Recurrent Neural Networks (RNN) with Keras

Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language. Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has seen so far. Ease of use: the built-in keras.layers.RNN, keras.layers.LSTM, keras.layers.GRU layers enable you to quickly build recurrent models without having to make difficult configuration choices. Ease of customization: You can also define your own RNN cell layer (the inner part of the for loop) with custom behavior, and use it with the generic keras.layers.RNN layer (the for loop itself). This allows you to quickly prototype different research ideas in a flexible way with minimal code.