As I feel like there is very little help to this in the documentation or somewhere else I am just trying to figuere out if I did everything correctly. At first I only changed bidirectional to True and to my suprise my model did still run without any error message. Now I also changed the dimensions of the next linear layer and my forward method.
Here is my thought process:

I am thinking, since bidirection doubles my lstm layer I need to double the Input size of the next layer. Which is weird because, like I said, after changing bidirectional to True without changing the input size of the next layer my model still ran.

I need to take both outputs of my lstm hn[0] and hn[1] and bot feed them to the next layer. So I then thought, maybe my model did not give me an error since I didnt have this implemented yet.
Would be just nice to know for sure how to correctly change the lstm structure. Any help appreciated.
EDIT: I just realised that ptorch documantation states
My LSTM Class now looks like this.
class Model_LSTM(nn.Module):
def __init__(self, n_features, n_classes, n_hidden, n_layers): # bidirectional mÃ¶glich
super().__init__()
self.lstm = nn.LSTM(
input_size=n_features,
hidden_size=n_hidden,
num_layers=n_layers,
batch_first=True,
dropout=0,
bidirectional=True
)
self.dense1 = nn.Linear(n_hidden*2, n_hidden)
self.classifier = nn.Linear(n_hidden, n_classes)
torch.nn.init.xavier_uniform_(self.lstm.weight_ih_l0)
torch.nn.init.xavier_uniform_(self.lstm.weight_hh_l0)
torch.nn.init.xavier_uniform_(self.classifier.weight)
torch.nn.init.xavier_uniform_(self.dense1.weight)
def forward(self, x):
_, (hn, _) = self.lstm(x)
out=torch.concat([hn[0], hn[1]], dim=1)
out = F.relu(self.dense1(out))
return self.classifier(out)