public static Model CreateModel(int classCount)
{
var activation = tf.keras.activations.elu_fn;
const int filterCount = 8;
int[] resNetFilters = { filterCount, filterCount, filterCount };
return(new Sequential(new Layer[] {
new Dropout(rate: 0.05),
Conv2D.NewDyn(filters: filterCount, kernel_size: 5, padding: "same"),
Activation.NewDyn(activation),
new MaxPool2D(pool_size: 2),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new MaxPool2D(),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new MaxPool2D(),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new ResNetBlock(kernelSize: 3, filters: resNetFilters, activation: activation),
new AvgPool2D(pool_size: 2),
new Flatten(),
new Dense(units: classCount, activation: tf.nn.softmax_fn),
}));
}
public ResNetBlock(int kernelSize, int[] filters)
{
for (int part = 0; part < PartCount; part++)
{
this.convs.Add(this.Track(part == 1
? Conv2D.NewDyn(filters[part], kernel_size: kernelSize, padding: "same")
: Conv2D.NewDyn(filters[part], kernel_size: (1, 1))));
this.batchNorms.Add(this.Track(new BatchNormalization()));
}
}
public ResNetBlock(int kernelSize, int[] filters, PythonFunctionContainer?activation = null)
{
this.activation = activation ?? tf.keras.activations.relu_fn;
for (int part = 0; part < PartCount; part++)
{
this.convs.Add(this.Track(part == 1
? Conv2D.NewDyn(filters: filters[part], kernel_size: kernelSize, padding: "same")
: Conv2D.NewDyn(filters[part], kernel_size: (1, 1))));
this.batchNorms.Add(this.Track(new BatchNormalization()));
}
this.outputChannels = filters[PartCount - 1];
}