thirdwing commented on issue #3368: custom loss symbol in R/Python URL: https://github.com/apache/incubator-mxnet/issues/3368#issuecomment-320108394 @khalida I have updated the document using your example. ```r # Network config optimizer <- "rmsprop" batchSize <- 60 nRounds <- 50 nHidden <- 14 verbose <- FALSE array.layout <- "rowmajor" library(mxnet) data(BostonHousing, package="mlbench") BostonHousing[, sapply(BostonHousing, is.factor)] <- as.numeric(as.character(BostonHousing[, sapply(BostonHousing, is.factor)])) BostonHousing <- data.frame(scale(BostonHousing)) test.ind = seq(1, 506, 5) # 1 pt in 5 used for testing train.x = data.matrix(BostonHousing[-test.ind, -14]) train.y = BostonHousing[-test.ind, 14] test.x = data.matrix(BostonHousing[--test.ind, -14]) test.y = BostonHousing[--test.ind, 14] data <- mx.symbol.Variable("data") label <- mx.symbol.Variable("label") fc1 <- mx.symbol.FullyConnected(data, num_hidden=nHidden, name="fc1") tanh1 <- mx.symbol.Activation(fc1, act_type="tanh", name="tanh1") fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden=1, name="fc2") lro <- mx.symbol.LinearRegressionOutput(fc2, name="lro") mx.set.seed(0) model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=nRounds, array.batch.size=batchSize, eval.metric=mx.metric.rmse, optimizer=optimizer, verbose=verbose, array.layout=array.layout) pred <- predict(model, test.x) lro2 <- mx.symbol.MakeLoss(mx.symbol.square(mx.symbol.Reshape(fc2, shape = 0) - label), name="lro2") mx.set.seed(0) model2 <- mx.model.FeedForward.create(lro2, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=nRounds, array.batch.size=batchSize, eval.metric=mx.metric.rmse, optimizer=optimizer, verbose=verbose, array.layout=array.layout) internals = internals(model2$symbol) fc_symbol = internals[[match("fc2_output", outputs(internals))]] model3 <- list(symbol = fc_symbol, arg.params = model2$arg.params, aux.params = model2$aux.params) class(model3) <- "MXFeedForwardModel" pred3 <- predict(model3, test.x) # Plotting of fits par(mfrow=c(1,2)) # Train fits plot(test.y, pred[1,], main="nnet Train Fit", xlab="Target", ylab="Response") abline(0,1, col="red", lwd=2) plot(test.y, pred3[1,], main="nnet MakeLoss square Train Fit", xlab="Target", ylab="Response") abline(0,1, col="red", lwd=2) ```  The output of `mx.symbol.MakeLoss` is the gradient of loss with respect to the input data. So currently the metric doesn't work with `MakeLoss` during the training process. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
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