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小編給大家分享一下pytorch如何處理類別不平衡的問(wèn)題,相信大部分人都還不怎么了解,因此分享這篇文章給大家參考一下,希望大家閱讀完這篇文章后大有收獲,下面讓我們一起去了解一下吧!
下面的代碼展示了如何使用WeightedRandomSampler來(lái)完成抽樣。
numDataPoints = 1000 data_dim = 5 bs = 100 # Create dummy data with class imbalance 9 to 1 data = torch.FloatTensor(numDataPoints, data_dim) target = np.hstack((np.zeros(int(numDataPoints * 0.9), dtype=np.int32), np.ones(int(numDataPoints * 0.1), dtype=np.int32))) print 'target train 0/1: {}/{}'.format( len(np.where(target == 0)[0]), len(np.where(target == 1)[0])) class_sample_count = np.array( [len(np.where(target == t)[0]) for t in np.unique(target)]) weight = 1. / class_sample_count samples_weight = np.array([weight[t] for t in target]) samples_weight = torch.from_numpy(samples_weight) samples_weight = samples_weight.double() sampler = WeightedRandomSampler(samples_weight, len(samples_weight)) target = torch.from_numpy(target).long() train_dataset = torch.utils.data.TensorDataset(data, target) train_loader = DataLoader( train_dataset, batch_size=bs, num_workers=1, sampler=sampler) for i, (data, target) in enumerate(train_loader): print "batch index {}, 0/1: {}/{}".format( i, len(np.where(target.numpy() == 0)[0]), len(np.where(target.numpy() == 1)[0]))
核心部分為實(shí)際使用時(shí)替換下變量把sampler傳遞給DataLoader即可,注意使用了sampler就不能使用shuffle,另外需要指定采樣點(diǎn)個(gè)數(shù):
class_sample_count = np.array( [len(np.where(target == t)[0]) for t in np.unique(target)]) weight = 1. / class_sample_count samples_weight = np.array([weight[t] for t in target]) samples_weight = torch.from_numpy(samples_weight) samples_weight = samples_weight.double() sampler = WeightedRandomSampler(samples_weight, len(samples_weight))
以上是“pytorch如何處理類別不平衡的問(wèn)題”這篇文章的所有內(nèi)容,感謝各位的閱讀!相信大家都有了一定的了解,希望分享的內(nèi)容對(duì)大家有所幫助,如果還想學(xué)習(xí)更多知識(shí),歡迎關(guān)注億速云行業(yè)資訊頻道!
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