diyang commented on issue #10805: SKIP RNN is incorrect in LSTnet URL: https://github.com/apache/incubator-mxnet/issues/10805#issuecomment-386563096 I have used MxNet R to implement SKIP RNN You may find it in this function. https://github.com/diyang/deeplearning.mxnet/blob/master/LSTnet/src/lstnet_model.R I used queue to contain the hidden states of 24 hours, then I will pop the queue head, and then push the newly yielded hidden state of current hour into the queue tail. ```R rnn.skip.unroll<-function(data, num.rnn.layer=1, seq.len, num.hidden, seasonal.period, dropout=0, config="gru") { param.cells <- list() last.states <- list() for( i in 1:num.rnn.layer){ if(config == "gru"){ param.cells[[i]] <- list(gates.i2h.weight = mx.symbol.Variable(paste0("l", i, ".gates.i2h.weight")), gates.i2h.bias = mx.symbol.Variable(paste0("l", i, ".gates.i2h.bias")), gates.h2h.weight = mx.symbol.Variable(paste0("l", i, ".gates.h2h.weight")), gates.h2h.bias = mx.symbol.Variable(paste0("l", i, ".gates.h2h.bias")), trans.i2h.weight = mx.symbol.Variable(paste0("l", i, ".trans.i2h.weight")), trans.i2h.bias = mx.symbol.Variable(paste0("l", i, ".trans.i2h.bias")), trans.h2h.weight = mx.symbol.Variable(paste0("l", i, ".trans.h2h.weight")), trans.h2h.bias = mx.symbol.Variable(paste0("l", i, ".trans.h2h.bias"))) state <- list(h=mx.symbol.Variable(paste0("l", i, ".gru.init.h"))) }else{ param.cells[[i]] <- list(i2h.weight = mx.symbol.Variable(paste0("l", i, ".i2h.weight")), i2h.bias = mx.symbol.Variable(paste0("l", i, ".i2h.bias")), h2h.weight = mx.symbol.Variable(paste0("l", i, ".h2h.weight")), h2h.bias = mx.symbol.Variable(paste0("l", i, ".h2h.bias"))) state <- list(c=mx.symbol.Variable(paste0("l", i, ".lstm.init.c")), h=mx.symbol.Variable(paste0("l", i, ".lstm.init.h"))) } last.states[[i]] <- state } data_seq_slice = mx.symbol.SliceChannel(data=data, num_outputs=seq.len, axis=2, squeeze_axis=1) last.hidden <- list() #it's a queue seasonal.states <- list() for (seqidx in 1:seq.len){ hidden <- data_seq_slice[[seqidx]] # stack lstm if(seqidx <= seasonal.period){ for (i in 1:num.rnn.layer){ dropout <- ifelse(i==1, 0, dropout) prev.state <- last.states[[i]] if(config == "gru"){ next.state <- gru.cell(num.hidden, indata = hidden, prev.state = prev.state, param = param.cells[[i]], seqidx = seqidx, layeridx = i, dropout = dropout) }else{ next.state <- lstm.cell(num.hidden, indata = hidden, prev.state = prev.state, param = param.cells[[i]], seqidx = seqidx, layeridx = i, dropout = dropout) } hidden <- next.state$h last.states[[i]] <- next.state } seasonal.states <- c(seasonal.states, last.states) }else{ for (i in 1:num.rnn.layer){ dropout <- ifelse(i==1, 0, dropout) prev.state <- seasonal.states[[1]] seasonal.states <- seasonal.states[-1] if(config == "gru"){ next.state <- gru.cell(num.hidden, indata = hidden, prev.state = prev.state, param = param.cells[[i]], seqidx = seqidx, layeridx = i, dropout = dropout) }else{ next.state <- lstm.cell(num.hidden, indata = hidden, prev.state = prev.state, param = param.cells[[i]], seqidx = seqidx, layeridx = i, dropout = dropout) } hidden <- next.state$h last.states[[i]] <- next.state } seasonal.states <- c(seasonal.states, last.states) } # Aggeregate outputs from each timestep last.hidden <- c(last.hidden, hidden) } list.all <- list(outputs = last.hidden, last.states = last.states) return(list.all) } ```
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