我需要实现这样的自定义图层：
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引入实现自定义图层的方式一样。 我需要给这个层提供两个输入：
def main():
with np.load('datasets/simple_tree.npz') as dataset:
inputsize = dataset['inputsize']
train_length = dataset['train_length']
train_data = dataset['train_data']
valid_length = dataset['valid_length']
valid_data = dataset['valid_data']
test_length = dataset['test_length']
test_data = dataset['test_data']
params = dataset['params']
num_of_all_masks = 20
num_of_hlayer = 6
hlayer_size = 5
graph_size = 4
all_masks = generate_all_masks(num_of_all_masks, num_of_hlayer, hlayer_size, graph_size)
input_layer = Input(shape=(4,))
mask_1 = Input( shape = (graph_size , hlayer_size) )
mask_2 = Input( shape = (hlayer_size , hlayer_size) )
mask_3 = Input( shape = (hlayer_size , hlayer_size) )
mask_4 = Input( shape = (hlayer_size , hlayer_size) )
mask_5 = Input( shape = (hlayer_size , hlayer_size) )
mask_6 = Input( shape = (hlayer_size , hlayer_size) )
mask_7 = Input( shape = (hlayer_size , graph_size) )
hlayer1 = MaskedDenseLayer(hlayer_size, 'relu')( [input_layer, mask_1] )
hlayer2 = MaskedDenseLayer(hlayer_size, 'relu')( [hlayer1, mask_2] )
hlayer3 = MaskedDenseLayer(hlayer_size, 'relu')( [hlayer2, mask_3] )
hlayer4 = MaskedDenseLayer(hlayer_size, 'relu')( [hlayer3, mask_4] )
hlayer5 = MaskedDenseLayer(hlayer_size, 'relu')( [hlayer4, mask_5] )
hlayer6 = MaskedDenseLayer(hlayer_size, 'relu')( [hlayer5, mask_6] )
output_layer = MaskedDenseLayer(graph_size, 'sigmoid')( [hlayer6, mask_7] )
autoencoder = Model(inputs=[input_layer, mask_1, mask_2, mask_3,
mask_4, mask_5, mask_6, mask_7], outputs=[output_layer])
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
#reassign_mask = ReassignMask()
for i in range(0, num_of_all_masks):
state = np.random.randint(0,20)
autoencoder.fit(x=[train_data,
np.tile(all_masks[state][0], [300, 1, 1]),
np.tile(all_masks[state][1], [300, 1, 1]),
np.tile(all_masks[state][2], [300, 1, 1]),
np.tile(all_masks[state][3], [300, 1, 1]),
np.tile(all_masks[state][4], [300, 1, 1]),
np.tile(all_masks[state][5], [300, 1, 1]),
np.tile(all_masks[state][6], [300, 1, 1])],
y=[train_data],
epochs=1,
batch_size=20,
shuffle=True,
#validation_data=(valid_data, valid_data),
#callbacks=[reassign_mask],
verbose=1)
不幸的是，当我运行此代码时，我收到以下错误：
TypeError: can only concatenate tuple (not "int") to tuple
我需要的是一种实现自定义图层的方法，该图层包含两个包含前一层和蒙版矩阵的输入。 这里的 all_mask 变量是一个包含所有图层的预生成掩码的列表。
有人可以帮忙吗？我的代码在这里出了什么问题。
更新
一些参数：
训练数据：（300,4）
隐藏层数：6
隐藏图层单元：5
mask :(前一层的大小，当前图层的大小）
这是我的模型摘要：
__________________________________________________________________________________________________
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
答案 0 :(得分：5)
您的input_shape是元组列表。
input_shape: [(None, 4), (None, 4, 5)]
您不能简单地使用input_shape[0]或input_shape[1]。如果要使用实际值，则必须选择哪个元组，然后选择哪个值。例如：
self.kernel = self.add_weight(name='kernel',
#here:
shape=(input_shape[0][1], self.output_dim),
initializer='glorot_uniform',
trainable=True)
在方法compute_output_shape中，同样的必要（遵循自己的形状规则），你想要的是连接元组：
return input_shape[0] + (self.output_dim,)
请勿忘记取消注释super(MaskedDenseLayer, self).build(input_shape)行。