类型错误：目标数据丢失。您的模型具有“loss”：binary_crossentropy，因此期望目标数据在“fit()”中传递

我正在尝试运行一个模型并使用分层 K 折验证来验证它。我已将训练和测试图像一起存储在一个新文件夹中，并将训练和测试的基本事实存储在 CSV 中以用于获取标签。

我正在使用 binary_crossentropy 作为损失函数，因为我正在研究二元分类。

CSV 文件包含 2 列：Image（图像的名称）和 ID（相应图像的标签）。

这是代码：

EPOCHS = 1
N_SPLIT = 3

image_dir = 'path of the folder where all the image is contained'

image_label = pd.read_csv('groundtruths of the images.csv')
df = image_label.copy()
    
main_pred = [] #a list to store the scores of each fold
error = [] #
data_kfold = pd.DataFrame()

train_y = df.ID #stores the label of the images
train_x = df.Image #stores the name of the images

train_datagen=ImageDataGenerator(horizontal_flip=True,vertical_flip=True,rotation_range=90) #data augmentation
validation_datagen = ImageDataGenerator()
kfold = StratifiedKFold(n_splits=N_SPLIT,shuffle=True,random_state=42) #making folds

j = 0 # a variable to count the fold number

for train_idx, val_idx in list(kfold.split(train_x,train_y)):
    x_train_df = df.iloc[train_idx] #training data after split
    x_valid_df = df.iloc[val_idx] #validation data after split
    j+=1
    #loading training images
    training_set = train_datagen.flow_from_dataframe(dataframe=x_train_df, directory=image_dir,
                                                 x_col="Image", y_col="ID",
                                                 class_mode=None,
                                                 target_size=(image_size,image_size), batch_size=batch_size)
    #loading validation images
    validation_set = validation_datagen.flow_from_dataframe(dataframe=x_valid_df, directory=image_dir,
                                                 x_col="Image", y_col="ID",
                                                 class_mode=None,
                                                 target_size=(image_size,image_size), batch_size=batch_size)

    #training THIS IS THE LINE WHERE THE ERROR OCCURS
    history = parallel_model.fit(training_set,
                                 validation_data=validation_set,
                                 epochs = EPOCHS,
                                 steps_per_epoch=x_train_df.shape[0] // batch_size
                                 )

    test_generator = ImageDataGenerator(rescale = 1./255)

    test_set = test_generator.flow_from_dataframe(dataframe=image_label, directory=image_dir,
                                                 x_col="Image",y_col=None,
                                                 class_mode=None,
                                                 target_size=(image_size,image_size))

    pred= parallel_model.predict_generator(test_set, len(image_label) // batch_size)
    predicted_class_indices=np.argmax(pred,axis=1)
    data_kfold[j] = predicted_class_indices
    gc.collect()

我得到的错误：

Found 800 validated image filenames.
Found 400 validated image filenames.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-26-6b473ab35caf> in <module>()
     20                                  validation_data=validation_set,
     21                                  epochs = EPOCHS,
---> 22                                  steps_per_epoch=x_train_df.shape[0] // batch_size
     23                                  )
     24 

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1127           except Exception as e:  # pylint:disable=broad-except
   1128             if hasattr(e, "ag_error_metadata"):
-> 1129               raise e.ag_error_metadata.to_exception(e)
   1130             else:
   1131               raise

TypeError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 813, in train_step
        f'Target data is missing. Your model has `loss`: {self.loss}, '

    TypeError: Target data is missing. Your model has `loss`: binary_crossentropy, and therefore expects target data to be passed in `fit()`.

这是您的数据集的错误。

训练时，Tensorflow 需要模型的输入，以及作为基本事实的输出标签。

尝试迭代你的tf.data.Dataset对象并查看它是否仅变成单个值（导致此错误的原因）或元组中的 2 个值（形式为(model_input, label)).

错误输出示例：

for item in dataset:
    print(item) # OR, in the output below for brevity, item.shape

(64, 64, 64, 1)
(64, 64, 64, 1)
(64, 64, 64, 1)
(64, 64, 64, 1)
(64, 64, 64, 1)
...

正确输出示例：

for item in dataset:
    print([ i.shape for i in item ])

[TensorShape([64, 64, 64, 1]), TensorShape([64, 64, 64, 2])]
[TensorShape([64, 64, 64, 1]), TensorShape([64, 64, 64, 2])]
[TensorShape([64, 64, 64, 1]), TensorShape([64, 64, 64, 2])]
[TensorShape([64, 64, 64, 1]), TensorShape([64, 64, 64, 2])]
...

示例取自我正在研究的模型。