/// <summary> /// Error: 19.1 /// </summary> public static BackpropAlgorithm CreateKaggleCatOrDogDemo_Pretrained() { Console.WriteLine("init CreateKaggleCatOrDogDemo_Pretrained"); ConvNet net; var assembly = Assembly.GetExecutingAssembly(); using (var stream = assembly.GetManifestResourceStream("ML.DeepTests.Pretrained.cn_e16_p37.65.mld")) { net = ConvNet.Deserialize(stream); net.IsTraining = true; } var lrate = 0.01D; var alg = new BackpropAlgorithm(net) { LossFunction = Loss.CrossEntropySoftMax, EpochCount = 500, LearningRate = lrate, BatchSize = 4, UseBatchParallelization = true, MaxBatchThreadCount = 8, Optimizer = Optimizer.Adadelta, Regularizator = Regularizator.L2(0.001D), LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D) }; alg.Build(); return(alg); }
public static BackpropAlgorithm CreateKaggleCatOrDogFiltersDemo1_Pretrained(string fpath) { Console.WriteLine("init CreateKaggleCatOrDogFiltersDemo1_Pretrained"); ConvNet net; var assembly = Assembly.GetExecutingAssembly(); using (var stream = System.IO.File.Open(fpath, System.IO.FileMode.Open, System.IO.FileAccess.Read)) { net = ConvNet.Deserialize(stream); net.IsTraining = true; } var lrate = 0.001D; var alg = new BackpropAlgorithm(net) { LossFunction = Loss.CrossEntropySoftMax, EpochCount = 500, LearningRate = lrate, BatchSize = 8, UseBatchParallelization = true, MaxBatchThreadCount = 8, Optimizer = Optimizer.Adadelta, Regularizator = Regularizator.L2(0.001D), LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D) }; alg.Build(); return(alg); }
public static BackpropAlgorithm CreateMainColorsDemo1() { Console.WriteLine("init CreateMainColorsDemo1"); var activation = Activation.ReLU; var net = new ConvNet(3, 48) { IsTraining = true }; net.AddLayer(new FlattenLayer(outputDim: 128, activation: activation)); net.AddLayer(new FlattenLayer(outputDim: 128, activation: activation)); net.AddLayer(new DenseLayer(outputDim: 12, activation: activation)); net._Build(); net.RandomizeParameters(seed: 0); var lrate = 1.1D; var alg = new BackpropAlgorithm(net) { EpochCount = 500, LearningRate = lrate, BatchSize = 8, UseBatchParallelization = true, MaxBatchThreadCount = 8, LossFunction = Loss.Euclidean, Optimizer = Optimizer.Adadelta, Regularizator = Regularizator.L2(0.0001D), LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D) }; alg.Build(); return(alg); }
public void Gradient_1ConvLayer_1Iter_Euclidean() { // arrange var net = new ConvNet(3, 1, 1) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 2, windowSize: 1, activation: Activation.Atan)); net._Build(); net.RandomizeParameters(seed: 0); var point1 = RandomPoint(3, 1, 1); var point2 = RandomPoint(3, 1, 1); // just for 2 dim output var sample = new ClassifiedSample <double[][, ]>(); sample[point1] = CLASSES[0]; sample[point2] = CLASSES[1]; var alg = new BackpropAlgorithm(net) { LearningRate = 0.1D, LossFunction = Loss.Euclidean }; alg.Build(); // act alg.RunIteration(point1, EXPECTED[0]); // assert AssertNetGradient(alg, point1, EXPECTED[0]); }
