'NoneType' 对象不可订阅 - Keras 自定义回调出错 class
'NoneType' object is not subscriptable - error at Keras custom callback class
我在定义自定义回调函数时遇到此错误。
'NoneType' object is not subscriptable
代码示例
class Metrics(tf.keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self._data = []
def on_epoch_end(self, batch, logs={}):
X_val, y_val = self.validation_data[0], self.validation_data[1]
y_predict = np.asarray(model.predict(X_val))
y_val = np.argmax(y_val, axis=1)
y_predict = np.argmax(y_predict, axis=1)
self._data.append({
'val_jaccard': jaccard(y_val, y_predict),
'val_f1': f1_score(y_val, y_predict),
'val_precision': recall_score(y_val, y_predict),
'val_jaccard': precision_score(y_val, y_predict),
})
return
metrics = Metrics()
model.fit((item for item in image_data),steps_per_epoch=steps_per_epoch,
epochs=20, validation_data = (item for item in image_data_val), validation_steps = valid_step , callbacks = [metrics], verbose=2)
之前我遇到了另一个错误："AttributeError: 'Sequential' object has no attribute 'validation_data'. 为了解决这个错误，我按照建议 here 删除了 self.model.validation_data 和 self.validation_data。之后我遇到了这个错误。我无法理解此错误的来源
Keras 版本为 2.2.4
按要求完成回溯
Epoch 1/20
Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x7f8fbe48fa20>>
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1473, in __del__
self._session._session, self._handle)
tensorflow.python.framework.errors_impl.CancelledError: (None, None, 'Session has been closed.')
Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x7f8fbe42f2e8>>
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1473, in __del__
self._session._session, self._handle)
tensorflow.python.framework.errors_impl.CancelledError: (None, None, 'Session has been closed.')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-102-43fa8dafc96c> in <module>()
5 model.fit((item for item in image_data),steps_per_epoch=steps_per_epoch, epochs=20,
6 validation_data = (item for item in image_data_val),
----> 7 validation_steps = valid_step , callbacks = [metrics], verbose=2)
8 eval_model=model.evaluate(image_batch, label_batch)
9 eval_model
4 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
671 use_multiprocessing=use_multiprocessing,
672 shuffle=shuffle,
--> 673 initial_epoch=initial_epoch)
674 if training_utils.is_eager_dataset_or_iterator(x):
675 # Make sure that y, sample_weights, validation_split are not passed.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1431 shuffle=shuffle,
1432 initial_epoch=initial_epoch,
-> 1433 steps_name='steps_per_epoch')
1434
1435 def evaluate_generator(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
329 if mode == ModeKeys.TRAIN:
330 # Epochs only apply to `fit`.
--> 331 callbacks.on_epoch_end(epoch, epoch_logs)
332 progbar.on_epoch_end(epoch, epoch_logs)
333
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py in on_epoch_end(self, epoch, logs)
309 logs = logs or {}
310 for callback in self.callbacks:
--> 311 callback.on_epoch_end(epoch, logs)
312
313 def on_train_batch_begin(self, batch, logs=None):
<ipython-input-101-71d6af0b20ff> in on_epoch_end(self, batch, logs)
14
15 def on_epoch_end(self, batch, logs={}):
---> 16 X_val, y_val = self.validation_data[0], self.validation_data[1]
17 y_predict = np.asarray(model.predict(X_val))
18 y_val = np.argmax(y_val, axis=1)
TypeError: 'NoneType' object is not subscriptable
更新
根据建议，我正在使用 keras 进行回调，即 class Metrics(keras.callback.Callback)。在此解决方案之后，我原来的错误消失了，但我收到另一个错误 AttributeError: 'Metrics' object has no attribute 'on_train_batch_begin'
我真的不知道这个错误。
新错误的回溯是
Epoch 1/20
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-60-43fa8dafc96c> in <module>()
5 model.fit((item for item in image_data),steps_per_epoch=steps_per_epoch, epochs=20,
6 validation_data = (item for item in image_data_val),
----> 7 validation_steps = valid_step , callbacks = [metrics], verbose=2)
8 eval_model=model.evaluate(image_batch, label_batch)
9 eval_model
3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
671 use_multiprocessing=use_multiprocessing,
672 shuffle=shuffle,
--> 673 initial_epoch=initial_epoch)
674 if training_utils.is_eager_dataset_or_iterator(x):
675 # Make sure that y, sample_weights, validation_split are not passed.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1431 shuffle=shuffle,
1432 initial_epoch=initial_epoch,
-> 1433 steps_name='steps_per_epoch')
1434
1435 def evaluate_generator(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
258 # Callbacks batch begin.
