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Resulted network does not seem to work

See original GitHub issue

Hi, I ran the Neat Python XOR example and a neural network it found has the following properties:

Best individual in generation 22 meets fitness threshold - complexity: (2, 8) Number of evaluations: 3450

Best genome: Nodes: NodeGene(id=0, type=INPUT, bias=0.0, response=4.924273, activation=sigmoid) NodeGene(id=1, type=INPUT, bias=0.0, response=4.924273, activation=sigmoid) NodeGene(id=2, type=OUTPUT, bias=0.0, response=4.921960812206318, activation=sigmoid) NodeGene(id=3, type=HIDDEN, bias=-2.340619398413843, response=4.930091408143276, activation=sigmoid) NodeGene(id=4, type=HIDDEN, bias=0.4674809078722945, response=4.918937894580367, activation=sigmoid) Connections: ConnectionGene(in=0, out=2, weight=1.0534262789427578, enabled=True, innov=0) ConnectionGene(in=1, out=2, weight=-1.6479324848766828, enabled=True, innov=1) ConnectionGene(in=0, out=3, weight=-3.5479647367966765, enabled=True, innov=2) ConnectionGene(in=3, out=2, weight=5.970619032226201, enabled=True, innov=3) ConnectionGene(in=1, out=3, weight=4.634338591600734, enabled=True, innov=4) ConnectionGene(in=0, out=4, weight=0.19203601809123594, enabled=True, innov=5) ConnectionGene(in=4, out=2, weight=-0.6588709650159562, enabled=True, innov=6) ConnectionGene(in=4, out=3, weight=-3.0010419817357072, enabled=True, innov=8) Node order: [4, 3]

Output: expected 0.00000 got 0.11990 expected 1.00000 got 1.00000 expected 1.00000 got 0.92939 expected 0.00000 got 0.00395 {2} {2} {0, 1, 3, 4} {0, 1, 2, 3, 4}

However, when trying to implement the neural network in Python, I don’t get the same results:

import math

def sigmoid(x):
    f = 1 / (1 + math.exp(-x))
    return f

node0 = 1
node1 = 1
node2 = 0

node3_bias = -2.340619398413843
node4_bias = 0.4674809078722945

connection_0_2 = 1.0534262789427578
connection_1_2 = -1.6479324848766828
connection_0_3 = -3.5479647367966765
connection_3_2 = 5.970619032226201
connection_1_3 = 4.634338591600734
connection_0_4 = 0.19203601809123594
connection_4_2 = -0.6588709650159562
connection_4_3 = -3.0010419817357072

def main():
    activated_node4 = sigmoid(node4_bias * 1 +
                              node0 * connection_0_4)
    activated_node3 = sigmoid(node3_bias * 1 +
                              activated_node4 * connection_4_3 +
                              node0 * connection_0_3 +
                              node1 * connection_1_3)
    activated_node2 = sigmoid(activated_node4 * connection_4_2 +
                              activated_node3 * connection_3_2 +
                              node1 * connection_1_2 +
                              node0 * connection_0_2)

    print("The value is: ", activated_node2)

if __name__ == "__main__":


For nodes 1 and 1 I get:

The value is: 0.30957048732254944

which does not match the expected output above.

Can anyone please help me with some pointers as to what I am getting wrong?

Thank you.

Issue Analytics

  • State:closed
  • Created 7 years ago
  • Comments:6 (4 by maintainers)

github_iconTop GitHub Comments

radu-nedelcu-decommented, Mar 5, 2017

Hi guys,

Thank you so much! I was ignoring both the response as I did not know it is required and the sigmoid activation function, thinking it would be identical.

It’s working fine now.

CodeReclaimerscommented, Mar 5, 2017

Glad to help–it’s always those little back-end details that are hard to spot right away. 😃 Thanks for taking an evolved network and doing the experiment of using it elsewhere!

Read more comments on GitHub >

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