TypeError: 'NoneType' 对象不可调用 Tensorflow [英] TypeError: 'NoneType' object is not callable Tensorflow

问题描述

目前正在使用 tf2.0 解决回归问题.为了准备我的数据集，我使用了以下代码:

Currently working on a regression problem with tf2.0. In order to prepare my dataset, I have used the following code:

train = tf.data.Dataset.from_tensor_slices(([train_X], [train_y])).batch(BATCH_SIZE).repeat()
val = tf.data.Dataset.from_tensor_slices(([val_X], [val_y])).batch(BATCH_SIZE).repeat() 

现在，如果我们看看它们的形状:

Now if we look at their shapes:

<RepeatDataset shapes: ((None, 42315, 20), (None, 42315)), types: (tf.float64, tf.float64)>
<RepeatDataset shapes: ((None, 2228, 20), (None, 2228)), types: (tf.float64, tf.float64)>

我认为这是相当正确的.现在，如果我通过如下所示的模型运行它们，它们似乎可以训练并且工作得很好:

Which I believe are fairly right. Now if I run these through the model as shown below, they seem to train and work just fine:

simple_lstm_model = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(8),
    tf.keras.layers.Dense(1)
])

simple_lstm_model.compile(optimizer='adam', loss='mae')

history = simple_lstm_model.fit(train, epochs=EPOCHS,
                      steps_per_epoch=EVALUATION_INTERVAL,
                      validation_data=val, validation_steps=50)

然而，当我让我的模型稍微复杂一些并尝试编译它时，它给了我这个问题的标题的错误.有关错误的详细信息位于此问题的最底部.复杂模型如下图所示:

However, when I make my model slightly more complicated and try to compile it, it gives me the error which is the heading of this question. Details about the error are at the very bottom of this question. The complicated model is shown below:

comp_lstm = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(64),
    tf.keras.layers.LSTM(64),
    tf.keras.layers.LSTM(64),
    tf.keras.layers.Dense(1)
])

comp_lstm.compile(optimizer='adam', loss='mae')

history = comp_lstm.fit(train, 
                      epochs=EPOCHS,
                      steps_per_epoch=EVALUATION_INTERVAL,
                      validation_data=val, validation_steps=50)

事实上，我想尝试一个双向 LSTM，但似乎多个 LSTM 堆栈本身给我带来了如下所述的问题.

I, infact, wanted to try a bidirectional LSTM but it seems like a multiple stack of LSTMs itself is giving me the issues as stated below.

TypeError                                 Traceback (most recent call last)
<ipython-input-21-8a86aab8a730> in <module>
      2 EPOCHS = 20
      3 
----> 4 history = comp_lstm.fit(train, 
      5                       epochs=EPOCHS,
      6                       steps_per_epoch=EVALUATION_INTERVAL,

~/python_envs/p2/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

~/python_envs/p2/lib/python3.8/site-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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
    846                 batch_size=batch_size):
    847               callbacks.on_train_batch_begin(step)
--> 848               tmp_logs = train_function(iterator)
    849               # Catch OutOfRangeError for Datasets of unknown size.
    850               # This blocks until the batch has finished executing.

~/python_envs/p2/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    578         xla_context.Exit()
    579     else:
--> 580       result = self._call(*args, **kwds)
    581 
    582     if tracing_count == self._get_tracing_count():

~/python_envs/p2/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    609       # In this case we have created variables on the first call, so we run the
    610       # defunned version which is guaranteed to never create variables.
--> 611       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    612     elif self._stateful_fn is not None:
    613       # Release the lock early so that multiple threads can perform the call

TypeError: 'NoneType' object is not callable

推荐答案

问题是当你堆叠多个 LSTM 时，我们应该使用 LSTM 层中的参数，return_sequences = True.

Problem is that when you stack multiple LSTMs, we should use the argument, return_sequences = True in LSTM Layer.

这是因为如果return_sequences = False(默认行为)，LSTM 将返回最后一个时间步的输出.但是当我们堆叠 LSTM 时，我们将需要 Complete Sequence 的 Output 而不仅仅是最后的 Time Step.

It is because if return_sequences = False (default behavior), LSTM will return the Output of the Last Time Step. But when we stack LSTMs, we will need the Output of the Complete Sequence rather than just the Last Time Step.

将模型更改为

comp_lstm = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(64, return_sequences = True),
    tf.keras.layers.LSTM(64, return_sequences = True),
    tf.keras.layers.LSTM(64),
    tf.keras.layers.Dense(1)
])

应该解决错误.

这样，您也可以使用Bi-Directional LSTMs.

如果您遇到任何其他错误，请告诉我，我很乐意为您提供帮助.

Please let me know if you face any other error and I will be Happy to help you.

希望这会有所帮助.快乐学习！

Hope this helps. Happy Learning!

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