public static Model ConvolutionalNeuralNetworkModel()
{
var images = Variable <float>();
var labels = Variable <float>();
ILayer <float> net = new Reshape <float>(images, PartialShape.Create(-1, 1, 28, 28));
net = new Convolution2D <float>(net.Output, 5, 5, 16);
net = new ActivationReLU <float>(net.Output);
net = new Pooling2D <float>(net.Output, PoolingMode.MAX, 2, 2, 2, 2);
net = new Convolution2D <float>(net.Output, 5, 5, 32);
net = new ActivationTanh <float>(net.Output);
net = new Pooling2D <float>(net.Output, PoolingMode.MAX, 2, 2, 2, 2);
net = new Reshape <float>(net.Output, PartialShape.Create(-1, net.Output.Shape.Skip(1).Aggregate(ScalarOps.Mul)));
net = new FullyConnected <float>(net.Output, 50);
net = new ActivationTanh <float>(net.Output);
net = new FullyConnected <float>(net.Output, 10);
return(new Model {
Loss = new SoftmaxCrossEntropy <float>(net.Output, labels),
Images = images,
Labels = labels
});
}
public IActivationFunction GetActivationFunction()
{
IActivationFunction activation;
switch (ActivationFunction)
{
case ActivationFunctionType.Linear:
activation = new ActivationLinear();
break;
case ActivationFunctionType.Sigmoid:
activation = new ActivationSigmoid();
break;
case ActivationFunctionType.TanH:
activation = new ActivationTANH();
break;
case ActivationFunctionType.SoftMax:
activation = new ActivationSoftMax();
break;
case ActivationFunctionType.ReLU:
activation = new ActivationReLU();
break;
default:
throw new ArgumentOutOfRangeException();
}
return(activation);
}
public static Model MultiLayerPerceptronModel()
{
var images = Variable <float>(PartialShape.Create(-1, 28 * 28));
ILayer <float> net = new FullyConnected <float>(images, 128);
net = new ActivationReLU <float>(net.Output);
net = new FullyConnected <float>(net.Output, 64);
net = new ActivationReLU <float>(net.Output);
net = new FullyConnected <float>(net.Output, 10);
var labels = Variable <float>(PartialShape.Create(-1, 10));
return(new Model {
Loss = new SoftmaxCrossEntropy <float>(net.Output, labels),
Images = images,
Labels = labels
});
}
