public void SerializationTest()
{
// Create a RegressionLayer
var layer = new RegressionLayer();
layer.Init(10, 10, 3);
RegressionLayer deserialized;
using (var ms = new MemoryStream())
{
// Serialize
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(ms, layer);
// Deserialize
ms.Position = 0;
deserialized = formatter.Deserialize(ms) as RegressionLayer;
}
Assert.AreEqual(layer.InputDepth, deserialized.InputDepth);
Assert.AreEqual(layer.InputHeight, deserialized.InputHeight);
Assert.AreEqual(layer.InputWidth, deserialized.InputWidth);
Assert.AreEqual(layer.OutputDepth, deserialized.OutputDepth);
Assert.AreEqual(layer.OutputHeight, deserialized.OutputHeight);
Assert.AreEqual(layer.OutputWidth, deserialized.OutputWidth);
}
public static RegressionLayer Regression(this LayerBase layer)
{
var regression = new RegressionLayer();
layer.ConnectTo(regression);
return(regression);
}
//public static SvmLayer<T> Svm<T>(this LayerBase<T> layer, int classCount) where T : struct, IEquatable<T>, IFormattable
//{
// var svm = new SvmLayer<T>(classCount);
// layer.ConnectTo(svm);
// return svm;
//}
public static RegressionLayer <T> Regression <T>(this LayerBase <T> layer) where T : struct, IEquatable <T>, IFormattable
{
var regression = new RegressionLayer <T>();
layer.ConnectTo(regression);
return(regression);
}
private static Network CreateNewNetwork()
{
var num_inputs = 27; // 9 eyes, each sees 3 numbers (wall, green, red thing proximity)
var num_actions = 5; // 5 possible angles agent can turn
var temporal_window = 1; // amount of temporal memory. 0 = agent lives in-the-moment :)
var network_size = num_inputs * temporal_window + num_actions * temporal_window + num_inputs;
Network net = new Network();
InputLayer il = new InputLayer();
il.OutputWidth = 1;
il.OutputHeight = 1;
il.OutputDepth = network_size;
net.Layers.Add(il);
ConvLayer conv = new ConvLayer(16, 5, 5, il.OutputDepth, il.OutputWidth, il.OutputHeight, 1, 2, 0, 1, 0.1);
net.Layers.Add(conv);
ReluLayer rlv = new ReluLayer(conv.OutputDepth, conv.OutputWidth, conv.OutputHeight);
net.Layers.Add(rlv);
MaxPoolLayer pl = new MaxPoolLayer(2, 2, rlv.OutputDepth, rlv.OutputWidth, rlv.OutputHeight, 2, 0, 0);
net.Layers.Add(pl);
FullyConnLayer fc = new FullyConnLayer(50, pl.OutputDepth, pl.OutputWidth, pl.OutputHeight, 0, 1, 0);
net.Layers.Add(fc);
ReluLayer rl = new ReluLayer(fc.OutputDepth, fc.OutputWidth, fc.OutputHeight);
net.Layers.Add(rl);
FullyConnLayer fc2 = new FullyConnLayer(50, rl.OutputDepth, rl.OutputWidth, rl.OutputHeight, 0, 1, 0);
net.Layers.Add(fc2);
ReluLayer rl2 = new ReluLayer(fc2.OutputDepth, fc2.OutputWidth, fc2.OutputHeight);
net.Layers.Add(rl2);
FullyConnLayer fc8 = new FullyConnLayer(5, rl2.OutputDepth, rl2.OutputWidth, rl2.OutputHeight, 0, 1, 0);
net.Layers.Add(fc8);
RegressionLayer sl = new RegressionLayer(fc8.OutputDepth, fc8.OutputWidth, fc8.OutputHeight);
net.LossLayer = sl;
return(net);
}
private static Network CreateNewNetwork()
{
Network net = new Network();
InputLayer il = new InputLayer();
il.OutputWidth = 1;
il.OutputHeight = 1;
il.OutputDepth = 2;
net.Layers.Add(il);
FullyConnLayer fc = new FullyConnLayer(50, il.OutputDepth, il.OutputWidth, il.OutputHeight, 0, 1, 0);
net.Layers.Add(fc);
ReluLayer rl = new ReluLayer(fc.OutputDepth, fc.OutputWidth, fc.OutputHeight);
net.Layers.Add(rl);
FullyConnLayer fc2 = new FullyConnLayer(50, rl.OutputDepth, rl.OutputWidth, rl.OutputHeight, 0, 1, 0);
net.Layers.Add(fc2);
ReluLayer rl2 = new ReluLayer(fc2.OutputDepth, fc2.OutputWidth, fc2.OutputHeight);
net.Layers.Add(rl2);
FullyConnLayer fc8 = new FullyConnLayer(3, rl2.OutputDepth, rl2.OutputWidth, rl2.OutputHeight, 0, 1, 0);
net.Layers.Add(fc8);
RegressionLayer sl = new RegressionLayer(fc8.OutputDepth, fc8.OutputWidth, fc8.OutputHeight);
net.LossLayer = sl;
return(net);
}
public void RegressionLayerSerialization()
{
var layer = new RegressionLayer();
layer.Init(28, 24, 1);
var data = layer.GetData();
Assert.AreEqual(28, data["InputWidth"]);
Assert.AreEqual(24, data["InputHeight"]);
Assert.AreEqual(1, data["InputDepth"]);
var deserialized = LayerBase <double> .FromData(data) as RegressionLayer;
Assert.IsNotNull(deserialized);
Assert.AreEqual(28, deserialized.InputWidth);
Assert.AreEqual(24, deserialized.InputHeight);
Assert.AreEqual(1, deserialized.InputDepth);
Assert.AreEqual(layer.OutputWidth, deserialized.OutputWidth);
Assert.AreEqual(layer.OutputHeight, deserialized.OutputHeight);
Assert.AreEqual(layer.OutputDepth, deserialized.OutputDepth);
}
