This example takes values from two different networks and multiplies them. Any function can be done in this way - by converting a python function
import nef
net=nef.Network('Test Network')
net.add_to(world)
input1=net.make_input('input1',values=[0])
input2=net.make_input('input2',values=[0])
A=net.make('A',neurons=100,dimensions=1)
B=net.make('B',neurons=100,dimensions=1)
C=net.make('C',neurons=100,dimensions=2)
D=net.make('D',neurons=100,dimensions=1)
net.connect(input1,A)
net.connect(input2,B)
# transform - the brackets are the dimensions of the receiving network
# the values in the brackets are the transpose weights
net.connect(A,C,transform=[[1],[0]])
net.connect(B,C,transform=[[0],[1]])
# define a python function
def multiply(x):
return x[0]*x[1]
# compute that function here
net.connect(C,D,func=multiply)
import nef
net=nef.Network('Test Network')
net.add_to(world)
input1=net.make_input('input1',values=[0])
input2=net.make_input('input2',values=[0])
A=net.make('A',neurons=100,dimensions=1)
B=net.make('B',neurons=100,dimensions=1)
C=net.make('C',neurons=100,dimensions=2)
D=net.make('D',neurons=100,dimensions=1)
net.connect(input1,A)
net.connect(input2,B)
# transform - the brackets are the dimensions of the receiving network
# the values in the brackets are the transpose weights
net.connect(A,C,transform=[[1],[0]])
net.connect(B,C,transform=[[0],[1]])
# define a python function
def multiply(x):
return x[0]*x[1]
# compute that function here
net.connect(C,D,func=multiply)
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