https://github.com/Microsoft/CNTK
Tip revision: 392e7ef5240a50775388092f57979ddf0141c3ae authored by Bowen Bao on 06 December 2018, 22:54:11 UTC
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Tip revision: 392e7ef
Trainer.cpp
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#include "stdafx.h"
#include "CNTKLibrary.h"
#include "Utils.h"
#include "Learner.h"
#include "PerformanceProfiler.h"
#include "CompositeFunction.h"
#include "Serialization.h"
namespace
{
const std::wstring versionPropertyName = L"Version";
const std::wstring learnersPropertyName = L"Learners";
const std::wstring externalStatePropertyName = L"ExternalState";
const std::wstring distributedStatePropertyName = L"DistributedState";
// Version history:
// 0 -- a version number before the versioning was introduced for the trainer's checkpoints.
// 1 -- initial version: added a key-value pair for the checkpoint version info, added
// distributed state key to save all local state collected from distributed workers.
static const size_t trainerCheckpointVersion = 1;
}
namespace CNTK
{
Trainer::Trainer(const FunctionPtr& model, const FunctionPtr& lossFunction,
const std::vector<LearnerPtr>& parameterLearners,
const std::vector<ProgressWriterPtr>& progressWriters)
: Trainer(model, lossFunction, nullptr, parameterLearners, progressWriters)
{}
Trainer::Trainer(const FunctionPtr& model, const FunctionPtr& lossFunction, const FunctionPtr& evaluationFunction,
const std::vector<LearnerPtr>& parameterLearners,
const std::vector<ProgressWriterPtr>& progressWriters)
: Evaluator(evaluationFunction, progressWriters, false),
m_model(model),
m_lossFunction(lossFunction),
m_parameterLearners(std::make_shared<Learners>(parameterLearners)),
m_prevMinibatchNumSamples(0),
m_distributed(false),
m_aggregatedTrainingLossValue(std::make_shared<Accumulator>()),
m_aggregatedTrainingEvalCriterionValue(),
m_prevDistributedTotalNumSamples(0)
{
std::vector<Variable> combinedFunctionArgs;
if (m_model) // model is optional, since it may not be adding any information on top of lossFunction
combinedFunctionArgs = m_model->Outputs();
combinedFunctionArgs.push_back(m_lossFunction);
if (m_lossFunction->Output().GetDataType() == DataType::Float16)
fprintf(stderr, "WARNING: using Float16 for loss function may cause overflow, please cast to float");
if (!m_lossFunction->Output().DynamicAxes().empty())
{
m_aggregatedLossFunction = ReduceSum(lossFunction, Axis::AllAxes(), L"aggregateLoss");
combinedFunctionArgs.push_back(m_aggregatedLossFunction);
m_trainingSampleCountVar = m_lossFunction;
}
else
{
m_aggregatedLossFunction = m_lossFunction;
std::function<std::pair<Variable, bool>(const FunctionPtr& root)> FindTrainingSampleCountVar;
FindTrainingSampleCountVar = [&FindTrainingSampleCountVar](const FunctionPtr& root) -> std::pair<Variable, bool> {
const auto& outputs = root->Outputs();
auto firstOutputWithDynamicAxes = std::find_if(outputs.begin(), outputs.end(), [](const Variable& var) { return !var.DynamicAxes().empty(); });
if (firstOutputWithDynamicAxes != outputs.end())
return std::make_pair(*firstOutputWithDynamicAxes, true);
const auto& inputs = root->Inputs();
for (const auto& input : inputs)
{
if (!input.DynamicAxes().empty())
return std::make_pair(input, true);
if (input.IsOutput())
{
auto retVal = FindTrainingSampleCountVar(input.Owner());
if (retVal.second)
return retVal;
}
}
return std::make_pair(Variable(), false);
};
auto findTrainingSampleCountVarRetVal = FindTrainingSampleCountVar(m_lossFunction->RootFunction());
if (!findTrainingSampleCountVarRetVal.second)
