getting started with neural networks the result is shown in figure listing plot ing validat ion scoresexcluding he first dat point def smooth_curve(pointsfactor= )smoothed_points [for point in pointsif smoothed_pointsprevious smoothed_points[- smoothed_points append(previous factor point ( factor)elsesmoothed_points append(pointreturn smoothed_points smooth_mae_history smooth_curve(average_mae_history[ :]plt plot(range( len(smooth_mae_history )smooth_mae_historyplt xlabel('epochs'plt ylabel('validation mae'plt show(figure validation mae by epochexcluding he first dat point according to this plotvalidation mae stops improving significantly after epochs past that pointyou start overfitting once you're finished tuning other parameters of the model (in addition to the number of epochsyou could also adjust the size of the hidden layers)you can train final production model on all of the training datawith the best parametersand then look at its performance on the test data listing training he final model gets freshcompiled model model build_model(trains it on the entirety of the data model fit(train_datatrain_targetsepochs= batch_size= verbose= test_mse_scoretest_mae_score model evaluate(test_datatest_targetslicensed to