我正在尝试计算二进制类彩色图像分类问题中的真阳性、真阴性、假阳性、假阴性比率。
我有二进制类、面部和背景彩色图像，我必须使用 MLP 对它们进行分类。
我的问题是：我收到错误：
ValueError：要求检索元素 0，但序列的长度为 0
编辑：完整回溯
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
128 raise ValueError('{} is not supported in multi-worker mode.'.format(
129 method.__name__))
--> 130 return method(self, *args, **kwargs)
131
132 return tf_decorator.make_decorator(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1577 use_multiprocessing=use_multiprocessing,
1578 model=self,
-> 1579 steps_per_execution=self._steps_per_execution)
1580
1581 # Container that configures and calls `tf.keras.Callback`s.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
1115 use_multiprocessing=use_multiprocessing,
1116 distribution_strategy=ds_context.get_strategy(),
-> 1117 model=model)
1118
1119 strategy = ds_context.get_strategy()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, shuffle, workers, use_multiprocessing, max_queue_size, model, **kwargs)
914 max_queue_size=max_queue_size,
915 model=model,
--> 916 **kwargs)
917
918 @staticmethod
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
784 # Since we have to know the dtype of the python generator when we build the
785 # dataset, we have to look at a batch to infer the structure.
--> 786 peek, x = self._peek_and_restore(x)
787 peek = self._standardize_batch(peek)
788 peek = _process_tensorlike(peek)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _peek_and_restore(x)
918 @staticmethod
919 def _peek_and_restore(x):
--> 920 return x[0], x
921
922 def _handle_multiprocessing(self, x, workers, use_multiprocessing,
/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
55 'but the Sequence '
56 'has length {length}'.format(idx=idx,
---> 57 length=len(self)))
58 if self.seed is not None:
59 np.random.seed(self.seed + self.total_batches_seen)
ValueError: Asked to retrieve element 0, but the Sequence has length 0
- 在尝试分别从 2 个类预测每个文件夹时（而不是包含 2 个文件夹的根文件夹，每个类一个用于训练）。
我产生错误的代码是：
test_face_dir = "/content/test/TESTSET/face"
test_background_dir = "/content/test/TESTSET/background"
# Face DG
test_datagen_face = ImageDataGenerator(rescale=1./255)
test_generator_face = test_datagen_face.flow_from_directory(
test_face_dir,
target_size=img_window[:2],
batch_size=batch_size,
class_mode='binary',
color_mode='rgb'
)
# Background DG
test_datagen_background = ImageDataGenerator(rescale=1./255)
test_generator_background = test_datagen_background.flow_from_directory(
test_background_dir,
target_size=img_window[:2],
batch_size=batch_size,
class_mode='binary',
color_mode='rgb'
)
#-----------------------------------------
prediction_face = simpleMLP.predict(test_generator_face)
prediction_background = simpleMLP.predict(test_generator_background)
#-----------------------------------------
# th = 0.5 #threshold
# Face
prediction_face[prediction_face>=th]=1
prediction_face[prediction_face<th]=0
pred_face = np.squeeze(prediction_face)
print('pred shape: ', pred_face.shape,int(np.sum(pred_face)))
# Background
prediction_background[prediction_background>=th]=1
prediction_background[prediction_background<th]=0
pred_background = np.squeeze(prediction_background)
print('pred shape: ', pred_background.shape,int(np.sum(pred_background)))