在指定时间后无法收敛时停止 Keras

一种 Select 是定义一个调用自身的函数，可能在执行之前执行设置种子之类的操作.

library(keras)
d <- dataset_mnist()
x_train <- d$train$x
y_train <- d$train$y
x_test <- d$test$x
y_test <- d$test$y

x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))
x_train <- x_train / 255
x_test <- x_test / 255
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = 'softmax')

model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
)

我们可以创建一个递归函数，在超时后调用它自己.

timed_fit <- function(t = 5) {
  Sys.sleep(1)
  set.seed(t)
  message("seed set to ", t)
  setTimeLimit(cpu = t, elapsed = t, transient = TRUE)
  on.exit({setTimeLimit(cpu = Inf, elapsed = Inf, transient = FALSE)})
  tryCatch({
    model %>% fit(
      x_train, y_train, 
      epochs = 4, batch_size = 128, 
      validation_split = 0.2
    )
  }, error = function(e) {
    if (grepl("reached elapsed time limit|reached CPU time limit", e$message)) {
      message("\n timed out!\n") # or set another seed, continue
      timed_fit(t + 10)
    } else {
      # error not related to timeout
      stop(e)
    }
  })
}

timed_fit()

插入Sys.sleep(1)是为了避免出现错误，尽管出现了错误，但不会正确中断流程.