this is the example convnet for the imdb dataset listing training and evaluat ing simple convnet on he imdb dat from keras models import sequential from keras import layers from keras optimizers import rmsprop model sequential(model add(layers embedding(max_features input_length=max_len)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 summary(model compile(optimizer=rmsprop(lr= - )loss='binary_crossentropy'metrics=['acc']history model fit(x_trainy_trainepochs= batch_size= validation_split= figures and show the training and validation results validation accuracy is somewhat less than that of the lstmbut runtime is faster on both cpu and gpu (the exact increase in speed will vary greatly depending on your exact configurationat this pointyou could retrain this model for the right number of epochs (eightand run it on the test set this is convincing demonstration that convnet can offer fastcheap alternative to recurrent network on word-level sentiment-classification task figure training and validation loss on imdb wit simple convnet licensed to