InvalidArgument("Trainer: Failed to find a variable underlying the graph rooted at specified loss function '%S', from which the training sample count can be determined.", m_lossFunction->RootFunction()->AsString().c_str());
m_trainingSampleCountVar = findTrainingSampleCountVarRetVal.first;
if (GetTraceLevel() >= TraceLevel::Info)
fprintf(stderr, "Info: Trainer loss Function '%S' output does not have a batch axis; the first Variable '%S' with a batch axis found in the graph underlying the scalar "
"loss Function will be used to determine minibatch training sample count.\n", m_lossFunction->AsString().c_str(), m_trainingSampleCountVar.AsString().c_str());
if (std::find(combinedFunctionArgs.begin(), combinedFunctionArgs.end(), m_trainingSampleCountVar) == combinedFunctionArgs.end())
combinedFunctionArgs.push_back(m_trainingSampleCountVar);
}
if (evaluationFunction)
{
auto evalArgs = GetCombinedEvalFunctionArgs();
combinedFunctionArgs.insert(combinedFunctionArgs.end(), evalArgs.begin(), evalArgs.end());
m_aggregatedTrainingEvalCriterionValue = std::make_shared<Accumulator>();
}
// create a default eval value in case there's no criterion
m_prevMinibatchAggregateEvalCriterionValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(0, m_aggregatedLossFunction->Output().GetDataType(), NDShape{}, DeviceDescriptor::CPUDevice()));
m_combinedTrainingFunction = Combine(combinedFunctionArgs);
SetCombinedEvalFunction(m_combinedTrainingFunction);
auto modelParameters = m_combinedTrainingFunction->Parameters();
m_learnerParameters = m_parameterLearners->GetParameters();
std::unordered_set<Parameter> modelParametersSet(modelParameters.begin(), modelParameters.end());
std::unordered_set<Parameter> learnerParametersNotPartOfModel;
for (const auto& learnerParameter : m_learnerParameters)
{
if (modelParametersSet.find(learnerParameter) == modelParametersSet.end())
learnerParametersNotPartOfModel.insert(learnerParameter);
}
for (const auto& modelParameter : modelParametersSet)
{
if (m_learnerParameters.find(modelParameter) == m_learnerParameters.end())
m_modelParametersNotCoveredByLearners.insert(modelParameter);
}
if (!learnerParametersNotPartOfModel.empty())
InvalidArgument("Trainer ctor: %d of the learner parameters '%S' are not part of the model specified",
(int)learnerParametersNotPartOfModel.size(), NamedListString(learnerParametersNotPartOfModel).c_str());
if (!m_modelParametersNotCoveredByLearners.empty())
fprintf(stderr, "[Note:] Trainer ctor: %d of the model parameters are not covered by any of the specified Learners; these parameters will not be learned\n", (int)m_modelParametersNotCoveredByLearners.size());
m_distributed = m_parameterLearners->IsDistributed();
if (m_distributed)
Evaluator::SetCommunicator(dynamic_cast<DistributedLearner*>(m_parameterLearners->ParameterLearners()[0].get())->GetCommunicator());
for (auto& learner : m_parameterLearners->ParameterLearners())
{
learner->AddProgressWriters(progressWriters);
}
}
bool Trainer::TrainMinibatch(const std::unordered_map<Variable, MinibatchData>& arguments, const DeviceDescriptor& computeDevice /*= DeviceDescriptor::UseDefaultDevice()*/)
{
std::unordered_map<Variable, ValuePtr> outputsToFetch = {};
return TrainMinibatch(arguments, outputsToFetch, computeDevice);
}
bool Trainer::TrainMinibatch(const std::unordered_map<Variable, MinibatchData>& arguments, std::unordered_map<Variable, ValuePtr>& outputsToFetch, const DeviceDescriptor& computeDevice /*= DeviceDescriptor::UseDefaultDevice()*/)
{
#ifndef CNTK_UWP
auto profMinibatch = Microsoft::MSR::CNTK::ScopeProfile(Microsoft::MSR::CNTK::profilerEvtMainMinibatch);
#endif
bool result = (!m_distributed) ?