public void SimpleNet_Euclidean_OneIter() { // arrange var net = Mocks.SimpleLinearNetwork(); var sample = new ClassifiedSample <double[][, ]>(); var point = new double[1][, ] { new[, ] { { 1.0D } } }; sample[point] = new Class("a", 0); var alg = new BackpropAlgorithm(net); alg.LearningRate = 2.0D; alg.LossFunction = Loss.Euclidean; alg.Build(); // act alg.RunIteration(point, new double[] { 1.0D }); // assert Assert.AreEqual(12, alg.Values[0][0][0, 0]); Assert.AreEqual(33, alg.Values[1][0][0, 0]); Assert.AreEqual(-62, alg.Values[2][0][0, 0]); Assert.AreEqual(3, net[0].ActivationFunction.DerivativeFromValue(alg.Values[0][0][0, 0])); Assert.AreEqual(3, net[1].ActivationFunction.DerivativeFromValue(alg.Values[1][0][0, 0])); Assert.AreEqual(2, net[2].ActivationFunction.DerivativeFromValue(alg.Values[2][0][0, 0])); Assert.AreEqual(-126, alg.Errors[2][0][0, 0]); Assert.AreEqual(378, alg.Errors[1][0][0, 0]); Assert.AreEqual(1134, alg.Errors[0][0][0, 0]); Assert.AreEqual(-126 * 33, alg.Gradient[2][0]); Assert.AreEqual(-126, alg.Gradient[2][1]); Assert.AreEqual(378 * 12, alg.Gradient[1][0]); Assert.AreEqual(378, alg.Gradient[1][1]); Assert.AreEqual(1134 * 1, alg.Gradient[0][0]); Assert.AreEqual(1134, alg.Gradient[0][1]); alg.FlushGradient(); Assert.AreEqual(-1 + 2 * 126 * 33, net[2].Weights[0]); Assert.AreEqual(2 + 2 * 126, net[2].Weights[1]); Assert.AreEqual(1 + 2 * (-378 * 12), net[1].Weights[0]); Assert.AreEqual(-1 + 2 * (-378), net[1].Weights[1]); Assert.AreEqual(3 + 2 * (-1134 * 1), net[0].Weights[0]); Assert.AreEqual(1 + 2 * (-1134), net[0].Weights[1]); }
public void Gradient_DifferentLayers_1Iter_CrossEntropy_Regularization() { // arrange var activation = Activation.ReLU; var net = new ConvNet(1, 5) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 2, windowSize: 3, padding: 1)); net.AddLayer(new MaxPoolingLayer(windowSize: 3, stride: 2, activation: Activation.Exp)); net.AddLayer(new ActivationLayer(activation: Activation.Tanh)); net.AddLayer(new FlattenLayer(outputDim: 10, activation: activation)); net.AddLayer(new DropoutLayer(rate: 0.5D)); net.AddLayer(new DenseLayer(outputDim: 3, activation: Activation.Exp)); net._Build(); net.RandomizeParameters(seed: 0); var sample = new ClassifiedSample <double[][, ]>(); for (int i = 0; i < 3; i++) { var point = RandomPoint(1, 5, 5); sample[point] = new Class(i.ToString(), i); } var regularizator = Regularizator.Composite(Regularizator.L1(0.1D), Regularizator.L2(0.3D)); var alg = new BackpropAlgorithm(net) { LearningRate = 0.1D, LossFunction = Loss.CrossEntropySoftMax, Regularizator = regularizator }; alg.Build(); // act var data = sample.First(); var expected = new double[3] { 1.0D, 0.0D, 0.0D }; alg.RunIteration(data.Key, expected); regularizator.Apply(alg.Gradient, alg.Net.Weights); ((DropoutLayer)alg.Net[4]).ApplyCustomMask = true; // assert AssertNetGradient(alg, data.Key, expected); }
public void Gradient_MNISTSimple_1Iter() { // arrange var activation = Activation.ReLU; var net = new ConvNet(1, 14) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 4, windowSize: 5)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2, activation: activation)); net.AddLayer(new ConvLayer(outputDepth: 8, windowSize: 5)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2, activation: activation)); net.AddLayer(new FlattenLayer(outputDim: 10, activation: activation)); net._Build(); Randomize(net.Weights, -1.0D, 1.0D); var sample = new ClassifiedSample <double[][, ]>(); for (int i = 0; i < 10; i++) { var point = RandomPoint(1, 14, 14); sample[point] = new Class(i.ToString(), i); } var alg = new BackpropAlgorithm(net) { LearningRate = 0.005D, LossFunction = Loss.Euclidean }; alg.Build(); // act var data = sample.First(); var expected = new double[10] { 1.0D, 0.0D, 0.0D, 0.0D, 0.0D, 0.0D, 0.0D, 0.0D, 0.0D, 0.0D }; alg.RunIteration(data.Key, expected); // assert AssertNetGradient(alg, data.Key, expected); }
