Hi,

I am unable to increase the precision and recall on my time-series classifier in below code.

Can someone please help to let me know what can be done further to improve the precision and recall.

```
class lstmfcn(nn.Module):
def __init__(self, input_shape, size):
super(lstmfcn, self).__init__()
self.lstm = nn.LSTM(input_size=input_shape[1], hidden_size=size, batch_first=True)
self.conv1d_1 = nn.Conv1d(input_shape[0], size, kernel_size=8, padding='same')
self.batch_norm_1 = nn.BatchNorm1d(size)
self.activation_1 = nn.ReLU()
self.conv1d_2 = nn.Conv1d(size, size*2, kernel_size=5, padding='same')
self.batch_norm_2 = nn.BatchNorm1d(size*2)
self.activation_2 = nn.ReLU()
self.conv1d_3 = nn.Conv1d(size*2, size, kernel_size=3, padding='same')
self.batch_norm_3 = nn.BatchNorm1d(size)
self.activation_3 = nn.ReLU()
self.global_avg_pooling_1d = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Linear(size*2, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
h_lstm, _ = self.lstm(x)
y = self.conv1d_1(x)
y = self.batch_norm_1(y)
y = self.activation_1(y)
y = self.conv1d_2(y)
y = self.batch_norm_2(y)
y = self.activation_2(y)
y = self.conv1d_3(y)
y = self.batch_norm_3(y)
y = self.activation_3(y)
y = self.global_avg_pooling_1d(y)
x = torch.cat((h_lstm[:, -1], y.squeeze(dim=2)), dim=1)
x = self.fc(x)
x = self.sigmoid(x)
return x
```

Has already done scaling and resampling.

Increasing the size is not helping.