ValueError：尺寸必须等于RNN -KERAS

Question

我正在构建具有不同x和y长度的RNN模型。
从NLP中，我知道这应该是可能的（即 - 如果您有翻译模型，您将没有相同的长度输入句子/单词和输出句子/单词...）

不幸的是，这对我不起作用，我收到以下错误：（

ValueError: Dimensions must be equal, but are 3 and 405 for '{{node mean_absolute_error/sub}} = Sub[T=DT_FLOAT](sequential_47/time_distributed_46/Reshape_1, IteratorGetNext:1)' with input shapes: [?,3,1], [?,405,1].

下面的完整错误）

我也在线检查并找到这个线程 Chollet本人似乎在2021年参与并关闭了该线程，但是那里的最后一条评论（2019年）没有解决，并且与我有同样的问题。

我正在使用Google Colab，因此可以肯定的是，它不是旧版本的问题（tf .__版本__ ==＆gt; 2.8.2 and tf.keras .__ version _____版本__ ==＆gt; 2.8 .0

我的模型是：

model_gru = Sequential()
model_gru.add(GRU(75, return_sequences=True,input_shape=(train_X.shape[1], train_X.shape[2])))# , unroll=True))
model_gru.add(GRU(units=30, return_sequences=True))
model_gru.add(TimeDistributed(Dense(1)))
model_gru.compile(loss='mae', optimizer='adam')
model_gru.summary()

gru_history = model_gru.fit(train_X, train_y, epochs=30, batch_size=64, validation_data=(test_X, test_y), shuffle=False)

如何解决问题

---

？

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-212-1b00467cbad2> in <module>()
----> 1 gru_history = model_gru.fit(train_X, train_y, epochs=30, batch_size=64, validation_data=(test_X, test_y), shuffle=False, callbacks=[callback])

1 frames
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1145           except Exception as e:  # pylint:disable=broad-except
   1146             if hasattr(e, "ag_error_metadata"):
-> 1147               raise e.ag_error_metadata.to_exception(e)
   1148             else:
   1149               raise

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, 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 1000, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
        loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
        y, y_pred, sample_weight, regularization_losses=self.losses)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
        losses = call_fn(y_true, y_pred)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1457, in mean_absolute_error
        return backend.mean(tf.abs(y_pred - y_true), axis=-1)

    ValueError: Dimensions must be equal, but are 3 and 405 for '{{node mean_absolute_error/sub}} = Sub[T=DT_FLOAT](sequential_47/time_distributed_46/Reshape_1, IteratorGetNext:1)' with input shapes: [?,3,1], [?,405,1].

Answer 1

RNN单元格需要相同长度的输入和标签。

RNN为每个相应的输入产生输出。无论您使用RNN，GRU，LSTM等。RNN都采用序列，然后从序列中的第一个元素开始，它会生成输出和隐藏状态。它存储输出，然后将隐藏状态和序列中的下一个元素传递，并生成下一个输出和新的隐藏状态。一旦完成了序列中的所有元素，它将在每个时间步和最终隐藏状态下为您提供输出。因此，输入和标签的长度确实需要相同。

RNN翻译通过编码输入，然后将编码状态传递到依次解码它的解码器来起作用。 IE使用RNN的翻译不是完成整个工作的单个RNN。