Integrate yuqtang/StagingFixNewLearner2 into master
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@ -4460,13 +4460,13 @@ namespace CNTK
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///
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/// A special value that can be used for the minibatchSize to indicate that the reference minibatch size is not specified.
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///
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static const size_t UnspecifiedMinibatchSize = 0;
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static const size_t IgnoredMinibatchSize = 0;
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///
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/// Create a schedule with a constant parameter value.
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/// @param value a single value to populate the schedule
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/// @param minibatchSize a minibatch size that the @e value specifies for.
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///
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CNTK_API TrainingParameterSchedule(T value, size_t minibatchSize = UnspecifiedMinibatchSize);
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CNTK_API TrainingParameterSchedule(T value, size_t minibatchSize = IgnoredMinibatchSize);
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#ifndef SWIG
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///
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@ -4475,7 +4475,7 @@ namespace CNTK
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/// and so on. The last value is then used repeatedly until the end of training.
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/// @e minibatchSize is the a minibatch size that each schedule[i] specifies for.
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///
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CNTK_API TrainingParameterSchedule(const std::vector<T>& schedule, size_t epochSize = FullDataSweep, size_t minibatchSize = UnspecifiedMinibatchSize);
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CNTK_API TrainingParameterSchedule(const std::vector<T>& schedule, size_t epochSize = FullDataSweep, size_t minibatchSize = IgnoredMinibatchSize);
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#endif
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///
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@ -4488,7 +4488,7 @@ namespace CNTK
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/// after which the values is switched to '0.005'.
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/// @e minibatchSize is the a minibatch size that each schedule[i] specifies for.
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///
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CNTK_API TrainingParameterSchedule(const std::vector<std::pair<size_t, T>>& schedule, size_t epochSize = FullDataSweep, size_t minibatchSize = UnspecifiedMinibatchSize);
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CNTK_API TrainingParameterSchedule(const std::vector<std::pair<size_t, T>>& schedule, size_t epochSize = FullDataSweep, size_t minibatchSize = IgnoredMinibatchSize);
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///
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@ -4635,7 +4635,7 @@ namespace CNTK
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///
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/// A special value that can be used for the minibatchSize to indicate that the reference minibatch size is not specified.
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///
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CNTK_API static const size_t UnspecifiedMinibatchSize;
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CNTK_API static const size_t IgnoredMinibatchSize;
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public:
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//
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@ -4717,7 +4717,7 @@ namespace CNTK
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///setting and be specialized to its own reference minibatch size. However, this is only suggested for advanced
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///users.
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CNTK_API void SetMinibatchSize(std::size_t minibatchSize) { GetOptions().Add(MinibatchSizeKey, minibatchSize); }
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CNTK_API std::size_t GetMinibatchSize() const { return GetOptions().GetOrElse(MinibatchSizeKey, UnspecifiedMinibatchSize); }
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CNTK_API std::size_t GetMinibatchSize() const { return GetOptions().GetOrElse(MinibatchSizeKey, IgnoredMinibatchSize); }
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CNTK_API void SetLearningRateSchedule(const LearningRateSchedule& learningRateSchedule) { m_learningRateSchedule = learningRateSchedule; }
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CNTK_API const LearningRateSchedule& GetLearningRateSchedule() const { return m_learningRateSchedule; }
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@ -4726,7 +4726,7 @@ namespace CNTK
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template<typename T>
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static bool IsCompatibleMode(const TrainingParameterSchedule<T>& schedule)
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{
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return schedule.GetMinibatchSize() == UnspecifiedMinibatchSize;
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return schedule.GetMinibatchSize() == IgnoredMinibatchSize;
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}
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///
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@ -4737,7 +4737,7 @@ namespace CNTK
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{
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if (GetOptions().Contains(MinibatchSizeKey))
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{
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return GetMinibatchSize() == UnspecifiedMinibatchSize;
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return GetMinibatchSize() == IgnoredMinibatchSize;
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}
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else
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//if the learner minbiatch size is not set, by default it is not in compatible mode.
