a25_unet_training_v1_on_my_segmentation.py 文件源码

def run_cross_validation_create_models_unet1(nfolds=5):
    from sklearn.model_selection import KFold
    files_full = glob.glob(INPUT_PATH + "*/*.png")
    files = []
    for f in files_full:
        if '_mask' in f:
            continue
        files.append(f)

    kf = KFold(n_splits=nfolds, shuffle=True, random_state=66)
    num_fold = 0
    sum_score = 0
    for train_index, test_index in kf.split(range(len(files))):
        num_fold += 1
        print('Start KFold number {} from {}'.format(num_fold, nfolds))
        print('Split train: ', len(train_index))
        print('Split valid: ', len(test_index))
        score = train_single_model(num_fold, train_index, test_index, files)
        sum_score += score

    print('Avg loss: {}'.format(sum_score/nfolds))