点积需要在Tensor输出上计算...在编码器中，您正确定义了encoder_output，在解码器中，您必须添加decoder_outputs, state_h, state_c = decoder_lstm(enc_emb, initial_state=encoder_states)
点积现在是

attention = dot([decoder_outputs, encoder_outputs], axes=[2, 2])
attention = Activation('softmax')(attention)
context = dot([attention, encoder_outputs], axes=[2,1])

连接不需要initial_states。您必须在您的rnn层中定义它：decoder_outputs, state_h, state_c = decoder_lstm(enc_emb, initial_state=encoder_states)个
这里，完整的例子
编码器+解码器

# dummy variables
num_encoder_tokens = 30
num_decoder_tokens = 10
latent_dim = 100

encoder_inputs = Input(shape=(None,))
enc_emb =  Embedding(num_encoder_tokens, latent_dim, mask_zero = True)(encoder_inputs)
encoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
encoder_outputs, state_h, state_c = encoder_lstm(enc_emb)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None,))
dec_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero = True)
dec_emb = dec_emb_layer(decoder_inputs)
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(dec_emb,
                                     initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.summary()

解码器

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None,))
dec_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero = True)
dec_emb = dec_emb_layer(decoder_inputs)
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, state_h, state_c = decoder_lstm(dec_emb, initial_state=encoder_states)
attention = dot([decoder_outputs, encoder_outputs], axes=[2, 2])
attention = Activation('softmax')(attention)
context = dot([attention, encoder_outputs], axes=[2,1])
decoder_outputs = concatenate([context, decoder_outputs])
decoder_dense = Dense(num_decoder_tokens, activation='softmax')(decoder_outputs)

# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_dense)
model.summary()

Marco的答案是可行的，但是我们必须改变第二个代码块中涉及dot函数的代码行，它需要一个位置参数，就像tensorflow的例子here一样。
最后，下面的大块包括了修正和将工作：

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None,))
dec_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero = True)
dec_emb = dec_emb_layer(decoder_inputs)
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, state_h, state_c = decoder_lstm(dec_emb, initial_state=encoder_states)
attention = Dot(axes=[2, 2])([decoder_outputs, encoder_outputs])
attention = Activation('softmax')(attention)
context = Dot(axes=[2,1])([attention, encoder_outputs])
decoder_outputs = concatenate([context, decoder_outputs])
decoder_dense = Dense(num_decoder_tokens, activation='softmax')(decoder_outputs)

# Define the model that will turn
# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_dense)
model.summary()