计算多分类时的每个类别的F1

• 接口

sklearn.metrics.classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2, output_dict=False)

示例：

from sklearn.metrics import classification_report
y_true = [0,0, 1, 2, 2, 2, 0]
y_pred = [0, 1, 0, 2, 2, 1, 0]
target_names = ['dog', 'pig', 'cat']
result = classification_report(y_true, y_pred, target_names=target_names, output_dict=True)
print(result)

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pytorch 使用K-折交叉验证

pytorch 使用K-折交叉验证

核心代码

  # Define the K-fold Cross Validator
  kfold = KFold(n_splits=k_folds, shuffle=True)

  # K-fold Cross Validation model evaluation
  for fold, (train_ids, test_ids) in enumerate(kfold.split(dataset))
    
    # Sample elements randomly from a given list of ids, no replacement.
    train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids)
    test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids)
    
    # Define data loaders for training and testing data in this fold
    trainloader = torch.utils.data.DataLoader(
                      dataset, 
                      batch_size=10, sampler=train_subsampler)
    testloader = torch.utils.data.DataLoader(
                      dataset,
                      batch_size=10, sampler=test_subsampler)