added a verbose option to mx.mlp in R package. close #1608

apache · Mar 20, 2016 · 569c486 · 569c486
1 parent fc922f0
commit 569c486
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Showing 7 changed files with 113 additions and 116 deletions.
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
@@ -103,3 +103,4 @@ List of Contributors
 * [Dan Becker](https://github.com/dansbecker)
 * [Yun Yan](https://github.com/Puriney)
 * [Tao Wei](https://github.com/taoari)
+* [Max Kuhn](https://github.com/topepo)
diff --git a/R-package/R/callback.R b/R-package/R/callback.R
@@ -4,10 +4,10 @@ mx.metric.logger <- setRefClass("mx.metric.logger", fields = list(train = "numer
 #' Log training metric each period
 #' @export
 mx.callback.log.train.metric <- function(period, logger=NULL) {
-  function(iteration, nbatch, env) {
+  function(iteration, nbatch, env, verbose=TRUE) {
     if (nbatch %% period == 0 && !is.null(env$metric)) {
       result <- env$metric$get(env$train.metric)
-      if (nbatch != 0)
+      if (nbatch != 0 & verbose)
         cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n"))
       if (!is.null(logger)) {
         if (class(logger) != "mx.metric.logger") {
@@ -16,7 +16,7 @@ mx.callback.log.train.metric <- function(period, logger=NULL) {
         logger$train <- c(logger$train, result$value)
         if (!is.null(env$eval.metric)) {
           result <- env$metric$get(env$eval.metric)
-          if (nbatch != 0)
+          if (nbatch != 0 & verbose)
             cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n"))
           logger$eval <- c(logger$eval, result$value)
         }
@@ -33,10 +33,10 @@ mx.callback.log.train.metric <- function(period, logger=NULL) {
 #'
 #' @export
 mx.callback.save.checkpoint <- function(prefix, period=1) {
-  function(iteration, nbatch, env) {
+  function(iteration, nbatch, env, verbose=TRUE) {
     if (iteration %% period == 0) {
       mx.model.save(env$model, prefix, iteration)
-      cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
+      if(verbose) cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
     }
     return(TRUE)
   }

diff --git a/R-package/R/lr_scheduler.R b/R-package/R/lr_scheduler.R
@@ -1,6 +1,6 @@
 
 #' @export
-FactorScheduler <- function(step, factor_val) {
+FactorScheduler <- function(step, factor_val, verbose=TRUE) {
   function(optimizerEnv){
     num_update <- optimizerEnv$num_update
     count      <- optimizerEnv$count
@@ -9,10 +9,9 @@ FactorScheduler <- function(step, factor_val) {
     if (num_update > count + step){
       count = count + step
       lr = lr * factor_val
-      cat(paste0("Update[", num_update, "]: learning rate is changed to ", lr, "\n"))
+      if(verbose) cat(paste0("Update[", num_update, "]: learning rate is changed to ", lr, "\n"))
       optimizerEnv$lr <- lr
       optimizerEnv$count <- count
     }
-    # return(optimizerEnv)
   }
 }
diff --git a/R-package/R/model.R b/R-package/R/model.R
@@ -72,7 +72,7 @@ mx.model.extract.model <- function(symbol, train.execs) {
 }
 
 # decide what type of kvstore to use
-mx.model.create.kvstore <- function(kvstore, arg.params, ndevice) {
+mx.model.create.kvstore <- function(kvstore, arg.params, ndevice, verbose=TRUE) {
   if (is.MXKVStore(kvstore)) return (kvstore)
   if (!is.character(kvstore)) {
     stop("kvstore msut be either MXKVStore or a string")
@@ -85,7 +85,7 @@ mx.model.create.kvstore <- function(kvstore, arg.params, ndevice) {
     } else {
       kvstore <- 'local_allreduce_cpu'
     }
-    cat(paste0("Auto-select kvstore type = ", kvstore, "\n"))
+    if(verbose) cat(paste0("Auto-select kvstore type = ", kvstore, "\n"))
   }
   return(mx.kv.create(kvstore))
 }
@@ -98,9 +98,10 @@ mx.model.train <- function(symbol, ctx, input.shape,
                            metric,
                            epoch.end.callback,
                            batch.end.callback,
-                           kvstore) {
+                           kvstore,
+                           verbose=TRUE) {
   ndevice <- length(ctx)
-  cat(paste0("Start training with ", ndevice, " devices\n"))
+  if(verbose) cat(paste0("Start training with ", ndevice, " devices\n"))
   # create the executors
   sliceinfo <- mx.model.slice.shape(input.shape, ndevice)
   train.execs <- lapply(1:ndevice, function(i) {
@@ -203,7 +204,7 @@ mx.model.train <- function(symbol, ctx, input.shape,
     train.data$reset()
     if (!is.null(metric)) {
       result <- metric$get(train.metric)
-      cat(paste0("[", iteration, "] Train-", result$name, "=", result$value, "\n"))
+      if(verbose) cat(paste0("[", iteration, "] Train-", result$name, "=", result$value, "\n"))
     }
     if (!is.null(eval.data)) {
       if (!is.null(metric)) {
@@ -237,7 +238,7 @@ mx.model.train <- function(symbol, ctx, input.shape,
       eval.data$reset()
       if (!is.null(metric)) {
         result <- metric$get(eval.metric)
-        cat(paste0("[", iteration, "] Validation-", result$name, "=", result$value, "\n"))
+        if(verbose) cat(paste0("[", iteration, "] Validation-", result$name, "=", result$value, "\n"))
       }
     } else {
       eval.metric <- NULL
@@ -247,7 +248,7 @@ mx.model.train <- function(symbol, ctx, input.shape,
 
