deep learning for text and sequences model compile(optimizer='rmsprop'loss='binary_crossentropy'metrics=['acc']history model fit(x_trainy_trainepochs= batch_size= validation_split= it performs slightly better than the regular lstm you tried in the previous sectionachieving over validation accuracy it also seems to overfit more quicklywhich is unsurprising because bidirectional layer has twice as many parameters as chronological lstm with some regularizationthe bidirectional approach would likely be strong performer on this task now let' try the same approach on the temperature-prediction task listing training bidirect ional gru from keras models import sequential from keras import layers from keras optimizers import rmsprop model sequential(model add(layers bidirectionallayers gru( )input_shape=(nonefloat_data shape[- ]))model add(layers dense( )model compile(optimizer=rmsprop()loss='mae'history model fit_generator(train_gensteps_per_epoch= epochs= validation_data=val_genvalidation_steps=val_stepsthis performs about as well as the regular gru layer it' easy to understand whyall the predictive capacity must come from the chronological half of the networkbecause the antichronological half is known to be severely underperforming on this task (againbecause the recent past matters much more than the distant past in this casegoing even further there are many other things you could tryin order to improve performance on the temperature-forecasting problemadjust the number of units in each recurrent layer in the stacked setup the current choices are largely arbitrary and thus probably suboptimal adjust the learning rate used by the rmsprop optimizer try using lstm layers instead of gru layers try using bigger densely connected regressor on top of the recurrent layersthat isa bigger dense layer or even stack of dense layers don' forget to eventually run the best-performing models (in terms of validation maeon the test setotherwiseyou'll develop architectures that are overfitting to the validation set licensed to