imdb_bidirectional_lstm

library(keras)

# Define maximum number of input features
max_features <- 20000

# Cut texts after this number of words
# (among top max_features most common words)
maxlen <- 100

batch_size <- 32

# Load imdb dataset 
cat('Loading data...\n')
imdb <- dataset_imdb(num_words = max_features)

# Define training and test sets
x_train <- imdb$train$x
y_train <- imdb$train$y
x_test <- imdb$test$x
y_test <- imdb$test$y

# Output lengths of testing and training sets
cat(length(x_train), 'train sequences\n')
cat(length(x_test), 'test sequences\n')

cat('Pad sequences (samples x time)\n')

# Pad training and test inputs
x_train <- pad_sequences(x_train, maxlen = maxlen)
x_test <- pad_sequences(x_test, maxlen = maxlen)

# Output dimensions of training and test inputs
cat('x_train shape:', dim(x_train), '\n')
cat('x_test shape:', dim(x_test), '\n')

# Initialize model
model <- keras_model_sequential()
model %>%
  # Creates dense embedding layer; outputs 3D tensor
  # with shape (batch_size, sequence_length, output_dim)
  layer_embedding(input_dim = max_features, 
                  output_dim = 128, 
                  input_length = maxlen) %>% 
  bidirectional(layer_lstm(units = 64)) %>%
  layer_dropout(rate = 0.5) %>% 
  layer_dense(units = 1, activation = 'sigmoid')

# Try using different optimizers and different optimizer configs
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)

# Train model over four epochs
cat('Train...\n')
model %>% fit(
  x_train, y_train,
  batch_size = batch_size,
  epochs = 4,
  validation_data = list(x_test, y_test)
)