deep learning for text and sequences train_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index= shuffle=truestep=stepbatch_size=batch_sizeval_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index= step=stepbatch_size=batch_sizetest_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index=nonestep=stepbatch_size=batch_sizehow many steps to draw from val_gen in order to see the entire validation set val_steps ( lookbacktest_steps (len(float_data lookbackhow many steps to draw from test_gen in order to see the entire test set common-sensenon-machine-learning baseline before you start using black-box deep-learning models to solve the temperatureprediction problemlet' try simplecommon-sense approach it will serve as sanity checkand it will establish baseline that you'll have to beat in order to demonstrate the usefulness of more-advanced machine-learning models such common-sense baselines can be useful when you're approaching new problem for which there is no known solution (yeta classic example is that of unbalanced classification taskswhere some classes are much more common than others if your dataset contains instances of class and instances of class bthen common-sense approach to the classification task is to always predict "awhen presented with new sample such classifier is accurate overalland any learning-based approach should therefore beat this score in order to demonstrate usefulness sometimessuch elementary baselines can prove surprisingly hard to beat in this casethe temperature timeseries can safely be assumed to be continuous (the temperatures tomorrow are likely to be close to the temperatures todayas well as periodical with daily period thus common-sense approach is to always predict that the temperature hours from now will be equal to the temperature right now let' evaluate this approachusing the mean absolute error (maemetricnp mean(np abs(preds targets)licensed to