# a neat trick to save screen space:
if i % 25 == 1:
print(f'epoch: {i} loss: {loss.item()}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
# graphy stuff
print(f'epoch: {i:3} loss: {loss.item():10.8f}') # print the last line
print(f'\nDuration: {time.time() - start_time:.0f} seconds'
) # print the time elapsed
plt.plot(range(epochs), losses)
plt.ylabel('RMSE Loss')
plt.xlabel('epoch')
with torch.no_grad():
y_val = model(cat_test, con_test)
loss = torch.sqrt(criterion(y_val, y_test))
print(f'RMSE: {loss:.8f}')
print(f'{"PREDICTED":>12} {"ACTUAL":>8} {"DIFF":>8}')
for i in range(50):
diff = np.abs(y_val[i].item() - y_test[i].item())
print(
f'{i+1:2}. {y_val[i].item():8.4f} {y_test[i].item():8.4f} {diff:8.4f}')
torch.save(model.state_dict(), 'TaxiFareRegrModel.pt')
