/// <summary>
/// Create new NeuralNetworkTrainer.
/// </summary>
/// <param name="nn">NeuralNetwork to train.</param>
/// <param name="inputDat">Input data.</param>
/// <param name="targetDat">Target data.</param>
/// <param name="maxUnrollLen">Memory state unroll times, for recurring layers.</param>
/// <param name="losType">Loss calculation type, NeuralNetworkTrainer.LOSS_TYPE_AVERAGE/MAX/CROSSENTROPY.</param>
public NeuralNetworkTrainer(NeuralNetwork nn, float[][] inputDat, float[][] targetDat, int maxUnrollLen, int losType)
{
neuralNetwork = nn;
inputData = inputDat;
targetData = targetDat;
maxUnrollLength = maxUnrollLen;
if (maxUnrollLength < 1)
{
maxUnrollLength = 1;
}
lossType = losType;
//check for recurring layer, if need to stack and unroll
if (nn.outputLayer.recurring)
{
hasRecurring = true;
}
else
{
for (int i = 0; i < nn.hiddenLayers.Length; i++)
{
if (nn.hiddenLayers[i].recurring)
{
hasRecurring = true;
break;
}
}
}
derivatives.Setup(nn);
adagradMemory.Setup(nn);
adagradMemory.Reset();
int tunrollLen = maxUnrollLength;
if (!hasRecurring)
{
tunrollLen = 1;
}
stackedRuntimeContext = new NeuralNetworkContext[tunrollLen];
stackedFullContext = new NeuralNetworkFullContext[tunrollLen];
stackedDerivativeMemory = new NeuralNetworkPropagationState[tunrollLen];
for (int i = 0; i < stackedRuntimeContext.Length; i++)
{
stackedRuntimeContext[i] = new NeuralNetworkContext();
stackedRuntimeContext[i].Setup(nn);
stackedFullContext[i] = new NeuralNetworkFullContext();
stackedFullContext[i].Setup(nn);
stackedDerivativeMemory[i] = new NeuralNetworkPropagationState();
stackedDerivativeMemory[i].Setup(nn, stackedRuntimeContext[i], stackedFullContext[i], derivatives);
}
}
/// <summary>
/// Create new NeuralNetworkQLearning system.
/// </summary>
/// <param name="nn">NeuralNetwork to train.</param>
/// <param name="inputDat">Input data.</param>
/// <param name="targetDat">Target data.</param>
public NeuralNetworkQLearning(NeuralNetwork nn, int maxUnrollLen, string sessionsFileName)
{
neuralNetwork = nn;
learningSessionsFile = sessionsFileName;
maxUnrollLength = maxUnrollLen;
if (maxUnrollLength < 1)
{
maxUnrollLength = 1;
}
//check for recurring layer, if need to stack and unroll
for (int i = 0; i < nn.hiddenLayers.Length; i++)
{
if (nn.hiddenLayers[i].recurring)
{
hasRecurring = true;
break;
}
}
derivatives.Setup(nn);
if (!hasRecurring)
{
maxUnrollLength = 1;
}
stackedRuntimeContext = new NeuralNetworkContext[maxUnrollLength];
stackedFullContext = new NeuralNetworkFullContext[maxUnrollLength];
stackedDerivativeMemory = new NeuralNetworkPropagationState[maxUnrollLength];
for (int i = 0; i < stackedRuntimeContext.Length; i++)
{
stackedRuntimeContext[i] = new NeuralNetworkContext();
stackedRuntimeContext[i].Setup(nn);
stackedFullContext[i] = new NeuralNetworkFullContext();
stackedFullContext[i].Setup(nn);
stackedDerivativeMemory[i] = new NeuralNetworkPropagationState();
stackedDerivativeMemory[i].Setup(nn, stackedRuntimeContext[i], stackedFullContext[i], derivatives);
}
/*
* if (hasRecurring)
* {
* recurringMemoryState = new float[nn.hiddenLayers.Length][];
* for (int i = 0; i < nn.hiddenLayers.Length; i++)
* {
* if (nn.hiddenLayers[i].recurring)
* {
* recurringMemoryState[i] = new float[nn.hiddenLayers[i].numberOfNeurons];
* }
* }
* }*/
}
/// <summary>
/// Create new NeuralNetworkGenerator.
/// </summary>
/// <param name="nn">NeuralNetwork to train.</param>
public NeuralNetworkGenerator(NeuralNetwork nn, int maxUnrollLen)
{
neuralNetwork = nn;
maxUnrollLength = maxUnrollLen;
//check for recurring layer, if need to stack and unroll
for (int i = 0; i < nn.hiddenLayers.Length; i++)
{
if (nn.hiddenLayers[i].recurring)
{
hasRecurring = true;
break;
}
}
derivatives.Setup(nn);
if (hasRecurring)
{
recurringBPBuffer = new float[nn.hiddenLayers.Length][];
for (int i = 0; i < recurringBPBuffer.Length - 1; i++)
{
if (nn.hiddenLayers[i].recurring)
{
recurringBPBuffer[i] = new float[nn.hiddenLayers[i].numberOfNeurons];
}
}
}
else
{
maxUnrollLen = 1;
}
stackedRuntimeContext = new NeuralNetworkContext[maxUnrollLen];
stackedFullContext = new NeuralNetworkFullContext[maxUnrollLen];
stackedDerivativeMemory = new NeuralNetworkPropagationState[maxUnrollLen];
for (int i = 0; i < maxUnrollLen; i++)
{
stackedRuntimeContext[i] = new NeuralNetworkContext();
stackedRuntimeContext[i].Setup(nn);
stackedFullContext[i] = new NeuralNetworkFullContext();
stackedFullContext[i].Setup(nn);
stackedDerivativeMemory[i] = new NeuralNetworkPropagationState();
stackedDerivativeMemory[i].Setup(nn, stackedRuntimeContext[i], stackedFullContext[i], derivatives);
stackedDerivativeMemory[i].inputMem = new float[nn.inputLayer.numberOfNeurons];
}
}