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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