我正在尝试基于bAbI Task 8 example构建Q＆amp; A模型，但我无法将两个输入图层合并到一个图层中。这是我目前的模型架构：
story_input = Input(shape=(story_maxlen,vocab_size), name='story_input')
story_input_proc = Embedding(vocab_size, latent_dim, name='story_input_embed', input_length=story_maxlen)(story_input)
story_input_proc = Reshape((latent_dim,story_maxlen), name='story_input_reshape')(story_input_proc)
query_input = Input(shape=(query_maxlen,vocab_size), name='query_input')
query_input_proc = Embedding(vocab_size, latent_dim, name='query_input_embed', input_length=query_maxlen)(query_input)
query_input_proc = Reshape((latent_dim,query_maxlen), name='query_input_reshape')(query_input_proc)
story_query = dot([story_input_proc, query_input_proc], axes=(1, 1), name='story_query_merge')
encoder = LSTM(latent_dim, return_state=True, name='encoder')
encoder_output, state_h, state_c = encoder(story_query)
encoder_output = RepeatVector(3, name='encoder_3dim')(encoder_output)
encoder_states = [state_h, state_c]
decoder = LSTM(latent_dim, return_sequences=True, name='decoder')(encoder_output, initial_state=encoder_states)
answer_output = Dense(vocab_size, activation='softmax', name='answer_output')(decoder)
model = Model([story_input, query_input], answer_output)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
，这是model.summary（）的输出
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
story_input (InputLayer) (None, 358, 38) 0
__________________________________________________________________________________________________
query_input (InputLayer) (None, 5, 38) 0
__________________________________________________________________________________________________
story_input_embed (Embedding) (None, 358, 64) 2432 story_input[0][0]
__________________________________________________________________________________________________
query_input_embed (Embedding) (None, 5, 64) 2432 query_input[0][0]
__________________________________________________________________________________________________
story_input_reshape (Reshape) (None, 64, 358) 0 story_input_embed[0][0]
__________________________________________________________________________________________________
query_input_reshape (Reshape) (None, 64, 5) 0 query_input_embed[0][0]
__________________________________________________________________________________________________
story_query_merge (Dot) (None, 358, 5) 0 story_input_reshape[0][0]
query_input_reshape[0][0]
__________________________________________________________________________________________________
encoder (LSTM) [(None, 64), (None, 17920 story_query_merge[0][0]
__________________________________________________________________________________________________
encoder_3dim (RepeatVector) (None, 3, 64) 0 encoder[0][0]
__________________________________________________________________________________________________
decoder (LSTM) (None, 3, 64) 33024 encoder_3dim[0][0]
encoder[0][1]
encoder[0][2]
__________________________________________________________________________________________________
answer_output (Dense) (None, 3, 38) 2470 decoder[0][0]
==================================================================================================
Total params: 58,278
Trainable params: 58,278
Non-trainable params: 0
__________________________________________________________________________________________________
其中vocab_size = 38，story_maxlen = 358，query_maxlen = 5，latent_dim = 64，批量大小= 64。
当我尝试训练此模型时，我收到错误：
Input to reshape is a tensor with 778240 values, but the requested shape has 20480
以下是这两个值的公式：
input_to_reshape = batch_size * latent_dim * query_maxlen * vocab_size
requested_shape = batch_size * latent_dim * query_maxlen
我相信错误信息是说输入query_input_reshape层的张量的形状是（？，5,38,64），但是期待形状的张量（？，5,64）（见上面的公式），但我可能错了。
当我将Reshape的target_shape输入更改为3D（即Reshape((latent_dim,query_maxlen,vocab_size)）时，我收到错误total size of new array must be unchanged，这对我没有任何意义，因为输入是3D。你会认为Reshape((latent_dim,query_maxlen))会给我这个错误，因为它会将3D张量变成2D张量，但编译得很好，所以我不知道那里发生了什么。
我使用Reshape的唯一原因是因为我需要将两个张量合并为LSTM编码器的输入。当我试图摆脱Reshape图层时，我只是在尝试编译模型时遇到尺寸不匹配错误。上面的模型架构至少编译但我无法训练它。
有人可以帮我弄清楚我如何合并story_input和query_input图层？谢谢！