public void ErrorTranclatedCorrect()
{
var uselessLayer = new NeuronsLayer(2, 1);
double[,] TEST_WEIGHTS = { { 1, 0.5 } };
double[] TEST_ERROR = { 1, -0.5 };
double[] CORRECT_ERROR = { 0.75 };
uselessLayer.Weights = TEST_WEIGHTS;
uselessLayer.Error = TEST_ERROR;
uselessLayer.TranslateError(_layer);
CollectionAssert.AreEqual(CORRECT_ERROR, _layer.Error);
}
/// <summary>
/// Reads information about a network from the network file.
/// </summary>
/// <param name="file">The .unf file.</param>
/// <returns>A input size - layers pair.</returns>
public (int, List <INeuronsLayer>) ReadNetworkFile(string file)
{
_file = file;
var localStorage = new Dictionary <string, string>();
string[] info;
using (var reader = new StreamReader(_file))
{
info = reader.ReadLine().Split(',', StringSplitOptions.RemoveEmptyEntries);
ConvertStringsToPairs(info);
_inputSize = int.Parse(localStorage["is"]);
_layers = new List <INeuronsLayer>();
var layersCount = int.Parse(localStorage["lc"]);
for (int i = 0; i < layersCount; i++)
{
localStorage.Clear();
reader.ReadLine();
info = reader.ReadLine().Split(',', StringSplitOptions.RemoveEmptyEntries);
ConvertStringsToPairs(info);
var inputSize = int.Parse(localStorage["is"]);
var size = int.Parse(localStorage["ls"]);
var activationFunction = GetFunctionType(localStorage["af"]);
var newLayer = new NeuronsLayer(size, inputSize);
newLayer.ActivationFunction = activationFunction;
for (int j = 0; j < size; j++)
{
var weights = reader.ReadLine().Split(' ', StringSplitOptions.RemoveEmptyEntries);
newLayer.Bias[j] = double.Parse(weights[0]);
for (int k = 0; k < inputSize; k++)
{
newLayer.Weights[k, j] = double.Parse(weights[k + 1]);
}
}
_layers.Add(newLayer);
}
}
return(_inputSize, _layers);
void ConvertStringsToPairs(string[] strings)
{
foreach (var param in strings)
{
var pair = param.Split('=', StringSplitOptions.RemoveEmptyEntries);
localStorage.Add(pair[0], pair[1]);
}
}
ActivationFunctionType GetFunctionType(string name)
{
return(name switch
{
"LI" => ActivationFunctionType.Linear,
"S" => ActivationFunctionType.Sigmoid,
"T" => ActivationFunctionType.TanH,
"R" => ActivationFunctionType.ReLU,
"LR" => ActivationFunctionType.LReLU
});
}
/// <summary>
/// Reads a information about network from config and weights files.
/// </summary>
/// <param name="file">A .ncfg file.</param>
/// <returns>A input size - layers pair.</returns>
public (int, List <INeuronsLayer>) ReadNetworkFile(string file)
{
(int, List <INeuronsLayer>)result;
var localStorage = new Dictionary <string, string>();
using (var reader = new StreamReader(file))
{
for (int i = 0; i < 3; i++)
{
var current = reader.ReadLine()
.Replace(" ", "")
.Split('=', StringSplitOptions.RemoveEmptyEntries);
localStorage.Add(current[0], current[1]);
}
result.Item1 = int.Parse(localStorage["input_size"]);
result.Item2 = new List <INeuronsLayer>();
var layersCount = int.Parse(localStorage["layers_count"]);
var basePath = Path.Join(file, @"../", localStorage["path_to_weights"]);
for (int i = 0; i < layersCount; i++)
{
localStorage.Clear();
reader.ReadLine();
for (int j = 0; j < 4; j++)
{
var current = reader.ReadLine()
.Replace(" ", "")
.Split('=', StringSplitOptions.RemoveEmptyEntries);
localStorage.Add(current[0], current[1]);
}
var layerSize = int.Parse(localStorage["layer_size"]);
var inputSize = int.Parse(localStorage["input_size"]);
var functionType = GetFunctionType(localStorage["activation_function"]);
var weightsFileStream = new FileStream(Path.Combine(basePath, localStorage["weights_file"]), FileMode.Open);
var newLayer = new NeuronsLayer(layerSize, inputSize);
newLayer.ActivationFunction = functionType;
ReadLayerWeights(newLayer, weightsFileStream);
result.Item2.Add(newLayer);
_filesTable.Add(newLayer, weightsFileStream);
}
}
return(result);
ActivationFunctionType GetFunctionType(string @string)
{
return(@string switch
{
"Linear" => ActivationFunctionType.Linear,
"Sigmoid" => ActivationFunctionType.Sigmoid,
"LReLU" => ActivationFunctionType.LReLU,
"ReLU" => ActivationFunctionType.ReLU,
"TanH" => ActivationFunctionType.TanH,
_ => throw new ArgumentException()
});
}
