deep learning for text and sequences look at data from longer ago (by increasing the lookback parameter of the data generatoror look at high-resolution timeseries (by decreasing the step parameter of the generatorheresomewhat arbitrarilyyou'll use step that' half as largeresulting in timeseries twice as longwhere the temperature data is sampled at rate of point per minutes the example reuses the generator function defined earlier listing unchanged preparing higher-resolut ion dat generat ors for he jena dat aset step lookback delay previously set to ( point per hour)now ( point per mintrain_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index= shuffle=truestep=stepval_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index= step=steptest_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index=nonestep=stepval_steps ( lookback/ test_steps (len(float_data lookback/ this is the modelstarting with two conv layers and following up with gru layer figure shows the results listing model combining convolut ional base and gru layer 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 gru( dropout= recurrent_dropout= )model add(layers dense( )model summary(model compile(optimizer=rmsprop()loss='mae'licensed to