public void HardSigmoidPrimeTest()
{
var a = new Matrix(2, 2);
a.InRandomize();
var b = a.Duplicate();
a = new HardSigmoid().Backward(a);
b.InMap((x) => x > 1 || x < 0 ? 0 : 1);
Assert.IsTrue(Math.Abs(a.FrobeniusNorm() - b.FrobeniusNorm()) < 0.1,
new HardSigmoid().Type().ToString() + " Derivative.");
}
public void HardSigmoidTest()
{
var a = new Matrix(2, 2);
a.InRandomize();
var b = a.Duplicate();
a = new HardSigmoid().Forward(a);
b.InMap((x) => x < 0 ? 0 : x < 1 ? x : 1);
Assert.IsTrue(Math.Abs(a.FrobeniusNorm() - b.FrobeniusNorm()) < 0.1,
new HardSigmoid().Type().ToString() + " Activation.");
}
public Layer(int nCount, int index, ActivationSettings activationSettings)
{
NCount = nCount;
Index = index;
ActivationType = activationSettings.Type();
// Activation Setup
switch (activationSettings.Type())
{
case EActivationType.Invalid:
Activation = null;
throw new ArgumentException("Activation Type Invalid.");
case EActivationType.Arctan:
Activation = new Arctan();
break;
case EActivationType.BinaryStep:
Activation = new BinaryStep();
break;
case EActivationType.BipolarSigmoid:
Activation = new BipolarSigmoid();
break;
case EActivationType.ELU:
Activation = new ELU((ELUSettings)activationSettings);
break;
case EActivationType.HardSigmoid:
Activation = new HardSigmoid();
break;
case EActivationType.HardTanh:
Activation = new HardTanh();
break;
case EActivationType.Identity:
Activation = new Identity();
break;
case EActivationType.Logit:
Activation = new Logit();
break;
case EActivationType.LReLU:
Activation = new LReLU((LReLUSettings)activationSettings);
break;
case EActivationType.Mish:
Activation = new Mish();
break;
case EActivationType.ReLU:
Activation = new ReLU();
break;
case EActivationType.SeLU:
Activation = new SeLU();
break;
case EActivationType.Sigmoid:
Activation = new Sigmoid();
break;
case EActivationType.Softmax:
Activation = new Softmax();
break;
case EActivationType.Softplus:
Activation = new Softplus();
break;
case EActivationType.Softsign:
Activation = new Softsign();
break;
case EActivationType.Tanh:
Activation = new Tanh();
break;
default:
throw new ArgumentException("Activation Type Invalid.");
}
}
private List <IKernelDescriptor> ReadDescriptors(JObject model)
{
List <IKernelDescriptor> dscps = model.SelectToken("descriptors").Select(layer => {
IKernelDescriptor descriptor = null;
String layerName = (String)layer.SelectToken("layer");
switch (layerName)
{
case "AvgPooling1D":
descriptor = new AvgPooling1D(
(int)layer.SelectToken("padding"),
(int)layer.SelectToken("stride"),
(int)layer.SelectToken("kernel_size"));
break;
case "GlobalAveragePooling1D":
descriptor = new GlobalAvgPooling1D();
break;
case "AvgPooling2D":
descriptor = new AvgPooling2D((int)layer.SelectToken("padding_vl"), (int)layer.SelectToken("padding_hz"),
(int)layer.SelectToken("stride_vl"), (int)layer.SelectToken("stride_hz"),
(int)layer.SelectToken("kernel_height"), (int)layer.SelectToken("kernel_width"));
break;
case "GlobalAveragePooling2D":
descriptor = new GlobalAvgPooling2D();
break;
case "BatchNormalization":
descriptor = new BatchNormalization(
(int)layer.SelectToken("epsilon"));
break;
case "Cropping1D":
descriptor = new Cropping1D(
(int)layer.SelectToken("trimBegin"),
