Hi, I have nueral network that is used to classify a patch as eye or not, give eye landmark and bounding box predictions.

All the three output layers are fully connected linear layers.

The problem with the model is, it is giving same output for the first output layer which has two nodes.

Here is my model architecture.

class Net(torch.nn.Module):

def **init**(self):

super(Net, self).**init**()

self.conv1 = nn.Conv2d(3, 32, 3)

torch.nn.init.xavier_normal_(self.conv1.weight)

self.pool1 = nn.MaxPool2d(3,stride=2,ceil_mode=True)

self.conv2 = nn.Conv2d(32, 64, 3)

torch.nn.init.xavier_normal_(self.conv2.weight)

self.pool2 = nn.MaxPool2d(3,stride=2,ceil_mode=True)

self.conv3 = nn.Conv2d(64, 64, 3)

torch.nn.init.xavier_normal_(self.conv3.weight)

self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)

self.conv4 = nn.Conv2d(64, 128, 2)

torch.nn.init.xavier_normal_(self.conv4.weight)

self.fc1 = nn.Linear(3*1*128, 256)

self.cls_prob_fc = nn.Linear(256,2)

self.bbox_pred_fc = nn.Linear(256,4)

self.lm_pred_fc = nn.Linear(256,12)

```
def forward(self, x):
x = self.pool1(F.relu(self.conv1(x)))
x = self.pool2(F.relu(self.conv2(x)))
x = self.pool3(F.relu(self.conv3(x)))
x = F.relu(self.conv4(x))
x = x.view(x.size(0), 1*3*128)
x = F.relu(self.fc1(x))
x_cls_prob = self.cls_prob_fc(x)
x_bbox_pred = self.bbox_pred_fc(x)
x_lm_pred = self.lm_pred_fc(x)
return x_cls_prob,x_bbox_pred,x_lm_pred
```

Could anyone please help me with this