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);
}
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);
}
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[] { 1.0D };
sample[point] = new Class("a", 0);
var alg = new BackpropAlgorithm(net);
alg.LearningRate = 2.0D;
alg.LossFunction = Loss.Euclidean;
// act
alg.RunIteration(point, new double[] { 1.0D });
// assert
Assert.AreEqual(12, net[0][0].Value);
Assert.AreEqual(66, net[1][0].Value);
Assert.AreEqual(-128, net[2][0].Value);
Assert.AreEqual(3, net[0][0].Derivative);
Assert.AreEqual(3 / drate, net[1][0].Derivative);
Assert.AreEqual(2, net[2][0].Derivative);
Assert.AreEqual(-129 * 2, alg.Errors[2][0]);
Assert.AreEqual(-1 * (-258) * 3 / drate, alg.Errors[1][0]);
Assert.AreEqual(1548 * 3, alg.Errors[0][0]);
Assert.AreEqual(-258 * 66, alg.Gradient[2][0, 0]);
AssertNetGradient(alg, point, 2, 0, 0);
Assert.AreEqual(-258, alg.Gradient[2][0, 1]);
AssertNetGradient(alg, point, 2, 0, 1);
Assert.AreEqual(1548 * 12, alg.Gradient[1][0, 0]);
AssertNetGradient(alg, point, 1, 0, 0);
Assert.AreEqual(1548, alg.Gradient[1][0, 1]);
AssertNetGradient(alg, point, 1, 0, 1);
Assert.AreEqual(4644 * 1, alg.Gradient[0][0, 0]);
AssertNetGradient(alg, point, 0, 0, 0);
Assert.AreEqual(4644, alg.Gradient[0][0, 1]);
AssertNetGradient(alg, point, 0, 0, 1);
// act
alg.FlushGradient();
// assert
Assert.AreEqual(2 + 2 * 258, net[2][0].Bias);
Assert.AreEqual(-1 + 2 * 258 * 66, net[2][0][0]);
Assert.AreEqual(-1 + 2 * (-1548), net[1][0].Bias);
Assert.AreEqual(1 + 2 * (-1548 * 12), net[1][0][0]);
Assert.AreEqual(1 + 2 * (-4644), net[0][0].Bias);
Assert.AreEqual(3 + 2 * (-4644 * 1), net[0][0][0]);
}
public void SimpleNet_OneIter()
{
// arrange
var net = Mocks.SimpleLinearNetwork();
var sample = new ClassifiedSample <double[]>();
var point = new[] { 1.0D };
sample[point] = new Class("a", 0);
var alg = new BackpropAlgorithm(net);
alg.LearningRate = 2.0D;
alg.LossFunction = Loss.Euclidean;
// act
alg.RunIteration(point, new double[] { 1.0D });
// assert
Assert.AreEqual(12, net[0][0].Value);
Assert.AreEqual(33, net[1][0].Value);
Assert.AreEqual(-62, net[2][0].Value);
Assert.AreEqual(3, net[0][0].Derivative);
Assert.AreEqual(3, net[1][0].Derivative);
Assert.AreEqual(2, net[2][0].Derivative);
Assert.AreEqual(-126, alg.Errors[2][0]);
Assert.AreEqual(378, alg.Errors[1][0]);
Assert.AreEqual(1134, alg.Errors[0][0]);
Assert.AreEqual(-126 * 33, alg.Gradient[2][0, 0]);
AssertNetGradient(alg, point, 2, 0, 0);
Assert.AreEqual(-126, alg.Gradient[2][0, 1]);
AssertNetGradient(alg, point, 2, 0, 1);
Assert.AreEqual(378 * 12, alg.Gradient[1][0, 0]);
AssertNetGradient(alg, point, 1, 0, 0);
Assert.AreEqual(378, alg.Gradient[1][0, 1]);
AssertNetGradient(alg, point, 1, 0, 1);
Assert.AreEqual(1134 * 1, alg.Gradient[0][0, 0]);
AssertNetGradient(alg, point, 0, 0, 0);
Assert.AreEqual(1134, alg.Gradient[0][0, 1]);
AssertNetGradient(alg, point, 0, 0, 1);
// act
alg.FlushGradient();
// assert
Assert.AreEqual(2 + 2 * 126, net[2][0].Bias);
Assert.AreEqual(-1 + 2 * 126 * 33, net[2][0][0]);
Assert.AreEqual(-1 + 2 * (-378), net[1][0].Bias);
Assert.AreEqual(1 + 2 * (-378 * 12), net[1][0][0]);
Assert.AreEqual(1 + 2 * (-1134), net[0][0].Bias);
Assert.AreEqual(3 + 2 * (-1134 * 1), net[0][0][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]);
}