Datasets:
jblitzar
/
github-python-corpus

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optimizer,
device,
epochs,
early_stopping=True,
early_num=20):
loss_list = [100]
early_epoch = 0
net = net.to(device)
print("training on ", device)
start = time.time()
train_loss_list = []
valida_loss_list = []
train_acc_list = []
valida_acc_list = []
for epoch in range(epochs):
train_acc_sum, n = 0.0, 0
time_epoch = time.time()
lr_adjust = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, 15, eta_min=0.0, last_epoch=-1)
for X, y in train_iter:
batch_count, train_l_sum = 0, 0
#X = X.permute(0, 3, 1, 2)
X = X.to(device)
y = y.to(device)
y_hat = net(X)
# print('y_hat', y_hat)
# print('y', y)
l = loss(y_hat, y.long())
optimizer.zero_grad()
l.backward()
optimizer.step()
train_l_sum += l.cpu().item()
train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()
n += y.shape[0]
batch_count += 1
lr_adjust.step()
valida_acc, valida_loss = record.evaluate_accuracy(
valida_iter, net, loss, device)
loss_list.append(valida_loss)
train_loss_list.append(train_l_sum) # / batch_count)
train_acc_list.append(train_acc_sum / n)
valida_loss_list.append(valida_loss)
valida_acc_list.append(valida_acc)
print(
'epoch %d, train loss %.6f, train acc %.3f, valida loss %.6f, valida acc %.3f, time %.1f sec'
% (epoch + 1, train_l_sum / batch_count, train_acc_sum / n,
valida_loss, valida_acc, time.time() - time_epoch))
PATH = "./net_DBA.pt"
# if loss_list[-1] <= 0.01 and valida_acc >= 0.95:
# torch.save(net.state_dict(), PATH)
# break
if early_stopping and loss_list[-2] < loss_list[-1]:
if early_epoch == 0: # and valida_acc > 0.9:
torch.save(net.state_dict(), PATH)
early_epoch += 1
loss_list[-1] = loss_list[-2]
if early_epoch == early_num:
net.load_state_dict(torch.load(PATH))
break
else:
early_epoch = 0
print('epoch %d, loss %.4f, train acc %.3f, time %.1f sec'
% (epoch + 1, train_l_sum / batch_count, train_acc_sum / n,
time.time() - start))
def sampling(proportion, ground_truth):
train = {}
test = {}
labels_loc = {}
m = max(ground_truth)
for i in range(m):
indexes = [
j for j, x in enumerate(ground_truth.ravel().tolist())
if x == i + 1
]
np.random.shuffle(indexes)
labels_loc[i] = indexes
if proportion != 1:
nb_val = max(int((1 - proportion) * len(indexes)), 3)
else:
nb_val = 0
train[i] = indexes[:nb_val]
test[i] = indexes[nb_val:]
train_indexes = []
test_indexes = []
for i in range(m):
train_indexes += train[i]
test_indexes += test[i]
np.random.shuffle(train_indexes)
np.random.shuffle(test_indexes)
return train_indexes, test_indexes