Keras seq2seq - word embedding

I finally done it. Here is the code:

Shared_Embedding = Embedding(output_dim=embedding, input_dim=vocab_size, name="Embedding")

encoder_inputs = Input(shape=(sentenceLength,), name="Encoder_input")
encoder = LSTM(n_units, return_state=True, name='Encoder_lstm') 
word_embedding_context = Shared_Embedding(encoder_inputs) 
encoder_outputs, state_h, state_c = encoder(word_embedding_context) 
encoder_states = [state_h, state_c] 
decoder_lstm = LSTM(n_units, return_sequences=True, return_state=True, name="Decoder_lstm")

decoder_inputs = Input(shape=(sentenceLength,), name="Decoder_input")
word_embedding_answer = Shared_Embedding(decoder_inputs) 
decoder_outputs, _, _ = decoder_lstm(word_embedding_answer, initial_state=encoder_states) 
decoder_dense = Dense(vocab_size, activation='softmax', name="Dense_layer") 
decoder_outputs = decoder_dense(decoder_outputs) 

model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

encoder_model = Model(encoder_inputs, encoder_states) 

decoder_state_input_h = Input(shape=(n_units,), name="H_state_input") 
decoder_state_input_c = Input(shape=(n_units,), name="C_state_input") 
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] 
decoder_outputs, state_h, state_c = decoder_lstm(word_embedding_answer, initial_state=decoder_states_inputs) 
decoder_states = [state_h, state_c] 
decoder_outputs = decoder_dense(decoder_outputs)

decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states)

"model" is training model encoder_model and decoder_model are inference models