/// <summary> /// Error 21.65 /// </summary> public static BackpropAlgorithm CreateCIFAR10Trunc2ClassesDemo2_SEALED() { Console.WriteLine("init CreateCIFAR10Trunc2ClassesDemo2"); var activation = Activation.ReLU; var net = new ConvNet(3, 32) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 16, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new ConvLayer(outputDepth: 16, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 3, stride: 2)); net.AddLayer(new DropoutLayer(0.25)); net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 3, stride: 2)); net.AddLayer(new DropoutLayer(0.25)); net.AddLayer(new FlattenLayer(outputDim: 256, activation: activation)); net.AddLayer(new DropoutLayer(0.5)); net.AddLayer(new DenseLayer(outputDim: 2, activation: Activation.Exp)); net._Build(); net.RandomizeParameters(seed: 0); var lrate = 0.01D; var alg = new BackpropAlgorithm(net) { LossFunction = Loss.CrossEntropySoftMax, EpochCount = 500, LearningRate = lrate, BatchSize = 4, UseBatchParallelization = true, MaxBatchThreadCount = 8, Optimizer = Optimizer.Adadelta, Regularizator = Regularizator.L2(0.001D), LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D) }; alg.Build(); return(alg); }
// https://code.google.com/archive/p/cuda-convnet/ - CIFAR archtectures+errors /// <summary> /// Creates CNN for CIFAR-10 training (from https://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html) /// </summary> public static BackpropAlgorithm CreateCIFAR10Demo2() { Console.WriteLine("init CreateCIFAR10Demo2"); var activation = Activation.LeakyReLU(); var net = new ConvNet(3, 32) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 5, padding: 2, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2)); net.AddLayer(new ConvLayer(outputDepth: 40, windowSize: 5, padding: 2, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2)); net.AddLayer(new ConvLayer(outputDepth: 60, windowSize: 5, padding: 2, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2)); net.AddLayer(new FlattenLayer(outputDim: 1024, activation: activation)); net.AddLayer(new DropoutLayer(0.5)); net.AddLayer(new DenseLayer(outputDim: 1024, activation: activation)); net.AddLayer(new DropoutLayer(0.25)); net.AddLayer(new DenseLayer(outputDim: 10, activation: activation)); net._Build(); net.RandomizeParameters(seed: 0); var lrate = 0.05D; var alg = new BackpropAlgorithm(net) { LossFunction = Loss.Euclidean, EpochCount = 500, LearningRate = lrate, BatchSize = 8, UseBatchParallelization = true, MaxBatchThreadCount = 8, Optimizer = Optimizer.Adadelta, LearningRateScheduler = LearningRateScheduler.TimeBased(lrate, 0.005D) }; alg.Build(); return(alg); }