TrainLocalMinibatch(GetInputs(arguments), outputsToFetch, IsAtSweepEnd(arguments), computeDevice) :
TrainDistributedMinibatch(GetInputs(arguments), outputsToFetch, IsAtSweepEnd(arguments), computeDevice);
// TODO: exclude updating progress writers from profiling?
UpdateTrainingProgress(m_prevMinibatchNumSamples, m_prevMinibatchAggregateTrainingLossValue,
m_prevMinibatchAggregateEvalCriterionValue, computeDevice);
return result;
}
bool Trainer::TrainMinibatch(const std::unordered_map<Variable, ValuePtr>& arguments, bool isSweepEndInArguments, const DeviceDescriptor& computeDevice /*= DeviceDescriptor::UseDefaultDevice()*/)
{
std::unordered_map<Variable, ValuePtr> outputsToFetch = {};
return TrainMinibatch(arguments, isSweepEndInArguments, outputsToFetch, computeDevice);
}
bool Trainer::TrainMinibatch(const std::unordered_map<Variable, ValuePtr>& arguments, bool isSweepEndInArguments, std::unordered_map<Variable, ValuePtr>& outputsToFetch, const DeviceDescriptor& computeDevice /*= DeviceDescriptor::UseDefaultDevice()*/)
{
#ifndef CNTK_UWP
auto profMinibatch = Microsoft::MSR::CNTK::ScopeProfile(Microsoft::MSR::CNTK::profilerEvtMainMinibatch);
#endif
bool result = (!m_distributed) ?
TrainLocalMinibatch(arguments, outputsToFetch, isSweepEndInArguments, computeDevice) :
TrainDistributedMinibatch(arguments, outputsToFetch, isSweepEndInArguments, computeDevice);
// TODO: exclude updating progress writers from profiling?
UpdateTrainingProgress(m_prevMinibatchNumSamples, m_prevMinibatchAggregateTrainingLossValue,
m_prevMinibatchAggregateEvalCriterionValue, computeDevice);
return result;
}
bool Trainer::TrainLocalMinibatch(const std::unordered_map<Variable, ValuePtr>& arguments, std::unordered_map<Variable, ValuePtr>& outputsToFetch, bool sweepEnd, const DeviceDescriptor& computeDevice /*= DeviceDescriptor::UseDefaultDevice()*/)
{
bool emptyMinibatch = arguments.empty() || (arguments.begin()->second == nullptr);
if (emptyMinibatch) // Nothing to train with.
{
m_prevMinibatchNumSamples = 0;
return false;
}
std::unordered_map<Variable, ValuePtr> parameterGradients;
ExecuteForwardBackward(arguments, outputsToFetch, computeDevice, parameterGradients);
#ifndef CNTK_UWP
auto profWeights = Microsoft::MSR::CNTK::ScopeProfile(Microsoft::MSR::CNTK::profilerEvtMainWeights);
#endif
std::unordered_map<Parameter, NDArrayViewPtr> gradients;
for (const auto& parameter : m_learnerParameters)
gradients[parameter] = parameterGradients[parameter]->Data();
return m_parameterLearners->Update(gradients, m_prevMinibatchNumSamples, sweepEnd);
}
bool Trainer::TrainDistributedMinibatch(const std::unordered_map<Variable, ValuePtr>& arguments, std::unordered_map<Variable, ValuePtr>& outputsToFetch, bool sweepEnd, const DeviceDescriptor& computeDevice /*= DeviceDescriptor::UseDefaultDevice()*/)
{
std::unordered_map<Parameter, NDArrayViewPtr> gradients;
gradients.reserve(m_learnerParameters.size());
bool emptyMinibatch = arguments.empty() || (arguments.begin()->second == nullptr);
NDArrayViewPtr trainingLoss = nullptr;
NDArrayViewPtr evalCriterion = nullptr;
if (emptyMinibatch)
{
m_prevMinibatchNumSamples = 0;
// Gradients are not existing.
