[R] fix image classification examples ( close apache#5080 ) (apache#5855

)

[R] fix image classification examples ( close apache#5080 )

example/image-classification/train_mnist.R

@@ -95,6 +95,8 @@ parse_args <- function() {
                         help='the batch size')
     parser$add_argument('--lr', type='double', default=.05,
                         help='the initial learning rate')
+    parser$add_argument('--mom', type='double', default=.9,
+                        help='momentum for sgd')
     parser$add_argument('--model-prefix', type='character',
                         help='the prefix of the model to load/save')
     parser$add_argument('--num-round', type='integer', default=10,
@@ -113,6 +115,32 @@ if (args$network == 'mlp') {
     data_shape <- c(28, 28, 1)
     net <- get_lenet()
 }
+
 # train
-source("train_model.R")
-train_model.fit(args, net, get_iterator(data_shape))
+data_loader <- get_iterator(data_shape)
+data <- data_loader(args)
+train <- data$train
+val <- data$value 
+
+if (is.null(args$gpus)) {
+  devs <- mx.cpu()  
+} else {
+  devs <- lapply(unlist(strsplit(args$gpus, ",")), function(i) {
+    mx.gpu(as.integer(i))
+  })
+}
+
+mx.set.seed(0)
+
+model <- mx.model.FeedForward.create(
+  X                  = train,
+  eval.data          = val,
+  ctx                = devs,
+  symbol             = net,
+  num.round          = args$num_round,
+  array.batch.size   = args$batch_size,
+  learning.rate      = args$lr,
+  momentum           = args$mom,
+  eval.metric        = mx.metric.accuracy,
+  initializer        = mx.init.uniform(0.07),
+  batch.end.callback = mx.callback.log.train.metric(100))

example/image-classification/train_model.R

@@ -33,9 +33,9 @@ train_model.fit <- function(args, network, data_loader) {
     }
 
     # data
-    data <- data_loader
+    data <- data_loader(args)
     train <- data$train
-    val <- data$value  
+    val <- data$value 
 
     # devices
     if (is.null(args$gpus)) {