Exemplos de getConfig em Python

Exemplo n.º 1

Arquivo: StandardVae_featureGeneration_reconstructionClassification.py Projeto: HenglinShi/Bidirectional-LSTM-VAE

def main():
    experimentName = 'StandardVae_featureGeneration_reconstructionClassification'

    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 = Ntu(mConfig.dataPath, mConfig)

    batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData_vaeReconstruction(
        mConfig)

    mNet = VaeBlstm_v13(sampleDim, segLen_max, mConfig)
    mNet.train(6000, mConfig.batchSize_train, batchSource_train,
               mConfig.batchSize_test, batchSource_test)

    pass

Exemplo n.º 2

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

        
    mConfig.baseLearningRate = 0.0002
    mData = Ntu(mConfig.dataPath, mConfig)
    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)

Exemplo n.º 3

Arquivo: MyVae_selfReconstruction_rawJoint_zClassification.py Projeto: HenglinShi/Bidirectional-LSTM-VAE

def main():
    args = parseArgs()
    experimentName = 'MyVae_selfReconstruction_rawJoint_zClassification'
    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 = Ntu(mConfig.dataPath, mConfig)
    batchSource_train, batchSource_test, batchSource_valid, segLen_max, sampleDim, frameLabelList_train, frameLabelList_test, frameLabelList_valid = mData.getData(
        mConfig)

    mNet = VaeBlstm_v7(sampleDim, segLen_max, mConfig)
    mNet.train(6000, mConfig.batchSize_train, batchSource_train,
               mConfig.batchSize_test, batchSource_test)

Exemplo n.º 4

Arquivo: StandardVae_selfReconstruction_rawJoint_zClassification.py Projeto: HenglinShi/Bidirectional-LSTM-VAE

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
    mConfig.baseLearningRate = 0.0001
    mData = Ntu(mConfig.dataPath, mConfig)
    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)