/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #pragma once #include namespace paddle { namespace enumeration_wrapper { enum PassType { PASS_TRAIN, // Train pass PASS_TEST, // Test pass PASS_GC, // Gradient Check pass PASS_METRIC, // pass for generate template output with no drop rate. // pass for metric learning training with metric learning error, only used // when we are doing KNN evaluation. PASS_METRIC_TRAIN, PASS_METRIC_TRAIN_WITH_NOERROR, // Pass for metric learning training // with no evaluation. }; enum ParameterType { PARAMETER_VALUE = 0, PARAMETER_GRADIENT, PARAMETER_MOMENTUM, // Used by ParameterAverager PARAMETER_SUM1, PARAMETER_SUM2, PARAMETER_SUM3, // also used by AdagradParameterUpdater/AdadeltaParameterUpdater PARAMETER_LEARNING_RATE, // Used by Sparse SGD update PARAMETER_UPDATE_TIME, // Used by async_sgd // Change of the parameter since last remote update PARAMETER_DELTA, // Used by BatchRemoteParameterUpdater PARAMETER_GRADIENT_SUM, // Used by AdagradParameterUpdater/AdadeltaParameterUpdater PARAMETER_GRADIENT_SQURESUM, PARAMETER_GRADIENT_SQURESUM1, // Used by SparseConnected layer PARAMETER_ROWS, PARAMETER_COLS, // Used by Adam Optimizer. PARAMETER_SECOND_MOMENTUM, // Used By AdaMax Optimizer. PARAMETER_WEIGHTED_INFINITY_NORM, // Used by remote parameter average PARAMETER_APPLY, // Used by sparse momentum PARAMETER_MOMENTUM_UT, PARAMETER_MOMENTUM_VT, NUM_PARAMETER_TYPES, }; } // namespace enumeration_wrapper //! explicit import enum into paddle namespace. using namespace enumeration_wrapper; // NOLINT class TrainAlgorithm { public: static const std::string SGD; static const std::string AsyncSGD; static const std::string OWLQN; static inline bool isValid(const std::string& algo) { return algo == SGD || algo == AsyncSGD || algo == OWLQN; } }; #ifdef __AVX__ const int ALIGN_HINT = 32; #else const int ALIGN_HINT = 16; #endif } // namespace paddle