def train(self, batch_num):
args = self.param.args
dm = self.param.volatile.dm
datakey = 'train'
for i in range(batch_num):
self.now_batch += 1
incoming = self.get_next_batch(dm, datakey)
incoming.args = Storage()
incoming.now_epoch = self.now_epoch
if (i + 1) % args.batch_num_per_gradient == 0:
self.zero_grad()
self.net.forward(incoming)
loss = incoming.result.loss
self.trainSummary(self.now_batch, storage_to_list(incoming.result))
logging.info("batch %d : gen loss=%f", self.now_batch,
loss.detach().cpu().numpy())
loss.backward()
if (i + 1) % args.batch_num_per_gradient == 0:
nn.utils.clip_grad_norm_(self.net.parameters(), args.grad_clip)
self.optimizer.step()
def train(self, batch_num):
args = self.param.args
dm = self.param.volatile.dm
datakey = 'train'
for i in range(batch_num):
self.now_batch += 1
incoming = self.get_next_batch(dm, datakey)
incoming.args = Storage()
if (i + 1) % args.batch_num_per_gradient == 0:
self.zero_grad()
self.net.forward(incoming)
loss = incoming.result.loss
accuracy = np.mean(
(incoming.result.label == incoming.result.prediction
).float().detach().cpu().numpy())
detail_arr = storage_to_list(incoming.result)
detail_arr.update({'accuracy_on_batch': accuracy})
self.trainSummary(self.now_batch, detail_arr)
logging.info("batch %d : classification loss=%f, batch accuracy=%f", \
self.now_batch, loss.detach().cpu().numpy(), accuracy)
loss.backward()
if (i + 1) % args.batch_num_per_gradient == 0:
nn.utils.clip_grad_norm_(self.net.parameters(), args.grad_clip)
self.optimizer.step()
def train(self, batch_num, total_step_counter):
args = self.param.args
dm = self.param.volatile.dm
datakey = 'train'
for i in range(batch_num):
self.now_batch += 1
incoming = self.get_next_batch(dm, datakey)
incoming.args = Storage()
incoming.args.sampling_proba = 1. - \
inverse_sigmoid_decay(args.decay_factor, total_step_counter)
if (i + 1) % args.batch_num_per_gradient == 0:
self.zero_grad()
self.net.forward(incoming)
loss = incoming.result.loss
self.trainSummary(self.now_batch, storage_to_list(incoming.result))
logging.info("batch %d : gen loss=%f", self.now_batch,
loss.detach().cpu().numpy())
loss.backward()
if (i + 1) % args.batch_num_per_gradient == 0:
nn.utils.clip_grad_norm_(self.net.parameters(), args.grad_clip)
self.optimizer.step()
total_step_counter += 1
return total_step_counter