public void SimpleNet_CrossEntropySoftMax_OneIter() { // arrange var net = Mocks.SimpleLinearNetwork2(Activation.ReLU); net[2].ActivationFunction = Activation.Logistic(1); var sample = new ClassifiedSample <double[][, ]>(); var point1 = new double[1][, ] { new[, ] { { 1.0D } } }; var point2 = new double[1][, ] { new[, ] { { -1.0D } } }; var cls1 = new Class("a", 0); var cls2 = new Class("b", 1); sample[point1] = cls1; sample[point2] = cls2; var alg = new BackpropAlgorithm(sample, net); alg.LearningRate = 2.0D; alg.LossFunction = Loss.CrossEntropySoftMax; alg.Build(); // act alg.RunIteration(point1, cls1); // assert AssertNetGradient(alg, point1, 2, 1); AssertNetGradient(alg, point1, 1, 0); AssertNetGradient(alg, point1, 1, 1); AssertNetGradient(alg, point1, 0, 0); AssertNetGradient(alg, point1, 0, 1); }
/// <summary> /// Error = 0.92 /// </summary> public static BackpropAlgorithm CreateMNISTSimpleDemo_SEALED() { Console.WriteLine("init CreateMNISTSimpleDemo_SEALED"); var activation = Activation.LeakyReLU(); var net = new ConvNet(1, 28) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 12, windowSize: 5, padding: 2)); net.AddLayer(new ConvLayer(outputDepth: 12, windowSize: 5, padding: 2)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2, activation: activation)); net.AddLayer(new ConvLayer(outputDepth: 24, windowSize: 5, padding: 2)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2, activation: activation)); net.AddLayer(new FlattenLayer(outputDim: 32, activation: activation)); net.AddLayer(new DropoutLayer(rate: 0.5D)); net.AddLayer(new DenseLayer(outputDim: 10, activation: activation)); net._Build(); net.RandomizeParameters(seed: 0); var lrate = 0.001D; var alg = new BackpropAlgorithm(net) { EpochCount = 500, LearningRate = lrate, BatchSize = 4, UseBatchParallelization = true, MaxBatchThreadCount = 4, LossFunction = Loss.Euclidean, Optimizer = Optimizer.RMSProp, Regularizator = Regularizator.L2(0.0001D), LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D) }; alg.Build(); return(alg); }
public static BackpropAlgorithm CreateMNISTHardDemo() { Console.WriteLine("init CreateMNISTHardDemo"); var activation = Activation.ReLU; var net = new ConvNet(1, 28) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new ConvLayer(outputDepth: 64, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2)); net.AddLayer(new DropoutLayer(0.25)); net.AddLayer(new FlattenLayer(outputDim: 128, activation: activation)); net.AddLayer(new DropoutLayer(0.5)); net.AddLayer(new FlattenLayer(outputDim: 10, activation: Activation.Exp)); net._Build(); net.RandomizeParameters(seed: 0); var lrate = 0.005D; var alg = new BackpropAlgorithm(net) { EpochCount = 50, LearningRate = lrate, BatchSize = 8, UseBatchParallelization = true, MaxBatchThreadCount = 8, LossFunction = Loss.CrossEntropySoftMax, Optimizer = Optimizer.RMSProp, LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D) }; alg.Build(); return(alg); }
/// <summary> /// Creates CNN for CIFAR-10 training /// (from http://machinelearningmastery.com/object-recognition-convolutional-neural-networks-keras-deep-learning-library/) /// </summary> public static BackpropAlgorithm CreateCIFAR10Demo3() { Console.WriteLine("init CreateCIFAR10Demo3"); var activation = Activation.ReLU; var net = new ConvNet(3, 32) { IsTraining = true }; net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new DropoutLayer(0.2D)); net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 3, padding: 1, activation: activation)); net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2)); net.AddLayer(new