Hi, I was training my model but some issues occurred.
The dataloader was supposed to update batches but instead I got this:
Output
- [0, 0] loss: 0.009
- [1, 0] loss: 0.009
- [2, 0] loss: 0.009
- [3, 0] loss: 0.009
- [4, 0] loss: 0.009
- [5, 0] loss: 0.009
- [6, 0] loss: 0.009
- [7, 0] loss: 0.009
- [8, 0] loss: 0.009
- [9, 0] loss: 0.009
Anyone know what’s wrong? My codes are as below
DataLoader
class MSourceDataSet(Dataset):
def __init__(self, clean_dir, mix_dir, clean_label_dir, mix_label_dir):
with open(clean_dir + 'clean1.json') as f:
clean0 = torch.Tensor(json.load(f))
with open(mix_dir + 'mix1.json') as f:
mix0 = torch.Tensor(json.load(f))
with open(clean_label_dir + 'clean_label1.json') as f:
clean_label0 = torch.Tensor(json.load(f))
with open(mix_label_dir + 'mix_label1.json') as f:
mix_label0 = torch.Tensor(json.load(f))
self.spec = torch.cat([clean0, mix0], 0)
self.label = torch.cat([clean_label0, mix_label0], 0)
def __len__(self):
return self.spec.shape[0]
def __getitem__(self, index):
spec = self.spec[index]
label = self.label[index]
return spec, label
In [78]:
trainset = MSourceDataSet(clean_dir, mix_dir, clean_label_dir, mix_label_dir)
trainloader = torch.utils.data.DataLoader(dataset = trainset,
batch_size = 4,
shuffle = True)
# testloader = torch.utils.data.DataLoader(dataset = testset,
# batch_size = 4,
# shuffle = True)
Training
model.train()
for epoch in range(10):
running_loss = 0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 1000 == 0:
print ('[%d, %5d] loss: %.3f' % (epoch, i, running_loss/ 1000))
running_loss = 0
torch.save(model, 'FeatureNet.pkl')