Hi!
I've written a little code of numpy code that does a neural network
feedforward calculation:
def feedforward(self,x):
for activation, w, b in zip( self.activations, self.weights,
self.biases ):
x = activation( np.dot(w, x) + b)
This works fine when my activation funct
2016-05-05 11:38 GMT+02:00 Øystein Schønning-Johansen :
> Hi!
>
> I've written a little code of numpy code that does a neural network
> feedforward calculation:
>
> def feedforward(self,x):
> for activation, w, b in zip( self.activations, self.weights,
> self.biases ):
> x
Thanks for your answer, Francesc. Knowing that there is no numpy solution
saves the work of searching for this. I've not tried the solution described
at SO, but it looks like a real performance killer. I'll rather try to
override malloc with glibs malloc_hooks or LD_PRELOAD tricks. Do you think
tha
On Thu, May 5, 2016 at 2:10 PM, Øystein Schønning-Johansen <
oyste...@gmail.com> wrote:
> Thanks for your answer, Francesc. Knowing that there is no numpy solution
> saves the work of searching for this. I've not tried the solution described
> at SO, but it looks like a real performance killer. I'