for (const auto& parameter : m_learnerParameters)
gradients[parameter] = nullptr;
trainingLoss = MakeSharedObject<NDArrayView>(0, (m_aggregatedLossFunction ? m_aggregatedLossFunction->Output().GetDataType() : DataType::Float), NDShape{}, computeDevice);
evalCriterion = MakeSharedObject<NDArrayView>(0, (m_aggregatedEvaluationFunction ? m_aggregatedEvaluationFunction->Output().GetDataType() : DataType::Float), NDShape{}, computeDevice);
}
else
{
// Get gradients after forward/backward pass.
std::unordered_map<Variable, ValuePtr> parameterGradients;
// ExecuteForwardBackward updates m_prevMinibatchNumSamples to the local value.
ExecuteForwardBackward(arguments, outputsToFetch, computeDevice, parameterGradients);
for (const auto& parameter : m_learnerParameters)
gradients[parameter] = parameterGradients[parameter]->Data();
trainingLoss = m_prevMinibatchAggregateTrainingLossValue->Data();
evalCriterion = m_prevMinibatchAggregateEvalCriterionValue->Data();
}
auto currentWorkerNumSamples = m_prevMinibatchNumSamples;
auto prevTotalNumSamples = TotalNumberOfSamplesSeen();
MinibatchInfo info{ arguments.empty(), sweepEnd, m_prevMinibatchNumSamples, trainingLoss, evalCriterion };
bool updated = m_parameterLearners->Update(gradients, info);
// Here we update m_prevMinibatchNumSamples with aggregated value in the
// case of distributed learner.
m_prevMinibatchNumSamples = info.numberOfSamples;
// Update internal state.
if (emptyMinibatch)
{
// Have to reassign loss and criterion.
m_prevMinibatchAggregateEvalCriterionValue = std::make_shared<Value>(info.evalCriterionValue);
m_prevMinibatchAggregateTrainingLossValue = std::make_shared<Value>(info.trainingLossValue);
}
// Did we do a distributed sync?
// We determine this by checking if the increase in total #samples is > #samples processed by local worker
auto currentTotalNumSamples = TotalNumberOfSamplesSeen();
if ((currentTotalNumSamples - prevTotalNumSamples) > currentWorkerNumSamples)
{
for (auto& progressWriter : m_progressWriters)
progressWriter->UpdateDistributedSync(currentTotalNumSamples - m_prevDistributedTotalNumSamples, nullptr);
m_prevDistributedTotalNumSamples = currentTotalNumSamples;
}
return updated;
}
void Trainer::UpdateTrainingProgress(size_t numSamples, const ValuePtr& loss, const ValuePtr& evalCriterion,
const DeviceDescriptor& computeDevice)
{
if (numSamples == 0)
{
return;
}
m_aggregatedTrainingLossValue->Update(loss, computeDevice);
if (m_aggregatedTrainingEvalCriterionValue)
{
m_aggregatedTrainingEvalCriterionValue->Update(evalCriterion, computeDevice);
}
for (auto& progressWriter : m_progressWriters)
{
progressWriter->UpdateTraining(numSamples, m_aggregatedTrainingLossValue, m_aggregatedTrainingEvalCriterionValue);
}
}
void Trainer::SummarizeTrainingProgress()
{
// Aggregate across workers training loss and eval criteria. Needed for BMUF like learner which don't aggregate after every minibatch.
