To use recurrent layers such as LSTM or GRU layers in a Keras model, you can use the LSTM or GRU layer from the keras.layers module. These layers allow you to model sequential data and capture temporal dependencies in the data.
Here is an example of how to use an LSTM layer in a Keras model:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
model = Sequential()
model.add(LSTM(32, input_shape=(None, 10)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
In this example, we have a model with one LSTM layer with 32 units and a Dense output layer with a sigmoid activation function. The input shape of the LSTM layer is (None, 10), meaning that it can accept sequences of any length with 10 features.
You can also use bidirectional recurrent layers by wrapping the LSTM or GRU layer in a Bidirectional layer from the keras.layers module.
Once you have defined your model, you can train the model using the fit() method and evaluate it using the evaluate() method. You may want to experiment with different hyperparameters and architectures to achieve your desired performance.