Note on criterion and optimizer

I am experimenting the LSTM to learn my stock market sequence and this NN and pair of criterion and optimizer does not learn.

# LSTM model
class SineLSTM(nn.Module):
    def init(self, input_size, hidden_size, num_layers, output_size):
        super(SineLSTM, self).init()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
    def forward(self, x):
        batch_size = x.size(0)
        h0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, batch_size, self.hiddensize).to(x.device)
        out,  = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out
# Initialize model, loss function, and optimizer
model = SineLSTM(input_size, hidden_size, num_layers, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

Claude give me answer on the combination of them

Here are the common combinations of loss functions (criterion) and optimizers that could be used with the LSTM model you provided:

1. MSE Loss with Adam:
```python
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
```

2. MSE Loss with SGD:
```python
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
```

3. L1 Loss (Mean Absolute Error) with Adam:
```python
criterion = nn.L1Loss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
```

4. L1 Loss with SGD:
```python
criterion = nn.L1Loss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
```

5. Huber Loss with Adam:
```python
criterion = nn.HuberLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
```

6. Huber Loss with SGD:
```python
criterion = nn.HuberLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
```

7. MSE Loss with RMSprop:
```python
criterion = nn.MSELoss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)
```

8. L1 Loss with RMSprop:
```python
criterion = nn.L1Loss()
optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)
```

These are some of the most common combinations. The choice depends on your specific problem and data characteristics. Would you like me to explain any of these combinations in more detail?