我正在尝试用这种设计在 keras 中实现一个神经（ish）网络：http: //nlp.cs.rpi.edu/paper/AAAI15.pdf
该算法本质上具有三个输入。输入 2 和输入 3 乘以相同的权重矩阵 W1 以产生 O2 和 O3。输入 1 乘以 W2 得到 O1。然后，我们需要取 O1 * O2 和 O1 * O3 的点积。
我正在尝试在 keras 中实现这一点。
我的第一个想法是使用 kerasGraph类并使 W1 成为具有两个输入和两个输出的共享节点层。到目前为止还好。
然后出现的问题是如何使用 O1 获取这两个输出的点积。
我试图定义一个自定义函数：
def layer_mult(X, Y):
return K.dot(X * K.transpose(Y))
然后：
ntm.add_node(Lambda(layer_mult, output_shape = (1,1)), name = "ls_pos", inputs = ["O1", "O2"])
ntm.add_node(Lambda(layer_mult, output_shape = (1,1)), name = "ls_neg", inputs = ["O1", "O3"])
编译时出现的问题是 keras 只想给 Lambda 层一个输入：
1045 func = types.FunctionType(func, globals())
1046 if hasattr(self, 'previous'):
-> 1047 return func(self.previous.get_output(train))
1048 else:
1049 return func(self.input)
TypeError: layer_mult() takes exactly 2 arguments (1 given)
我认为另一种选择可能是使用一个Merge类，该类具有dot允许的合并类型。但是，必须将类的输入层Merge传递给构造函数。因此，似乎没有办法将共享节点的输出Merge添加Merge到Graph.
如果我使用Sequential容器，我可以将它们放入Merge. 但是，那么就没有办法实现Sequential两层需要共享相同的权重矩阵。
我曾想过尝试将 O1、O2 和 O3 连接成一个向量作为输出层，然后在目标函数中进行乘法运算。但是，这需要目标函数拆分其输入，这在 keras 中似乎是不可能的（相关的 Theano 函数没有传递到 keras API）。
有知道解决办法的吗？
编辑：
我认为我已经取得了一些进展，因为我发现它shared_node正在实施dot（即使它不在文档中）。
所以我必须：
ntm = Graph()
ntm.add_input(name='g', input_shape=(300,)) # Vector of 300 units, normally distributed around zero
ntm.add_node([pretrained bit], name = "lt", input = "g") # 300 * 128, output = (,128)
n_docs = 1000
ntm.add_input("d_pos", input_shape = (n_docs,)) # (,n_docs)
ntm.add_input("d_neg", input_shape = (n_docs,)) # (,n_docs)
ntm.add_shared_node(Dense(128, activation = "softmax",
# weights = pretrained_W1,
W_constraint = unitnorm(),
W_regularizer = l2(0.001)
), name = "ld",
inputs = ["d_pos", "d_neg"],
outputs = ["ld_pos", "ld_neg"],
merge_mode=None) # n_docs * 128, output = (,128) * 2
ntm.add_shared_node(ActivityRegularization(0,0), #ActivityRegularization is being used as a passthrough - the function of the node is to dot* its inputs
name = "ls_pos",
inputs = ["lt", "d_pos"],
merge_mode = 'dot') # output = (,1)
ntm.add_shared_node(ActivityRegularization(0,0),
name = "ls_neg",
inputs = ["lt", "d_neg"],
merge_mode = 'dot') # output = (,1)
ntm.add_shared_node(ActivityRegularization(0,0),
name = "summed",
inputs = ["ls_pos", "ls_neg"],
merge_mode = 'sum') # output = (,1)
ntm.add_node(ThresholdedReLU(0.5),
input = "summed", name = "loss") # output = (,1)
ntm.add_output(name = "loss_out",
input= "loss")
def obj(X, Y):
return K.sum(Y)
ntm.compile(loss = {'loss_out' : obj}, optimizer = "sgd")
现在的错误是：
>>> ntm.compile(loss = {'loss_out' : obj}, optimizer = "sgd")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "build/bdist.macosx-10.5-x86_64/egg/keras/models.py", line 602, in compile
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/advanced_activations.py", line 149, in get_output
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 117, in get_input
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 1334, in get_output
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 1282, in get_output_sum
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 1266, in get_output_at
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 730, in get_output
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 117, in get_input
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 1340, in get_output
File "build/bdist.macosx-10.5-x86_64/egg/keras/layers/core.py", line 1312, in get_output_dot
File "/Volumes/home500/anaconda/envs/[-]/lib/python2.7/site-packages/theano/tensor/var.py", line 360, in dimshuffle
pattern)
File "/Volumes/home500/anaconda/envs/[-]/lib/python2.7/site-packages/theano/tensor/elemwise.py", line 164, in __init__
(input_broadcastable, new_order))
ValueError: ('You cannot drop a non-broadcastable dimension.', ((False, False, False, False), (0, 'x')))