// species a 2-layer neural network with one hidden layer of 20 neurons
  var net = new Net();

  // input layer declares size of input. here: 2-D data
  // ConvNetSharp works on 3-Dimensional volumes (width, height, depth), but if you're not dealing with images
  // then the first two dimensions (width, height) will always be kept at size 1
  net.AddLayer(new InputLayer(1, 1, 2));

  // declare 20 neurons
  net.AddLayer(new FullyConnLayer(20));

  // declare a ReLU (rectified linear unit non-linearity)
  net.AddLayer(new ReluLayer());

  // declare a fully connected layer that will be used by the softmax layer
  net.AddLayer(new FullyConnLayer(10));

  // declare the linear classifier on top of the previous hidden layer
  net.AddLayer(new SoftmaxLayer(10));

  // forward a random data point through the network
  var x = new Volume(new[] {0.3, -0.5});

  var prob = net.Forward(x);

  // prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
  Console.WriteLine("probability that x is class 0: " + prob.Weights[0]); // prints e.g. 0.50101

  var trainer = new SgdTrainer(net) {LearningRate = 0.01, L2Decay = 0.001};
  trainer.Train(x, 0); // train the network, specifying that x is class zero

  var prob2 = net.Forward(x);
  Console.WriteLine("probability that x is class 0: " + prob2.Weights[0]);
  // now prints 0.50374, slightly higher than previous 0.50101: the networks
  // weights have been adjusted by the Trainer to give a higher probability to
  // the class we trained the network with (zero)

var net = FluentNet.Create(24, 24, 1)
                   .Conv(5, 5, 8).Stride(1).Pad(2)
                   .Relu()
                   .Pool(2, 2).Stride(2)
                   .Conv(5, 5, 16).Stride(1).Pad(2)
                   .Relu()
                   .Pool(3, 3).Stride(3)
                   .FullyConn(10)
                   .Softmax(10)
                   .Build();

// Serialize to json 
var json = net.ToJSON();

// Deserialize from json
Net deserialized = SerializationExtensions.FromJSON(json);

// Serialize to binary
 using (var fs = new FileStream(filename, FileMode.Create))
 {
    net.SaveBinary(fs);
 }
 
 // Deserialize from binary
 using (var fs = new FileStream(filename, FileMode.Open))
 {
    INet deserialized = SerializationExtensions.LoadBinary(fs);
 }