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@ -36,7 +36,7 @@ namespace CNTK
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///
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/// A special value that can be used for the minibatchSize to indicate that the reference minibatch size is not specified.
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///
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CNTK_API const size_t Learner::UnspecifiedMinibatchSize = TrainingParameterSchedule<double>::UnspecifiedMinibatchSize;
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CNTK_API const size_t Learner::IgnoredMinibatchSize = TrainingParameterSchedule<double>::IgnoredMinibatchSize;
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// This method completely replaces the current schedule with the new schedule. However, since
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@ -162,13 +162,13 @@ void TestTrainingParametersSchedule()
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assert(schedule3[100] == 0.3);
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LearningRateSchedule schedule4(vector<double>{ 0.5 }, 10 ); // without vector<> gcc complains that conversion here is ambiguousS
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assert(schedule4.GetMinibatchSize() == LearningRateSchedule::UnspecifiedMinibatchSize);
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assert(schedule4.GetMinibatchSize() == LearningRateSchedule::IgnoredMinibatchSize);
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assert(schedule4[0] == 0.5);
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assert(schedule4[10] == 0.5);
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assert(schedule4[100] == 0.5);
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LearningRateSchedule schedule5{ std::vector<double>{ 0.5, 0.3, 0.2 }, 10 };
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assert(schedule5.GetMinibatchSize() == LearningRateSchedule::UnspecifiedMinibatchSize); //unspecified reference minibatch size is 0
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assert(schedule5.GetMinibatchSize() == LearningRateSchedule::IgnoredMinibatchSize); //unspecified reference minibatch size is 0
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assert(schedule5[0] == 0.5);
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assert(schedule5[9] == 0.5);
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assert(schedule5[10] == 0.3);
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@ -177,20 +177,20 @@ void TestTrainingParametersSchedule()
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assert(schedule5[100] == 0.2);
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MomentumSchedule schedule6{ { make_pair(1, 0.5) } }; // without make_pair this is interpreted as a vector of doubles
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assert(schedule6.GetMinibatchSize() == MomentumSchedule::UnspecifiedMinibatchSize);
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assert(schedule6.GetMinibatchSize() == MomentumSchedule::IgnoredMinibatchSize);
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assert(schedule6[0] == 0.5);
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assert(schedule6[10] == 0.5);
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assert(schedule6[100] == 0.5);
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LearningRateSchedule schedule7{ std::vector<std::pair<size_t, double>>{ { 1, 0.5 }, { 1, 0.3 }, { 1, 0.2 } } };
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assert(schedule7.GetMinibatchSize() == LearningRateSchedule::UnspecifiedMinibatchSize);
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assert(schedule7.GetMinibatchSize() == LearningRateSchedule::IgnoredMinibatchSize);
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assert(schedule7[0] == 0.5);
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assert(schedule7[1] == 0.3);
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assert(schedule7[2] == 0.2);
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assert(schedule7[100] == 0.2);
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MomentumSchedule schedule8{ std::vector<std::pair<size_t, double>>{ { 1, 0.5 }, { 1, 0.3 }, { 1, 0.2 } }, 10 };
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assert(schedule8.GetMinibatchSize() == MomentumSchedule::UnspecifiedMinibatchSize);
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assert(schedule8.GetMinibatchSize() == MomentumSchedule::IgnoredMinibatchSize);
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assert(schedule8[0] == 0.5);
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assert(schedule8[9] == 0.5);
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assert(schedule8[10] == 0.3);
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@ -208,7 +208,7 @@ void TestTrainingParametersSchedule()
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assert(schedule9[100] == 0.2);
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MomentumSchedule schedule10 = { std::vector<std::pair<size_t, double>>{ { 3, 0.5 }, { 2, 0.3 }, { 1, 0.2 } }, 10 };
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assert(schedule10.GetMinibatchSize() == MomentumSchedule::UnspecifiedMinibatchSize);
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assert(schedule10.GetMinibatchSize() == MomentumSchedule::IgnoredMinibatchSize);
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assert(schedule10[0] == 0.5);
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assert(schedule10[29] == 0.5);
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assert(schedule10[30] == 0.3);
