I’m quite new in python and pytorch.
I have perhaps simple problem.
I get negative loss numbers returned.
This is the output:
Epoch [1/100], Step [10/63], Loss: 0.1118
Epoch [1/100], Step [20/63], Loss: -0.3783
Epoch [1/100], Step [30/63], Loss: -1.4037
Epoch [1/100], Step [40/63], Loss: -3.8306
Epoch [1/100], Step [50/63], Loss: -7.8438
Epoch [1/100], Step [60/63], Loss: -14.6053
Epoch [2/100], Step [10/63], Loss: -27.3804
Epoch [2/100], Step [20/63], Loss: -36.3176
Epoch [2/100], Step [30/63], Loss: -41.2981
etc...
My datareader from CSV file:
class CSV_Data(Dataset):
def __init__(self, path):
xy = np.loadtxt("data/"+path, delimiter=",", dtype=np.float32, skiprows=1, usecols=(0,1,2,3))
self.x = torch.tensor(xy[:, 0:2], requires_grad=True)
self.y = torch.from_numpy(xy[:, 2:])
self.n_samples = xy.shape[0]
def __getitem__(self, index):
sample = self.x[index], self.y[index]
return sample
def __len__(self):
# len(dataset)
return self.n_samples
My model:
class RegressionReluNN(nn.Module):
def __init__(self, input_size, hidden_size):
super(RegressionReluNN, self).__init__()
self.linear1 = nn.Linear(in_features=input_size, out_features=hidden_size)
self.relu = nn.ELU()
# Or maybe Relu ???
self.linear2 = nn.Linear(in_features=hidden_size, out_features=2)
def forward(self, x):
out = self.linear1(x)
out = self.relu(out)
out = self.linear2(out)
# sigmoid at the end ???
y_predict = torch.sigmoid(out)
return y_predict
Training loop:
# criterion = nn.CrossEntropyLoss()
criterion = nn.MultiLabelSoftMarginLoss()
n_total_steps = len(train_loader) # This is for test
for epoch in range(num_epochs):
for i, (open_close, min_max) in enumerate(train_loader):
# Forward
outputs = model(open_close)
# print(outputs)
# sys.exit()
# min_max_target = torch.argmax(min_max, 1)
# print(min_max_target)
# sys.exit()
loss = criterion(outputs, min_max)
# Backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 10 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')
Data I have for the input and target is:
input:
[[float32, float32]]
target:
[[float32, float32]]
I also tied to use CrossEntropuLoss but but got confused with conversion, the actual: min_max_target = torch.argmax(min_max, 1) didn’t get me any positive results.