我在用Keras编写一个序列到序列模型时遇到了这个错误：
ValueError: Input 0 of layer lstm_59 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [None, 20]这是我的数据：
print(database['sentence1'][0], database['sentence2'][0])>>> 'It does not matter how you win it , just as long as you do .',>>> 'It does not matter how you win , only as long as you do it .'我为我的数据创建了一个序数编码（每个单词是一个类别），因此我为输入和目标句子创建了一个字典，以下是一些变量的形状：
number of samples = 2500unique_input_words = 12738unique_output_words = 12230input_length = 20output_length = 20encoding_input.shape = (2500, 20)decoding_input.shape = (2500, 20)decoding_output.shape = (2500, 20)基本上，编码/解码数组是2500个样本的列表，每个样本有20个元素的长度，（解码将返回一个句子）：
print(encoding_input[0])[12049 5684 3021 11494 8362 8598 8968 8371 3622 5583 8362 840 4061 8917 11710 4860 4491 4860 6411 4166]这是我使用LSTM层的RNN模型（使用Keras的函数式API）：
def create_model( input_length=20, output_length=20): encoder_input = tf.keras.Input(shape=(None, input_length,)) decoder_input = tf.keras.Input(shape=(None, output_length,)) encoder, state_h, state_c = tf.keras.layers.LSTM(64, return_state=True, return_sequences=False)(encoder_input) decoder = tf.keras.layers.LSTM(64, return_sequences=True)(decoder_input, initial_state=[state_h, state_c]) decoder = tf.keras.layers.Dense(20, activation="softmax")(decoder) model = tf.keras.Model(inputs=[encoder_input, decoder_input], outputs=[decoder]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return modelmodel = create_model() 如果我用我的数据来拟合模型：
model.fit([encoder_input, decoder_input], decoder_output, batch_size=64, epochs=5)首先我得到了这个警告：
WARNING:tensorflow:Model was constructed with shape (None, None, 20) for input Tensor("input_67:0", shape=(None, None, 20), dtype=float32), but it was called on an input with incompatible shape (None, 20).WARNING:tensorflow:Model was constructed with shape (None, None, 20) for input Tensor("input_68:0", shape=(None, None, 20), dtype=float32), but it was called on an input with incompatible shape (None, 20).然后是完整的错误堆栈跟踪：
ValueError: in user code: /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function * return step_function(self, iterator) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica return fn(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step ** outputs = model.train_step(data) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step y_pred = self(x, training=True) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__ outputs = call_fn(inputs, *args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call inputs, training=training, mask=mask) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph outputs = node.layer(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/recurrent.py:663 __call__ return super(RNN, self).__call__(inputs, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__ self.name) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility str(x.shape.as_list())) ValueError: Input 0 of layer lstm_59 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [None, 20]Model.summary()：
Model: "functional_45"__________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to ==================================================================================================input_67 (InputLayer) [(None, None, 20)] 0 __________________________________________________________________________________________________input_68 (InputLayer) [(None, None, 20)] 0 __________________________________________________________________________________________________lstm_59 (LSTM) [(None, 64), (None, 21760 input_67[0][0] __________________________________________________________________________________________________lstm_60 (LSTM) (None, None, 64) 21760 input_68[0][0] lstm_59[0][1] lstm_59[0][2] __________________________________________________________________________________________________dense_22 (Dense) (None, None, 20) 1300 lstm_60[0][0] ==================================================================================================Total params: 44,820Trainable params: 44,820Non-trainable params: 0__________________________________________________________________________________________________我知道这个错误可能是因为我的输出的维度，但我实际上已经尝试了很多解决方案，但都没有奏效。
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