A bidirectional LSTM, as the name indicates, not only uses the sequence of integers provided as input but also makes use of its reverse order as additional input. There could be situations where this approach may help to achieve further model classification performance improvements by capturing useful patterns in the data that may not have been captured by the original LSTM network.
For this experiment, we will modify the LSTM layer in the first experiment, as shown in the following code:
# Model architecturemodel <- keras_model_sequential() %>% layer_embedding(input_dim = 500, output_dim = 32) %>% bidirectional(layer_lstm(units = 32)) %>% layer_dense(units = 1, activation = "sigmoid")# Model ...