if (m_parameterLearners->DoAggregateMetricsIfNeededLambda)
{
NDArrayViewPtr localLossValue = nullptr;
if (m_aggregatedTrainingLossValue && m_aggregatedTrainingLossValue->IsInitialized())
{
localLossValue = m_aggregatedTrainingLossValue->Data();
}
NDArrayViewPtr localEvalCriterion = nullptr;
if (m_aggregatedTrainingEvalCriterionValue && m_aggregatedTrainingEvalCriterionValue->IsInitialized())
{
localEvalCriterion = m_aggregatedTrainingEvalCriterionValue->Data();
}
m_parameterLearners->DoAggregateMetricsIfNeededLambda(localLossValue, localEvalCriterion);
}
for (auto& progressWriter : m_progressWriters)
{
progressWriter->WriteTrainingSummary(m_aggregatedTrainingLossValue, m_aggregatedTrainingEvalCriterionValue);
}
m_aggregatedTrainingLossValue->Reset();
if (m_aggregatedTrainingEvalCriterionValue)
{
m_aggregatedTrainingEvalCriterionValue->Reset();
}
}
void Trainer::AddProgressWriters(const std::vector<ProgressWriterPtr>& progressWriters)
{
for (auto& learner : m_parameterLearners->ParameterLearners())
{
learner->AddProgressWriters(progressWriters);
}
m_progressWriters.insert(progressWriters.begin(), progressWriters.end());
}
void Trainer::PrintNodeTiming()
{
if (m_combinedTrainingFunction)
{
m_combinedTrainingFunction->PrintNodeTiming();
}
}
void Trainer::ExecuteForwardBackward(const std::unordered_map<Variable, ValuePtr>& arguments, std::unordered_map<Variable, ValuePtr>& outputsToFetch, const DeviceDescriptor& computeDevice, std::unordered_map<Variable, ValuePtr>& parameterGradients)
{
#ifndef CNTK_UWP
auto profForwardBackward = Microsoft::MSR::CNTK::ScopeProfile(Microsoft::MSR::CNTK::profilerEvtMainFB);
#endif
std::unordered_map<Variable, ValuePtr> outputs = { { m_aggregatedLossFunction, nullptr }, { m_trainingSampleCountVar, nullptr } };
if (m_aggregatedEvaluationFunction)
outputs.insert({ m_aggregatedEvaluationFunction, nullptr });
outputs.insert(outputsToFetch.begin(), outputsToFetch.end());
auto backPropSate = m_combinedTrainingFunction->Forward(arguments, outputs, computeDevice, { m_aggregatedLossFunction }, m_modelParametersNotCoveredByLearners);
m_prevMinibatchAggregateTrainingLossValue = outputs[m_aggregatedLossFunction];
if (m_aggregatedEvaluationFunction)
m_prevMinibatchAggregateEvalCriterionValue = outputs[m_aggregatedEvaluationFunction];
for (auto outputToFetch : outputsToFetch)
{
if (outputToFetch.second == nullptr)
outputsToFetch[outputToFetch.first] = outputs[outputToFetch.first];
}
if(!m_rootGradientValue ||
m_aggregatedLossFunction->Output().GetDataType() != m_rootGradientValue->GetDataType() ||
m_prevMinibatchAggregateTrainingLossValue->Shape() != m_rootGradientValue->Shape() ||
computeDevice != m_rootGradientValue->Device() ||
outputs.at(m_aggregatedLossFunction)->Mask() != m_rootGradientValue->Mask())
{
m_rootGradientValue = MakeSharedObject<Value>(MakeSharedObject<NDArrayView>(m_aggregatedLossFunction->Output().GetDataType(), m_prevMinibatchAggregateTrainingLossValue->Shape(), computeDevice), outputs.at(m_aggregatedLossFunction)->Mask());
}
DataType aggregateDataType = m_aggregatedLossFunction->Output().GetDataType();
if (aggregateDataType == DataType::Float)
m_rootGradientValue->Data()->SetValue(1.0f);
else if (aggregateDataType == DataType::Double)
m_rootGradientValue->Data()->SetValue(1.0);
else if (aggregateDataType == DataType::Float16)
m_rootGradientValue->Data()->SetValue(float16(1.0));
else
RuntimeError("DataType %s is not supported for root gradients", DataTypeName(aggregateDataType));
for (const auto& parameter : m_learnerParameters)
parameterGradients[parameter] = nullptr;
// TODO: Why Backward signature does not take Parameter instead of Variable for gradients?
