public static NeuralLayeredNetwork Pad(this NeuralLayeredNetwork network, int paddingSize)
{
network.AddLayer(new ZeroPaddingLayer(paddingSize));
return(network);
}
public static NeuralLayeredNetwork Conv(this NeuralLayeredNetwork network, int filtersCount, int kernelSize, int stride, IWeightsInitializer initializer)
{
network.AddLayer(new ConvolutionLayer(filtersCount, kernelSize, stride, initializer));
return(network);
}
public static NeuralLayeredNetwork Sigmoid(this NeuralLayeredNetwork network)
{
network.AddLayer(new ActivationLayer(new Sigmoid()));
return(network);
}
public static NeuralLayeredNetwork Tanh(this NeuralLayeredNetwork network)
{
network.AddLayer(new ActivationLayer(new Tanh()));
return(network);
}
public static NeuralLayeredNetwork Softmax(this NeuralLayeredNetwork network)
{
network.AddLayer(new Softmax());
return(network);
}
public static NeuralLayeredNetwork Fully(this NeuralLayeredNetwork network, int neuronsCount)
{
network.AddLayer(new FullyConnectedLayer(neuronsCount));
return(network);
}
public static NeuralLayeredNetwork Fully(this NeuralLayeredNetwork network, int neuronsCount, IWeightsInitializer initializer)
{
network.AddLayer(new FullyConnectedLayer(neuronsCount, initializer));
return(network);
}
public static NeuralLayeredNetwork Flatten(this NeuralLayeredNetwork network)
{
network.AddLayer(new FlattenLayer());
return(network);
}
public static NeuralLayeredNetwork MaxPool(this NeuralLayeredNetwork network, int poolSize, int stride)
{
network.AddLayer(new PoolingLayer(poolSize, stride));
return(network);
}
