Trains a LSTM on the IMDB sentiment classification task.
The dataset is actually too small for LSTM to be of any advantage compared to simpler, much faster methods such as TF-IDF + LogReg.
RNNs are tricky. Choice of batch size is important, choice of loss and optimizer is critical, etc. Some configurations won’t converge.
LSTM loss decrease patterns during training can be quite different from what you see with CNNs/MLPs/etc.
library(keras) max_features <- 20000 maxlen <- 80 # cut texts after this number of words (among top max_features most common words) batch_size <- 32 cat('Loading data...\n') imdb <- dataset_imdb(num_words = max_features) x_train <- imdb$train$x y_train <- imdb$train$y x_test <- imdb$test$x y_test <- imdb$test$y cat(length(x_train), 'train sequences\n') cat(length(x_test), 'test sequences\n') cat('Pad sequences (samples x time)\n') x_train <- pad_sequences(x_train, maxlen = maxlen) x_test <- pad_sequences(x_test, maxlen = maxlen) cat('x_train shape:', dim(x_train), '\n') cat('x_test shape:', dim(x_test), '\n') cat('Build model...\n') model <- keras_model_sequential() model %>% layer_embedding(input_dim = max_features, output_dim = 128) %>% layer_lstm(units = 64, dropout = 0.2, recurrent_dropout = 0.2) %>% layer_dense(units = 1, activation = 'sigmoid') # try using different optimizers and different optimizer configs model %>% compile( loss = 'binary_crossentropy', optimizer = 'adam', metrics = c('accuracy') ) cat('Train...\n') model %>% fit( x_train, y_train, batch_size = batch_size, epochs = 15, validation_data = list(x_test, y_test) ) scores <- model %>% evaluate( x_test, y_test, batch_size = batch_size ) cat('Test score:', scores[]) cat('Test accuracy', scores[])