thirdwing commented on issue #7336: io.cc:54: Data and label shape in-consistent
URL: 
https://github.com/apache/incubator-mxnet/issues/7336#issuecomment-320472863
 
 
   Are you using the prebuilt pkg?
   
   On my Linux machine with the latest code, it works well
   
   ```r
   library(readr)
   DataNeurona <- read_csv("MyData2.csv")
   DataNeurona <- as.matrix(DataNeurona)
   
   train.ind <- c(1:100)
   train.x <- DataNeurona[train.ind, -length(DataNeurona[1, ])]
   train.y <- DataNeurona[train.ind, length(DataNeurona[1, ])]
   test.x <- DataNeurona[-train.ind, length(DataNeurona[1, ])]
   test.y <- DataNeurona[-train.ind, length(DataNeurona[1, ])]
   
   library(mxnet)
   
   data <- mx.symbol.Variable("data")
   fc1 <-
     mx.symbol.FullyConnected(data, name = "fc1", num_hidden = 40) #weight=w,
   act1 <- mx.symbol.Activation(fc1, name = "sigmoid1", act_type = "sigmoid")
   fc2 <- mx.symbol.FullyConnected(act1, name = "fc2", num_hidden = 50)
   act2 <- mx.symbol.Activation(fc2, name = "tanh1", act_type = "sigmoid")
   fc4 <- mx.symbol.FullyConnected(act2, name = "fc4", num_hidden = 1)
   lro <- mx.symbol.LinearRegressionOutput(data = fc4, grad.scale = 1)
   mx.set.seed(0)
   
   model <- mx.model.FeedForward.create(
     symbol = lro,
     X = train.x,
     y = train.y,
     ctx = mx.cpu(),
     num.round = 25,
     array.batch.size = 20,
     learning.rate = 0.05,
     momentum = 0.9,
     eval.metric = mx.metric.mse
   )
   ```
   
   ```
   Start training with 1 devices
   [1] Train-mse=0.189338970604364
   [2] Train-mse=0.182918034135271
   [3] Train-mse=0.139369296805449
   [4] Train-mse=0.0981672365474814
   [5] Train-mse=0.090259424948582
   [6] Train-mse=0.091202815317992
   [7] Train-mse=0.0735728471551251
   [8] Train-mse=0.0755325079735637
   [9] Train-mse=0.0776457090260113
   [10] Train-mse=0.0703273524582357
   [11] Train-mse=0.0727759200215761
   [12] Train-mse=0.073669241397235
   [13] Train-mse=0.0705466593146721
   [14] Train-mse=0.0721189324018792
   [15] Train-mse=0.0723587683907677
   [16] Train-mse=0.0710235608978592
   [17] Train-mse=0.0718809927804486
   [18] Train-mse=0.0718809992951069
   [19] Train-mse=0.0713206787760761
   [20] Train-mse=0.0717545830533599
   [21] Train-mse=0.071694452664629
   [22] Train-mse=0.0714664637380885
   [23] Train-mse=0.0716749304881256
   [24] Train-mse=0.0716162021040038
   [25] Train-mse=0.0715267213384503
   Warning message:
   In mx.model.select.layout.train(X, y) :
     Auto detect layout of input matrix, use rowmajor..
   ```
 
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