My model has the following structure. I am able to train, and test, but when it comes to using new data later, it fails with the error Expected hidden[0] size (2, 188, 64), got (2, 150, 64).
Model
class BasicLSTM(nn.Module):
def __init__(self,
device,
input_dims: int,
hidden_dims: int,
output_dims: int,
num_layers: int = 2,
dropout: float = 0.5):
super(BasicLSTM, self).__init__()
self.model_type = "BasicLSTM"
self.device = device
self.input_dims = input_dims
self.output_dims = output_dims
self.hidden_dims = hidden_dims
self.num_layers = num_layers
self.dropout = dropout
self.lstm1 = nn.LSTM(input_size=input_dims,
hidden_size=self.hidden_dims,
num_layers=self.num_layers,
dropout=self.dropout,
# batch_first=True
)
self.fc = nn.Linear(self.hidden_dims, self.output_dims)
hidden_state = torch.randn(self.num_layers, batch_size, self.hidden_dims).to(self.device)
cell_state = torch.randn(self.num_layers, batch_size, self.hidden_dims).to(self.device)
self.hidden_cell = (hidden_state, cell_state)
def forward(self, x: Tensor):
x = x.unsqueeze(0).float().cuda()
out, self.hidden_cell = self.lstm1(x, self.hidden_cell)
preds = self.fc(out)
return preds[-1]
New predictions
def predict_future(time: int, future_data):
model.eval()
for i in range(time):
seq = torch.FloatTensor(future_data)
with torch.no_grad():
np.append(future_data, model(seq).item())
The type of future_data is a numpy n-dimensional array.