R Deep Learning Projects

Other LSTM architectures

Bidirectional LSTM seemed to be a good idea, right? What about a simpler network architecture? 

Instead of a bidirectional LSTM, we can consider a simple LSTM. To do this, we can replace the preceding model (after doing the same preprocessing; that is, feeding the data in the same format) with a simple LSTM:

model <- keras_model_sequential()model %>% layer_embedding(input_dim = length(vocab),  output_dim = 128,  input_length = 100) %>% layer_lstm(units = 64, dropout = 0.2, recurrent_dropout = 0.2) %>%  layer_dense(units = 1, activation = 'sigmoid') model %>% compile(     loss = "binary_crossentropy",     optimizer = "adam",

     metrics = "accuracy")

After training, we get the following results:

Training an LSTM in the IMDb ...