NoneType' 对象没有属性 '_inbound_nodes' [英] NoneType' object has no attribute '_inbound_nodes'

问题描述

我正在尝试构建一个混合专家神经网络.我在这里找到了一个代码:http://blog.sina.com.cn/s/blog_dc3c53e90102x9xu.html.我的目标是门和专家来自不同的数据，但具有相同的维度.

Hi I am trying to build a Mixture-of-experts neural network. I found a code here: http://blog.sina.com.cn/s/blog_dc3c53e90102x9xu.html. My goal is that the gate and expert come from different data, but with same dimensions.

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'对象在尝试添加多个 keras 密集层时没有属性 '_inbound_nodes' 但问题已通过 keras 的 Lambda 函数转换为层来解决.

However, I got "'NoneType' object has no attribute '_inbound_nodes'". I checked other similar questions here: AttributeError: 'NoneType' object has no attribute '_inbound_nodes' while trying to add multiple keras Dense layers but the problem is fixed with the Lambda function of keras to convert into a layer.

推荐答案

嗯，你需要把 tf.multiply() 放在一个 Lambda 层里面才能得到一个 Keras张量作为输出(而不是张量):

Well, you need to put tf.multiply() inside a Lambda layer to get a Keras Tensor as output (and not a Tensor):

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

这篇关于NoneType' 对象没有属性 '_inbound_nodes'的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！