我有一个Keras模型，该模型具有input shape = (frames, height, width, channels)并具有两个标量输出（请参阅下一个代码par）。我的模型确实使用了LSTM，这就是为什么我必须添加额外的尺寸。
height = 32
width = 64
channels = 3
frames = 2
img_shape = (height, width, channels)
input_shape = (frames, height, width, channels)
如果已按以下方式定义了批处理生成器
def generator(df, batch_size, frames_per_scene=frames_per_scene):
### read data frame columns
# inputs
img_list = df['filename']
# outputs
happiness= df['happiness']
anger = df['anger']
# create empty arrays for input and output
batch_img = np.zeros((batch_size, frames_per_scene) + img_shape)
batch_label = np.zeros((batch_size, 2))
index = 0
while True:
for i in range(batch_size):
for j in range(frames_per_scene):
label = [happiness.iloc[index], anger.iloc[index]]
img_name = img_list[index]
pil_img = image.load_img(img_name)
batch_img[i, j, :] = image.img_to_array(pil_img)
batch_label[i] = label
index += 1
if index == len(img_list):
img_list = df['filename']
happiness = df['happiness']
anger = df['anger']
index = 0
yield batch_img, batch_label
当我尝试使用以下模型方法时。
model.fit_generator(
train_batch, train_steps, epochs=epochs, verbose=verbose,
callbacks=callbacks_list, validation_data=validation_batch,
validation_steps=val_steps)
我收到以下错误
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-289c48f3bbf8> in <module>
----> 1 train_model(_episode=0)
<ipython-input-6-b4f2c2235a41> in train_model(_episode)
56 TensorBoard(log_dir=path_tensorboard, histogram_freq=0, write_graph=False, write_images=False)]
57
---> 58 model.fit_generator(train_batch, train_steps, epochs=epochs, verbose=verbose, callbacks=callbacks_list, validation_data=validation_batch, validation_steps=val_steps)
c:\envs\lstm\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
c:\envs\lstm\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
2167 max_queue_size=max_queue_size,
2168 workers=workers,
-> 2169 use_multiprocessing=use_multiprocessing)
2170 else:
2171 # No need for try/except because
c:\envs\lstm\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
c:\envs\lstm\lib\site-packages\keras\engine\training.py in evaluate_generator(self, generator, steps, max_queue_size, workers, use_multiprocessing)
2278
2279 while steps_done < steps:
-> 2280 generator_output = next(output_generator)
2281 if not hasattr(generator_output, '__len__'):
2282 raise ValueError('Output of generator should be a tuple '
c:\envs\lstm\lib\site-packages\keras\utils\data_utils.py in get(self)
733 success, value = self.queue.get()
734 if not success:
--> 735 six.reraise(value.__class__, value, value.__traceback__)
c:\envs\lstm\lib\site-packages\six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
c:\envs\lstm\lib\site-packages\keras\utils\data_utils.py in data_generator_task()
633 try:
634 if self._use_multiprocessing or self.queue.qsize() < max_queue_size:
--> 635 generator_output = next(self._generator)
636 self.queue.put((True, generator_output))
637 else:
<ipython-input-5-b23230fc675a> in generator(df, batch_size, frames_per_scene)
18 for j in range(frames_per_scene):
19 label = [steer.iloc[index], throttle.iloc[index]]
---> 20 img_name = img_list[index]
21
22 pil_img = image.load_img(img_name)
c:\pyenvs\ca\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
765 key = com._apply_if_callable(key, self)
766 try:
--> 767 result = self.index.get_value(self, key)
768
769 if not is_scalar(result):
c:\envs\lstm\lib\site-packages\pandas\core\indexes\base.py in get_value(self, series, key)
3116 try:
3117 return self._engine.get_value(s, k,
-> 3118 tz=getattr(series.dtype, 'tz', None))
3119 except KeyError as e1:
3120 if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 0
问题：有人遇到过类似的错误吗？
答案 0 :(得分：1)
我认为此错误可能来自您为数据框建立索引的方式。验证您在df中的索引为0。
一种解决方案可能是将基础的numpy数组存储到img_list，happiness和anger和pandas.Series对象中。
这将给出：
def generator(df, batch_size, frames_per_scene=frames_per_scene):
### read data frame columns
# inputs
img_list = df['filename'].values
# outputs
happiness= df['happiness'].values
anger = df['anger'].values
# create empty arrays for input and output
batch_img = np.zeros((batch_size, frames_per_scene) + img_shape)
batch_label = np.zeros((batch_size, 2))
index = 0
while True:
for i in range(batch_size):
for j in range(frames_per_scene):
label = [happiness[index], anger[index]]
img_name = img_list[index]
pil_img = image.load_img(img_name)
batch_img[i, j, :] = image.img_to_array(pil_img)
batch_label[i] = label
index += 1
if index == len(img_list):
img_list = df['filename'].values
happiness = df['happiness'].values
anger = df['anger'].values
index = 0
yield batch_img, batch_label