pablobarhamal...@gmail.com wrote:
> Ok, I'm working on a predator/prey simulation, which evolve using genetic
> algorithms. At the moment, they use a quite simple feed-forward neural
> network, which can change size over time. Each brain "tick" is performed
> by the following function (inside the Brain class):
>
> def tick(self):
> input_num = self.input_num
> hidden_num = self.hidden_num
> output_num = self.output_num
>
> hidden = [0]*hidden_num
> output = [0]*output_num
>
> inputs = self.input
> h_weight = self.h_weight
> o_weight = self.o_weight
>
> e = math.e
>
> count = -1
> for x in range(hidden_num):
> temp = 0
> for y in range(input_num):
> count += 1
> temp += inputs[y] * h_weight[count]
> hidden[x] = 1/(1+e**(-temp))
>
> count = -1
> for x in range(output_num):
> temp = 0
> for y in range(hidden_num):
> count += 1
> temp += hidden[y] * o_weight[count]
> output[x] = 1/(1+e**(-temp))
>
> self.output = output
>
> The function is actually quite fast (~0.040 seconds per 200 calls, using
> 10 input, 20 hidden and 3 output neurons), and used to be much slower
> untill I fiddled about with it a bit to make it faster. However, it is
> still somewhat slow for what I need it.
>
> My question to you is if you an see any obvious (or not so obvious) way of
> making this faster. I've heard about numpy and have been reading about it,
> but I really can't see how it could be implemented here.
>
> Cheers!
Assuming every list is replaced with a numpy.array,
h_weight.shape == (hidden_num, input_num)
o_weight.shape == (output_num, hidden_num)
and as untested as it gets:
def tick(self):
temp = numpy.dot(self.inputs, self.h_weight)
hidden = 1/(1+numpy.exp(-temp))
temp = numpy.dot(hidden, self.o_weight)
self.output = 1/(1+numpy.exp(-temp))
My prediction: this is probably wrong, but if you can fix the code it will
be stinkin' fast ;)
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