При объединении двух моделей нейросети, не удается сконкатенировать вектора

def createBOW():

    inp = Input(shape=(20000,))
    x1 = Dense(100, activation='relu')(inp)
    x1 = Dense(200, activation='relu')(x1)
    x1 = Dense(300, activation='relu')(x1)
    out = Dense(250, activation='relu')(x1)
    model = Model(inp, out)
    return model

def createEmb():

    inp = Input(shape=(1000,))
    x = Embedding(maxWord, 10, input_length=xLen)(inp)
    x = SpatialDropout1D(0.3)(x)
    x = LSTM(128, return_sequences=True)(x)
    x = Conv1D(64, 5, padding='same')(x)
    x = MaxPooling1D()(x)
    x = Conv1D(6, 5, padding='same')(x)
    x = MaxPooling1D()(x)
    out = GlobalMaxPooling1D()(x)

    model = Model(inp, out)

    return model


bow = createBOW()
emb = createEmb()
combined = concatenate([bow.outputs, emb.outputs])
x = Dense(64, activation='relu')(combined)
x = Dense(128, activation='relu')(x)
out = Dense(6, activation='softmax')(x)

model = Model(inputs=[bow.input, emb.input], outputs=out)

ValueError: A Concatenate layer should be called on a list of at least 1 input. Received: input_shape=[[(None, 250)], [(None, 6)]]

Выдает подобную ошибку при конкатенации моделей, подскажите, как решить данную проблему