TraceNode<ElemType>::TraceNode(const ScriptableObjects::IConfigRecordPtr configp) :
TraceNode(configp->Get(L"deviceId"), L"<placeholder>")
{
AttachInputsFromConfig(configp, this->GetExpectedNumInputs());
m_message = (const std::wstring&)configp->Get(L"say");
m_logFirst = configp->Get(L"logFirst");
m_logFrequency = configp->Get(L"logFrequency");
m_logGradientToo = configp->Get(L"logGradientToo");
m_formattingOptions = WriteFormattingOptions(*configp);
m_onlyUpToRow = configp->Get(L"onlyUpToRow");
m_onlyUpToT = configp->Get(L"onlyUpToT");
}
LearnableParameter<ElemType>::LearnableParameter(const ScriptableObjects::IConfigRecordPtr configp) :
LearnableParameter(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"shape"))
{
// TODO: Change dimensions to take a generic tensor instead. That will be a (minor) breaking change that will require fix-ups when converting from NDL to BrainScript.
AttachInputsFromConfig(configp, this->GetExpectedNumInputs());
// parameters[rows, [cols=1]] plus other optional parameters (learningRateMultiplier=[1|0|float], init=[uniform|gaussian|fixedvalue], initValueScale=[1|float], value=[0|float])
if (configp->Exists(L"learningRateMultiplier"))
SetLearningRateMultiplier(configp->Get(L"learningRateMultiplier"));
else if (configp->Exists(L"needsGradient") || configp->Exists(L"needGradient") || configp->Exists(L"computeGradient"))
InvalidArgument("Deprecated parameter names needsGradient|needGradient|computeGradient are not supported in BrainScript. Use learningRateMultiplier instead.");
wstring initString = configp->Get(L"init");
if (initString == L"fixedValue")
Value().SetValue((ElemType) configp->Get(L"value"));
else if (initString == L"uniform" || initString == L"gaussian")
{
// TODO: add these options also to old NDL
static unsigned long randomSeed = 1;
int forcedRandomSeed = configp->Get(L"randomSeed"); // forcing a specific random seed is useful for testing to get repeatable initialization independent of evaluation order
InitRandom((initString == L"uniform"), forcedRandomSeed < 0 ? randomSeed++ : (unsigned long) forcedRandomSeed, configp->Get(L"initValueScale"), configp->Get(L"initOnCPUOnly"));
}
else if (initString == L"fromFile")
{
wstring initFromFilePath = configp->Get(L"initFromFilePath");
if (initFromFilePath.empty())
RuntimeError("initFromFilePath parameter must be provided when using \"fromFile\" initialization method");
InitFromFile(initFromFilePath);
}
else if (initString == L"fromLiteral")
{
wstring initFromLiteral = configp->Get(L"initFromLiteral");
if (initFromLiteral.empty())
RuntimeError("initFromLiteral parameter must be provided when using \"fromLiteral\" initialization method");
size_t numRows, numCols;
auto array = File::LoadMatrixFromStringLiteral<ElemType>(msra::strfun::utf8(initFromLiteral), numRows, numCols);
InitFromArray(array, numRows, numCols);
}
else
RuntimeError("init must be one of the values of [ uniform | gaussian | fixedValue | fromFile ]");
}