[GitHub] alexmosc opened a new issue #12002: how I properly dimensionalize my array and tune `rnn.graph.unroll` to make the LSTM work for this multidimensional sequence

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alexmosc opened a new issue #12002: how I properly dimensionalize my array and tune `rnn.graph.unroll` to make the LSTM work for this multidimensional sequence
URL: https://github.com/apache/incubator-mxnet/issues/12002
 
 
   It is essentially a call for help rather than code-related issue.
   
   Assume a matrix with 5 rows and 20 columns. Each column is a sample of a multivariate timeseries. Each row is one dimension of the multivariate timeseries.
   
   I have also a vector of 20 output values.
   
   I am trying to build an LSTM model with sequence length = 20 which would iterate over samples 1 to 20 and regress output values associated.
   
   I get all sorts of "shape mismatch" and "You are trying to split the 0-th axis of input tensor with shape" error messages. 
   
   The question is how I properly dimensionalize my array of input data and tune `rnn.graph.unroll` to make the LSTM work for this multidimensional sequence.
   
   
   ```
   library(mxnet)
   
   rm(symbol)
   
   symbol <- rnn.graph.unroll(seq_len = 20, 
                              num_rnn_layer =  1, 
                              num_hidden = 50,
                              input_size = NULL,
                              num_embed = NULL, 
                              num_decode = 1,
                              masking = F, 
                              loss_output = "linear",
                              dropout = 0.2, 
                              ignore_label = -1,
                              cell_type = "lstm",
                              output_last_state = F,
                              config = "seq-to-one")
   
   #graph.viz(symbol, type = "graph", direction = "LR", graph.height.px = 600, graph.width.px = 800)
   
   # train.data <- mx.io.arrayiter(
   #           data = matrix(rnorm(100, 0, 1), ncol = 20)
   #           , label = rnorm(20, 0, 1)
   #           , batch.size = 20
   #           , shuffle = F
   #                  )
   
   train.x <- array(
                  t(matrix(rnorm(100, 0, 1), nrow = 1))
                  , dim = c(5, 20)
   )
   
   train.y <- matrix(rnorm(20, 0, 1), nrow = 1)
   
   nn_model <- mx.model.FeedForward.create(
        symbol,
        X = train.x,
        y = train.y,
        ctx = mx.cpu(),
        begin.round = 1,
        num.round = 1000,
        optimizer = "sgd",
        learning.rate = 0.01,
        initializer = mx.init.uniform(0.01),
        eval.metric = mx.metric.mse,
        array.batch.size = 1,
        array.layout = 'colmajor'
   )
   ```
   
   Alexey

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