Hello,
When running multi-class classification, I have to set the number of classes as n+1, as setting the number of classes as n returns error.
I attempted to modify CrossEntropyLoss as per instructions on this post, but I must have done something wrong.
I also read this post , but I could not find out a solution.
I have 10 variables split as (X_train,X_test) and 1 target variable split as (Y_train, Y_test). My target/label is a 3 class (1,2,3)
Here’s the code:
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.layer2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.relu(out)
out = self.layer2(out)
return out
tr_latent_X = data_utils.TensorDataset(train_x, train_y)
te_latent_X = data_utils.TensorDataset(test_x, test_y)
x_train_label = Y_train
x_test_label = y_test
train_loader_X = torch.utils.data.DataLoader(dataset=tr_latent_X,
batch_size=batch_size,
shuffle=False)
test_loader_X = torch.utils.data.DataLoader(dataset=te_latent_X,
batch_size=batch_size,
shuffle=False)
If I set the number of dimensions as 3(instead of 4) here later I obtain the error:
vae = NeuralNet(10,2,3)
loss_function = nn.CrossEntropyLoss(reduction='mean',ignore_index=-1)
optimizer = torch.optim.SGD(vae.parameters(), lr = 0.01)
def train(epoch):
vae.train()
train_loss = 0
for batch_idx, (data,label) in enumerate(train_loader_X):
optimizer.zero_grad()
out = vae(data)
loss = loss_function(out, label)
loss.backward()
train_loss += loss.item()
optimizer.step()
if batch_idx % 5000 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader_X.dataset),
100. * batch_idx / len(train_loader_X), loss.item() / len(data)))
print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader_X.dataset)))
for epoch in range(1, 10):
train(epoch)
This is the error I obtained (last part of the long sequenece)
4 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1836 .format(input.size(0), target.size(0)))
1837 if dim == 2:
-> 1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1839 elif dim == 4:
1840 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:97
If instead of setting 3 classes, I set 4, then it runs well:
vae = NeuralNet(10,2,4)
loss_function = nn.CrossEntropyLoss(reduction='mean',ignore_index=-1)
optimizer = torch.optim.SGD(vae.parameters(), lr = 0.01)
If anyone could shed some light on this, I would be very grateful.
Sincerely,
