用于计算张量积的 R 向量内存已耗尽

Question

我在 RStudio（版本 3.5.2）中计算 60,000 对 28*28 矩阵的张量积，控制台显示“错误：向量内存已用尽（达到限制？）”。我认为我的 MacBook Pro 不会有这么低的容量（16GB RAM）。我尝试了mclapply 方法进行并行计算，但仍然无法正常工作。谁能给我一些见解？非常感谢！

如有必要，以下是我的代码。我无法运行最后一行。

install.packages("keras")
library(keras)
install_keras()
install_keras(method = "conda")

library(keras)
mnist <- dataset_mnist()
str(mnist)

trainx <- mnist$train$x
trainy <- mnist$train$y
testx <- mnist$test$x
testy <- mnist$test$y

trainxr <- trainx
trainxg <- trainx
trainxb <- trainx
testxr <- testx
testxg <- testx
testxb <- testx

#training data
i <- 1
for(i in i:60000){
  randomr <- sample (0:255, 1)
  randomg <- sample (0:255, 1)
  randomb <- sample (0:255, 1)
  trainxr[i,,] <- (randomr/255)*(trainx[i,,]/255)
  trainxg[i,,] <- (randomg/255)*(trainx[i,,]/255)
  trainxb[i,,] <- (randomb/255)*(trainx[i,,]/255)
  i <- i+1
}

#testing data
j <- 1
for(j in j:10000){
  randomr <- sample (0:255, 1)
  randomg <- sample (0:255, 1)
  randomb <- sample (0:255, 1)
  testxr[j,,] <- (randomr/255)*(testx[j,,]/255)
  testxg[j,,] <- (randomg/255)*(testx[j,,]/255)
  testxb[j,,] <- (randomb/255)*(testx[j,,]/255)
  j <- j+1
}

#for training
k <- 1
for(k in k:60000){
  randomminus <- sample (0:255, 1)
  matrixminus <- matrix((randomminus/255):(randomminus/255), nrow = 28, ncol = 28)
  trainxr[k,,] <- trainxr[k,,] - matrixminus
  trainxr[k,,] <- abs(trainxr[k,,])
  trainxg[k,,] <- trainxg[k,,] - matrixminus
  trainxg[k,,] <- abs(trainxg[k,,])
  trainxb[k,,] <- trainxb[k,,] - matrixminus
  trainxb[k,,] <- abs(trainxb[k,,])
  k <- k+1
}

#for testing
l <- 1
for(l in l:10000){
  randomminus <- sample (0:255, 1)
  matrixminus <- matrix((randomminus/255):(randomminus/255), nrow = 28, ncol = 28)
  trainxr[l,,] <- trainxr[l,,] - matrixminus
  trainxr[l,,] <- abs(trainxr[l,,])
  trainxg[l,,] <- trainxg[l,,] - matrixminus
  trainxg[l,,] <- abs(trainxg[l,,])
  trainxb[l,,] <- trainxb[l,,] - matrixminus
  trainxb[l,,] <- abs(trainxb[l,,])
  l <- l+1
}

#tensor product
stepone <- matrix(1:1, nrow=21952, ncol=28)
steptwo <- matrix(1:1, nrow=28, ncol=28)
trainxtensor_a <- trainxr %x% trainxg

Answer 1

我猜您打算折叠该张量积中的第 2 维和第 3 维。也许你想要这样的东西：

library(tensor)
trainxtensor_a <- tensor(trainxr, trainxg, c(2,3), c(2,3))

虽然您应该先尝试使用较小的数据集来检查它是否符合您的预期：

trainxtensor_a <- tensor(trainxr[1:5,,], trainxg[1:5,,], c(2,3), c(2,3))