I am trying to build a minimal working example in PyTorch using an LSTM. I use a mock example that predicts for a sequence of 3 time steps one out of 4 labels.

I get an error message which says

Expected object of scalar type Double but got scalar type Float for argument #2 ‘mat2’ in call to _th_mm

I am not sure what is missing. I assume if you are more familiar with PyTorch problem is easy to spot but my understanding is that my data is already double (i.e. float64=double, no?) Can someone give me a pointer what is wrong?

```
from torch.utils.data import DataLoader,Dataset
import random
class MyDataset(Dataset):
def __init__(self, data, labels):
self.data=data
self.labels=labels
def __getitem__(self, index):
return self.data[index], self.labels[index]
def __len__(self):
return len(self.data)
mock_data=torch.tensor([
[[.3, .2], [.2,.57], [.93,.9]],
[[.33, .23], [.25,.57], [.63,.29]],
[[.63, .4], [.25,.9], [.23,.29]],
[[.343, .223], [.125,.57], [.163,.29]],
[[.3, .2], [.2,.57], [.93,.9]],
[[.33, .23], [.25,.57], [.63,.29]],
[[.63, .4], [.25,.9], [.23,.29]],
[[.343, .223], [.125,.57], [.163,.29]],
]).double()
mock_label=torch.tensor([1, 2, 0, 3, 2, 2, 1, 0]).long()
MAX_LABELS=4
ds = MyDataset(mock_data, mock_label)
dl = DataLoader(ds, shuffle=True, batch_size=4)
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class TheNet(nn.Module):
def __init__(self, dim_x):
super(TheNet, self).__init__()
self.lstm = nn.LSTM(input_size=dim_x, hidden_size=64, batch_first=True)
self.out = nn.Linear(64, MAX_LABELS)
def forward(self, data):
lstm_out, _ = self.lstm(data)
out = self.out(lstm_out.view(len(data), -1))
label_score = F.log_softmax(out, dim=1)
return label_score
model = TheNet(2)
loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
for epoch in range(300):
for seq, label in dl:
model.zero_grad()
print(seq.shape)
scores = model(seq)
break
# loss = loss_function(scores, labels)
# loss.backward()
# optimizer.step()
```