from tensorlog import learn from tensorlog import plearn from tensorlog import comline if __name__=="__main__": logging.basicConfig(level=logging.INFO) logging.info('level is info') db = comline.parseDBSpec('tmp-cache/cora.db|inputs/cora.cfacts') trainData = comline.parseDatasetSpec('tmp-cache/cora-train.dset|inputs/train.examples', db) testData = comline.parseDatasetSpec('tmp-cache/cora-test.dset|inputs/test.examples', db) prog = comline.parseProgSpec("cora.ppr",db,proppr=True) prog.setRuleWeights() prog.db.markAsParam('kaw',1) prog.db.markAsParam('ktw',1) prog.db.markAsParam('kvw',1) prog.maxDepth = 1 # learner = learn.FixedRateGDLearner(prog,regularizer=learn.L2Regularizer(),epochs=5) learner = plearn.ParallelFixedRateGDLearner(prog,regularizer=learn.L2Regularizer(),parallel=5,epochs=5) params = {'prog':prog, 'trainData':trainData, 'testData':testData, 'targetMode':'samebib/io', 'savedModel':'tmp-cache/cora-trained.db', 'savedTestPredictions':'tmp-cache/cora-test.solutions.txt', 'savedTrainExamples':'tmp-cache/cora-train.examples', 'savedTestExamples':'tmp-cache/cora-test.examples', 'learner':learner } print 'maxdepth',prog.maxDepth expt.Expt(params).run()
def runMain(): params,_ = setExptParams() return expt.Expt(params).run()
def runMain(num=250): logging.basicConfig(level=logging.INFO) masterconfig.masterConfig().matrixdb.allow_weighted_tuples=False params = setExptParams(num) return expt.Expt(params).run()
def runMain(): logging.basicConfig(level=logging.INFO) params = setExptParams() return expt.Expt(params).run()