pred_valid = neural_net.forward(x_valid)
valid_error = mse_loss(pred_valid, target_valid) + regularization * l2_norm
valid_over_epoch.append(valid_error.data[0])
for param in neural_net.parameters():
param.grad *= alpha
loss.backward()
for param in neural_net.parameters():
param.data -= learning_rate * param.grad.data
if epoch % animator.EPOCHS_PER_FRAME == 0:
print('Epoch {:4d}, loss: {:6.5f}, validation error: {:6.5f}'.format(
epoch, loss.data[0], valid_error.data[0]))
pred_train_list = pp.revert(pred_train).data.tolist()
pred_valid_list = pp.revert(pred_valid).data.tolist()
animator.add_epoch(epoch, pred_train_list, pred_valid_list)
set_global_min, stop_flag = early_stopper.check_for_early_stop(
valid_error.data[0])
dma_over_epoch.append(early_stopper.dma)
if set_global_min:
for optimal, param in zip(optimal_params, neural_net.parameters()):
optimal = param
if stop_flag in EarlyStop.BREAK_FLAGS:
for optimal, param in zip(optimal_params, neural_net.parameters()):
param = optimal
print('Epoch {:4d}, loss: {:6.5f}, validation error: {:6.5f}'.format(
