[GitHub] [incubator-mxnet] RuilinZhuIntel commented on issue #15420: [R] MKL-DNN support: "Unknown exception" in mx.nd.internal.as.array

[GitBox <gi...@apache.org>]

RuilinZhuIntel commented on issue #15420: [R] MKL-DNN support:  "Unknown exception" in mx.nd.internal.as.array
URL: https://github.com/apache/incubator-mxnet/issues/15420#issuecomment-509509558
 
 
   @Crunchy9 Hi, please try this code for training on your kaggle dataset,
   library(mxnet)
   dataset<-mx.io.ImageRecordIter(path.imgrec="C:\\Users\\ruilinzh\\Downloads\\caltech-256-image-dataset\\data_train.rec", path.imglist="C:\\Users\\ruilinzh\\Downloads\\caltech-256-image-dataset\\data_train.lst", batch.size = 8, data_shape = c(224, 224, 3))
   data <- mx.symbol.Variable("data") 
   conv <- mx.symbol.Convolution(data, kernel = c(7, 7) , stride = c(2, 2), pad = c(0, 0), num.filter = 24, name = paste0("test", "_conv1")) 
   act <- mx.symbol.LeakyReLU(conv, act.type = "leaky", name = paste0("test", "_act1")) 
   fc <- mx.symbol.FullyConnected(act, num_hidden = 256, name = paste0("test", "_FC")) 
   softmax <- mx.symbol.SoftmaxOutput(fc, name = "softmax") 
   devices <- mx.cpu() 
   model <- mx.model.FeedForward.create(softmax, initializer=mx.init.Xavier(factor_type = "in", magnitude=2), X=dataset, ctx=devices, num.round=2, begin.round=epoch+1, eval.data=NULL, optimizer=mx.opt.create("sgd", learning.rate = 0.005, momentum = 0.9, wd = 0, lr_scheduler = NULL), eval.metric=mx.metric.accuracy)

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