R/mxnet中lstm递归神经网络数据的正确格式化

我想使用R包mxnet中的mx.lstm函数来训练一个lstm神经网络。我的数据包括n个特征向量、一个带标签类的向量和一个时间向量，很像这个虚拟示例，其中X1、X2、X3是特征：

dat <- data.frame(
  X1 = rnorm(100, 1, sd = 1),
  X2 = rnorm(100, 2, sd = 1),
  X3 = rnorm(100, 3, sd = 1),
  class = sample(c(1,0), replace = T, 100),
  time =  seq(0.01,1,0.01))

---

dat我试图重现您的错误，得到了一条更详细的消息：

mx.io.internal.arrayItem（as.array（数据）、as.array（标签）、unif.rnds）中出错：
io.cc:50:X，y是以行主方式传递的，MXNetR采用列主约定。
请换成X的转置

我通过将数据和标签数组传递给aperm（）修复了错误

trainDat
library(mxnet)

# Convert dummy data into suitable format
trainDat <- list(data = array(c(dat$X1, dat$X2, dat$X3), dim = c(100,3)), 
label = array(dat[,4], dim = c(100,1)))

# Set the basic network parameters for the lstm (arbitrary for this example)
batch.size = 32
seq.len = 32
num.hidden = 16
num.embed = 16
num.lstm.layer = 1
num.round = 1
learning.rate = 0.1
wd = 0.00001
clip_gradient = 1
update.period = 1

# Run the model
model <- mx.lstm(train.data = trainDat,
             ctx=mx.cpu(),
             num.round=num.round, 
             update.period=update.period,
             num.lstm.layer=num.lstm.layer, 
             seq.len=seq.len,
             num.hidden=num.hidden, 
             num.embed=num.embed, 
             num.label=vocab,
             batch.size=batch.size, 
             input.size=vocab,
             initializer=mx.init.uniform(0.1), 
             learning.rate=learning.rate,
             wd=wd,
             clip_gradient=clip_gradient)

trainDat <- list(data = aperm(array(c(dat$X1, dat$X2, dat$X3), dim = c(100,3))), label = aperm(array(dat[,4], dim = c(100,1))))