def comparison_run(cls, runScale='full', dataScale='full_2occupations', useCPU = True): numSeqs = EmbeddingDataReader(EmbeddingDataReader.premade_sources()[dataScale], 'bucketing', 100, 40, padToFull=True).maxXLen params = [('initialLearningRate', [1e-3]), ('l2RegLambda', [1e-4]), ('maxNumSeqs', [numSeqs]), ('rnnCellUnitsNProbs', [([32, 32, 32], [.5]*3)]), ('convFilterSizesNKeepProbs', [([2, 3, 4], [.5]*3)]), ('convNumFeaturesPerFilter', [8]), ('pooledKeepProb', [0.5])] cls.run_thru_data(EmbeddingDataReader, dataScale, make_params_dict(params), runScale, useCPU, padToFull=True)
def quick_run(cls, runScale ='basic', dataScale='tiny_fake_2', useCPU = True): numSeqs = EmbeddingDataReader(EmbeddingDataReader.premade_sources()[dataScale], 'bucketing', 100, 40, padToFull=True).maxXLen params = [('initialLearningRate', [1e-3]), ('l2RegLambda', [0]), ('maxNumSeqs', [numSeqs]), ('rnnCellUnitsNProbs', [([3], [0.9]) ]), ('convFilterSizesNKeepProbs', [([2], [1.]) ]), ('convNumFeaturesPerFilter', [4]), ('pooledKeepProb', [1])] cls.run_thru_data(EmbeddingDataReader, dataScale, make_params_dict(params), runScale, useCPU, padToFull=True)
def quick_learn(cls, runScale='small', dataScale='small_2occupations', useCPU=True): numSeqs = EmbeddingDataReader( EmbeddingDataReader.premade_sources()[dataScale], 'bucketing', 100, 40, padToFull=True).maxXLen params = [('initialLearningRate', [1e-3]), ('l2RegLambda', [0]), ('maxNumSeqs', [numSeqs]), ('filterSizes', [[2, 4]]), ('numFeaturesPerFilter', [3]), ('pooledKeepProb', [1])] cls.run_thru_data(EmbeddingDataReader, dataScale, make_params_dict(params), runScale, useCPU, padToFull=True)
def comparison_run(cls, runScale='medium', dataScale='full_2occupations', useCPU=True): numSeqs = EmbeddingDataReader( EmbeddingDataReader.premade_sources()[dataScale], 'bucketing', 100, 40, padToFull=True).maxXLen params = [('initialLearningRate', [1e-3]), ('l2RegLambda', [1e-6]), ('maxNumSeqs', [numSeqs]), ('filterSizesNKeepProbs', [([1, 2, 3, 4], [0.9, 0.9, 0.9, 0.9])]), ('numFeaturesPerFilter', [128]), ('pooledKeepProb', [0.5, 0.85, 1])] cls.run_thru_data(EmbeddingDataReader, dataScale, make_params_dict(params), runScale, useCPU, padToFull=True)
def quick_learn(cls, runScale='small', dataScale='small_2occupations', useCPU=True): numSeqs = EmbeddingDataReader( EmbeddingDataReader.premade_sources()[dataScale], 'bucketing', 100, 40, padToFull=True).maxXLen params = [('initialLearningRate', [1e-3]), ('l2RegLambda', [1e-4]), ('maxNumSeqs', [numSeqs]), ('convFilterShapesNKeepProbs', [([(3, -1)], [1])]), ('convNumFeaturesPerFilter', [32]), ('rnnCellUnitsNProbs', [([16], [0.9])])] cls.run_thru_data(EmbeddingDataReader, dataScale, make_params_dict(params), runScale, useCPU, padToFull=True)