torch.zeros(1, 1, model.hidden_layer))
predictions = model(seq)
single_loss = loss(predictions, label)
single_loss.backward()
optimizer.step()
if i % 10 == 0:
print("Epoch: {0} Loss: {1:10.10f}".format(i, single_loss))
to_pred = 9128
test_inputs = target_norm[-train_window:].tolist()
for i in range(to_pred):
seq = torch.FloatTensor(test_inputs[-train_window:])
with torch.no_grad():
model.hidden = (torch.zeros(1, 1, model.hidden_layer),
torch.zeros(1, 1, model.hidden_layer))
test_inputs.append(model(seq).item())
inversed_preds = scaler.inverse_transform(
np.array(test_inputs[train_window:]).reshape(-1, 1))
plt.ion()
plt.title("Actuals vs. Predictions")
plt.ylabel("Traffic Volume")
plt.autoscale(True, 'x', True)
plt.plot(target)
plt.plot(inversed_preds)
plt.show()
plt.plot(target[-train_window:])
plt.plot(inversed_preds)
