static void Main(string[] args)
{
string net = args[0];
string pn1 = args[1];
string pn2 = args[2];
int equal = 0;
int unequal = 0;
Options.InitializeNNAnalysis();
NeuralNet nn = MNIST.GetNN(net);
Console.WriteLine("Initialized network");
Bitmap x1 = new Bitmap(pn1);
Bitmap x2 = new Bitmap(pn2);
int[] dat1 = UDraw.FromBitmap(x1, MNIST.InputCoordinates.RowCount, MNIST.InputCoordinates.ColumnCount, false);
int[] dat2 = UDraw.FromBitmap(x2, MNIST.InputCoordinates.RowCount, MNIST.InputCoordinates.ColumnCount, false);
int lab1 = NNAnalysis.Utils.ULabel.Label(nn, UArray.ToDoubleArray(dat1), true);
int lab2 = NNAnalysis.Utils.ULabel.Label(nn, UArray.ToDoubleArray(dat2), true);
if (lab1 == lab2)
{
equal++;
}
else
{
unequal++;
}
Console.Write("Label for {0} is: {1}-{2}", pn1, lab1, lab2);
Console.WriteLine(", equals = {0}", (lab1 == lab2));
//Console.WriteLine("Label for {0} is: {1}", pn2, lab2);
}
static void Main(string[] args)
{
string MNISTFile = null;
string MNISTData = null;
string MNISTLabels = null;
var p = new OptionSet();
bool just_accuracy = false;
bool just_loss = false;
p.Add("nnet=", "MNIST neural network file name", x => MNISTFile = x);
p.Add("datafile=", "MNIST data file name", x => MNISTData = x);
p.Add("labelfile=", "MNIST label file name", x => MNISTLabels = x);
p.Add <bool>("optimization=", "Do optimization (Default: true)", (x => RobustnessOptions.DoOptimization = x));
p.Add <double>("bound=", "Linfinity-ball to search", (x => RobustnessOptions.Epsilon = x));
p.Add <double>("sub=", "Subsample from 'live' constraints (0.0-1.0)", (x => RobustnessOptions.LiveConstraintSamplingRatio = x));
p.Add <string>("registry=", "Unique name to store output examples and statistics", (x => RobustnessOptions.Registry = x));
p.Add <bool>("cegar=", "Do CEGAR (default: true)", (x => RobustnessOptions.CEGAR = x));
p.Add <string>("only-accuracy", "Only evaluate accuracy", (x => just_accuracy = (x != null)));
p.Add <string>("only-loss", "Only evaluate loss", (x => just_loss = (x != null)));
p.Add <string>("no-quant-safety", "Quantization integrality safety off", (x => RobustnessOptions.QuantizationSafety = (x == null)));
p.Add <string>("max-conf", "Use max-conf objective", (x => {
if (x != null)
{
RobustnessOptions.ObjectiveKind = LPSObjectiveKind.MaxConf;
}
}));
p.Add <double>("winner-diff=", "Winning label should be that much different than second best", (x => RobustnessOptions.LabelConfidenceDiff = x));
p.Add <string>("log-png", "Log png files", (x => RobustnessOptions.SavePNGCounterexamples = (x != null)));
bool only_misclass = false;
p.Add("only-filter-misclass", "Only keep the misclassifications", (x => only_misclass = (x != null)));
Cmd.RunOptionSet(p, args);
if (MNISTFile == null || MNISTData == null || MNISTLabels == null)
{
Console.WriteLine("Invalid arguments, use --help");
Environment.Exit(1);
}
RobustnessOptions.Dump();
Options.InitializeNNAnalysis();
NeuralNet nn = MNIST.GetNN(MNISTFile);
ImageDataset data = MNIST.ReadData(MNISTLabels, MNISTData, MNIST.ALL_IMAGES, 0);
if (just_accuracy)
{
NNAccuracy.GetAccuracy(nn, data.Dataset);
return;
}
if (just_loss)
{
NNAccuracy.GetLoss(nn, data.Dataset);
return;
}
if (only_misclass)
{
string filtered = RobustnessOptions.Registry + "-misclass";
Console.WriteLine("Orig {0} data", data.Dataset.Count());
var ds = NNAccuracy.KeepMisclass(nn, data.Dataset);
Console.WriteLine("Kept {0} data", ds.Count());
ImageDataset ret = new ImageDataset(ds,
MNIST.InputCoordinates.ChannelCount,
MNIST.InputCoordinates.RowCount,
MNIST.InputCoordinates.ColumnCount, true);
MNIST.WriteData(filtered + "-labels", filtered + "-images", ret);
return;
}
// NB: No snapshotting for MNIST since it never crashes ...
ImageDataset synth = Robustness.SynthesizeCounterexamplesAndStore(nn, data, x => { return; });
MNIST.WriteData(RobustnessOptions.Registry + "-synth-labels",
RobustnessOptions.Registry + "-synth-images", synth);
}