jeremiedb commented on issue #12431: [R] use of mx.io.arrayiter completely crashes R environment URL: https://github.com/apache/incubator-mxnet/issues/12431#issuecomment-423410481 I think the issue comes from the evaluation metric. The following code include a modified rmse metric that flatten the pred and label vector. I think it's bug that the eval metric fails when the predictions are not in a flat setting, I'll open a PR to get it fixed. ``` data.A <- mx.nd.random.normal(shape = c(3,3,1,10)) data.A.2 <- mx.nd.random.normal(shape = c(3,3,1,10)) batch_size <- 5 train_iter = mx.io.arrayiter(data = as.array(data.A), label = as.array(data.A.2), batch.size = batch_size) data <- mx.symbol.Variable('data') label <- mx.symbol.Variable('label') conv_1 <- mx.symbol.Convolution(data= data, kernel = c(1,1), num_filter = 4, name="conv_1") conv_act_1 <- mx.symbol.Activation(data= conv_1, act_type = "relu", name="conv_act_1") flat <- mx.symbol.flatten(data = conv_act_1, name="flatten") fcl_1 <- mx.symbol.FullyConnected(data = flat, num_hidden = 9, name="fc_1") fcl_2 <- mx.symbol.reshape(fcl_1, shape=c(3, 3, 1, batch_size)) NN_Model <- mx.symbol.LinearRegressionOutput(data=fcl_2 , label=label, name="lro") fcl_2$infer.shape(list(data = c(3,3,1,batch_size))) NN_Model$infer.shape(list(data = c(3,3,1,batch_size))) mx.metric.rmse <- mx.metric.custom("rmse", function(label, pred) { pred <- mx.nd.reshape(pred, shape = -1) label <- mx.nd.reshape(label, shape = -1) res <- mx.nd.sqrt(mx.nd.mean(mx.nd.square(label-pred))) return(as.array(res)) }) mx.set.seed(99) autoencoder <- mx.model.FeedForward.create( NN_Model, X = train_iter, initializer = mx.init.uniform(0.01), ctx=mx.cpu(), num.round=5, eval.metric = mx.metric.rmse, optimizer = mx.opt.create("sgd"), verbose = TRUE) ```
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