def test(dset_test, batch_size, baseGRU):
test_loader = DL(dset_test,
batch_size=batch_size,
shuffle=True,
collate_fn=pad_collate)
vocab_size = len(dset_test.QA.VOCAB)
losses = []
accs = []
for batch_idx, data in enumerate(test_loader):
contexts, questions, answers = data
b_size = len(contexts)
c = contexts.view(b_size, -1).long()
q = questions.view(b_size, -1).long()
c.transpose_(0, 1)
q.transpose_(0, 1)
hidden1, hidden2 = baseGRU.initHidden(b_size)
out = baseGRU(c, q, hidden1, hidden2)
topv, topi = out.data.topk(1)
topi = topi.view(1, -1).squeeze(0)
acc = torch.mean((topi.data == answers.data).float())
accs.append(acc.item())
if batch_idx % 10 == 0:
visualizeSample(dset_test, contexts[0], questions[0], answers[0],
topi[0])
#if batch_idx == 2: break
accuracy = sum(accs) / len(accs)
print("test accuracy is: %f" % accuracy)
def train(dset_train, batch_size, epoch, dmn):
optim = torch.optim.Adam(dmn.parameters())
losses = []
accs = []
for e in range(epoch):
train_loader = DL(dset_train,
batch_size=batch_size,
shuffle=True,
collate_fn=pad_collate)
dmn.train()
print('epoch %d is in progress' % e)
for batch_idx, data in enumerate(train_loader):
contexts, questions, answers = data
b_size = len(contexts)
optim.zero_grad()
c = Variable(contexts.long().cuda())
q = Variable(questions.long().cuda())
answers = Variable(answers.cuda())
loss, acc, _ = dmn.get_loss(c, q, answers)
losses.append(loss.item())
accs.append(acc.item())
loss.backward()
optim.step()
#if batch_idx == 50: break
if e % 16 == 0:
plt.figure()
plt.plot(losses)
plt.title('training loss')
plt.show()
plt.figure()
plt.plot(accs)
plt.title('training accuracy')
plt.show()
def test(dset_test, batch_size, dmn):
test_loader = DL(dset_test, batch_size=batch_size, shuffle=True, collate_fn=pad_collate)
vocab_size = len(dset_test.QA.VOCAB)
losses = []
accs = []
for batch_idx, data in enumerate(test_loader):
contexts, questions, answers = data
b_size = len(contexts)
c = Variable(contexts.long())
q = Variable(questions.long())
answers = Variable(answers)
_, acc, topi = dmn.get_loss(c, q, answers)
accs.append(acc.item())
if batch_idx % 10 == 0:
visualizeSample(dset_test, contexts[0], questions[0], answers[0], topi[0])
#if batch_idx == 2: break
accuracy = sum(accs) / len(accs)
print("test accuracy is: %f" % accuracy)
def train(dset_train, batch_size, epoch, baseGRU):
criterion = nn.NLLLoss()
optim = torch.optim.Adam(baseGRU.parameters())
losses = []
accs = []
for e in range(epoch):
train_loader = DL(dset_train,
batch_size=batch_size,
shuffle=True,
collate_fn=pad_collate)
print('epoch %d is in progress' % e)
for batch_idx, data in enumerate(train_loader):
contexts, questions, answers = data
b_size = len(contexts)
optim.zero_grad()
c = contexts.view(b_size, -1).long()
q = questions.view(b_size, -1).long()
c.transpose_(0, 1)
q.transpose_(0, 1)
hidden1, hidden2 = baseGRU.initHidden(b_size)
out = baseGRU(c, q, hidden1, hidden2)
topv, topi = out.data.topk(1)
topi = topi.view(1, -1)
acc = torch.mean((topi.data == answers.data).float())
loss = criterion(out, answers)
losses.append(loss.item())
accs.append(acc.item())
loss.backward()
optim.step()
#if batch_idx == 50: break
if e % 16 == 0:
plt.figure()
plt.plot(losses)
plt.title('training loss')
plt.show()
plt.figure()
plt.plot(accs)
plt.title('training accuracy')
plt.show()
def train(dset, batch_size, epochs, dmn):
early_stopping_cnt = 0
early_stopping_flag = False
best_acc = 0
optim = torch.optim.Adam(dmn.parameters())
for epoch in range(epochs):
dset.set_mode('train')
train_loader = DL(
dset, batch_size=batch_size, shuffle=True, collate_fn=pad_collate
)
dmn.train()
if not early_stopping_flag:
total_acc = 0
cnt = 0
for batch_idx, data in enumerate(train_loader):
optim.zero_grad()
contexts, questions, answers = data
b_size = contexts.size()[0]
contexts = Variable(contexts.long())
questions = Variable(questions.long())
answers = Variable(answers)
loss, acc, _ = dmn.get_loss(contexts, questions, answers)
loss.backward()
total_acc += acc * b_size
cnt += b_size
if batch_idx % 20 == 0:
#print(f'[Task {task_id}, Epoch {epoch}] [Training] loss : {loss.data[0]: {10}.{8}}, acc : {total_acc / cnt: {5}.{4}}, batch_idx : {batch_idx}')
print('[Epoch %d] [Training] loss : %f, acc : %f, batch_idx : %d' % (epoch, loss.data[0], (total_acc/cnt), batch_idx))
optim.step()
dset.set_mode('valid')
valid_loader = DL(
dset, batch_size=batch_size, shuffle=False, collate_fn=pad_collate
)
dmn.eval()
total_acc = 0
cnt = 0
for batch_idx, data in enumerate(valid_loader):
contexts, questions, answers = data
b_size = contexts.size()[0]
contexts = Variable(contexts.long())
questions = Variable(questions.long())
answers = Variable(answers)
_, acc, _ = dmn.get_loss(contexts, questions, answers)
total_acc += acc * batch_size
cnt += batch_size
total_acc = total_acc / cnt
if total_acc > best_acc:
best_acc = total_acc
best_state = dmn.state_dict()
early_stopping_cnt = 0
else:
early_stopping_cnt += 1
if early_stopping_cnt > 20:
early_stopping_flag = True
#print(f'[Run {run}, Task {task_id}, Epoch {epoch}] [Validate] Accuracy : {total_acc: {5}.{4}}')
print('[Epoch %d] [Validate] Accuracy : %f' % (epoch, total_acc))
if total_acc == 1.0:
break
else:
#print(f'[Run {run}, Task {task_id}] Early Stopping at Epoch {epoch}, Valid Accuracy : {best_acc: {5}.{4}}')
print('Early Stopping at Epoch %d, Valid Accuracy : %f' % (epoch, best_acc))
break
dmn.load_state_dict(best_state)