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[R] custom iter in model training; MF demo in R (apache#6673)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,67 @@ | ||
| library(mxnet) | ||
| DF <- read.table("./ml-100k/u.data", header = F, sep = "\t") | ||
| names(DF) <- c("user", "item", "score", "time") | ||
| max_user <- max(DF$user) | ||
| max_item <- max(DF$item) | ||
| DF_mat_x <- data.matrix(t(DF[, 1:2])) | ||
| DF_y <- DF[, 3] | ||
| k <- 64 | ||
| user <- mx.symbol.Variable("user") | ||
| item <- mx.symbol.Variable("item") | ||
| score <- mx.symbol.Variable("label") | ||
| user1 <-mx.symbol.Embedding(data = mx.symbol.BlockGrad(user), input_dim = max_user, | ||
| output_dim = k, name = "user1") | ||
| item1 <- mx.symbol.Embedding(data = mx.symbol.BlockGrad(item), input_dim = max_item, | ||
| output_dim = k, name = "item1") | ||
| pred <- user1 * item1 | ||
| pred1 <- mx.symbol.sum_axis(pred, axis = 1, name = "pred1") | ||
| pred2 <- mx.symbol.Flatten(pred1, name = "pred2") | ||
| pred3 <- mx.symbol.LinearRegressionOutput(data = pred2, label = score, name = "pred3") | ||
| devices <- mx.cpu() | ||
| mx.set.seed(123) | ||
|
|
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| CustomIter <- setRefClass("CustomIter", fields = c("iter1", "iter2"), | ||
| contains = "Rcpp_MXArrayDataIter", | ||
| methods = list( | ||
| initialize = function(iter1, iter2) { | ||
| .self$iter1 <- iter1 | ||
| .self$iter2 <- iter2 | ||
| .self | ||
| }, | ||
| value = function() { | ||
| user <- .self$iter1$value()$data | ||
| item <- .self$iter2$value()$data | ||
| label <- .self$iter1$value()$label | ||
| list(user = user, | ||
| item = item, | ||
| label = label) | ||
| }, | ||
| iter.next = function() { | ||
| .self$iter1$iter.next() | ||
| .self$iter2$iter.next() | ||
| }, | ||
| reset = function() { | ||
| .self$iter1$reset() | ||
| .self$iter2$reset() | ||
| }, | ||
| num.pad = function() { | ||
| .self$iter1$num.pad() | ||
| }, | ||
| finalize = function() { | ||
| .self$iter1$finalize() | ||
| .self$iter2$finalize() | ||
| } | ||
| ) | ||
| ) | ||
|
|
||
| user_iter = mx.io.arrayiter(data = DF[, 1], label = DF[, 3], batch.size = k) | ||
|
|
||
| item_iter = mx.io.arrayiter(data = DF[, 2], label = DF[, 3], batch.size = k) | ||
|
|
||
| train_iter <- CustomIter$new(user_iter, item_iter) | ||
|
|
||
| model <- mx.model.FeedForward.create(pred3, X = train_iter, ctx = devices, | ||
| num.round = 10, initializer = mx.init.uniform(0.07), | ||
| learning.rate = 0.07, eval.metric = mx.metric.rmse, | ||
| momentum = 0.9, epoch.end.callback = mx.callback.log.train.metric(1), | ||
| input.names = c("user", "item"), output.names = "label") |