如何在Keras中实现多输入自定义层

Question

我需要实现这样的自定义层：

class MaskedDenseLayer(Layer):
    def __init__(self, output_dim, activation, **kwargs):
        self.output_dim = output_dim
        super(MaskedDenseLayer, self).__init__(**kwargs)
        self._activation = activations.get(activation)
    def build(self, input_shape):

        # Create a trainable weight variable for this layer.
        self.kernel = self.add_weight(name='kernel', 
                                  shape=(input_shape[0][1], self.output_dim),
                                  initializer='glorot_uniform',
                                  trainable=True)
        super(MaskedDenseLayer, self).build(input_shape)  

    def call(self, l):
        self.x = l[0]
        self._mask = l[1][1]
        print('kernel:', self.kernel)
        masked = Multiply()([self.kernel, self._mask])
        self._output = K.dot(self.x, masked)
        return self._activation(self._output)


    def compute_output_shape(self, input_shape):
    return (input_shape[0][0], self.output_dim)

这就像Keras API引入的实现自定义层的方式一样。 我需要给这个层两个输入：

^{pr2}$

不幸的是，当我运行此代码时，我得到以下错误：

TypeError: can only concatenate tuple (not "int") to tuple

我需要的是一种方法来实现一个自定义层，它有两个输入，包含前一层和一个掩码矩阵。 这里all_mask变量是一个列表，其中包含所有层的一些预先生成的掩码。在

有人能帮忙吗？我的代码怎么了。在

更新

一些参数：

列车数据：（300，4）

隐藏层数：6

隐藏层单位：5

掩码：（前一层的大小，当前层的大小）

下面是我的模型摘要：

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_361 (InputLayer)          (None, 4)            0                                            
__________________________________________________________________________________________________
input_362 (InputLayer)          (None, 4, 5)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_281 (MaskedD (None, 5)            20          input_361[0][0]                  
                                                                 input_362[0][0]                  
__________________________________________________________________________________________________
input_363 (InputLayer)          (None, 5, 5)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_282 (MaskedD (None, 5)            25          masked_dense_layer_281[0][0]     
                                                                 input_363[0][0]                  
__________________________________________________________________________________________________
input_364 (InputLayer)          (None, 5, 5)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_283 (MaskedD (None, 5)            25          masked_dense_layer_282[0][0]     
                                                                 input_364[0][0]                  
__________________________________________________________________________________________________
input_365 (InputLayer)          (None, 5, 5)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_284 (MaskedD (None, 5)            25          masked_dense_layer_283[0][0]     
                                                                 input_365[0][0]                  
__________________________________________________________________________________________________
input_366 (InputLayer)          (None, 5, 5)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_285 (MaskedD (None, 5)            25          masked_dense_layer_284[0][0]     
                                                                 input_366[0][0]                  
__________________________________________________________________________________________________
input_367 (InputLayer)          (None, 5, 5)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_286 (MaskedD (None, 5)            25          masked_dense_layer_285[0][0]     
                                                                 input_367[0][0]                  
__________________________________________________________________________________________________
input_368 (InputLayer)          (None, 5, 4)         0                                            
__________________________________________________________________________________________________
masked_dense_layer_287 (MaskedD (None, 4)            20          masked_dense_layer_286[0][0]     
                                                                 input_368[0][0]                  
==================================================================================================
Total params: 165
Trainable params: 165
Non-trainable params: 0

Answer 1

您的input_shape是元组的列表。在

input_shape:  [(None, 4), (None, 4, 5)]

不能简单地使用input_shape[0]或input_shape[1]。如果要使用实际值，必须先选择哪个元组，然后选择哪个值。示例：

^{pr2}$

在compute_output_shape方法中也需要相同的方法（遵循您自己的形状规则），其中似乎您想要的是连接元组：

return input_shape[0] + (self.output_dim,)

别忘了取消super(MaskedDenseLayer, self).build(input_shape)行的注释。在