Recurrent neural networks (RNNs) are a type of neural network that can handle sequential data, such as time series data or natural language text. They differ from traditional feedforward neural networks in that they have loops in them, which allow them to pass information from earlier time steps to later time steps.
The basic building block of an RNN is the "cell," which contains a hidden state that is updated at each time step. The updated hidden state is then used to make a prediction for the current time step.
One common type of RNN cell is the Long Short-Term Memory (LSTM) cell, which allows information to be selectively remembered or forgotten over time. Another type is the Gated Recurrent Unit (GRU) cell, which is similar to LSTM but has fewer parameters.
In Keras, you can implement an RNN by using one of the built-in RNN layers, such as ‘LSTM‘ or ‘GRU‘. For example, here is some code to define an LSTM-based RNN to classify movie reviews:
from keras.layers import LSTM, Embedding, Dense
from keras.models import Sequential
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))Here, we first add an ‘Embedding‘ layer to convert our input data (movie reviews) into dense vector embeddings. Then, we add an ‘LSTM‘ layer with 128 hidden units and a dropout rate of 0.2 to prevent overfitting. Finally, we add a ‘Dense‘ layer with a sigmoid activation function to output a probability for each review.
Overall, RNNs are a powerful tool for handling sequential data, and Keras provides an easy way to implement them in practice.