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@ -117,10 +117,10 @@
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%rename(l1_regularization_weight) CNTK::AdditionalLearningOptions::l1RegularizationWeight;
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%rename(l2_regularization_weight) CNTK::AdditionalLearningOptions::l2RegularizationWeight;
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%rename(unspecified_minibatch_size) CNTK::TrainingParameterSchedule<double>::UnspecifiedMinibatchSize;
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%rename(unspecified_minibatch_size) CNTK::TrainingParameterSchedule<std::size_t>::UnspecifiedMinibatchSize;
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%rename(ignored_minibatch_size) CNTK::TrainingParameterSchedule<double>::IgnoredMinibatchSize;
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%rename(ignored_minibatch_size) CNTK::TrainingParameterSchedule<std::size_t>::IgnoredMinibatchSize;
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%rename(_MINIBATCH_SIZE) CNTK::Learner::MinibatchSizeKey; // L"MinibatchSize"
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%rename(unspecified_minibatch_size) CNTK::Learner::UnspecifiedMinibatchSize;
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%rename(ignored_minibatch_size) CNTK::Learner::IgnoredMinibatchSize;
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%rename(_options) CNTK::Learner::GetOptions;
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%rename(ndcg_at_1) CNTK::NDCGAt1;
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@ -107,7 +107,7 @@ class QLearning(AgentBaseClass):
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self._q.parameters,
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C.learners.learning_rate_schedule(
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self._parameters.initial_eta, C.learners.UnitType.sample),
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minibatch_size=minibatch_size,
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use_mean_gradient=True,
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momentum=C.learners.momentum_schedule(self._parameters.momentum),
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variance_momentum=C.learners.momentum_schedule(0.999),
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gradient_clipping_threshold_per_sample=
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@ -154,7 +154,7 @@ class Learner(cntk_py.Learner):
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_verify_learning_rate_type(learning_rate)
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if not learning_rate.is_minibatch_size_explicitly_specified:
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#If the schedule minibatch size is not explicitly specified, the learner's specification will take over
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if self.minibatch_size is not None and self.minibatch_size != self.unspecified_minibatch_size:
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if self.minibatch_size is not None and self.minibatch_size != self.ignored_minibatch_size:
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learning_rate.minibatch_size = self.minibatch_size
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return super(Learner, self).reset_learning_rate(learning_rate)
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@ -164,7 +164,7 @@ class Learner(cntk_py.Learner):
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'''
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return super(Learner, self).learning_rate()
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IGNORE = Learner.unspecified_minibatch_size
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IGNORE = Learner.ignored_minibatch_size
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'''
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Indicate that the minibatch size is ignored in learning's hyper-parameter schedule.
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'''
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@ -288,7 +288,7 @@ def training_parameter_schedule(schedule, unit=UnitType.minibatch, epoch_size=No
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if unit == UnitType.sample:
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ref_minibatch_size = 1
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else: # unit == UnitType.minibatch
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ref_minibatch_size = cntk_py.training_double_parameter_schedule.unspecified_minibatch_size
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ref_minibatch_size = cntk_py.training_double_parameter_schedule.ignored_minibatch_size
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if isinstance(schedule, cntk_py.training_double_parameter_schedule):
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schedule.is_minibatch_size_explicitly_specified = True #legacy learning parameter always have the specification
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@ -325,9 +325,9 @@ def learning_parameter_schedule(schedule, minibatch_size=None, epoch_size=None):
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pair, i.e. [(num_epoch_1, p_1), (num_epoch_n, p_2), .., (num_epoch_n, p_n)], the i-th parameter is used as a
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value from the (``epoch_size`` * (num_epoch_0 + ... + num_epoch_2 + ... + num_epoch_(i-1) + 1)-th sample to the
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(``epoch_size`` * num_epoch_i)-th sample (taking num_epoch_0 = 0 as a special initialization).
