here' the evaluation loop listing comput ing he common-sense baseline mae def evaluate_naive_method()batch_maes [for step in range(val_steps)samplestargets next(val_genpreds samples[:- mae np mean(np abs(preds targets)batch_maes append(maeprint(np mean(batch_maes)evaluate_naive_method(this yields an mae of because the temperature data has been normalized to be centered on and have standard deviation of this number isn' immediately interpretable it translates to an average absolute error of temperature_std degrees celsius @ listing convert ing he mae back celsius error celsius_mae std[ that' fairly large average absolute error now the game is to use your knowledge of deep learning to do better basic machine-learning approach in the same way that it' useful to establish common-sense baseline before trying machine-learning approachesit' useful to try simplecheap machine-learning models (such as smalldensely connected networksbefore looking into complicated and computationally expensive models such as rnns this is the best way to make sure any further complexity you throw at the problem is legitimate and delivers real benefits the following listing shows fully connected model that starts by flattening the data and then runs it through two dense layers note the lack of activation function on the last dense layerwhich is typical for regression problem you use mae as the loss because you evaluate on the exact same data and with the exact same metric you did with the common-sense approachthe results will be directly comparable listing training and evaluat ing densely connect ed model from keras models import sequential from keras import layers from keras optimizers import rmsprop model sequential(model add(layers flatten(input_shape=(lookback /stepfloat_data shape[- ]))model add(layers dense( activation='relu')model add(layers dense( )licensed to