Trying to solve a multi label image classification problem,an 8 class multi label problem . My labels look like this
	[0. 1. 0. 0. 1. 0. 0. 0.]
	[0. 1. 0. 0. 0. 0. 0. 0.]
	[0. 1. 0. 1. 0. 0. 0. 0.]

The code looks like this
model = models.resnet50(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 8)
model = model.cuda()
# Decay LR by a factor of 0.1 every 7 epochs
# exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
loss_fn = torch.nn.BCELoss()



# def train(epoch):
for epoch in range(15):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # import pdb;pdb.set_trace()
        data, target = data.cuda(), target.float().cuda()
        optimizer.zero_grad()
        output = model(data)
        output = torch.sigmoid(output)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()

For now the results are not good, am i doing something wrong?
Any suggestions would be really helpful.Thanks in advance.