public static Tuple <INetwork, IFactory> SmallLoLa(string FileName, bool Encrypt) { Console.WriteLine("Small LoLa mode"); Console.Write("Generating keys in "); var start = DateTime.Now; var Factory = (Encrypt) ? (IFactory) new EncryptedSealBfvFactory(new ulong[] { 2277377, 2424833 }, 8192, DecompositionBitCount: 40, GaloisDecompositionBitCount: 40, SmallModulusCount: 3) : new RawFactory(8192); var end = DateTime.Now; Console.WriteLine("{0} seconds", (end - start).TotalSeconds); int weightscale = 64; // with weightscale of 64 the accuracy is 96.92% and the maximal value is 534491448976 var readerLayer = new LLConvReader() { FileName = FileName, SparseFormat = true, NormalizationFactor = 1.0 / 256.0, Scale = 16.0, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, }; var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = Factory }; var ConvLayer1 = new LLPoolLayer() { Source = encryptLayer, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, MapCount = new int[] { 5, 1 }, WeightsScale = weightscale, Weights = SmallModel.Weights_0 }; var VectorizeLayer2 = new LLVectorizeLayer() { Source = ConvLayer1 }; var ActivationLayer3 = new SquareActivation() { Source = VectorizeLayer2 }; var DenseLayer4 = new LLDenseLayer() { Source = ActivationLayer3, Bias = SmallModel.Biases_1, Weights = SmallModel.Weights_1, WeightsScale = weightscale, InputFormat = EVectorFormat.dense }; return(new Tuple <INetwork, IFactory>(DenseLayer4, Factory)); }
public void CalPrediction() { var FileName = "cal_deep_test.tsv"; var ini = new IniReader(@"cal.model.ini", 4096, 102); double weightscale = 1e+6; var readerLayer = new LLSingleLineReader() { FileName = FileName, SparseFormat = true, NormalizationFactor = 1.0, Scale = 1E+10, }; var debugLayer = new DebugLayer() { Source = readerLayer }; var encryptLayer = new EncryptLayer() { Source = debugLayer }; var denseLayer = new LLDenseLayer() { Source = encryptLayer, Weights = ini.Weights, Bias = ini.Bias, WeightsScale = weightscale, InputFormat = EVectorFormat.dense }; var network = denseLayer; network.PrepareNetwork(); var pred = network.GetNext().GetColumn(0).Decrypt(null); Assert.AreEqual(102, pred.Count); for (int i = 0; i < 10; i++) { Assert.AreEqual(debugLayer.scores[i], pred[i], 1e-3); } }
public static Tuple<INetwork, IFactory> LargeLoLa(string FileName, bool Encrypt) { Console.WriteLine("Large LoLa mode"); WeightsReader wr = new WeightsReader("MnistLargeWeight.csv", "MnistLargeBias.csv"); Console.Write("Generating keys in "); var start = DateTime.Now; var Factory = Encrypt ? (IFactory)new EncryptedSealBfvFactory(new ulong[] { 2148728833, 2148794369, 2149810177 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 7) : new RawFactory(16384); var end = DateTime.Now; Console.WriteLine("{0} seconds", (end - start).TotalSeconds); var readerLayer = new LLConvReader { FileName = FileName, SparseFormat = true, InputShape = new int[] { 1, 28, 28 }, KernelShape = new int[] { 1, 8, 8 }, Upperpadding = new int[] { 0, 1, 1 }, Lowerpadding = new int[] { 0, 1, 1 }, Stride = new int[] { 1000, 2, 2 }, NormalizationFactor = 1.0, Scale = 16.0 }; var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = Factory }; var convLayer1 = new LLPoolLayer() { Source = encryptLayer, InputShape = new int[] { 1, 28, 28 }, KernelShape = new int[] { 1, 8, 8 }, Upperpadding = new int[] { 0, 1, 1 }, Lowerpadding = new int[] { 0, 1, 1 }, Stride = new int[] { 1000, 2, 2 }, MapCount = new int[] { 83, 1, 1 }, WeightsScale = 4096, Weights = ((double[])wr.Weights[0]).Select(x => x/256).ToArray(), Bias = (double[])wr.Biases[0] }; var VectorizeLayer2 = new LLVectorizeLayer() { Source = convLayer1 }; //var activationLayer3 = new SquareActivation() { Source = VectorizeLayer2 }; var activationLayer3 = new AppxReLUActivation() { Source = VectorizeLayer2 }; //var activationLayer3 = new LeakyReLUActivation() { Source = VectorizeLayer2 }; //var activationLayer3 = new ReLUActivation() { Source = VectorizeLayer2 }; var convEngine = new ConvolutionEngine() { InputShape = new int[] { 83, 12, 12 }, KernelShape = new int[] { 83, 6, 6 }, Padding = new bool[] { false, false, false }, Stride = new int[] { 83, 2, 2 }, MapCount = new int[] { 163, 1, 1 } }; var denseLayer4 = new LLDenseLayer { Source = activationLayer3, WeightsScale = 64, Weights = convEngine.GetDenseWeights((double[])wr.Weights[1]), Bias = convEngine.GetDenseBias((double[])wr.Biases[1]), InputFormat = EVectorFormat.dense, ForceDenseFormat = true }; //var activationLayer5 = new SquareActivation() { Source = denseLayer4 }; var activationLayer5 = new AppxReLUActivation() { Source = denseLayer4 }; //var activationLayer5 = new LeakyReLUActivation() { Source = denseLayer4 }; //var activationLayer5 = new ReLUActivation() { Source = denseLayer4 }; var denseLayer6 = new LLDenseLayer() { Source = activationLayer5, Weights = (double[])wr.Weights[2], Bias = (double[])wr.Biases[2], WeightsScale = 512, InputFormat = EVectorFormat.dense }; return new Tuple<INetwork, IFactory>(denseLayer6, Factory); }
public static Tuple<INetwork, IFactory> LoLa(string FileName, bool Encrypt) { Console.WriteLine("LoLa mode"); Console.Write("Generating keys in "); var start = DateTime.Now; var Factory = Encrypt ? (IFactory)new EncryptedSealBfvFactory(new ulong[] { 557057, 638977, 737281, 786433 }, 8192) : new RawFactory(8192); var end = DateTime.Now; Console.WriteLine("{0} seconds", (end - start).TotalSeconds); int weightscale = 32; var readerLayer = new LLConvReader() { FileName = FileName, SparseFormat = true, NormalizationFactor = 1.0 / 256.0, Scale = 16.0, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, }; var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = Factory }; var ConvLayer1 = new LLPoolLayer() { Source = encryptLayer, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, MapCount = new int[] { 5, 1 }, WeightsScale = weightscale, Weights = Weights.Weights_0 }; var VectorizeLayer2 = new LLVectorizeLayer() { Source = ConvLayer1 }; //var ActivationLayer3 = new SquareActivation() { Source = VectorizeLayer2 }; var ActivationLayer3 = new AppxReLUActivation() { Source = VectorizeLayer2 }; //var ActivationLayer3 = new LeakyReLUActivation() { Source = VectorizeLayer2 }; //var ActivationLayer3 = new ReLUActivation() { Source = VectorizeLayer2 }; var DuplicateLayer4 = new LLDuplicateLayer() { Source = ActivationLayer3, Count = 8 }; var DenseLayer5 = new LLPackedDenseLayer() { Source = DuplicateLayer4, Weights = Transpose(Weights.Weights_1, 5 * 13 * 13, 100), Bias = Weights.Biases_2, WeightsScale = weightscale * weightscale, PackingCount = DuplicateLayer4.Count, PackingShift = 1024 }; var InterleaveLayer6 = new LLInterleaveLayer() { Source = DenseLayer5, Shift = -1, SelectedIndices = Enumerable.Range(0, (int)DuplicateLayer4.Count).Select(i => 1023 + i * 1024).ToList() }; //var ActivationLayer7 = new SquareActivation() { Source = InterleaveLayer6 }; var ActivationLayer7 = new AppxReLUActivation() { Source = InterleaveLayer6 }; //var ActivationLayer7 = new LeakyReLUActivation() { Source = InterleaveLayer6 }; //var ActivationLayer7 = new ReLUActivation() { Source = InterleaveLayer6 }; var DenseLayer8 = new LLInterleavedDenseLayer() { Source = ActivationLayer7, Weights = Weights.Weights_3, Bias = Weights.Biases_3, WeightsScale = weightscale, Shift = -1, SelectedIndices = Enumerable.Range(0, (int)DuplicateLayer4.Count).Select(i => 1023 + i * 1024).ToList() }; return new Tuple<INetwork, IFactory>(DenseLayer8, Factory); }