m_combinedTrainingFunction->Backward(backPropSate, { { m_aggregatedLossFunction, m_rootGradientValue } }, parameterGradients);
m_prevMinibatchNumSamples = GetSampleCount(m_trainingSampleCountVar, outputs[m_trainingSampleCountVar]);
}
static std::wstring GetTrainerStateCheckpointFilePath(const std::wstring& modelFilePath)
{
const wchar_t* checkpointExt = L".ckp";
return modelFilePath + checkpointExt;
}
void Trainer::SaveCheckpoint(const std::wstring& modelFilePath, Dictionary externalState)
{
auto learnersState = m_parameterLearners->CreateCheckpoint();
if (!m_distributed)
return Save(modelFilePath, learnersState, externalState);
auto compositeFunction = dynamic_cast<CompositeFunction*>(m_combinedTrainingFunction.get());
Dictionary state;
state[internalWorkerStateKey] = compositeFunction->GetInternalState(); // this is the local worker's state.
state[externalWorkerStateKey] = externalState;
// Collect distributed external state.
DistributedCommunicatorPtr communicator = MPICommunicator();
communicator->Barrier();
std::vector<DictionaryPtr> remoteState;
communicator->Gather(state, remoteState, communicator->Workers());
Dictionary aggregatedState;
for (const auto& w : communicator->Workers())
{
aggregatedState[std::to_wstring(w.m_globalRank)] = *remoteState[w.m_globalRank];
}
if (communicator->CurrentWorker().IsMain())
Save(modelFilePath, learnersState, externalState, aggregatedState);
// all workers need to sync up after saving model to avoid read-after-write hazard
// i.e. one worker is in the middle of write while another tries to read
communicator->Barrier();
}
void Trainer::Save(const std::wstring& modelFilePath, const std::vector<DictionaryValue>& learnerState, const Dictionary& externalState, const Dictionary& distributedState)
{
std::wstring tempModelFile = modelFilePath + L".tmp";
Dictionary state;
state[versionPropertyName] = trainerCheckpointVersion;
state[learnersPropertyName] = learnerState;
state[externalStatePropertyName] = externalState;
state[distributedStatePropertyName] = distributedState;
m_combinedTrainingFunction->Save(tempModelFile);
std::wstring trainerStateCheckpointFilePath = GetTrainerStateCheckpointFilePath(modelFilePath);
std::wstring tempCheckpointFile = trainerStateCheckpointFilePath + L".tmp";
state.Save(tempCheckpointFile);
// The return value is ignored here.
_wunlink(modelFilePath.c_str());
_wunlink(trainerStateCheckpointFilePath.c_str());
renameOrDie(tempModelFile, modelFilePath);
renameOrDie(tempCheckpointFile, trainerStateCheckpointFilePath);
}
Dictionary Trainer::RestoreFromCheckpoint(const std::wstring& modelFilePath)
{
// Restore the model's parameters
m_combinedTrainingFunction->Restore(modelFilePath);
Dictionary checkpoint = Dictionary::Load(GetTrainerStateCheckpointFilePath(modelFilePath));
size_t version = 0;
if (checkpoint.Contains(versionPropertyName))
version = checkpoint[versionPropertyName].Value<size_t>();
auto learnerState = checkpoint[learnersPropertyName].Value<std::vector<DictionaryValue>>();
auto externalState = checkpoint[externalStatePropertyName].Value<Dictionary>();
m_parameterLearners->RestoreFromCheckpoint(learnerState);
if (!m_distributed)
{
return externalState;
}
// this ensures that nobody will start writing to the model/checkpoint files, until
// everybody is done reading them.
