private void CreateDNet()
{
ConvolutionLayer conv0 = new ConvolutionLayer(inputDimension, filterSize: 3, filterCount: 32, zeroPadding: true);
ActivationLayer activation0 = new ActivationLayer(new Relu(leaky: true));
MaxPooling2DLayer pool0 = new MaxPooling2DLayer();
ConvolutionLayer conv1 = new ConvolutionLayer(inputDimension, filterSize: 3, filterCount: 32, zeroPadding: true);
ActivationLayer activation1 = new ActivationLayer(new Relu(leaky: true));
MaxPooling2DLayer pool1 = new MaxPooling2DLayer();
FlattenLayer flatten = new FlattenLayer();
LinearLayer linear0 = new LinearLayer(numNeurons: 128);
ActivationLayer activation2 = new ActivationLayer(new Relu(leaky: true));
LinearLayer linear1 = new LinearLayer(numNeurons: 1);
ActivationLayer activation3 = new ActivationLayer(new Sigmoid());
dNet.Add(conv0);
dNet.Add(activation0);
dNet.Add(pool0);
dNet.Add(conv1);
dNet.Add(activation1);
dNet.Add(pool1);
dNet.Add(flatten);
dNet.Add(linear0);
dNet.Add(activation2);
dNet.Add(linear1);
dNet.Add(activation3);
dNet.Compile(new BinaryCrossEntropy(), new Adam(0.001d));
}
public ReaderKerasModel(string fname)
{
JObject model = JObject.Parse(File.ReadAllText(fname));
String modelType = (String)model.SelectToken("model_type");
if (!modelType.Equals("Sequential"))
{
throw new Exception("This reader only supports Sequential type models!");
}
SequentialModel seq = new SequentialModel();
List <IKernelDescriptor> descriptors = ReadDescriptors(model);
foreach (var d in descriptors)
{
seq.Add(d);
}
seq.Compile(new DefaultExecutor());
List <IData> weights = ReadWeights(model, descriptors);
seq.SetWeights(weights);
sequential = seq;
}
private void CreateGNet()
{
ConvolutionLayer conv1 = new ConvolutionLayer(inputDimension, filterSize: 3, filterCount: 4, zeroPadding: true);
ActivationLayer act = new ActivationLayer(new Relu(leaky: true));
ConvolutionLayer conv = new ConvolutionLayer(inputDimension, filterSize: 3, filterCount: 4, zeroPadding: true);
ActivationLayer act1 = new ActivationLayer(new Relu(leaky: true));
ConvolutionLayer convolutionOut = new ConvolutionLayer(inputDimension, filterSize: 3, filterCount: 4, zeroPadding: true);
ActivationLayer actOut = new ActivationLayer(new Relu(leaky: true));
gNet.Add(conv1);
gNet.Add(act);
gNet.Add(conv);
gNet.Add(act1);
gNet.Add(convolutionOut);
gNet.Compile(new BinaryCrossEntropy(), new Adam(0.001d));
}