     epoch_continue <- TRUE
     if (!is.null(epoch.end.callback)) {
-      epoch_continue <- epoch.end.callback(iteration, 0, environment())
+      epoch_continue <- epoch.end.callback(iteration, 0, environment(), verbose = verbose)
     }
 
     if (!epoch_continue) {
@@ -295,10 +296,10 @@ mx.model.select.layout.train <- function(X, y) {
     stop("Cannot auto select array.layout, please specify this parameter")
   }
   if (rowmajor == 1) {
-    cat("Auto detect layout of input matrix, use rowmajor..\n")
+    warning("Auto detect layout of input matrix, use rowmajor..\n")
     return("rowmajor")
   } else{
-    cat("Auto detect layout input matrix, use colmajor..\n")
+    warning("Auto detect layout input matrix, use colmajor..\n")
     return("colmajor")
   }
 }
@@ -333,10 +334,10 @@ mx.model.select.layout.predict <- function(X, model) {
     stop("Cannot auto select array.layout, please specify this parameter")
   }
   if (rowmajor == 1) {
-    cat("Auto detect layout of input matrix, use rowmajor..\n")
+    warning("Auto detect layout of input matrix, use rowmajor..\n")
     return("rowmajor")
   } else{
-    cat("Auto detect layout input matrix, use colmajor..\n")
+    warning("Auto detect layout input matrix, use colmajor..\n")
     return("colmajor")
   }
 }
@@ -375,6 +376,8 @@ mx.model.select.layout.predict <- function(X, model) {
 #'      and will report error when X is a square matrix to ask user to explicitly specify layout.
 #' @param kvstore string (default="local")
 #'     The parameter synchronization scheme in multiple devices.
+#' @param verbose logical (default=TRUE)
+#'     Specifies whether to print information on the iterations during training.     
 #' @return model A trained mxnet model.
 #'
 #' @export
@@ -386,6 +389,7 @@ function(symbol, X, y=NULL, ctx=NULL,
          epoch.end.callback=NULL, batch.end.callback=NULL,
          array.batch.size=128, array.layout="auto",
          kvstore="local",
+         verbose=TRUE,
          ...) {
   if (is.array(X) || is.matrix(X)) {
     if (array.layout == "auto") {
@@ -429,15 +433,16 @@ function(symbol, X, y=NULL, ctx=NULL,
     }
     eval.data <- mx.model.init.iter(eval.data$data, eval.data$label, batch.size=array.batch.size, is.train = TRUE)
   }
-  kvstore <- mx.model.create.kvstore(kvstore, params$arg.params, length(ctx))
+  kvstore <- mx.model.create.kvstore(kvstore, params$arg.params, length(ctx), verbose=verbose)
   model <- mx.model.train(symbol, ctx, input.shape,
                           params$arg.params, params$aux.params,
                           1, num.round, optimizer=optimizer,
                           train.data=X, eval.data=eval.data,
                           metric=eval.metric,
                           epoch.end.callback=epoch.end.callback,
                           batch.end.callback=batch.end.callback,
-                          kvstore=kvstore)
+                          kvstore=kvstore, 
+                          verbose=verbose)
   return (model)
 }
 

diff --git a/R-package/R/mxnet_generated.R b/R-package/R/mxnet_generated.R
@@ -267,7 +267,7 @@ mx.io.CSVIter <- function(...) {
 #'     Batch Param: Batch size.
 #' @param round.batch  boolean, optional, default=True
 #'     Batch Param: Use round robin to handle overflow batch.
-#' @param prefetch.buffer  long (non-negative), optional, default=4
+#' @param prefetch.buffer  , optional, default=4
 #'     Backend Param: Number of prefetched parameters
 #' @param rand.crop  boolean, optional, default=False
 #'     Augmentation Param: Whether to random crop on the image
@@ -354,7 +354,7 @@ mx.io.ImageRecordIter <- function(...) {
 #'     partition the data into multiple parts
 #' @param part.index  int, optional, default='0'
 #'     the index of the part will read
-#' @param prefetch.buffer  long (non-negative), optional, default=4
+#' @param prefetch.buffer  , optional, default=4
 #'     Backend Param: Number of prefetched parameters
 #' @return iter The result mx.dataiter
 #' 

diff --git a/R-package/vignettes/CallbackFunctionTutorial.Rmd b/R-package/vignettes/CallbackFunctionTutorial.Rmd
@@ -88,7 +88,7 @@ Basically, all callback functions follow the structure below:
 
 ```{r, eval=FALSE}
 mx.callback.fun <- function() {
-  function(iteration, nbatch, env) {
+  function(iteration, nbatch, env, verbose) {
   }
 }
 ```
@@ -97,10 +97,10 @@ The `mx.callback.save.checkpoint` function below is stateless. It just get the m
 
 ```{r, eval=FALSE}
 mx.callback.save.checkpoint <- function(prefix, period=1) {
-  function(iteration, nbatch, env) {
+  function(iteration, nbatch, env, verbose) {
     if (iteration %% period == 0) {
       mx.model.save(env$model, prefix, iteration)
-      cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
+      if(verbose) cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration))
     }
     return(TRUE)
   }
@@ -112,11 +112,11 @@ process.
 
 ```{r, eval=FALSE}
 mx.callback.log.train.metric <- function(period, logger=NULL) {
-  function(iteration, nbatch, env) {
+  function(iteration, nbatch, env, verbose) {
     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(verbose) 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.")
@@ -142,7 +142,7 @@ Yes! You can stop the training early by `return(FALSE)`. See the examples below.
 
 ```{r}
 mx.callback.early.stop <- function(eval.metric) {
-  function(iteration, nbatch, env) {
+  function(iteration, nbatch, env, verbose) {
     if (!is.null(env$metric)) {
       if (!is.null(eval.metric)) {
         result <- env$metric$get(env$eval.metric)