example/gan/CGAN_mnist_R/iterators.R

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G_iterator <- function(batch_size) {
  
  batch <- 0
  batch_per_epoch <- 5
  
  reset <- function() {
    batch <<- 0
  }
  
  iter.next <- function() {
    batch <<- batch + 1
    if (batch > batch_per_epoch) {
      return(FALSE)
    } else {
      return(TRUE)
    }
  }
  
  value <- function() {
    set.seed(123 + batch)
    digit <- mx.nd.array(sample(0:9, size = batch_size, replace = T))
    data <- mx.nd.one.hot(indices = digit, depth = 10)
    data <- mx.nd.reshape(data = data, shape = c(1, 1, -1, batch_size))
    return(list(data = data, digit = digit))
  }
  
  return(list(reset = reset, iter.next = iter.next, value = value, batch_size = batch_size, 
    batch = batch))
}

D_iterator <- function(batch_size) {
  
  batch <- 0
  batch_per_epoch <- 5
  
  reset <- function() {
    batch <<- 0
  }
  
  iter.next <- function() {
    batch <<- batch + 1
    if (batch > batch_per_epoch) {
      return(FALSE)
    } else {
      return(TRUE)
    }
  }
  
  value <- function() {
    set.seed(123 + batch)
    idx <- sample(length(train_label), size = batch_size, replace = T)
    data <- train_data[, , , idx, drop = F]
    label <- mx.nd.array(train_label[idx])
    digit <- mx.nd.one.hot(indices = label, depth = 10)
    
    return(list(data = mx.nd.array(data), digit = digit, label = label))
  }
  
  return(list(reset = reset, iter.next = iter.next, value = value, batch_size = batch_size, 
    batch = batch))
}