[GitHub] ArturIndio opened a new issue #7446: format input data using mx.rnn

[gi...@git.apache.org]

ArturIndio opened a new issue #7446: format input data using mx.rnn
URL: https://github.com/apache/incubator-mxnet/issues/7446
 
 
   
   [data_ex_git.zip](https://github.com/apache/incubator-mxnet/files/1220978/data_ex_git.zip)
   I've trying to forecast time-series using mx.rnn model but I can't shape the data to input  format asked from mx.rnn. I want do predict a variable Q_t using some past values from itself and another variables. Using mx.model.FeedForward.create is very easy to define. I can't understand the "labels" input on mx.rnn model. This is my code:
   
   ```
   load("data_ex_git.RData") # attached
   
   
   train <- data[1:dias_train,]
   test <- data[(dias_train+1):nrow(data),]
   
   # Neural net fitting
   
   # Scaling data for the NN
   maxs <- apply(data, 2, max)
   mins <- apply(data, 2, min)
   scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins))
   
   train_ <- scaled[1:dias_train,]
   test_ <- scaled[(dias_train+1):nrow(data),]
   
   library(mxnet)
   train.x <- data.matrix(train_[,-1])
   train.y <- train_[,1]
   test.x <- data.matrix(test_[,-1])
   test.y <- test_[,1]
   
   X.train <- list(data=t(train.x), label=t(train.y))
   X.val <- list(data=t(test.x), label=t(test.y))
   
   batch.size = 5
   seq.len = 5
   num.hidden = 3
   num.embed = 3
   num.rnn.layer = 1
   num.lstm.layer = 1
   num.round = 1
   update.period = 1
   learning.rate= 0.1
   wd=0.00001
   clip_gradient=1
   
   mx.set.seed(0)
   model <- mx.rnn(X.train, NULL, num.rnn.layer=num.rnn.layer, seq.len=seq.len, num.hidden=num.hidden,
                   num.embed=num.embed, num.label=5, batch.size=batch.size, input.size=5, ctx = mx.cpu(),
                   num.round = num.round, update.period = update.period, initializer = mx.init.uniform(0.01),
                   dropout = 0, optimizer = "sgd", batch.norm = FALSE,
                   learning.rate=learning.rate, wd=wd, clip_gradient=clip_gradient)
   
   preds = predict(model,t(test.x))
   ```
   That's my error:
   
   `[19:10:06] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor\./matrix_op-inl.h:141: Using target_shape will be deprecated.
   [19:10:06] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor\./matrix_op-inl.h:141: Using target_shape will be deprecated.
   [19:10:06] d:\program files (x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor\./matrix_op-inl.h:141: Using target_shape will be deprecated.
   [19:10:06] D:\Program Files (x86)\Jenkins\workspace\mxnet\mxnet\dmlc-core\include\dmlc/logging.h:308: [19:10:06] D:\Program Files (x86)\Jenkins\workspace\mxnet\mxnet\src\ndarray\ndarray.cc:329: Check failed: from.shape() == to->shape() operands shape mismatchfrom.shape = (5,14) to.shape=(5,5)
   Error in exec$update.arg.arrays(arg.arrays, match.name, skip.null) : 
     [19:10:06] D:\Program Files (x86)\Jenkins\workspace\mxnet\mxnet\src\ndarray\ndarray.cc:329: Check failed: from.shape() == to->shape() operands shape mismatchfrom.shape = (5,14) to.shape=(5,5)`
 
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