【task4(2天)】用pytorch實現多層網路
1.引入模組，讀取資料
2.構建計算圖（構建網路模型）
3.損失函式與優化器
4.開始訓練模型
5.對訓練的模型**結果進行評估
參考：import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from torch.autograd import variable
import torch.nn as nn
import torch.nn.functional as f
import torch.optim as optim
transform = transforms.compose([transforms.totensor(), transforms.normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.cifar10(root=』./data』, train=true, download=true, transform=transform)
trainloader = torch.utils.data.dataloader(trainset, batch_size=4, shuffle=true, num_workers=0)
testset = torchvision.datasets.cifar10(root=』./data』, train=false, download=true, transform=transform)
testloader = torch.utils.data.dataloader(testset, batch_size=4, shuffle=false, num_workers=0)
classes = (『plane』, 『car』, 『bird』, 『cat』, 『deer』, 『dog』, 『frog』, 『horse』, 『ship』, 『truck』)
def imshow(img):
img = img/2 +0.5
nping = img.numpy()
plt.imshow(np.transpose(nping, (1, 2, 0)))
plt.show()
dataiter = iter(trainloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print(』 『.join(』%5s』 % classes[labels[j]] for j in range(4)))
#自定義網路，繼承torch.nn.module類
class net(nn.module):
definit(self):
super(net, self).init()
self.conv1 = nn.conv2d(3, 6, 5)
self.pool = nn.maxpool2d(2, 2)
self.conv2 = nn.conv2d(6, 16, 5)
self.fc1 = nn.linear(1655, 120)
self.fc2 = nn.linear(120, 84)
self.fc3 = nn.linear(84, 10)
def forward(self, x):
x = self.pool(f.relu(self.conv1(x)))
x = self.pool(f.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = f.relu(self.fc1(x))
x = f.relu(self.fc2(x))
x = self.fc3(x)
return x
criterion = nn.crossentropyloss()
optimizer = optim.sgd(net.parameters(), lr=0.001, momentum=0.9)
#開始訓練
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = variable(inputs), variable(labels)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/2000))
running_loss = 0.0
dataiter2 = iter(testloader)
images, labels = dataiter2.next()
imshow(torchvision.utils.make_grid(images))
print('groundtruth: 『, 』 『.join(』%5s』 % classes[labels[j]] for j in range(4)))
outputs = net(variable(images))
_, predicted = torch.max(outputs.data, 1)
print('prediced: 『, 』 『.join(』%5s』 % classes[predicted[j]] for j in range(4)))
correct = 0
total = 0
for data in testloader:
images, labels = data
outputs = net(variable(images))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print(『accuracy of the network on the 10000 test images: %d %%』 % (100*correct/total))
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
for data in testloader:
images, labels = data
outputs = net(variable(images))
_, predicted = torch.max(outputs.data, 1)
c = (predicted==labels).squeeze().numpy()
for i in range(4):
label = labels[i]
class_correct[label] += c[i]
class_total[label] += 1
for i in range(10):
print(class_total[i], class_correct[i])
print(『accuracy of %5s : %.2f %%』 % (classes[i], 100*class_correct[i]/class_total[i]))
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