def main(): args = parseArgs() finetune = args.finetune print finetune experimentName = 'MyVae_featureGeneration_reconstructionClassification' if not os.path.exists(workingDir): os.mkdir(workingDir) experimentDir = os.path.join(workingDir, experimentName) if not os.path.exists(experimentDir): os.mkdir(experimentDir) #finetune = False mConfig = getConfig(experimentName, finetune) mConfig.workingDir = experimentDir mData = ChaLearn(mConfig.dataPath, 1) batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData_vaeReconstruction(mConfig) mNet = VaeBlstm_v5(sampleDim, segLen_max, mConfig) mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test) pass
def main(): experimentName = 'StandardVae_featureGeneration_zClassification' args = parseArgs() finetune = args.finetune mConfig = getConfig(experimentName, finetune) dataset = 'chalearn' experimentDir = os.path.join(workingDir, experimentName) if not os.path.exists(experimentDir): os.mkdir(experimentDir) mConfig.workingDir = experimentDir mData = ChaLearn(mConfig.dataPath, 1) batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData_vaeReconstruction(mConfig) mNet = VaeBlstm_v14(sampleDim, segLen_max, mConfig) mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)
def main(): experimentName = 'Blstm_rawJoint' mConfig = getConfig(experimentName, 'False') dataset = 'chalearn' #workingDir = '/wrk/hshi/DONOTREMOVE/git/FeatureLearningAndGestureRecognition/ExperimentArchive/New/20180418' experimentDir = os.path.join(workingDir, experimentName) if not os.path.exists(experimentDir): os.mkdir(experimentDir) mConfig.workingDir = experimentDir mData = ChaLearn(mConfig.dataPath, 1) batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData(mConfig) mNet = Blstm_v1(sampleDim, segLen_max, mConfig) mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)
def main(): experimentName = 'StandardVae_selfReconstruction_rawJoint_zClassification' dataset = 'chalearn' #workingDir = '/wrk/hshi/DONOTREMOVE/git/FeatureLearningAndGestureRecognition/ExperimentArchive/New/20180418' experimentDir = os.path.join(workingDir, experimentName) if not os.path.exists(experimentDir): os.mkdir(experimentDir) args = parseArgs() finetune = args.finetune mConfig = getConfig(experimentName, finetune) mConfig.workingDir = experimentDir mData = ChaLearn(mConfig.dataPath, 1) batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData(mConfig) mNet = VaeBlstm_v11(sampleDim, segLen_max, mConfig) mNet.train(6000, mConfig.batchSize_train, batchSource_train, mConfig.batchSize_test, batchSource_test)