I’m getting Target 1 is out of bounds error and not able to debug the error. Can anyone please help me out with this error. I have also added a snapshot of my model summary.
My code :
# Define the loss function (negative log-likelihood)
criterion = nn.NLLLoss()
# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=base_learning_rate, momentum=momentum, weight_decay=weight_decay)
# Initialize lists to store accuracy values
train_accuracy_history = []
# Training loop
train_loss_history = []
verbose = True
l2_lambda = 0.0001
for epoch in range(num_epochs):
model.train()
train_loss = 0.0
correct = 0
total = 0
for batch_data, batch_labels in train_loader:
optimizer.zero_grad()
# Forward pass
output = model(batch_data)
batch_labels = batch_labels.view(-1)
# Compute loss
ce_loss = criterion(output, batch_labels.long())
l2_reg = 0.0
for param in model.parameters():
l2_reg += torch.norm(param, p=2)
# Combine the cross-entropy loss and L2 regularization term
loss = ce_loss + l2_lambda * l2_reg
# Backward pass and optimization
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(output.data, 1)
total += batch_labels.size(0)
correct += (predicted == batch_labels).sum().item()
train_loss /= len(train_loader)
accuracy = correct / total
train_accuracy_history.append(accuracy)
train_loss_history.append(train_loss)
if verbose:
print(f"\nEpoch [{epoch+1}/{num_epochs}] | Train Loss: {train_loss:.4f} | Train Accuracy: {accuracy:.2f}")
My error:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Input In [29], in <cell line: 14>()
26 batch_labels = batch_labels.view(-1)
28 # Compute loss
---> 29 ce_loss = criterion(output, batch_labels.long())
30 #ce_loss = criterion(output, batch_labels)
32 l2_reg = 0.0
File ~\anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ~\anaconda3\lib\site-packages\torch\nn\modules\loss.py:211, in NLLLoss.forward(self, input, target)
210 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 211 return F.nll_loss(input, target, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction)
File ~\anaconda3\lib\site-packages\torch\nn\functional.py:2689, in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
2687 if size_average is not None or reduce is not None:
2688 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2689 return torch._C._nn.nll_loss_nd(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
IndexError: Target 1 is out of bounds.
