# CrossEntropyLoss: Index Error (Target 3 is out of bounds)

Hi there,

I am using code from a CIFAR classification problem (num_classes = 10) and want to use the code for my dataset (CheXpert with num_classes = 3). Therefore, I changed the num_classes in the ResNet model from 10 to 3.

class ResNet(FitModule):
def __init__(self, block, num_blocks, num_classes=3):    #changed from 10 to 3
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = conv3x3(3, 64)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

self.linear = nn.Linear(1024 * block.expansion, num_classes)
...
def forward(self, x):
x = x.float()
out = F.relu(self.bn1(self.conv1(x).float()).float())
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = torch.cat([self.mp(out), self.ap(out)], 1)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out

Then the IndexError: Target 3 is out of bounds occurs in my fit-methode when using CrossEntropyLoss.
10 pictures of size 3x32x32 are given into the model. Thatâ€™s why X_batch has size [10, 3, 32, 32], after going through the model, y_batch_pred has size [10, 3] as I changed num_classes to 3.
When using the CrossEntropyLoss with y_batch_pred [10, 3] and the initial labels y_batch [10] the IndexError occurs.

def fit(self, X, y, batch_size=32, epochs=10, verbose=1, validation_split=0.,
validation_data=None, shuffle=True, initial_epoch=0, seed=None,
loss=CrossEntropyLoss(), optimizer=partial(SGD, lr=0.001, momentum=0.9),
metrics=None):

...
# Run batches
for batch_i, batch_data in enumerate(train_data):
# Get batch data
# Backprop
y_batch_pred = self(X_batch).float()             # picture goes through model
batch_loss = loss(y_batch_pred, y_batch)    # IndexError !!
batch_loss.backward()
opt.step()
# Update status
epoch_loss += batch_loss.item()
for param in self.parameters():
log['loss'] = float(epoch_loss) / (batch_i + 1)
if verbose:
pb.bar(batch_i, log_to_message(log))
...
return logs

There does not seem to be a big difference to between the original CIFAR problem with 10 classes and my problem with only 3 classes. When running my images on a 10 classes classification problem everything works just fine.

Can anyone help?