1

我需要使用分层 kfold（不平衡的多类任务）交叉验证 keras 模型。是否可以在 (folds = list(StratifiedKFold(k, shuffle=True, random_state=1).split(x_train, y_train)) 中将 x_train/y_train 与 imagedatagenerator (flow_from_directory) 一起使用？在 Kaggle ( https:// /www.kaggle.com/stefanie04736/simple-keras-model-with-k-fold-cross-validation?select=train.json.7z），但是 x_train, y_train = next(train_generator) 没有映射数据和标签正确。任何帮助将不胜感激！

train_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(
 directory=train_path,
 target_size=input_img[:-1],
 color_mode="rgb",
 batch_size=BATCH_SIZE,
 classes=target_names,
 class_mode="input")

test_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(
 directory=test_path,
 target_size=input_img[:-1],
 color_mode="rgb",
 batch_size=BATCH_SIZE,
 classes=target_names,
 class_mode="input",
 shuffle=False)

test_labels = test_generator.classes

#Instantiate to load data and generate k stratified folds
k = 5
def load_data_kfold(k):
 #For StratifiedKFold, labels (y_train) must be 1-D array of labels (Cannot be one-hot)
 x_train, y_train = next(train_generator)
 print(x_train)
 print(y_train)
 folds = list(StratifiedKFold(k, shuffle=True, random_state=1).split(x_train, y_train))

 return folds, x_train, y_train

folds, x_train, y_train = load_data_kfold(k)

Traceback (most recent call last):
File "C:/Users/LaRoche Lab/PycharmProjects/pythonProject2/R.py", line 122, in <module>
folds, x_train, y_train = load_data_kfold(k)
File "C:/Users/LaRoche Lab/PycharmProjects/pythonProject2/R.py", line 118, in load_data_kfold
folds = list(StratifiedKFold(k, shuffle=True, random_state=1).split(x_train, y_train))
File "C:\Users\LaRoche Lab\Anaconda3\envs\tensorflow\lib\site- 
packages\sklearn\model_selection\_split.py", line 735, in split
y = check_array(y, ensure_2d=False, dtype=None)
File "C:\Users\LaRoche Lab\Anaconda3\envs\tensorflow\lib\site-packages\sklearn\utils\validation.py", 
line 73, in inner_f return f(**kwargs)File "C:\Users\LaRoche Lab\Anaconda3\envs\tensorflow\lib\site- 
packages\sklearn\utils\validation.py", line 642, in check_array
% (array.ndim, estimator_name)) 
ValueError: Found array with dim 4. Estimator expected <= 2.
4

0 回答 0