Dataset and DataLoader's parts are ok, I recycled from another code that I built, but got an infinite loop at that part in my code:
def train(train_loader, MLP, epoch, criterion, optimizer):
MLP.train()
epoch_loss = []
for batch in train_loader:
optimizer.zero_grad()
sample, label = batch
#Forward
pred = MLP(sample)
loss = criterion(pred, label)
epoch_loss.append(loss.data)
#Backward
loss.backward()
optimizer.step()
epoch_loss = np.asarray(epoch_loss)
print('Epoch: {}, Loss: {:.4f} +/- {:.4f}'.format(epoch+1,
epoch_loss.mean(), epoch_loss.std()))
def test(test_loader, MLP, epoch, criterion):
MLP.eval()
with torch.no_grad():
epoch_loss = []
for batch in train_loader:
sample, label = batch
#Forward
pred = MLP(sample)
loss = criterion(pred, label)
epoch_loss.append(loss.data)
epoch_loss = np.asarray(epoch_loss)
print('Epoch: {}, Loss: {:.4f} +/- {:.4f}'.format(epoch+1,
epoch_loss.mean(), epoch_loss.std()))
Than, I put it to iterate over the epochs:
for epoch in range(args['num_epochs']):
train(train_loader, MLP, epoch, criterion, optimizer)
test(test_loader, MLP, epoch, criterion)
print('-----------------------')
As it doesn't print even the first loss data, I believe that the logic error is in the training function, but I don't know where it is.
Edit: Here is my MLP Class, the problem can be here too:
class BikeRegressor(nn.Module):
def __init__(self, input_size, hidden_size, out_size):
super(BikeRegressor, self).__init__()
self.features = nn.Sequential(nn.Linear(input_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, hidden_size),
nn.ReLU())
self.out = nn.Sequential(nn.Linear(hidden_size, out_size),
nn.ReLU())
def forward(self, X):
hidden = self.features(X)
output = self.out(hidden)
return output
Edit 2: Dataset and Dataloader:
class Bikes(Dataset):
def __init__(self, data): #data is a Dataframe from Pandas
self.datas = data.to_numpy()
def __getitem__(self, idx):
sample = self.datas[idx][2:14]
label = self.datas[idx][-1:]
sample = torch.from_numpy(sample.astype(np.float32))
label = torch.from_numpy(label.astype(np.float32))
return sample, label
def __len__(self):
return len(self.datas)
train_set = Bikes(ds_train)
test_set = Bikes(ds_test)
train_loader = DataLoader(train_set, batch_size=args['batch_size'], shuffle=True, num_workers=args['num_workers'])
test_loader = DataLoader(test_set, batch_size=args['batch_size'], shuffle=True, num_workers=args['num_workers'])
forwardmethod (in the model) between each layer, so you can see if the model receives the input, if there's a shape mismatch soomewhere, and if your model returns the expected output shape. – Marcus