private Function DefineModel_A(Variable input, int nbLabels, Example_103_Util util)
{
Func <Variable, Function> activation = CNTKLib.ReLU;
var layer1 = util.Convolution(input, new[] { 5, 5 }, 32, activation);
var layer2 = util.PoolingMax(layer1, new[] { 3, 3 }, new[] { 2, 2 });
var layer3 = util.Convolution(layer2, new[] { 5, 5 }, 32, activation);
var layer4 = util.PoolingMax(layer3, new[] { 3, 3 }, new[] { 2, 2 });
var layer5 = util.Convolution(layer4, new[] { 5, 5 }, 64, activation);
var layer6 = util.PoolingMax(layer5, new[] { 3, 3 }, new[] { 2, 2 });
var layer7 = util.DenseNode(layer6, 64, activation); // linear layer
var lastLayer = util.DenseNode(layer7, nbLabels, null); // linear layer
return(lastLayer);
}
private Function DefineModel_E(Variable input, int nbLabels, Example_103_Util util)
{
Func <Variable, Function> activation = CNTKLib.ReLU;
Variable net = input;
net = util.ConvolutionWithBatchNormalization(net, new[] { 3, 3 }, 16, activation);
net = util.ResNetBasicStack(net, 16, 3, activation);
net = util.ResNetInc(net, 32, activation);
net = util.ResNetBasicStack(net, 32, 2, activation);
net = util.ResNetInc(net, 64, activation);
net = util.ResNetBasicStack(net, 64, 2, activation);
net = util.PoolingMax(net, new[] { 8, 8 }, new[] { 1, 1 });
net = util.DenseNode(net, nbLabels, null);
return(net);
}
private Function DefineModel_D_Pourri(Variable input, int nbLabels, Example_103_Util util)
{
Func <Variable, Function> activation = CNTKLib.ReLU;
Variable net = input;
foreach (int dims in new[] { 64, 96, 128 })
{
net = util.Convolution(net, new[] { 3, 3 }, dims, activation);
net = util.Convolution(net, new[] { 3, 3 }, dims, activation);
net = util.PoolingMax(net, new[] { 3, 3 }, new int[] { 2, 2 });
}
for (int i = 0; i < 2; i++)
{
net = util.DenseNode(net, 1024, activation);
}
net = util.DenseNode(net, nbLabels, CNTKLib.Softmax);
return(net);
}
