lstm_text_generation

Source: https://github.com/rstudio/keras/blob/master/vignettes/examples/lstm_text_generation.R

Example script to generate text from Nietzsche’s writings.

At least 20 epochs are required before the generated text starts sounding coherent.

It is recommended to run this script on GPU, as recurrent networks are quite computationally intensive.

If you try this script on new data, make sure your corpus has at least ~100k characters. ~1M is better.

library(keras)
library(readr)
library(stringr)
library(purrr)
library(tokenizers)

# Parameters --------------------------------------------------------------

maxlen <- 40

# Data Preparation --------------------------------------------------------

# Retrieve text
path <- get_file(
  'nietzsche.txt', 
  origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt'
  )

# Load, collapse, and tokenize text
text <- read_lines(path) %>%
  str_to_lower() %>%
  str_c(collapse = "\n") %>%
  tokenize_characters(strip_non_alphanum = FALSE, simplify = TRUE)

print(sprintf("corpus length: %d", length(text)))

chars <- text %>%
  unique() %>%
  sort()

print(sprintf("total chars: %d", length(chars)))  

# Cut the text in semi-redundant sequences of maxlen characters
dataset <- map(
  seq(1, length(text) - maxlen - 1, by = 3), 
  ~list(sentece = text[.x:(.x + maxlen - 1)], next_char = text[.x + maxlen])
  )

dataset <- transpose(dataset)

# Vectorization
x <- array(0, dim = c(length(dataset$sentece), maxlen, length(chars)))
y <- array(0, dim = c(length(dataset$sentece), length(chars)))

for(i in 1:length(dataset$sentece)){
  
  x[i,,] <- sapply(chars, function(x){
    as.integer(x == dataset$sentece[[i]])
  })
  
  y[i,] <- as.integer(chars == dataset$next_char[[i]])
  
}

# Model Definition --------------------------------------------------------

model <- keras_model_sequential()

model %>%
  layer_lstm(128, input_shape = c(maxlen, length(chars))) %>%
  layer_dense(length(chars)) %>%
  layer_activation("softmax")

optimizer <- optimizer_rmsprop(lr = 0.01)

model %>% compile(
  loss = "categorical_crossentropy", 
  optimizer = optimizer
)

# Training & Results ----------------------------------------------------

sample_mod <- function(preds, temperature = 1){
  preds <- log(preds)/temperature
  exp_preds <- exp(preds)
  preds <- exp_preds/sum(exp(preds))
  
  rmultinom(1, 1, preds) %>% 
    as.integer() %>%
    which.max()
}

on_epoch_end <- function(epoch, logs) {
  
  cat(sprintf("epoch: %02d ---------------\n\n", epoch))
  
  for(diversity in c(0.2, 0.5, 1, 1.2)){
    
    cat(sprintf("diversity: %f ---------------\n\n", diversity))
    
    start_index <- sample(1:(length(text) - maxlen), size = 1)
    sentence <- text[start_index:(start_index + maxlen - 1)]
    generated <- ""
    
    for(i in 1:400){
      
      x <- sapply(chars, function(x){
        as.integer(x == sentence)
      })
      x <- array_reshape(x, c(1, dim(x)))
      
      preds <- predict(model, x)
      next_index <- sample_mod(preds, diversity)
      next_char <- chars[next_index]
      
      generated <- str_c(generated, next_char, collapse = "")
      sentence <- c(sentence[-1], next_char)
      
    }
    
    cat(generated)
    cat("\n\n")
    
  }
}

print_callback <- callback_lambda(on_epoch_end = on_epoch_end)

model %>% fit(
  x, y,
  batch_size = 128,
  epochs = 1,
  callbacks = print_callback
)