def optimize(self, inputs, extra_inputs=None, callback=None):
if not inputs:
# Assumes that we should always sample mini-batches
raise NotImplementedError
if self._opt_fun is None:
raise Exception(
'Use update_opt() to setup the loss function first.')
f_loss = self._opt_fun['f_loss']
if extra_inputs is None:
extra_inputs = tuple()
last_loss = f_loss(*(tuple(inputs) + extra_inputs))
start_time = time.time()
dataset = BatchDataset(inputs,
self._batch_size,
extra_inputs=extra_inputs)
sess = tf.compat.v1.get_default_session()
for epoch in range(self._max_epochs):
if self._verbose:
logger.log('Epoch {}'.format(epoch))
progbar = pyprind.ProgBar(len(inputs[0]))
for batch in dataset.iterate(update=True):
sess.run(self._train_op,
dict(list(zip(self._input_vars, batch))))
if self._verbose:
progbar.update(len(batch[0]))
if self._verbose:
if progbar.active:
progbar.stop()
new_loss = f_loss(*(tuple(inputs) + extra_inputs))
if self._verbose:
logger.log('Epoch: {} | Loss: {}'.format(epoch, new_loss))
if self._callback or callback:
elapsed = time.time() - start_time
callback_args = dict(
loss=new_loss,
params=self._target.get_param_values(
trainable=True) if self._target else None,
itr=epoch,
elapsed=elapsed,
)
if self._callback:
self._callback(callback_args)
if callback:
callback(**callback_args)
if abs(last_loss - new_loss) < self._tolerance:
break
last_loss = new_loss
def get_minibatch(self, *inputs):
r"""Yields a batch of inputs.
Notes: P is the size of minibatch (self._minibatch_size)
Args:
*inputs (list[torch.Tensor]): A list of inputs. Each input has
shape :math:`(N \dot [T], *)`.
Yields:
list[torch.Tensor]: A list batch of inputs. Each batch has shape
:math:`(P, *)`.
"""
batch_dataset = BatchDataset(inputs, self._minibatch_size)
for _ in range(self._max_optimization_epochs):
for dataset in batch_dataset.iterate():
yield dataset
def optimize(self, inputs, extra_inputs=None, callback=None):
"""Perform optimization.
Args:
inputs (list[numpy.ndarray]): List of input values.
extra_inputs (list[numpy.ndarray]): List of extra input values.
callback (callable): Function to call during each epoch. Default
is None.
Raises:
NotImplementedError: If inputs are invalid.
Exception: If loss function is None, i.e. not defined.
"""
if not inputs:
# Assumes that we should always sample mini-batches
raise NotImplementedError('No inputs are fed to optimizer.')
if self._opt_fun is None:
raise Exception(
'Use update_opt() to setup the loss function first.')
f_loss = self._opt_fun['f_loss']
if extra_inputs is None:
extra_inputs = tuple()
last_loss = f_loss(*(tuple(inputs) + extra_inputs))
start_time = time.time()
dataset = BatchDataset(inputs,
self._batch_size,
extra_inputs=extra_inputs)
sess = tf.compat.v1.get_default_session()
for epoch in range(self._max_optimization_epochs):
if self._verbose:
logger.log('Epoch {}'.format(epoch))
with click.progressbar(length=len(inputs[0]),
label='Optimizing minibatches') as pbar:
for batch in dataset.iterate(update=True):
sess.run(self._train_op,
dict(list(zip(self._input_vars, batch))))
pbar.update(len(batch[0]))
new_loss = f_loss(*(tuple(inputs) + extra_inputs))
if self._verbose:
logger.log('Epoch: {} | Loss: {}'.format(epoch, new_loss))
if self._callback or callback:
elapsed = time.time() - start_time
callback_args = dict(
loss=new_loss,
params=self._target.get_param_values()
if self._target else None,
itr=epoch,
elapsed=elapsed,
)
if self._callback:
self._callback(callback_args)
if callback:
callback(**callback_args)
if abs(last_loss - new_loss) < self._tolerance:
break
last_loss = new_loss