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pytorch visdom处理简单分类 pytorch + visdom 处理简单分类问题的示例

泛泛之素 人气:0

环境

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

包引用

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import visdom
import time
from torch import nn,optim

数据准备

use_gpu = True
ones = 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 +1
x3 = torch.normal(-6*torch.from_numpy(ones),2)
y3 = y1 +2
x4 = 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 accuracy
line = 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)

我们先用cpu运行一次,结果如下:

然后用gpu运行一下,结果如下:

发现cpu的速度比gpu快很多,但是我听说机器学习应该是gpu更快啊,百度了一下,知乎上的答案是:

我的理解就是gpu在处理图片识别大量矩阵运算等方面运算能力远高于cpu,在处理一些输入和输出都很少的,还是cpu更具优势。

添加神经层:

net = nn.Sequential(
  nn.Linear(2, 10),
  nn.ReLU(),  #激活函数
  nn.Linear(10, 4)
)

添加一层10单元神经层,看看效果是否会有所提升:

使用cpu:

 

使用gpu:

比较观察,似乎并没有什么区别,看来处理简单分类问题(输入,输出少)的问题,神经层和gpu不会对机器学习加持。

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