history model fit_generator(train_gensteps_per_epoch= epochs= validation_data=val_genvalidation_steps=val_stepsfigure training and validat ion loss on he jena temperat ureforecast ing ask wit convnet followed by gru judging from the validation lossthis setup isn' as good as the regularized gru alonebut it' significantly faster it looks at twice as much datawhich in this case doesn' appear to be hugely helpful but may be important for other datasets wrapping up here' what you should take away from this sectionin the same way that convnets perform well for processing visual patterns in space convnets perform well for processing temporal patterns they offer faster alternative to rnns on some problemsin particular naturallanguage processing tasks typically convnets are structured much like their equivalents from the world of computer visionthey consist of stacks of conv layers and maxpooling layersending in global pooling operation or flattening operation because rnns are extremely expensive for processing very long sequencesbut convnets are cheapit can be good idea to use convnet as preprocessing step before an rnnshortening the sequence and extracting useful representations for the rnn to process licensed to