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minibatch_size (int): an integer to specify the reference minibatch size that schedule are designed for;
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minibatch_size (int): an integer to specify the minibatch size that schedule are designed for.
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CNTK will scale the schedule internally so as to simulate the behavior of the schedule as much as possible
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to match the designed effect. If it is not specified, CNTK will set to the special value cntk.learners.unspecified_minibatch_size.
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to match the designed effect. If it is not specified, CNTK will set to the special value :attr:`IGNORE`.
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epoch_size (optional, int): number of samples as a scheduling unit.
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Parameters in the schedule change their values every ``epoch_size``
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samples. If no ``epoch_size`` is provided, this parameter is substituted
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@ -514,17 +514,17 @@ def _infer_ref_minibatch_size_from_legacy_use_mean_gradient(ref_minibatch_size,
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#if ref_minibatch_size and the legacy use_mean_gradient are neither specified
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return None
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if ref_minibatch_size is not None:
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if use_mean_gradient == True and ref_minibatch_size != cntk_py.Learner.unspecified_minibatch_size:
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if use_mean_gradient == True and ref_minibatch_size != cntk_py.Learner.ignored_minibatch_size:
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Warning(
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'Learner reference minibatch size is specified while use_mean_gradient (depreated option) is specified to True. Learner reference minibatch size will override the mean gradient behavior')
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#if the ref_minibatch_size is specified, it overrides the legacay use_mean_gradient specification
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return ref_minibatch_size
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elif use_mean_gradient is not None:
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#if the ref_minibatch_size is NOT specified, the legacay use_mean_gradient specification take in the effect
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return cntk_py.Learner.unspecified_minibatch_size if use_mean_gradient is True else None
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return cntk_py.Learner.ignored_minibatch_size if use_mean_gradient is True else None
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return None
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def _infer_learning_parameter_schedule(number_or_schedule, ref_minibatch_size, epoch_size):
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def _infer_learning_parameter_schedule(number_or_schedule, ref_minibatch_size, epoch_size, use_mean_gradient=None):
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#the input is a number, create a new training parameter
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if isinstance(number_or_schedule, (int, float)) or \
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(isinstance(number_or_schedule, list) and all(isinstance(r, (int, float, tuple)) for r in number_or_schedule)):
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@ -538,6 +538,13 @@ def _infer_learning_parameter_schedule(number_or_schedule, ref_minibatch_size, e
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if not number_or_schedule.is_minibatch_size_explicitly_specified and ref_minibatch_size is not None:
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#If the schedule minibatch size is not explicitly specified, the learner's specification will take over
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number_or_schedule.minibatch_size = ref_minibatch_size
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#for backward compatibility: use_mean_gradient = True and lr.unit = UnitType.sample
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#this combination was there to avoid the double-scaling of gradients when the gradients are already mean gradients
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if use_mean_gradient and number_or_schedule.minibatch_size == 1:
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#override the learning rate's minibatch_size to IGNORE
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number_or_schedule.minibatch_size = IGNORE
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Warning('use_mean_gradient=True and learning_rate_schedule.unit=UnitType.sample is a deprecated combination. '
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'Please use the new learner APIs: see https://www.cntk.ai/pythondocs/cntk.learners.html for details.')
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return number_or_schedule
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else:
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raise ValueError('training parameter schedule type (%s) not supported. '
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@ -549,7 +556,7 @@ def _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, ref_
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#if non-None reference_minibatch_size will take precedence otherwise according use_mean_gradient if it is True
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ref_minibatch_size = _infer_ref_minibatch_size_from_legacy_use_mean_gradient(ref_minibatch_size, use_mean_gradient)
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#if minibatch_size is not None, any schedules that are with unspecified reference minibatch size will be overrided.
