deep learning for text and sequences figure training and validat ion accuracy on imdb wit simple convnet combining cnns and rnns to process long sequences because convnets process input patches independentlythey aren' sensitive to the order of the timesteps (beyond local scalethe size of the convolution windows)unlike rnns of courseto recognize longer-term patternsyou can stack many convolution layers and pooling layersresulting in upper layers that will see long chunks of the original inputs--but that' still fairly weak way to induce order sensitivity one way to evidence this weakness is to try convnets on the temperature-forecasting problemwhere order-sensitivity is key to producing good predictions the following example reuses the following variables defined previouslyfloat_datatrain_genval_genand val_steps listing training and evaluat ing simple convnet on he jena dat from keras models import sequential from keras import layers from keras optimizers import rmsprop model sequential(model add(layers conv ( activation='relu'input_shape=(nonefloat_data shape[- ]))model add(layers maxpooling ( )model add(layers conv ( activation='relu')model add(layers maxpooling ( )model add(layers conv ( activation='relu')model add(layers globalmaxpooling ()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_stepslicensed to