pytorch + visdom 处理简单分类问题的示例

环境

系统 ： win 10
显卡：gtx965m
cpu ：i7-6700HQ
python 3.61
pytorch 0.3

包引用

import torchfrom torch.autograd import Variableimport torch.nn.functional as Fimport numpy as npimport visdomimport timefrom torch import nn,optim

数据准备

use_gpu = Trueones = np.ones((500,2))x1 = torch.normal(6*torch.from_numpy(ones),2)y1 = torch.zeros(500) x2 = torch.normal(6*torch.from_numpy(ones*[-1,1]),2)y2 = y1 +1x3 = torch.normal(-6*torch.from_numpy(ones),2)y3 = y1 +2x4 = torch.normal(6*torch.from_numpy(ones*[1,-1]),2)y4 = y1 +3 x = torch.cat((x1, x2, x3 ,x4), 0).float()y = torch.cat((y1, y2, y3, y4), ).long()  

可视化如下看一下：

visdom可视化准备

先建立需要观察的windows

viz = visdom.Visdom()colors = np.random.randint(0,255,(4,3)) #颜色随机#线图用来观察loss 和 accuracyline = viz.line(X=np.arange(1,10,1), Y=np.arange(1,10,1))#散点图用来观察分类变化scatter = viz.scatter(  X=x,  Y=y+1,   opts=dict(    markercolor = colors,    marksize = 5,    legend=["0","1","2","3"]),)#text 窗口用来显示loss 、accuracy 、时间text = viz.text("FOR TEST")#散点图做对比viz.scatter(  X=x,  Y=y+1,   opts=dict(    markercolor = colors,    marksize = 5,    legend=["0","1","2","3"]  ),)

效果如下：

逻辑回归处理

输入2，输出4

logstic = nn.Sequential(  nn.Linear(2,4))

gpu还是cpu选择：

if use_gpu:  gpu_status = torch.cuda.is_available()  if gpu_status:    logstic = logstic.cuda()    # net = net.cuda()    print("###############使用gpu##############")  else : print("###############使用cpu##############")else:  gpu_status = False  print("###############使用cpu##############")

优化器和loss函数：

loss_f = nn.CrossEntropyLoss()optimizer_l = optim.SGD(logstic.parameters(), lr=0.001)

训练2000次：

start_time = time.time()time_point, loss_point, accuracy_point = [], [], []for t in range(2000):  if gpu_status:    train_x = Variable(x).cuda()    train_y = Variable(y).cuda()  else:    train_x = Variable(x)    train_y = Variable(y)  # out = net(train_x)  out_l = logstic(train_x)  loss = loss_f(out_l,train_y)  optimizer_l.zero_grad()  loss.backward()  optimizer_l.step()

训练过成观察及可视化：

if t % 10 == 0:  prediction = torch.max(F.softmax(out_l, 1), 1)[1]  pred_y = prediction.data  accuracy = sum(pred_y ==train_y.data)/float(2000.0)  loss_point.append(loss.data[0])  accuracy_point.append(accuracy)  time_point.append(time.time()-start_time)  print("[{}/{}] | accuracy : {:.3f} | loss : {:.3f} | time : {:.2f} ".format(t + 1, 2000, accuracy, loss.data[0],                                  time.time() - start_time))  viz.line(X=np.column_stack((np.array(time_point),np.array(time_point))),       Y=np.column_stack((np.array(loss_point),np.array(accuracy_point))),       win=line,       opts=dict(legend=["loss", "accuracy"]))   #这里的数据如果用gpu跑会出错，要把数据换成cpu的数据 .cpu()即可  viz.scatter(X=train_x.cpu().data, Y=pred_y.cpu()+1, win=scatter,name="add",        opts=dict(markercolor=colors,legend=["0", "1", "2", "3"]))  viz.text("<h3 align='center' style='color:blue'>accuracy : {}</h3><br><h3 align='center' style='color:pink'>"       "loss : {:.4f}</h3><br><h3 align ='center' style='color:green'>time : {:.1f}</h3>"       .format(accuracy,loss.data[0],time.time()-start_time),win =text)