ConvNetSharp

C# port of ConvNetJS. You can use ConvNetSharp to train and evaluate convolutional neural networks (CNN).

Thank you very much to the original author (Andrej Karpathy) and to all the contributors to ConvNetJS!

Example Code

Here's a minimum example of defining a 2-layer neural network and training it on a single data point:

  // 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.Get(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.Get(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)

Fluent API (see FluentMnistDemo)

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();

Save and Load Network

###JSON serialization (not supported by FluentNet)

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

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

###Binary serialization

// 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);
 }