError('Epochs must == 1 when using iterator input.')
ds = xs
num_train_steps = max_steps
else:
# Raw sequences which we turn into a dataset.
ds = data.dataset_from_tensors(xs)
ds = ds.shuffle(buffer_size=1024).repeat().batch(batch_size)
num_train_steps = math.ceil((len(xs) * epochs) / float(batch_size))
if max_steps:
num_train_steps = min(num_train_steps, max_steps)
if not num_train_steps:
raise ValueError('Must set max_steps to nonzero value.')
metrics = []
start = time.time()
max_steps = max_steps or 10**6
for _, batch in zip(range(num_train_steps), ds):
metrics.append(self.fit_batch(batch))
finish = time.time()
average = evaluation.combine_metrics(metrics)
average['runtime'] = finish - start
average['rate'] = len(metrics) / (finish - start)
if self._store_metrics:
average = tree.map_structure(onp.array, average)
self._epoch_train.append(average)
return dict(last=evaluation.combine_metrics([metrics[-1]]),
average=average)
def fit(self,
xs,
ys=None,
weights=None,
epochs=1,
batch_size=None,
shuffle=True,
max_steps=None,
verbose=False):
"""Fits to sequences given as [N x length] token array."""
# TODO(ddohan): Use other kwargs.
del shuffle
del weights
del verbose
del ys
if batch_size is None:
batch_size = self._batch_size
if hasattr(xs, 'as_numpy_iterator'):
# TF Dataset
ds = xs.repeat(epochs)
num_train_steps = max_steps
elif hasattr(xs, 'element_spec'):
# Dataset iterator.
if epochs != 1:
raise ValueError('Epochs must == 1 when using iterator input.')
ds = xs
num_train_steps = max_steps
else:
# Raw sequences which we turn into a dataset.
ds = data.dataset_from_tensors(xs)
ds = ds.shuffle(buffer_size=1024).repeat().batch(batch_size)
num_train_steps = math.ceil((len(xs) * epochs) / float(batch_size))
if max_steps:
num_train_steps = min(num_train_steps, max_steps)
if not num_train_steps:
raise ValueError('Must set max_steps to nonzero value.')
for _, batch in zip(range(num_train_steps), ds):
batch = batch._numpy() # pylint: disable=protected-access
metrics = self.fit_batch(batch)