public static Tuple<INetwork, IFactory> LoLaDense(string FileName, bool Encrypt) { Console.WriteLine("LoLa-Dense mode"); Console.Write("Generating keys in "); var start = DateTime.Now; var Factory = (Encrypt) ? (IFactory)new EncryptedSealBfvFactory(new ulong[] { 34359771137, 34360754177 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 7) : (IFactory)new RawFactory(16384); var end = DateTime.Now; Console.WriteLine("{0} seconds", (end - start).TotalSeconds); int weightscale = 32; var readerLayer = new LLSingleLineReader() { FileName = FileName, SparseFormat = true, NormalizationFactor = 1.0 / 256.0, Scale = 16.0, }; var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = Factory }; var preConvLayer1 = new LLPreConvLayer() { Source = encryptLayer, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, UseAxisForBlocks = new bool[] { true, true } }; var ConvLayer2 = new LLPoolLayer() { Source = preConvLayer1, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, MapCount = new int[] { 5, 1 }, WeightsScale = weightscale, Weights = Weights.Weights_0, HotIndices = preConvLayer1.HotIndices }; var VectorizeLayer3 = new LLVectorizeLayer() { Source = ConvLayer2 }; //var ActivationLayer4 = new SquareActivation() { Source = VectorizeLayer3 }; var ActivationLayer4 = new AppxReLUActivation() { Source = VectorizeLayer3 }; //var ActivationLayer4 = new LeakyReLUActivation() { Source = VectorizeLayer3 }; //var ActivationLayer4 = new ReLUActivation() { Source = VectorizeLayer3 }; var DuplicateLayer5 = new LLDuplicateLayer() { Source = ActivationLayer4, Count = 16}; var DenseLayer6 = new LLPackedDenseLayer() { Source = DuplicateLayer5, Weights = preConvLayer1.RearrangeWeights(Transpose(Weights.Weights_1, 5 * 13 * 13, 100)), Bias = Weights.Biases_2, WeightsScale = weightscale * weightscale, PackingCount = DuplicateLayer5.Count, PackingShift = 1024, }; //var ActivationLayer7 = new SquareActivation() { Source = DenseLayer6 }; var ActivationLayer7 = new AppxReLUActivation() { Source = DenseLayer6 }; //var ActivationLayer7 = new LeakyReLUActivation() { Source = DenseLayer6 }; //var ActivationLayer7 = new ReLUActivation() { Source = DenseLayer6 }; var InterleaveLayer8 = new LLInterleaveLayer() { Source = ActivationLayer7, Shift = -1, SelectedIndices = Enumerable.Range(0, (int)DuplicateLayer5.Count).Select(i => 1023 + i * 1024).ToList() }; var DenseLayer8 = new LLInterleavedDenseLayer() { Source = InterleaveLayer8, Weights = Weights.Weights_3, Bias = Weights.Biases_3, WeightsScale = weightscale, Shift = -1, SelectedIndices = Enumerable.Range(0, (int)DuplicateLayer5.Count).Select(i => 1023 + i * 1024).ToList() }; var network = DenseLayer8; return new Tuple<INetwork, IFactory>(network, Factory); }