(int)layer.SelectToken("trimEnd"));
break;
case "Cropping2D":
descriptor = new Cropping2D(
(int)layer.SelectToken("topTrim"),
(int)layer.SelectToken("bottomTrim"),
(int)layer.SelectToken("leftTrim"),
(int)layer.SelectToken("rightTrim"));
break;
case "MaxPooling1D":
descriptor = new MaxPooling1D(
(int)layer.SelectToken("padding"),
(int)layer.SelectToken("stride"),
(int)layer.SelectToken("kernel_size"));
break;
case "GlobalMaxPooling1D":
descriptor = new GlobalMaxPooling1D();
break;
case "MaxPooling2D":
descriptor = new MaxPooling2D((int)layer.SelectToken("padding_vl"), (int)layer.SelectToken("padding_hz"),
(int)layer.SelectToken("stride_vl"), (int)layer.SelectToken("stride_hz"),
(int)layer.SelectToken("kernel_height"), (int)layer.SelectToken("kernel_width"));
break;
case "GlobalMaxPooling2D":
descriptor = new GlobalMaxPooling2D();
break;
case "Convolution1D":
descriptor = new Convolution1D(
(int)layer.SelectToken("padding"),
(int)layer.SelectToken("stride"),
(int)layer.SelectToken("kernel_size"),
(int)layer.SelectToken("kernel_num"));
break;
case "Convolution2D":
descriptor = new Convolution2D((int)layer.SelectToken("padding_vl"), (int)layer.SelectToken("padding_hz"),
(int)layer.SelectToken("stride_vl"), (int)layer.SelectToken("stride_hz"),
(int)layer.SelectToken("kernel_height"), (int)layer.SelectToken("kernel_width"),
(int)layer.SelectToken("kernel_num"));
break;
case "Dense2D":
descriptor = new Dense2D((int)layer.SelectToken("units"));
break;
case "Input2D":
descriptor = new Input2D((int)layer.SelectToken("height"), (int)layer.SelectToken("width"),
(int)layer.SelectToken("channel"), (int)layer.SelectToken("batch"));
break;
case "Bias2D":
descriptor = new Bias2D();
break;
case "Permute":
descriptor = new Permute(
(int)layer.SelectToken("dim1"),
(int)layer.SelectToken("dim2"),
(int)layer.SelectToken("dim3"));
break;
case "Reshape":
descriptor = new Reshape2D(
(int)layer.SelectToken("height"),
(int)layer.SelectToken("width"),
(int)layer.SelectToken("channel"),
1);
break;
case "RepeatVector":
descriptor = new RepeatVector(
(int)layer.SelectToken("num"));
break;
case "SimpleRNN":
descriptor = new SimpleRNN(
(int)layer.SelectToken("units"),
(int)layer.SelectToken("input_dim"),
ANR((string)layer.SelectToken("activation")));
break;
case "LSTM":
descriptor = new LSTM(
(int)layer.SelectToken("units"),
(int)layer.SelectToken("input_dim"),
ANR((string)layer.SelectToken("activation")),
ANR((string)layer.SelectToken("rec_act")));
break;
case "GRU":
descriptor = new GRU(
(int)layer.SelectToken("units"),
(int)layer.SelectToken("input_dim"),
ANR((string)layer.SelectToken("activation")),
ANR((string)layer.SelectToken("rec_act")));
break;
case "ELu":
descriptor = new ELu(1);
break;
case "HardSigmoid":
descriptor = new HardSigmoid();
break;
case "ReLu":
descriptor = new ReLu();
break;
case "Sigmoid":
descriptor = new Sigmoid();
break;
case "Flatten":
descriptor = new Flatten();
break;
case "Softmax":
descriptor = new Softmax();
break;
case "SoftPlus":
descriptor = new SoftPlus();
break;
case "SoftSign":
descriptor = new Softsign();
break;
case "TanH":
descriptor = new TanH();
break;
default:
throw new Exception("Unknown layer type!");
}
return(descriptor);
}).ToList();
return(dscps);
}
