[GitHub] thirdwing commented on issue #7364: MxnetR chunk-wise neural nets

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thirdwing commented on issue #7364: MxnetR chunk-wise neural nets
URL: 
https://github.com/apache/incubator-mxnet/issues/7364#issuecomment-321406878
 
 
   @train-test-laura If you are using the same model over different chunks of 
data, you can do something similar to below:
   
   ```r
   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]
   
   train.x2 <- train.x
   train.y2 <- train.y
   
   library(mxnet)
   
   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,
                                        ctx = mx.cpu(),
                                        num.round = 50,
                                        array.batch.size = 20,
                                        learning.rate = 2e-6,
                                        momentum = 0.9,
                                        eval.metric = mx.metric.rmse)
   
   model2 <- mx.model.FeedForward.create(lro,
                                         X = train.x2,
                                         y = train.y2,
                                         ctx = mx.cpu(),
                                         num.round = 50,
                                         array.batch.size = 20,
                                         learning.rate = 2e-6,
                                         momentum = 0.9,
                                         eval.metric = mx.metric.rmse,
                                         arg.params = model$arg.params,
                                         aux.params = model$aux.params)
   ```
   
 
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