anchor.diff:              "https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz";
               "https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz"]
   
  -let trainSet = MNIST("../data", urls=urls, train=true, transform=id, n=200)
  +let trainSet = MNIST("../data", urls=urls, train=true, n=1000)
   let trainLoader = trainSet.loader(batchSize=batchSize, shuffle=true)
  -let validSet = MNIST("../data", urls=urls, train=false, transform=id, n=10)
  +let validSet = MNIST("../data", urls=urls, train=false, n=10)
   let validLoader = validSet.loader(batchSize=batchSize, shuffle=false)
   
   
  
positive.code:     let mutable validLoss = dsharp.zero()
      let mutable correct = 0
      for j, data, target in validLoader.epoch() do
          print j
          let output = data --> classifier
          validLoss <- validLoss + dsharp.nllLoss(output, target, reduction="sum")
          let pred = output.argmax(1)
          correct <- correct + int (output.eq(target).sum())
          print correct
      validLoss <- validLoss / validSet.length
      printfn "Validation loss: %A" (float validLoss)
  
negative.code:     --> Linear(128, 10)
      --> dsharp.logsoftmax(dim=1)
  
  let epochs = 20
  let batchSize = 64
  let numSamples = 32
  
  