public MyMatrix[] GetActivisions(float[] input)
{
MyMatrix[] activisions = new MyMatrix[m_layerCount];
MyMatrix activision = new MyMatrix(input);
activisions[0] = activision;
for (int layerIndex = 0; layerIndex < m_layerCount - 1; layerIndex++)
{
// compensate weights scale for dropout
MyMatrix weightsCompensated = null;
if (m_dropoutKeepRate < 1)
{
weightsCompensated = new MyMatrix(m_weights[layerIndex], true);
weightsCompensated.MultiplyByFactor(m_dropoutKeepRate);
}
else
{
weightsCompensated = m_weights[layerIndex];
}
activision = GetActivisionFunction(MyMatrix.AddMatrix(MyMatrix.Dot(weightsCompensated, activision), m_biases[layerIndex]), layerIndex);
activisions[layerIndex + 1] = activision;
}
return(activisions);
}
private void FillActivisions(float[] input)
{
m_activisionValues[0] = new MyMatrix(input);
for (int layerIndex = 0; layerIndex < m_layerCount - 1; layerIndex++)
{
m_rawValues[layerIndex] = MyMatrix.AddMatrix(MyMatrix.Dot(m_weights[layerIndex], m_activisionValues[layerIndex]), m_biases[layerIndex]);
m_activisionValues[layerIndex + 1] = GetActivisionFunction(m_rawValues[layerIndex], layerIndex);
if (m_dropoutKeepRate < 1 && layerIndex != m_layerCount - 2)
{
if (m_dropoutKeepRate <= 0 || m_dropoutKeepRate > 1)
{
Debug.Log("Warning: m_regularizationKeepRate was corrupt! (" + m_dropoutKeepRate + ")");
}
MyMatrix regularizationMask = new MyMatrix(m_activisionValues[layerIndex + 1].m_rowCountY, 1);// new float[m_layerLengths[layerIndex] + 1];
for (int i = 0; i < regularizationMask.m_rowCountY; i++)
{
float random = 0;
if (m_initDropoutSeed >= 0)
{
random = Utility.GetRandomWithSeed(0f, 1f, m_currentDropoutSeed++);
}
else
{
Random.Range(0f, 1f);
}
if (random < m_dropoutKeepRate)
{
regularizationMask.m_data[i][0] = 1;
}
}
m_rawValues[layerIndex] = MyMatrix.MultiplyElementWise(m_rawValues[layerIndex], regularizationMask);
m_activisionValues[layerIndex + 1] = MyMatrix.MultiplyElementWise(m_activisionValues[layerIndex + 1], regularizationMask);
}
}
}