FlattenLayer(outputDim: 512, activation: Activation.ReLU)); net.AddLayer(new DropoutLayer(0.5)); net.AddLayer(new DenseLayer(outputDim: 10, activation: Activation.Logistic(1))); net._Build(); net.RandomizeParameters(seed: 0); var lrate = 0.01D; var alg = new BackpropAlgorithm(net) { LossFunction = Loss.CrossEntropySoftMax, EpochCount = 50, LearningRate = lrate, BatchSize = 8, UseBatchParallelization = true, MaxBatchThreadCount = 8, Optimizer = Optimizer.Momentum, LearningRateScheduler = LearningRateScheduler.TimeBased(lrate, 0.0005D) }; alg.Build(); return(alg); }
public void Gradient_SimpleDropout_1Iter_Euclidean() { // arrange var net = new ConvNet(3, 1) { IsTraining = true }; net.AddLayer(new DenseLayer(outputDim: 10, activation: Activation.Atan)); net.AddLayer(new DropoutLayer(rate: 0.5D)); net.AddLayer(new DenseLayer(outputDim: 2, activation: Activation.Atan)); net._Build(); net.RandomizeParameters(seed: 0); var point1 = RandomPoint(3, 1, 1); var point2 = RandomPoint(3, 1, 1); // just for 2 dim output var sample = new ClassifiedSample <double[][, ]>(); sample[point1] = CLASSES[0]; sample[point2] = CLASSES[1]; var alg = new BackpropAlgorithm(net) { LearningRate = 0.1D, LossFunction = Loss.Euclidean }; alg.Build(); // act alg.RunIteration(point1, EXPECTED[0]); ((DropoutLayer)alg.Net[1]).ApplyCustomMask = true; // assert AssertNetGradient(alg, point1, EXPECTED[0]); }
public void SimpleNet_OneIter_Dropout() { // arrange var drate = 0.5D; var dseed = 1; var net = Mocks.SimpleLinearNetworkWithDropout(drate, dseed); var sample = new ClassifiedSample <double[][, ]>(); var point = new double[1][, ] { new[, ] { { 1.0D } } }; sample[point] = new Class("a", 0); var alg = new BackpropAlgorithm(net); alg.LearningRate = 2.0D; alg.LossFunction = Loss.Euclidean; alg.Build(); // act alg.RunIteration(point, new double[] { 1.0D }); // assert Assert.AreEqual(12, alg.Values[0][0][0, 0]); Assert.AreEqual(33, alg.Values[1][0][0, 0]); Assert.AreEqual(66, alg.Values[2][0][0, 0]); Assert.AreEqual(-128, alg.Values[3][0][0, 0]); Assert.AreEqual(3, net[0].ActivationFunction.DerivativeFromValue(alg.Values[0][0][0, 0])); Assert.AreEqual(3, net[1].ActivationFunction.DerivativeFromValue(alg.Values[1][0][0, 0])); Assert.AreEqual(2, net[3].ActivationFunction.DerivativeFromValue(alg.Values[3][0][0, 0])); Assert.AreEqual(-129 * 2, alg.Errors[3][0][0, 0]); Assert.AreEqual(-258 * (-1), alg.Errors[2][0][0, 0]); Assert.AreEqual(258 * 3 / drate, alg.Errors[1][0][0, 0]); Assert.AreEqual(1548 * 3, alg.Errors[0][0][0, 0]); Assert.AreEqual(-258 * 66, alg.Gradient[3][0]); Assert.AreEqual(-258, alg.Gradient[3][1]); Assert.AreEqual(0, alg.Gradient[2].Length); Assert.AreEqual(0, alg.Gradient[2].Length); Assert.AreEqual(1548 * 12, alg.Gradient[1][0]); Assert.AreEqual(1548, alg.Gradient[1][1]); Assert.AreEqual(4644 * 1, alg.Gradient[0][0]); Assert.AreEqual(4644, alg.Gradient[0][1]); // act alg.FlushGradient(); // assert Assert.AreEqual(2 + 2 * 258, net[3].Weights[1]); Assert.AreEqual(-1 + 2 * 258 * 66, net[3].Weights[0]); Assert.AreEqual(-1 + 2 * (-1548), net[1].Weights[1]); Assert.AreEqual(1 + 2 * (-1548 * 12), net[1].Weights[0]); Assert.AreEqual(1 + 2 * (-4644), net[0].Weights[1]); Assert.AreEqual(3 + 2 * (-4644 * 1), net[0].Weights[0]); }