使用Pytorch加速PSO算法？

weixin_38077297 2019-09-12 03:35:16

我已经实施了PSO算法使用PyTorch,torch.cuda.FloatTensor。我设法加速了2次，但我期待的不止于此。它认为步骤检查限制(line 41-55)和参数更新(line 58-72)是原因。有一个“GPU任何”这样做？有是我的代码： import torch import numpy as np import matplotlib.pyplot as plt dtype = torch.cuda.FloatTensor def fitness(x,y): return -torch.abs(torch.sin(x)*torch.cos(y)*torch.exp(torch.abs(1-(torch.sqrt(x**2+y**2))/(3.1415)))) def velocity(v, gxbest, pxbest, pybest, x, pop, w, c1, c2): return w*torch.rand(pop).type(dtype)*v + \ c1*torch.rand(pop).type(dtype)*(pxbest - x) + \ c2*torch.rand(pop).type(dtype)*(gxbest.expand(x.size(0)) - x) def PSO(pop, xmax, xmin, niter, wmax, wmin, c1, c2): vx = torch.rand(pop).type(dtype) vy = torch.rand(pop).type(dtype) best = np.zeros(niter) x = (xmax - xmin)*torch.rand(pop).type(dtype) + xmin y = (xmax - xmin)*torch.rand(pop).type(dtype) + xmin z = fitness(x,y) [minz, indexminz] = z.min(0) gxbest = x[indexminz] gybest = y[indexminz] pxbest = x pybest = y pzbest = z for K in range(niter): w = wmax - ((wmax - wmin)/niter) * (K) vnextx = velocity(vx, gxbest, pxbest, pybest, x, pop, w, c1, c2) xnext = x + vnextx vnexty = velocity(vy, gxbest, pxbest, pybest, y, pop, w, c1, c2) ynext = y + vnexty **(41)** xnext = xnext.cpu() ynext = ynext.cpu() idxmax = (xnext > xmax) # elements that are bigger that upper limit idxmim = (xnext < xmin) # elements that are smaller that upper limit xnext[idxmax] = xmax xnext[idxmim] = xmin idymax = (ynext > xmax) # elements that are bigger that upper limit idymim = (ynext < xmin) # elements that are smaller that upper limit ynext[idymax] = xmax ynext[idymim] = xmin xnext = xnext.cuda() **(55)** ynext = ynext.cuda() znext = fitness(xnext,ynext) **(58)**[minznext, indexminznext] = znext.min(0) if (minznext[0] < minz[0]): minz = minznext gxbest = xnext[indexminznext] gybest = ynext[indexminznext] indexpbest = (znext < pzbest) pxbest[indexpbest] = xnext[indexpbest] pybest[indexpbest] = ynext[indexpbest] pzbest[indexpbest] = znext[indexpbest] x = xnext y = ynext vx = vnextx **(72)** vy = vnexty best[K] = minz.cpu().numpy() return gxbest, gybest , minz, best def main(): pop, xmax, xmin, niter = 10000, 10, -10, 10 wmax = 0.9 wmin = 0.4 c1 = 2.05 c2 = 2.05 xbest, ybest, fitbest, best = PSO(pop, xmax, xmin, niter, wmax, wmin, c1, c2) print(xbest) print(ybest) print(fitbest) t = np.linspace(0,niter,niter) plt.plot(t, best, 'k.-') plt.show() main() 感谢这么多的帮助。

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