This tutorial provides guidelines for using and writing callback functions, which can very useful in model training.
Let's begin with a small example. We can build and train a model with the following code:
library(mxnet) data(BostonHousing, package="mlbench") train.ind = seq(1, 506, 3) train.x = data.matrix(BostonHousing[train.ind, -14]) train.y = BostonHousing[train.ind, 14] test.x = data.matrix(BostonHousing[-train.ind, -14]) test.y = BostonHousing[-train.ind, 14] data <- mx.symbol.Variable("data") fc1 <- mx.symbol.FullyConnected(data, num_hidden=1) lro <- mx.symbol.LinearRegressionOutput(fc1) mx.set.seed(0) model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=16.063282524034 ## [1] Validation-rmse=10.1766446093622 ## [2] Train-rmse=12.2792375712573 ## [2] Validation-rmse=12.4331776190813 ## [3] Train-rmse=11.1984634005885 ## [3] Validation-rmse=10.3303041888193 ## [4] Train-rmse=10.2645236892904 ## [4] Validation-rmse=8.42760407903415 ## [5] Train-rmse=9.49711005504284 ## [5] Validation-rmse=8.44557808483234 ## [6] Train-rmse=9.07733734175182 ## [6] Validation-rmse=8.33225500266177 ## [7] Train-rmse=9.07884450847991 ## [7] Validation-rmse=8.38827833418459 ## [8] Train-rmse=9.10463850277417 ## [8] Validation-rmse=8.37394452365264 ## [9] Train-rmse=9.03977049028532 ## [9] Validation-rmse=8.25927979725672 ## [10] Train-rmse=8.96870685004475 ## [10] Validation-rmse=8.19509291481822
We also provide two optional parameters, batch.end.callback and epoch.end.callback, which can provide great flexibility in model training.
This package provides two callback functions:
mx.callback.save.checkpoint saves a checkpoint to files during each period iteration.model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, epoch.end.callback = mx.callback.save.checkpoint("boston"))
## Auto detect layout of input matrix, use row major..
## Start training with 1 devices
## [1] Train-rmse=19.1621424021617
## [1] Validation-rmse=20.721515592165
## Model checkpoint saved to boston-0001.params
## [2] Train-rmse=13.5127391952367
## [2] Validation-rmse=14.1822123675007
## Model checkpoint saved to boston-0002.params
mx.callback.log.train.metric logs a training metric each period. You can use it either as a batch.end.callback or an epoch.end.callback.model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, batch.end.callback = mx.callback.log.train.metric(5))
## Auto detect layout of input matrix, use row major..
## Start training with 1 devices
## Batch [5] Train-rmse=17.6514558545416
## [1] Train-rmse=15.2879610219001
## [1] Validation-rmse=12.3332062820921
## Batch [5] Train-rmse=11.939392828565
## [2] Train-rmse=11.4382242547217
## [2] Validation-rmse=9.91176550103181
............
You also can save the training and evaluation errors for later use by passing a reference class:
logger <- mx.metric.logger$new() model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, epoch.end.callback = mx.callback.log.train.metric(5, logger))
## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=19.1083228733256 ## [1] Validation-rmse=12.7150687428974 ## [2] Train-rmse=15.7684378116157 ## [2] Validation-rmse=14.8105319420491 ............
head(logger$train)
## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324
head(logger$eval)
## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087
You can find the source code for the two callback functions on GitHub and use it as a template:
Basically, all callback functions follow the following structure:
mx.callback.fun <- function() { function(iteration, nbatch, env) { } }
The following mx.callback.save.checkpoint function is stateless. It gets the model from the environment and saves it:.
mx.callback.save.checkpoint <- function(prefix, period=1) { function(iteration, nbatch, env) { if (iteration %% period == 0) { mx.model.save(env$model, prefix, iteration) cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration)) } return(TRUE) } }
The mx.callback.log.train.metric is a little more complex. It holds a reference class and updates it during the training process:
mx.callback.log.train.metric <- function(period, logger=NULL) { function(iteration, nbatch, env) { if (nbatch %% period == 0 && !is.null(env$metric)) { result <- env$metric$get(env$train.metric) if (nbatch != 0) cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n")) if (!is.null(logger)) { if (class(logger) != "mx.metric.logger") { stop("Invalid mx.metric.logger.") } logger$train <- c(logger$train, result$value) if (!is.null(env$eval.metric)) { result <- env$metric$get(env$eval.metric) if (nbatch != 0) cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n")) logger$eval <- c(logger$eval, result$value) } } } return(TRUE) } }
Now you might be curious why both callback functions return(TRUE).
Can we return(FALSE)?
Yes! You can stop the training early with return(FALSE). See the following examples.
mx.callback.early.stop <- function(eval.metric) { function(iteration, nbatch, env) { if (!is.null(env$metric)) { if (!is.null(eval.metric)) { result <- env$metric$get(env$eval.metric) if (result$value < eval.metric) { return(FALSE) } } } return(TRUE) } } model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, epoch.end.callback = mx.callback.early.stop(10))
## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=18.5897984387033 ## [1] Validation-rmse=13.5555213820571 ## [2] Train-rmse=12.5867564040256 ## [2] Validation-rmse=9.76304967080928
When the validation metric dips below the threshold we set, the training process stops.