from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class Graphconvlayer(nn.Module):
def __init__(self,adj,input_feature_neurons,output_neurons):
super(Graphconvlayer, self).__init__()
self.adj=adj
self.input_feature_neurons=input_feature_neurons
self.output_neurons=output_neurons
self.weights=Parameter(torch.normal(mean=0.0,std=torch.ones(input_feature_neurons,output_neurons)))
self.bias=Parameter(torch.normal(mean=0.0,std=torch.ones(input_feature_neurons)))
def forward(self,inputfeaturedata):
output1= torch.mm(self.adj,inputfeaturedata)
print(output1.shape)
print(self.weights.shape)
print(self.bias.shape)
output2= torch.matmul(output1,self.weights.t())+ self.bias
return output2
class GCN(nn.Module):
def __init__(self,lr,dropoutvalue,adjmatrix,inputneurons,hidden,outputneurons):
super(GCN, self).__init__()
self.lr=lr
self.dropoutvalue=dropoutvalue
self.adjmatrix=adjmatrix
self.inputneurons=inputneurons
self.hidden=hidden
self.outputneurons=outputneurons
self.gcn1 = Graphconvlayer(adjmatrix,inputneurons,hidden)
self.gcn2 = Graphconvlayer(adjmatrix,hidden,outputneurons)
def forward(self,x,adj):
x= F.relu(self.gcn1(adj,x,64))
x= F.dropout(x,self.dropoutvalue)
x= self.gcn2(adj,x,7)
return F.log_softmax(x,dim=1)
a=GCN(lr=0.001,dropoutvalue=0.5,adjmatrix=adj,inputneurons=features.shape[1],hidden=64,outputneurons=7)
a.forward(adj,features)
TypeError Traceback (most recent call last)
<ipython-input-85-7d1a2a73ecad> in <module>()
37
38 a=GCN(lr=0.001,dropoutvalue=0.5,adjmatrix=adj,inputneurons=features.shape[1],hidden=64,outputneurons=7)
---> 39 a.forward(adj,features)
1 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self.forward(*input, **kwargs)
888 for hook in itertools.chain(
--> 889 _global_forward_hooks.values(),
890 self._forward_hooks.values()):
891 hook_result = hook(self, input, result)
TypeError: forward() takes 2 positional arguments but 4 were given
print(a)
>>>
GCN(
(gcn1): Graphconvlayer()
(gcn2): Graphconvlayer()
)
This is a graph neural network. What I am trying to get is the output from the forward layer. I am not sure why I am getting the above error and what I should change for the code to work. Can anyone guide me through this?
Also I am if I pass class graphconvlayer to class GCN, do I have to now separately pass each of it's parameters also to the object ä of class GCN?
Your GCN
is composed of two Graphconvlayer
.
As defined in the code you posted, Graphconvlayer
's forward
method expects only one input argument: inputfeaturedata
. However, when GCN
calls self.gcn1
or self.gcn2
(in its forward
method) it passes 3 arguments: self.gcn1(adj,x,64)
and self.gcn2(adj,x,7)
.
Hence, instead of a single input argument, self.gcn1
and self.gcn2
are receiving 3 -- this is the error you are getting.