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);
}
/// <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 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);
}
/// <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);
}
/// <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);
}