SeedRandom[0];
n = 10000;
X = RandomReal[{-1, 1}, {n, 100, 5}];
Y = RandomInteger[{0, 1}, n];

SeedRandom[0];
netT = NetTrain[
  net,
  X -> Y,
  All,
  LossFunction -> CrossEntropyLossLayer["Index"],
  BatchSize -> 64, MaxTrainingRounds -> 10, TargetDevice -> "GPU"
  ]

SeedRandom[0];
netT = NetTrain[
  net,
  X -> Y,
  All,
  LossFunction -> CrossEntropyLossLayer["Index"],
  BatchSize -> 64, MaxTrainingRounds -> 10, TargetDevice -> "GPU"
  ]

cm = ClassifierMeasurements[netT["TrainedNet"], X -> Y]

cm["ConfusionMatrixPlot"]

cm["Precision"]

cm["Recall"]

proba = netT["TrainedNet"][X, "Probabilities"];
proba0 = Lookup[proba, 0];
proba1 = Lookup[proba, 1];

Table[
  {
    Select[Pick[proba0, Y, 0], # >= i &] // Length,
    Select[Pick[proba0, Y, 1], # >= i &] // Length
    } // {i, ##, N[#[[1]]/(#[[1]] + #[[2]])]} &,
  {i, 0.5, 0.7, 0.01}
  ] // MatrixForm

Table[
  {
    Select[Pick[proba0, Y, 0], # >= i &] // Length,
    Select[Pick[proba0, Y, 1], # >= i &] // Length
    } // {i, ##, N[#[[1]]/(#[[1]] + #[[2]])]} &,
  {i, 0.5, 0.7, 0.01}
  ] // MatrixForm

PairedHistogram[Pick[proba1, Y, 1], Pick[proba1, Y, 0]]

Table[
  {
    Select[Pick[proba1, Y, 1], # >= i &] // Length,
    Select[Pick[proba1, Y, 0], # >= i &] // Length
    } // {i, ##, N[#[[1]]/(#[[1]] + #[[2]])]} &,
  {i, 0.5, 0.7, 0.01}
  ] // MatrixForm

EV[p_] := p*80 - (1 - p)*100 - 4

Plot[
 {
   Select[Pick[proba1, Y, 1], # >= x &] // Length,
   Select[Pick[proba1, Y, 0], # >= x &] // Length
   } // EV[#[[1]]/(#[[1]] + #[[2]])]*(#[[1]] + #[[2]]) &,
 {x, 0.5, 0.7}
 ]

Plot[
 {
   Select[Pick[proba1, Y, 1], # >= x &] // Length,
   Select[Pick[proba1, Y, 0], # >= x &] // Length
   } // EV[#[[1]]/(#[[1]] + #[[2]])]*(#[[1]] + #[[2]]) &,
 {x, 0.5, 0.7}
 ]