人工智能撸代码,怎样可视化神经网络算法实现模型的训练过程

Posted 2020-11-28

感谢参考原文-http://bjbsair.com/2020-04-01/tech-info/18490.html

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正文

本文将介绍一个和pytorch紧密结合的机器学习库，visdom

Visdom的安装

Pip install visdom

如果安装失败

pip install --upgrade visdom

安装好之后，我们需要实时开启

Python -m visdom.server

然后会出现

在浏览输入这个网址就可以开启visdom了

当我们使用visdom画图的时候，我们需要

from visdom import Visdom

viz=Visdom()

然后就可以使用viz来进行画图了

画线的话可以使用viz.line

画图片的话可以使用viz.image

画文字的话可以使用viz.text

画线的时候，要先画一个起始点，然后后面的对它进行覆盖操作

from visdom import Visdom  
import numpy as np  
import torch  
x=np.arange(0,10)  
y=np.arange(0,10)*9  
print(x)  
viz=Visdom()  
viz.line([0.],[0.],win="first",opts=dict(title=‘first‘))  
viz.line(y,x,win="first",update=‘append‘)

画线的时候，可以先画一个其实的图，然后后面的对它进行添加操作，当然也可以直接来画图

viz.line([0.],[0.],win="first",opts=dict(title=‘first‘))

表示画起始点

viz.line(y,x,win="first",update=‘append‘)

表示添加操作

其中win=""first"表示画在first的区域，主题名为first

然后

viz.line(y,x,win="first",update=‘append‘)

win="first"表示对first区域添加画图，append表示添加

import torch  

import torch.nn as nn  

import torch.nn.functional as F  

import torch.optim as optim  

from torchvision import datasets, transforms  

from visdom import Visdom  

batch_size=200  

learning_rate=0.01  

epochs=10  

train_loader = torch.utils.data.DataLoader(  

datasets.MNIST(‘./data‘, train=True, download=True,  

transform=transforms.Compose([  

transforms.ToTensor(),  

])),  

batch_size=batch_size, shuffle=True)  

test_loader = torch.utils.data.DataLoader(  

datasets.MNIST(‘./data‘, train=False, transform=transforms.Compose([  

transforms.ToTensor(),  

])),  

batch_size=batch_size, shuffle=True)  

class MLP(nn.Module):  

def __init__(self):  

super(MLP, self).__init__()  

self.model = nn.Sequential(  

nn.Linear(784, 200),  

nn.LeakyReLU(inplace=True),  

nn.Linear(200, 200),  

nn.LeakyReLU(inplace=True),  

nn.Linear(200, 10),  

nn.LeakyReLU(inplace=True),  

)  

def forward(self, x):  

x = self.model(x)  

return x  

device = torch.device(‘cpu‘)  

net = MLP().to(device)  

optimizer = optim.SGD(net.parameters(), lr=learning_rate)  

criteon = nn.CrossEntropyLoss()  

viz = Visdom()  

viz.line([0.], [0.], win=‘train_loss‘, opts=dict(title=‘train loss‘))  

viz.line([[0.0, 0.0]], [0.], win=‘test‘, opts=dict(title=‘test loss&acc.‘,  

legend=[‘loss‘, ‘acc.‘]))  

global_step = 0  

for epoch in range(epochs):  

for batch_idx, (data, target) in enumerate(train_loader):  

data = data.view(-1, 28*28)  

data, target = data.to(device), target.to(device)  

logits = net(data)  
#print(target)  

loss = criteon(logits, target)  

optimizer.zero_grad()  

loss.backward()  

# print(w1.grad.norm(), w2.grad.norm())  

optimizer.step()  

global_step += 1  

viz.line([loss.item()], [global_step], win=‘train_loss‘, update=‘append‘)  

if batch_idx % 100 == 0:  

print(‘Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}‘.format(  

epoch, batch_idx * len(data), len(train_loader.dataset),  

100. * batch_idx / len(train_loader), loss.item()))  

test_loss = 0  

correct = 0  

for data, target in test_loader:  

data = data.view(-1, 28 * 28)  

data, target = data.to(device), target.to(device)  

logits = net(data)  

test_loss += criteon(logits, target).item()  

pred = logits.argmax(dim=1)  

correct += pred.eq(target).float().sum().item()  

viz.line([[test_loss, correct / len(test_loader.dataset)]],  

[global_step], win=‘test‘, update=‘append‘)  

viz.images(data.view(-1, 1, 28, 28), win=‘x‘)  

viz.text(str(pred.detach().cpu().numpy()), win=‘pred‘,  

opts=dict(title=‘pred‘))  

test_loss /= len(test_loader.dataset)  

print(‘
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
‘.format(  

test_loss, correct, len(test_loader.dataset),  

100. * correct / len(test_loader.dataset)))