imdb_lstm

    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.

    Notes: - 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
    batch_size <- 32
    
    # Cut texts after this number of words (among top max_features most common words)
    maxlen <- 80  
    
    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[[1]])
    cat('Test accuracy', scores[[2]])