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 static BackpropAlgorithm CreateMNISTHardDemo(ClassifiedSample <double[][, ]> training)
{
Console.WriteLine("init CreateMNISTHardDemo");
var activation = Activation.ReLU;
var net = new ConvNet(1, 28)
{
IsTraining = true
};
net.AddLayer(new ConvLayer(outputDepth: 32, windowSize: 3, activation: activation));
net.AddLayer(new ConvLayer(outputDepth: 64, windowSize: 3, 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.Logistic(1)));
net._Build();
net.RandomizeParameters(seed: 0);
var lrate = 0.005D;
var alg = new BackpropAlgorithm(training, net)
{
LossFunction = Loss.Euclidean,
EpochCount = 50,
LearningRate = lrate,
BatchSize = 1,
LearningRateScheduler = LearningRateScheduler.Constant(lrate)
};
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);
}
/// <summary>
///
/// </summary>
public static BackpropAlgorithm CreateMNISTSimpleDemoWithBatching(ClassifiedSample <double[][, ]> training)
{
Console.WriteLine("init CreateMNISTSimpleDemoWithBatching");
var activation = Activation.ReLU;
var net = new ConvNet(1, 28)
{
IsTraining = true
};
net.AddLayer(new ConvLayer(outputDepth: 8, windowSize: 5));
net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2, activation: activation));
net.AddLayer(new ConvLayer(outputDepth: 18, windowSize: 5));
net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2, activation: activation));
net.AddLayer(new FlattenLayer(outputDim: 10, activation: activation));
net._Build();
net.RandomizeParameters(seed: 0);
var lrate = 0.0001D;
var alg = new BackpropAlgorithm(training, net)
{
EpochCount = 50,
LearningRate = lrate,
BatchSize = 8,
UseBatchParallelization = true,
MaxBatchThreadCount = 8,
LossFunction = Loss.Euclidean,
Optimizer = Optimizer.RMSProp,
LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D)
};
return(alg);
}
/// <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);
}
/// <summary>
/// Creates CNN for CIFAR-10 training (from keras)
/// </summary>
public static BackpropAlgorithm CreateCIFAR10Demo1(ClassifiedSample <double[][, ]> training)
{
Console.WriteLine("init CreateCIFAR10Demo1");
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 ConvLayer(outputDepth: 32, windowSize: 3, padding: 1, activation: activation));
net.AddLayer(new MaxPoolingLayer(windowSize: 2, stride: 2));
net.AddLayer(new DropoutLayer(0.25));
net.AddLayer(new ConvLayer(outputDepth: 64, 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: 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(training, net)
{
LossFunction = Loss.CrossEntropySoftMax,
EpochCount = 50,
LearningRate = lrate,
BatchSize = 8,
UseBatchParallelization = true,
MaxBatchThreadCount = 8,
Optimizer = Optimizer.Adadelta,
LearningRateScheduler = LearningRateScheduler.DropBased(lrate, 5, 0.5D)
};
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);
}
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);
}
public IEnumerable <TrainingSession> GetIterator(int maxIteration = int.MaxValue)
{
_stopwatch = Stopwatch.StartNew();
_stop = false;
Epoch = 1;
EpochIncremented = false;
if (LearningRateScheduler != null)
{
Learner.ResetLearningRate(new TrainingParameterScheduleDouble(LearningRateScheduler.LearningRate));
}
for (Iteration = 1; Iteration <= maxIteration; ++Iteration)
{
var minibatch = Sampler.GetNextMinibatch(TrainingDevice);
if (minibatch == null)
{
break;
}
DataNameToInputMap.InitializeByMinibatch(minibatch);
var arguments = DataNameToInputMap.GetVariableValueMap(minibatch);
Trainer.TrainMinibatch(arguments, minibatch.SweepEnd, TrainingDevice);
SampleCount = (int)Trainer.PreviousMinibatchSampleCount();
Loss = Trainer.PreviousMinibatchLossAverage();
if (Trainer.EvaluationFunction() != null)
{
Metric = Trainer.PreviousMinibatchEvaluationAverage();
}
foreach (var cb in Callbacks)
{
cb.Run(this);
}
if (_stop)
{
break;
}
yield return(this);
if (LearningRateScheduler != null)
{
bool update = LearningRateScheduler.UpdateLearningRate(Epoch, Iteration, Loss);
if (update)
{
Learner.ResetLearningRate(new TrainingParameterScheduleDouble(LearningRateScheduler.LearningRate));
}
}
EpochIncremented = false;
if (minibatch.SweepEnd)
{
++Epoch;
EpochIncremented = true;
}
}
}