static void Main(string[] args) { string fileName = "MNIST-28x28-test.txt"; int batchSize = 8192; int numberOfRecords = 10000; var Factory = new EncryptedSealBfvFactory(new ulong[] { 2148728833, 2148794369, 2149810177 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 7); int weightscale = 32; var ReaderLayer = new BatchReader { FileName = fileName, SparseFormat = true, MaxSlots = batchSize, NormalizationFactor = 1.0 / 256.0, Scale = 16.0 }; var EncryptedLayer = new EncryptLayer() { Source = ReaderLayer, Factory = Factory }; var StartTimingLayer = new TimingLayer() { Source = EncryptedLayer, StartCounters = new string[] { "Batch-Time" } }; var ConvLayer1 = new PoolLayer() { Source = StartTimingLayer, InputShape = new int[] { 28, 28 }, KernelShape = new int[] { 5, 5 }, Upperpadding = new int[] { 1, 1 }, Stride = new int[] { 2, 2 }, MapCount = new int[] { 5, 1 }, WeightsScale = weightscale, Weights = Weights.Weights_0 }; //var ActivationLayer2 = new SquareActivation() { Source = ConvLayer1 }; var ActivationLayer2 = new AppxReLUActivation() { Source = ConvLayer1 }; var DenseLayer3 = new PoolLayer() { Source = ActivationLayer2, InputShape = new int[] { 5 * 13 * 13 }, KernelShape = new int[] { 5 * 13 * 13 }, Stride = new int[] { 1000 }, MapCount = new int[] { 100 }, Weights = Transpose(Weights.Weights_1, 5 * 13 * 13, 100), Bias = Weights.Biases_2, WeightsScale = weightscale * weightscale }; //var ActivationLayer4 = new SquareActivation() { Source = DenseLayer3 }; var ActivationLayer4 = new AppxReLUActivation() { Source = DenseLayer3 }; var DenseLayer5 = new PoolLayer() { Source = ActivationLayer4, InputShape = new int[] { 100 }, KernelShape = new int[] { 100 }, Stride = new int[] { 1000 }, MapCount = new int[] { 10 }, Weights = Weights.Weights_3, Bias = Weights.Biases_3, WeightsScale = weightscale }; var StopTimingLayer = new TimingLayer() { Source = DenseLayer5, StopCounters = new string[] { "Batch-Time" } }; var network = StopTimingLayer; OperationsCount.Reset(); Console.WriteLine("Preparing"); network.PrepareNetwork(); OperationsCount.Print(); OperationsCount.Reset(); for (var p = (INetwork)network; p != null; p = p.Source) { if (p is BaseLayer b) { b.Verbose = true; } } int count = 0; int errs = 0; while (count < numberOfRecords) { using (var m = network.GetNext()) Utils.ProcessInEnv(env => { var decrypted = m.Decrypt(env); for (int i = 0; i < decrypted.RowCount; i++) { int pred = 0; for (int j = 1; j < decrypted.ColumnCount; j++) { if (decrypted[i, j] > decrypted[i, pred]) { pred = j; } } if (pred != ReaderLayer.Labels[i]) { errs++; } count++; if (count % 100 == 0) { Console.WriteLine("errs {0}/{1} accuracy {2:0.000}% prediction {3} label {4}", errs, count, 100 - (100.0 * errs / (count)), pred, ReaderLayer.Labels[i]); } } Console.WriteLine("Batch size {0} {1}", batchSize, TimingLayer.GetStats()); }, Factory); } Console.WriteLine("errs {0}/{1} accuracy {2:0.000}%", errs, count, 100 - (100.0 * errs / (count))); network.DisposeNetwork(); }
public static void Main(string[] args) { WeightsReader wr = new WeightsReader("CifarWeight.csv", "CifarBias.csv"); Console.WriteLine("Generating encryption keys {0}", DateTime.Now); var factory = new EncryptedSealBfvFactory(new ulong[] { 2148728833, 2148794369, 2149810177 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 7); Console.WriteLine("Encryption keys ready {0}", DateTime.Now); string fileName = "cifar-test.tsv"; var readerLayer = new LLConvReader { FileName = fileName, SparseFormat = false, InputShape = new int[] { 3, 32, 32 }, KernelShape = new int[] { 3, 8, 8 }, Upperpadding = new int[] { 0, 1, 1 }, Lowerpadding = new int[] { 0, 1, 1 }, Stride = new int[] { 1000, 2, 2 }, NormalizationFactor = 1.0, Scale = 128.0 }; var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = factory }; var convLayer1 = new LLPoolLayer() { Source = encryptLayer, InputShape = new int[] { 3, 32, 32 }, KernelShape = new int[] { 3, 8, 8 }, Upperpadding = new int[] { 0, 1, 1 }, Lowerpadding = new int[] { 0, 1, 1 }, Stride = new int[] { 1000, 2, 2 }, MapCount = new int[] { 83, 1, 1 }, WeightsScale = 256.0, Weights = (double[])wr.Weights[0], Bias = (double[])wr.Biases[0] }; var VectorizeLayer2 = new LLVectorizeLayer() { Source = convLayer1 }; var activationLayer3 = new SquareActivation() { Source = VectorizeLayer2 }; var convEngine = new ConvolutionEngine() { InputShape = new int[] { 83, 14, 14 }, KernelShape = new int[] { 83, 6, 6 }, Padding = new bool[] { false, false, false }, Stride = new int[] { 83, 2, 2 }, MapCount = new int[] { 163, 1, 1 } }; var denseLayer4 = new LLDenseLayer { Source = activationLayer3, WeightsScale = 512.0, Weights = convEngine.GetDenseWeights((double[])wr.Weights[1]), Bias = convEngine.GetDenseBias((double[])wr.Biases[1]), InputFormat = EVectorFormat.dense, ForceDenseFormat = true }; var activationLayer5 = new SquareActivation() { Source = denseLayer4 }; var denseLayer6 = new LLDenseLayer() { Source = activationLayer5, Weights = (double[])wr.Weights[2], Bias = (double[])wr.Biases[2], WeightsScale = 1024.0, InputFormat = EVectorFormat.dense }; var network = denseLayer6; Console.WriteLine("Preparing"); // Visualize layer construction for (var p = (INetwork)network; p != null; p = p.Source) { if (p is BaseLayer b) { b.Verbose = true; } } network.PrepareNetwork(); var m = network.GetNext(); Utils.Show(m, factory, readerLayer.Labels); Console.WriteLine("Max computed value {0} ({1})", RawMatrix.Max, Math.Log(RawMatrix.Max) / Math.Log(2)); }
public static void Main(string[] args) { var options = new Options(); var parsed = Parser.Default.ParseArguments <Options>(args).WithParsed(x => options = x); if (parsed.Tag == ParserResultType.NotParsed) { Environment.Exit(-2); } WeightsReader wr = new WeightsReader("CifarWeight.csv", "CifarBias.csv"); // Model has accuracy of 76.5 // Current parameters (scale) provide accuracy of 76.31% and uses 78.55 + 1 bits in message length // It has a latency of 740 seconds on the reference machine (Azure B8ms server at rest) Console.WriteLine("Generating encryption keys {0}", DateTime.Now); IFactory factory = null; if (options.Encrypt) { factory = new EncryptedSealBfvFactory(new ulong[] { 957181001729, 957181034497 }, 16384, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 8); } else { factory = new RawFactory(16 * 1024); } Console.WriteLine("Encryption keys ready {0}", DateTime.Now); int numberOfRecords = 10000; bool verbose = options.Verbose; string fileName = "cifar-test.tsv"; var readerLayer = new LLConvReader { FileName = fileName, SparseFormat = false, InputShape = new int[] { 3, 32, 32 }, KernelShape = new int[] { 3, 8, 8 }, Upperpadding = new int[] { 0, 1, 1 }, Lowerpadding = new int[] { 0, 1, 1 }, Stride = new int[] { 1000, 2, 2 }, NormalizationFactor = 1.0 / 256.0, Scale = 8, Verbose = verbose }; var EncryptLayer = new EncryptLayer() { Source = readerLayer, Factory = factory }; var StartTimingLayer = new TimingLayer() { Source = EncryptLayer, StartCounters = new string[] { "Inference-Time" } }; var ConvLayer1 = new LLPoolLayer() { Source = StartTimingLayer, InputShape = new int[] { 3, 32, 32 }, KernelShape = new int[] { 3, 8, 8 }, Upperpadding = new int[] { 0, 1, 1 }, Lowerpadding = new int[] { 0, 1, 1 }, Stride = new int[] { 1000, 2, 2 }, MapCount = new int[] { 83, 1, 1 }, WeightsScale = 256.0, Weights = (double[])wr.Weights[0], Bias = (double[])wr.Biases[0], Verbose = verbose }; var VectorizeLayer2 = new LLVectorizeLayer() { Source = ConvLayer1, Verbose = verbose }; var ActivationLayer3 = new SquareActivation() { Source = VectorizeLayer2, Verbose = verbose }; var ConvEngine = new