layer (typeoutput shape param ===============================================================embedding_ (embedding(nonenone simplernn_ (simplernn(none ===============================================================total params , trainable params , non-trainable params the following example returns the full state sequencemodel sequential(model add(embedding( )model add(simplernn( return_sequences=true)model summary(layer (typeoutput shape param ===============================================================embedding_ (embedding(nonenone simplernn_ (simplernn(nonenone ===============================================================total params , trainable params , non-trainable params it' sometimes useful to stack several recurrent layers one after the other in order to increase the representational power of network in such setupyou have to get all of the intermediate layers to return full sequence of outputsmodel sequential(model add(embedding( )model add(simplernn( return_sequences=true)model add(simplernn( return_sequences=true)last layer only returns model add(simplernn( return_sequences=true)the last output model add(simplernn( )model summary(layer (typeoutput shape param ===============================================================embedding_ (embedding(nonenone simplernn_ (simplernn(nonenone simplernn_ (simplernn(nonenone simplernn_ (simplernn(nonenone simplernn_ (simplernn(none ===============================================================total params , trainable params , non-trainable params licensed to