DistributedCommunicatorPtr communicator = MPICommunicator();
communicator->Barrier();
auto mainWorkerId = std::to_wstring(0);
auto localWorkerId = std::to_wstring(communicator->CurrentWorker().m_globalRank);
// before version 1, there was no distributed state per se. Instead, the external state
// contained a dictionary of worker-specific external states.
if (version == 0)
{
auto key = externalState.Contains(localWorkerId) ? localWorkerId : mainWorkerId;
return externalState[key].Value<Dictionary>();
}
Dictionary distributedState = checkpoint[distributedStatePropertyName].Value<Dictionary>();
if (communicator->CurrentWorker().IsMain() || !distributedState.Contains(localWorkerId))
{
return externalState;
}
// the checkpoint contains internal state for this worker.
Dictionary localState = distributedState[localWorkerId].Value<Dictionary>();
auto internalState = localState[internalWorkerStateKey].Value<Dictionary>();
auto compositeFunction = std::dynamic_pointer_cast<CompositeFunction>(m_combinedTrainingFunction);
if (compositeFunction == nullptr)
RuntimeError("Combined training function is not a CompositeFunction.");
// this assumes the compositeFunction (restored form a checkpoint made by the main node) and
// the internal worker state both have identical UIDs.
compositeFunction->SetInternalState(internalState);
return localState[externalWorkerStateKey].Value<Dictionary>();
}
double Trainer::PreviousMinibatchLossAverage() const
{
// TODO: better return 0; it is then still valid to compute lossAverage * numSamples
if (m_prevMinibatchNumSamples == 0)
RuntimeError("There was no preceding call to TrainMinibatch or the minibatch was empty.");
return m_prevMinibatchAggregateTrainingLossValue->AsScalar<double>() / m_prevMinibatchNumSamples;
}
double Trainer::PreviousMinibatchEvaluationAverage() const
{
if (!m_evaluationFunction)
InvalidArgument("Trainer::PreviousMinibatchEvaluationAverage: Cannot get evaluation criterion value when no evaluation function was specified during 'this' trainer's construction");
if (m_prevMinibatchNumSamples == 0)
RuntimeError("There was no preceding call to TrainMinibatch or the minibatch was empty.");
return m_prevMinibatchAggregateEvalCriterionValue->AsScalar<double>() / m_prevMinibatchNumSamples;
}
const std::vector<LearnerPtr>& Trainer::ParameterLearners() const
{
return m_parameterLearners->ParameterLearners();
}
size_t Trainer::TotalNumberOfSamplesSeen() const
{
return m_parameterLearners->GetMetricAggregatingLearner()->TotalNumberOfSamplesSeen();
}
size_t Trainer::TotalNumberOfUnitsSeen(DataUnit unit) const
{
switch (unit)
{
case DataUnit::Minibatch:
return m_parameterLearners->GetMetricAggregatingLearner()->TotalNumberOfMinibatchesSeen();
break;
case DataUnit::Sweep:
return m_parameterLearners->GetMetricAggregatingLearner()->TotalNumberOfSweepsSeen();
break;
case DataUnit::Sample:
return m_parameterLearners->GetMetricAggregatingLearner()->TotalNumberOfSamplesSeen();
default:
//should not be here; whenever a new data unit is defined, there should be a new case in this function.
LogicError("Unsupported data unit: %d", (int)unit);
}
}
TrainerPtr CreateTrainer(const FunctionPtr& model, const FunctionPtr& lossFunction, const std::vector<LearnerPtr>& parameterLearners,
const std::vector<ProgressWriterPtr>& progressWriters)
{
return MakeSharedObject<Trainer>(model, lossFunction, parameterLearners, progressWriters);
}
TrainerPtr CreateTrainer(const FunctionPtr& model, const FunctionPtr& lossFunction, const FunctionPtr& evaluationFunction, const std::vector<LearnerPtr>& parameterLearners,
const std::vector<ProgressWriterPtr>& progressWriters)
{
return MakeSharedObject<Trainer>(model, lossFunction, evaluationFunction, parameterLearners, progressWriters);
}
}