Hello,

I am trying to input a signal of format [batch_size, signal] to a Bidirectional LSTM. The signal consists of 660 values and the batch size is 50, hence the input format to the LSTM according to me is [50, 660] tensor.

**I know that the input for an LSTM layer if “batch_first = True” is 3D input of format (batch_size, sequance_length, input_size) but I am not able to understand how to convert my given signal to an LSTM input format.** Hence, I am getting this “RuntimeError” as *“input must have 3 dimensions, got 2”*

I have two queries?

- How should I give my signal as input to the network and how should I reshape the signal?
- Will the network what I made be able to run the signal and generate embedding?

This my network architecture,

```
sequance_length = 660
input_size = 660
hidden_dim = 32
num_epochs = 50
learning_rate = 0.001
margin = 0.2
class NetNet(nn.Module):
def __init__(self, input_size, hidden_dim):
super(NetNet, self).__init__()
self.hidden_size = hidden_dim
self.lstm = nn.LSTM(input_size, self.hidden_size, bidirectional = True, batch_first = True)
self.fc = nn.Linear(self.hidden_size * 2, self.hidden_size)
def forward(self, x):
h0 = torch.zeros(2, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(2, x.size(0), self.hidden_size).to(device)
print('h0 size: ' +str(h0.size()))
print('c0 size: ' +str(c0.size()))
print('x size: ' +str(x.size()))
# Bidirectional LSTM
out, _ = self.lstm(x, (h0, c0))
# Applies avg pooling for temporal series
out = out.sum(dim = 1)
out = torch.tanh(out)
# First Fully Connected Layer
out = self.fc(out[:, -1, :])
out = torch.tanh(out)
# Second Fully Connected Layer
out = self.fc(out)
out = torch.tanh(out)
# L2-normalized output
norm = torch.norm(out, 2, 1, keep_dim = True)
output = out/norm
return output
```

Please guide me because I am stuck with this for quite some time now. Thank you!