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schedule = _infer_learning_parameter_schedule(schedule, ref_minibatch_size, epoch_size)
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schedule = _infer_learning_parameter_schedule(schedule, ref_minibatch_size, epoch_size, use_mean_gradient)
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_verify_learning_rate_type(schedule)
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return schedule, ref_minibatch_size
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@ -588,8 +595,8 @@ def sgd(parameters, lr,
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
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to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
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In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
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will have the parameters apply as it is preventing CNTK performing any parameter scaling. See also: :func:`learning_parameter_schedule`
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
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will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
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epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule`
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@ -658,8 +665,8 @@ def momentum_sgd(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
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to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
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In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
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will have the parameters apply as it is preventing CNTK performing any parameter scaling.
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
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will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
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epoch_size (optional, int): number of samples as a scheduling unit for learning rate and momentum. See also: :func:`learning_parameter_schedule`
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Returns:
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@ -727,8 +734,8 @@ def nesterov(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
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to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
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In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
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will have the parameters apply as it is preventing CNTK performing any parameter scaling.
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
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will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
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epoch_size (optional, int): number of samples as a scheduling unit for learning rate and momentum. See also: :func:`learning_parameter_schedule`
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Returns:
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@ -801,9 +808,8 @@ def adadelta(parameters, lr=learning_rate_schedule(1, UnitType.sample), rho=0.95
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
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to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
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In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
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will have the parameters apply as it is preventing CNTK performing any parameter scaling. If the learner's learning rate
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schedule ``lr`` has its own specification of reference minibatch size, the learning rate schedule's specification takes precedence.
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
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will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
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epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule`
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Returns:
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@ -870,8 +876,8 @@ def adagrad(parameters, lr, need_ave_multiplier=True,
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
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to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
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In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
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will have the parameters apply as it is preventing CNTK performing any parameter scaling.
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if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
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will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
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epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule`
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Returns:
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@ -945,8 +951,8 @@ def fsadagrad(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
|
||||
to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
|
||||
In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
|
||||
if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
|
||||
will have the parameters apply as it is preventing CNTK performing any parameter scaling.
|
||||
if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
|
||||
will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
|
||||
epoch_size (optional, int): number of samples as a scheduling unit for learning rate, momentum and variance_momentum. See also: :func:`learning_parameter_schedule`
|
||||
|
||||
Returns:
|
||||
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|
@ -1025,8 +1031,8 @@ def adam(parameters, lr, momentum, unit_gain=default_unit_gain_value(),
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|||
size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
|
||||
to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
|
||||
In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
|
||||
if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
|
||||
will have the parameters apply as it is preventing CNTK performing any parameter scaling.
|
||||
if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
|
||||
will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
|
||||
epoch_size (optional, int): number of samples as a scheduling unit for learning rate, momentum and variance_momentum. See also: :func:`learning_parameter_schedule`
|
||||
|
||||
Returns:
|
||||
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|
@ -1104,8 +1110,8 @@ def rmsprop(parameters, lr,
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size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters
|
||||
to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters.
|
||||
In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule;
|
||||
if the learning rate schedule does not specify the minibatch_size, CNTK will set it to 1. Setting minibatch_size to 0
|
||||
will have the parameters apply as it is preventing CNTK performing any parameter scaling.
|
||||
if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE`
|
||||
will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule`
|
||||
epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule`
|
||||
|
||||
Returns:
|
||||
|
|
|
|||
|
|
@ -138,6 +138,15 @@ def test_learner_init_legacy():
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assert learner.learning_rate() == 0.1
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assert learner.minibatch_size == C.learners.IGNORE # the learner's reference minibatch size is still 0
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||||
# this will be deprecated in future version: This is logical invalid combination but it was the only way to use mean gradient and set learning rate in the past.
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learner = sgd(res.parameters, lr=learning_rate_schedule(0.1, UnitType.sample), use_mean_gradient=True)
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assert learner.is_compatible_mode() == True
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||||
assert learner.learning_rate() == 0.1
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||||
#test the override in the new version
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assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
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||||
assert learner.minibatch_size == C.learners.IGNORE # the learner's reference minibatch size is still 0
|
||||
|
||||
|
||||
# for backcompatibility test
|
||||
# this will be deprecated in future version
|
||||
# The UnitType will provide per minibatch instruction for the learner
|
||||
|
|
|
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