Hello,

I have kind of a time-series prediction where the output shape is variable. Therefore I did interpolation on my dataset to strech or compress the data into equal sized length of 10. **Note, that the sum of the values in that tensor sum up to 1 - they are relative values**.

After running training for 50 epochs the epoch loss stays the same and does not decrease. I know there are several topics about that problem, but nothing is releated to a ‘multi regression output’ where the target values sum up to 1.

**My custom dataset:**

```
class BADataset(data.Dataset):
def __init__(self, X, y):
self.X = torch.tensor(X.to_numpy().astype(np.float32))
self.y = torch.tensor(y.to_numpy().astype(np.float32))
def __len__(self):
return len(self.X)
def __getitem__(self, index):
return self.X[index], self.y[index]
```

**My network:**

```
class Net(nn.Module):
def __init__(self, inputsize, hiddensize, outputsize, dropout):
super(Net, self).__init__()
self.fc1 = nn.Linear(inputsize, hiddensize)
self.fc2 = nn.Linear(hiddensize, outputsize)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.softmax(self.fc2(x), dim=1)
return x
```

**Training process:**

```
for epoch in range(EPOCHS):
net.train()
epoch_loss = list()
cum_loss_train = list()
running_loss = 0.0
for idx, data in enumerate(train_loader):
inputs, labels = data
inputs = Variable(inputs.to(device))
labels = Variable(labels.to(device))
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss.append(loss.item())
cum_loss_train.append(np.mean(epoch_loss))
print(f'{datetime.datetime.now()} | EPOCH: {epoch} | LOSS: {np.round(np.mean(epoch_loss), 5)}')
```

**Parameters:**

```
BATCH_SIZE = 8
LEARNING_RATE = 0.05
MOMENTUM = 0.00
EPOCHS = 50
INPUT_SIZE = 40
HIDDEN_SIZE = 90
OUTPUT_SIZE = 10
DROPOUT = 0.8
EVAL_STEP = 1
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=LEARNING_RATE)
```

I already played around with the parameters. Changed the learning rate, hidden layer size, dropout value, batch size etc. But nothing solves the problem.

I also tried the Adam optimizer and changed the loss function to L1Loss

Also very strange is the model output after training for a view epochs:

```
tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], grad_fn=<UnbindBackward0>)
tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], grad_fn=<UnbindBackward0>)
tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], grad_fn=<UnbindBackward0>)
tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], grad_fn=<UnbindBackward0>)
tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], grad_fn=<UnbindBackward0>)
tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], grad_fn=<UnbindBackward0>)
```

The model always sets the second output to 1 and the rest to 0.

A reasonable output of the segments should be like that:

```
-0.00103,0.16046,0.20881,0.57528,0.02601,-0.00107,0.00423,0.02641,0.00045,0.00045
0.01369,0.22102,0.67908,0.04893,0.00032,0.00958,0.02267,0.00380,0.00045,0.00045
0.16022,0.20850,0.57443,0.02597,-0.00107,0.00423,0.02637,0.00045,0.00045,0.00045
0.22323,0.68586,0.04942,0.00032,0.00968,0.02290,0.00723,0.00045,0.00045,0.00045
0.24072,0.66320,0.02998,-0.00123,0.00488,0.05265,0.00825,0.00052,0.00052,0.00052
0.85768,0.06180,0.00040,0.01210,-0.00040,0.05767,0.00904,0.00057,0.00057,0.00057
0.86787,0.03924,-0.00161,0.01325,-0.00048,0.06890,0.01080,0.00068,0.00068,0.00068
0.40020,0.00262,0.08491,0.07184,-0.00261,0.37345,0.05853,0.00368,0.00368,0.00368
```

Kinda like a multi label classification where the probability is distributed over 10 classes.

Finally here is the output of the training loss.

```
2023-02-02 16:02:36.476878 | EPOCH: 0 | LOSS: 522.06665
2023-02-02 16:02:51.127700 | EPOCH: 1 | LOSS: 522.06669
2023-02-02 16:03:06.418139 | EPOCH: 2 | LOSS: 522.06669
2023-02-02 16:03:21.640622 | EPOCH: 3 | LOSS: 522.06669
2023-02-02 16:03:36.421470 | EPOCH: 4 | LOSS: 522.06669
2023-02-02 16:03:50.987930 | EPOCH: 5 | LOSS: 522.06669
2023-02-02 16:04:05.824788 | EPOCH: 6 | LOSS: 522.06669
2023-02-02 16:04:20.751109 | EPOCH: 7 | LOSS: 522.06669
2023-02-02 16:04:35.314357 | EPOCH: 8 | LOSS: 522.06669
2023-02-02 16:04:49.541290 | EPOCH: 9 | LOSS: 522.06669
2023-02-02 16:05:04.630747 | EPOCH: 10 | LOSS: 522.06669
2023-02-02 16:05:19.952308 | EPOCH: 11 | LOSS: 522.06669
2023-02-02 16:05:35.211317 | EPOCH: 12 | LOSS: 522.06669
2023-02-02 16:05:50.412194 | EPOCH: 13 | LOSS: 522.06669
2023-02-02 16:06:05.951134 | EPOCH: 14 | LOSS: 522.06669
2023-02-02 16:06:22.523799 | EPOCH: 15 | LOSS: 522.06669
2023-02-02 16:06:37.155365 | EPOCH: 16 | LOSS: 522.06669
2023-02-02 16:06:51.703793 | EPOCH: 17 | LOSS: 522.06669
2023-02-02 16:07:06.309624 | EPOCH: 18 | LOSS: 522.06669
```

Any suggestions about the problem here?