public static ConvolutionalNeuralNetwork Parse(string filename)
{
var jobject = JObject.Parse(File.ReadAllText(filename));
var jsonLayers = jobject.GetValue("layers");
var network = new ConvolutionalNeuralNetwork();
var netLayers = new List <CNNLayer>();
foreach (var jsonLayer in jsonLayers.ToArray())
{
var type = jsonLayer["layer_type"].ToObject <string>();
CNNLayer layer;
switch (type)
{
case "input":
layer = new InputLayer();
break;
case "conv":
layer = new ConvolutionalLayer
{
FilterSize = jsonLayer["sx"].ToObject <int>(),
Stride = jsonLayer["stride"].ToObject <int>(),
L1Decay = jsonLayer["l1_decay_mul"].ToObject <double>(),
L2Decay = jsonLayer["l2_decay_mul"].ToObject <double>(),
Pad = jsonLayer["pad"].ToObject <int>(),
Biases = Volume.ParseJSON(jsonLayer["biases"]),
Kernels = Volume.ArrayParseJSON(jsonLayer["filters"])
};
break;
case "relu":
layer = new ReLuLayer();
break;
case "pool":
layer = new SubsamplingLayer
{
FilterSize = jsonLayer["sx"].ToObject <int>(),
Stride = jsonLayer["stride"].ToObject <int>(),
Pad = jsonLayer["pad"].ToObject <int>(),
};
break;
case "fc":
layer = new FullyConnectedLayer
{
L1Decay = jsonLayer["l1_decay_mul"].ToObject <double>(),
L2Decay = jsonLayer["l2_decay_mul"].ToObject <double>(),
NeuronsCount = jsonLayer["out_depth"].ToObject <int>(),
Biases = Volume.ParseJSON(jsonLayer["biases"]),
Weights = Volume.ArrayParseJSON(jsonLayer["filters"])
};
break;
case "softmax":
layer = new SoftmaxLayer();
break;
default:
throw new ArgumentException("Unknown layer type");
}
if (netLayers.Count != 0)
{
layer.InSize = netLayers.Last().OutSize;
layer.InDepth = netLayers.Last().OutDepth;
}
layer.OutDepth = jsonLayer["out_depth"].ToObject <int>();
layer.OutSize = new Size(
jsonLayer["out_sx"].ToObject <int>(),
jsonLayer["out_sy"].ToObject <int>());
if (type == "fc")
{
(layer as FullyConnectedLayer)._inputsCount =
layer.InSize.Width * layer.InSize.Height * layer.InDepth;
}
else if (type == "pool")
{
(layer as SubsamplingLayer)._oldX = new int[layer.OutSize.Width * layer.OutSize.Height * layer.OutDepth];
(layer as SubsamplingLayer)._oldY = new int[layer.OutSize.Width * layer.OutSize.Height * layer.OutDepth];
}
else if (type == "softmax")
{
(layer as SoftmaxLayer)._es = new double[layer.OutDepth];
}
layer.OutVolume = new Volume(layer.OutSize.Width, layer.OutSize.Height, layer.OutDepth, 0);
netLayers.Add(layer);
}
network.Layers = netLayers;
network.InputLayer = (InputLayer)netLayers.First();
return(network);
}
