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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