ConvolutionEngine() { InputShape = new int[] { 83, 14, 14 }, KernelShape = new int[] { 83, 10, 10 }, Upperpadding = new int[] { 0, 4, 4 }, Lowerpadding = new int[] { 0, 4, 4 }, Stride = new int[] { 83, 2, 2 }, MapCount = new int[] { 112, 1, 1 } }; var DenseLayer4 = new LLDenseLayer { Source = ActivationLayer3, WeightsScale = 512.0, Weights = ConvEngine.GetDenseWeights((double[])wr.Weights[1]), Bias = ConvEngine.GetDenseBias((double[])wr.Biases[1]), InputFormat = EVectorFormat.dense, ForceDenseFormat = true, Verbose = verbose }; var ActivationLayer5 = new SquareActivation() { Source = DenseLayer4, Verbose = verbose }; var DenseLayer6 = new LLDenseLayer() { Source = ActivationLayer5, Weights = (double[])wr.Weights[2], Bias = (double[])wr.Biases[2], WeightsScale = 512.0, InputFormat = EVectorFormat.dense, Verbose = verbose }; var StopTimingLayer = new TimingLayer() { Source = DenseLayer6, StopCounters = new string[] { "Inference-Time" } }; var network = StopTimingLayer; Console.WriteLine("Preparing"); network.PrepareNetwork(); int count = 0; int errs = 0; int batchSize = 1; while (count < numberOfRecords) { using (var m = network.GetNext()) { Utils.ProcessInEnv(env => { var decrypted = m.Decrypt(env); int pred = 0; for (int j = 1; j < decrypted.RowCount; j++) { if (decrypted[j, 0] > decrypted[pred, 0]) { pred = j; } } if (pred != readerLayer.Labels[0]) { errs++; } count++; if (count % batchSize == 0) { Console.Write("errs {0}/{1} accuracy {2:0.000}% prediction {3} label {4} {5}ms", errs, count, 100 - (100.0 * errs / (count)), pred, readerLayer.Labels[0], TimingLayer.GetStats()); if (options.Encrypt) { Console.WriteLine(); } else { Console.WriteLine(" 2^{0} largest-value", Math.Log(RawMatrix.Max) / Math.Log(2)); } } }, factory); } } Console.WriteLine("errs {0}/{1} accuracy {2:0.000}%", errs, count, 100 - (100.0 * errs / (count))); network.DisposeNetwork(); if (!options.Encrypt) { Console.WriteLine("Max computed value 2^{1}", Math.Log(RawMatrix.Max) / Math.Log(2)); } }
static void Main(string[] args) { var ini = new IniReader(@"cal.model.ini", 4096, 102); ini.Normalize(@"cal.AffineNormalizer.txt"); var start = DateTime.Now; var Factory = new EncryptedSealBfvFactory(new ulong[] { 4300801 }, 4096, DecompositionBitCount: 60, GaloisDecompositionBitCount: 60, SmallModulusCount: 2); double weightscale = 256; string FileName = "cal_deep_test.tsv"; if (!File.Exists(FileName)) { Console.WriteLine("ERROR: Can't find data file {0}", FileName); Console.WriteLine("Please use DataPreprocess to obtain the Caltech-101 dataset"); return; } var readerLayer = new LLSingleLineReader() { FileName = FileName, SparseFormat = true, NormalizationFactor = 1.0, Scale = 256, }; var encryptLayer = new EncryptLayer() { Source = readerLayer, Factory = Factory }; var denseLayer = new LLDenseLayer() { Source = encryptLayer, Weights = ini.Weights, Bias = ini.Bias, WeightsScale = weightscale, InputFormat = EVectorFormat.dense }; var network = denseLayer; network.PrepareNetwork(); int errs = 0; var N = 1020; IMatrix m = null; Utils.ProcessInEnv(env => { for (int i = 0; i < N; i++) { Utils.Time("Prediction+Encryption", () => m = network.GetNext()); var dec = m.Decrypt(env); m.Dispose(); var l = readerLayer.Labels[0]; int pred = 0; for (int j = 0; j < 101; j++) { if (dec[j, 0] > dec[pred, 0]) { pred = j; } } if (pred != l) { errs++; } Console.WriteLine("errs {0}/{1} accuracy {2:0.000}% {3} prediction {4} label {5}", errs, i + 1, 100 - (100.0 * errs / (i + 1)), TimingLayer.GetStats(), pred, l); } }, Factory); network.DisposeNetwork(); }