Question

嗨，我正在尝试构建专家混合物神经网络。我在这里找到了一个代码：http://blog.sina.com.cn/s/blog_dc3c53e90102x9xu.html。我的目标是使门和专家来自不同的数据，但具有相同的维度。

def sliced(x,expert_num):
    return x[:,:,:expert_num]

def reduce(x, axis):
    return K.sum(x, axis=axis, keepdims=True)

def gatExpertLayer(inputGate, inputExpert, expert_num, nb_class):
    #expert_num=30
    #nb_class=10
    input_vector1 = Input(shape=(inputGate.shape[1:]))
    input_vector2 = Input(shape=(inputExpert.shape[1:]))

    #The gate
    gate = Dense(expert_num*nb_class, activation='softmax')(input_vector1)
    gate = Reshape((1,nb_class, expert_num))(gate)
    gate = Lambda(sliced, output_shape=(nb_class, expert_num), arguments={'expert_num':expert_num})(gate)

    #The expert
    expert = Dense(nb_class*expert_num, activation='sigmoid')(input_vector2)
    expert = Reshape((nb_class, expert_num))(expert)

    #The output
    output = tf.multiply(gate, expert)
    #output = keras.layers.merge([gate, expert], mode='mul')
    output = Lambda(reduce, output_shape=(nb_class,), arguments={'axis': 2})(output)

    model = Model(input=[input_vector1, input_vector2], output=output)

    model.compile(loss='mean_squared_error', metrics=['mse'], optimizer='adam')

    return model

但是，我得到了“'NoneType'对象没有属性'_inbound_nodes'”。我在这里检查了其他类似的问题：AttributeError: 'NoneType' object has no attribute '_inbound_nodes' while trying to add multiple keras Dense layers，但是问题已通过keras的Lambda函数转换为图层而得以解决。

Answer 1

好吧，您需要将tf.multiply()放在Lambda层中才能获得Keras张量作为输出（而不是张量）：

output = Lambda(lambda x: tf.multiply(x[0], x[1]))([gate, expert])

NoneType'对象没有属性'_inbound_nodes'

1 个答案: