imdb_lstm.RmdTrains 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]])