private ML.DeepMethods.Algorithms.BackpropAlgorithm createCNNAlg_NN_ForTest()
{
var cnn = new ConvNet(2, 1)
{
IsTraining = true
};
cnn.AddLayer(new DenseLayer(15, activation: Activation.Logistic(1)));
cnn.AddLayer(new MaxPoolingLayer(1, 1));
//cnn.AddLayer(new _ActivationLayer(Activation.Logistic(1)));
cnn.AddLayer(new DropoutLayer(0.1));
cnn.AddLayer(new FlattenLayer(3, activation: Activation.Logistic(1)));
//cnn.AddLayer(new _ActivationLayer(Activation.Logistic(1)));
cnn.AddLayer(new MaxPoolingLayer(1, 1));
cnn._Build();
cnn.RandomizeParameters(0);
var sample = new ClassifiedSample <double[][, ]>();
foreach (var obj in Data.TrainingSample)
{
var data = obj.Key;
var key = new double[data.Length][, ];
for (int i = 0; i < data.Length; i++)
{
key[i] = new double[1, 1];
}
for (int i = 0; i < data.Length; i++)
{
key[i][0, 0] = data[i];
}
sample[key] = obj.Value;
}
var alg = new ML.DeepMethods.Algorithms.BackpropAlgorithm(sample, cnn);
alg.EpochCount = 6000;
alg.LearningRate = 0.01D;
alg.BatchSize = 1;
alg.LossFunction = Loss.Euclidean;
int epoch = 0;
alg.EpochEndedEvent += (o, e) =>
{
if (epoch++ % 300 != 0)
{
return;
}
Console.WriteLine("----------------Epoch #: {0}", epoch);
Console.WriteLine("L:\t{0}", alg.LossValue);
Console.WriteLine("DL:\t{0}", alg.LossDelta);
Console.WriteLine("DW:\t{0}", alg.Step2);
};
return(alg);
}
private ML.DeepMethods.Algorithms.BackpropAlgorithm createCNNAlg_NN_ForTest()
{
var cnn = new ConvNet(6, 1)
{
IsTraining = true
};
cnn.AddLayer(new DenseLayer(15, activation: Activation.Logistic(1)));
cnn.AddLayer(new DenseLayer(15, activation: Activation.Logistic(1)));
cnn.AddLayer(new DenseLayer(15, activation: Activation.Logistic(1)));
//cnn.AddLayer(new MaxPoolingLayer(1, 1));
//cnn.AddLayer(new _ActivationLayer(Activation.Logistic(1)));
//cnn.AddLayer(new DropoutLayer(0.1));
cnn.AddLayer(new FlattenLayer(2, activation: Activation.Logistic(1)));
//cnn.AddLayer(new _ActivationLayer(Activation.Logistic(1)));
//cnn.AddLayer(new MaxPoolingLayer(1, 1));
cnn._Build();
cnn.RandomizeParameters(0);
var alg = new ML.DeepMethods.Algorithms.BackpropAlgorithm(cnn);
alg.EpochCount = 6000;
alg.LearningRate = 0.01D;
alg.BatchSize = 1;
alg.LossFunction = Loss.Euclidean;
int epoch = 0;
alg.EpochEndedEvent += (o, e) =>
{
if (epoch++ % 300 != 0)
{
return;
}
Console.WriteLine("----------------Epoch #: {0}", epoch);
Console.WriteLine("L:\t{0}", alg.LossValue);
Console.WriteLine("DL:\t{0}", alg.LossDelta);
Console.WriteLine("DW:\t{0}", alg.Step2);
};
return(alg);
}