259 batch_logs = {'batch': step, 'size': batch_size}
--> 260 callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
261 progbar.on_batch_begin(step, batch_logs)
262
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py in _call_batch_hook(self, mode, hook, batch, logs)
245 t_before_callbacks = time.time()
246 for callback in self.callbacks:
--> 247 batch_hook = getattr(callback, hook_name)
248 batch_hook(batch, logs)
249 self._delta_ts[hook_name].append(time.time() - t_before_callbacks)
AttributeError: 'Metrics' object has no attribute 'on_train_batch_begin'
这是 tf.keras 中的一个错误，他们弃用了 validation_data 参数并且不再设置回调的 validation_data，它始终设置为 None。
您的选择是不使用 tf.keras 而只使用官方 keras 包，我测试了您的代码并且它在 Keras 2.2.4 中工作。或者，您也可以将验证数据传递给回调的 __init__ 并将其设置在那里。
我认为你可以通过将 validation_dataset 传递给你的 Metrics class 而不是依赖......来解决这个问题 self.validation_data 应该如何设置？
我在定义自定义回调函数时遇到此错误。
'NoneType' object is not subscriptable
代码示例
class Metrics(tf.keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self._data = []
def on_epoch_end(self, batch, logs={}):
X_val, y_val = self.validation_data[0], self.validation_data[1]
y_predict = np.asarray(model.predict(X_val))
y_val = np.argmax(y_val, axis=1)
y_predict = np.argmax(y_predict, axis=1)
self._data.append({
'val_jaccard': jaccard(y_val, y_predict),
'val_f1': f1_score(y_val, y_predict),
'val_precision': recall_score(y_val, y_predict),
'val_jaccard': precision_score(y_val, y_predict),
})
return
metrics = Metrics()
model.fit((item for item in image_data),steps_per_epoch=steps_per_epoch,
epochs=20, validation_data = (item for item in image_data_val), validation_steps = valid_step , callbacks = [metrics], verbose=2)
之前我遇到了另一个错误："AttributeError: 'Sequential' object has no attribute 'validation_data'. 为了解决这个错误，我按照建议 here 删除了 self.model.validation_data 和 self.validation_data。之后我遇到了这个错误。我无法理解此错误的来源
Keras 版本为 2.2.4
按要求完成回溯
Epoch 1/20
Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x7f8fbe48fa20>>
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1473, in __del__
self._session._session, self._handle)
tensorflow.python.framework.errors_impl.CancelledError: (None, None, 'Session has been closed.')
Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x7f8fbe42f2e8>>
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1473, in __del__
self._session._session, self._handle)
tensorflow.python.framework.errors_impl.CancelledError: (None, None, 'Session has been closed.')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-102-43fa8dafc96c> in <module>()
5 model.fit((item for item in image_data),steps_per_epoch=steps_per_epoch, epochs=20,
6 validation_data = (item for item in image_data_val),
----> 7 validation_steps = valid_step , callbacks = [metrics], verbose=2)
8 eval_model=model.evaluate(image_batch, label_batch)
9 eval_model
4 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
671 use_multiprocessing=use_multiprocessing,
672 shuffle=shuffle,
--> 673 initial_epoch=initial_epoch)
674 if training_utils.is_eager_dataset_or_iterator(x):
675 # Make sure that y, sample_weights, validation_split are not passed.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1431 shuffle=shuffle,
1432 initial_epoch=initial_epoch,
-> 1433 steps_name='steps_per_epoch')
1434
1435 def evaluate_generator(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
329 if mode == ModeKeys.TRAIN:
330 # Epochs only apply to `fit`.
--> 331 callbacks.on_epoch_end(epoch, epoch_logs)
332 progbar.on_epoch_end(epoch, epoch_logs)
333
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py in on_epoch_end(self, epoch, logs)
309 logs = logs or {}
310 for callback in self.callbacks:
--> 311 callback.on_epoch_end(epoch, logs)
312
313 def on_train_batch_begin(self, batch, logs=None):
<ipython-input-101-71d6af0b20ff> in on_epoch_end(self, batch, logs)
14
15 def on_epoch_end(self, batch, logs={}):
---> 16 X_val, y_val = self.validation_data[0], self.validation_data[1]
17 y_predict = np.asarray(model.predict(X_val))
18 y_val = np.argmax(y_val, axis=1)
TypeError: 'NoneType' object is not subscriptable
更新
根据建议，我正在使用 keras 进行回调，即 class Metrics(keras.callback.Callback)。在此解决方案之后，我原来的错误消失了，但我收到另一个错误 AttributeError: 'Metrics' object has no attribute 'on_train_batch_begin'
我真的不知道这个错误。
新错误的回溯是
Epoch 1/20
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-60-43fa8dafc96c> in <module>()
5 model.fit((item for item in image_data),steps_per_epoch=steps_per_epoch, epochs=20,
6 validation_data = (item for item in image_data_val),
----> 7 validation_steps = valid_step , callbacks = [metrics], verbose=2)
8 eval_model=model.evaluate(image_batch, label_batch)
9 eval_model
3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
671 use_multiprocessing=use_multiprocessing,
672 shuffle=shuffle,
--> 673 initial_epoch=initial_epoch)
674 if training_utils.is_eager_dataset_or_iterator(x):
675 # Make sure that y, sample_weights, validation_split are not passed.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1431 shuffle=shuffle,
1432 initial_epoch=initial_epoch,
-> 1433 steps_name='steps_per_epoch')
1434
1435 def evaluate_generator(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
258 # Callbacks batch begin.
259 batch_logs = {'batch': step, 'size': batch_size}
--> 260 callbacks._call_batch_hook(mode, 'begin', step, batch_logs)
261 progbar.on_batch_begin(step, batch_logs)
262
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py in _call_batch_hook(self, mode, hook, batch, logs)
245 t_before_callbacks = time.time()
246 for callback in self.callbacks:
--> 247 batch_hook = getattr(callback, hook_name)
248 batch_hook(batch, logs)
249 self._delta_ts[hook_name].append(time.time() - t_before_callbacks)
AttributeError: 'Metrics' object has no attribute 'on_train_batch_begin'
这是 tf.keras 中的一个错误，他们弃用了 validation_data 参数并且不再设置回调的 validation_data，它始终设置为 None。
您的选择是不使用 tf.keras 而只使用官方 keras 包，我测试了您的代码并且它在 Keras 2.2.4 中工作。或者，您也可以将验证数据传递给回调的 __init__ 并将其设置在那里。
我认为你可以通过将 validation_dataset 传递给你的 Metrics class 而不是依赖......来解决这个问题 self.validation_data 应该如何设置？