def train_epoch(data, model, optimizer, batch_size, device):
""" Trains a single epoch of the given model. """
torch.cuda.empty_cache()
model.train()
log_timer = LogTimer(100)
for batch_ind, sents in enumerate(batches(data, batch_size)):
model.zero_grad()
optimizer.zero_grad()
torch.cuda.empty_cache()
out, loss, y = step(model, sents, device)
torch.cuda.empty_cache()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.2)
try:
optimizer.step()
except Exception as e:
logging.fatal(e, exc_info=True)
if log_timer() or batch_ind == 0:
# Calculate perplexity.
prob = out.exp()[
torch.arange(0, y.data.shape[0], dtype=torch.int64), y.data]
perplexity = 2**prob.log2().neg().mean().item()
print("\tBatch %d, loss %.3f, perplexity %.2f", batch_ind,
loss.item(), perplexity)
logging.info("\tBatch %d, loss %.3f, perplexity %.2f", batch_ind,
loss.item(), perplexity)
def train_epoch(data, model, optimizer, args, device):
""" Trains a single epoch of the given model. """
model.train()
log_timer = LogTimer(5)
for batch_ind, sents in enumerate(batches(data, args.batch_size)):
model.zero_grad()
out, loss, y = step(model, sents, device)
loss.backward()
optimizer.step()
if log_timer() or batch_ind == 0:
# Calculate perplexity.
prob = out.exp()[
torch.arange(0, y.data.shape[0], dtype=torch.int64), y.data]
perplexity = 2**prob.log2().neg().mean().item()
logging.info("\tBatch %d, loss %.3f, perplexity %.2f", batch_ind,
loss.item(), perplexity)
def train_epoch(data, model, optimizer, args, device, id2word2):
""" Trains a single epoch of the given model. """
model.train()
log_timer = LogTimer(5)
for batch_ind, batched_uts in enumerate(batches(data, args.batch_size)):
model.zero_grad()
hidden_init = model.init_hidden(len(batched_uts)).to(device)
for index in batched_uts[0]:
print(id2word2[index.item()], end= ' ') # print an uttrance
out, loss, y, h_n = step(model, hidden_init, batched_uts, device)
loss.backward()
optimizer.step()
if log_timer() or batch_ind == 0:
# Calculate perplexity.
prob = out.exp()[
torch.arange(0, y.data.shape[0], dtype=torch.int64), y.data]
perplexity = 2 ** prob.log2().neg().mean().item()
logging.info("\tBatch %d, loss %.3f, perplexity %.2f",
batch_ind, loss.item(), perplexity)