比賽網址是https://www.kaggle.com/c/cifar-10
導入庫
# 本節的網絡需要較長的訓練時間
# 可以在Kaggle訪問:
# https://www.kaggle.com/boyuai/boyu-d2l-image-classification-cifar-10
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import os
import time
獲取和組織數據集
比賽數據分爲訓練集和測試集。訓練集包含 50,000 圖片。測試集包含 300,000 圖片。兩個數據集中的圖像格式均爲PNG,高度和寬度均爲32像素,並具有三個顏色通道(RGB)。圖像涵蓋10個類別:飛機,汽車,鳥類,貓,鹿,狗,青蛙,馬,船和卡車。 爲了更容易上手,我們提供了上述數據集的小樣本。“ train_tiny.zip”包含 80 訓練樣本,而“ test_tiny.zip”包含100個測試樣本。它們的未壓縮文件夾名稱分別是“ train_tiny”和“ test_tiny”。
圖像增強
data_transform = transforms.Compose([
transforms.Resize(40),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32),
transforms.ToTensor()
])
trainset = torchvision.datasets.ImageFolder(root='/home/kesci/input/CIFAR102891/cifar-10/train'
, transform=data_transform)
data = [d[0].data.cpu().numpy() for d in trainset]
np.mean(data)
# 圖像增強
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4), #先四周填充0,再把圖像隨機裁剪成32*32
transforms.RandomHorizontalFlip(), #圖像一半的概率翻轉,一半的概率不翻轉
transforms.ToTensor(),
transforms.Normalize((0.4731, 0.4822, 0.4465), (0.2212, 0.1994, 0.2010)), #R,G,B每層的歸一化用到的均值和方差
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4731, 0.4822, 0.4465), (0.2212, 0.1994, 0.2010)),
])
導入數據集
train_dir = '/home/kesci/input/CIFAR102891/cifar-10/train'
test_dir = '/home/kesci/input/CIFAR102891/cifar-10/test'
trainset = torchvision.datasets.ImageFolder(root=train_dir, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=256, shuffle=True)
testset = torchvision.datasets.ImageFolder(root=test_dir, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=256, shuffle=False)
classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'forg', 'horse', 'ship', 'truck']
定義模型
ResNet-18網絡結構:ResNet全名Residual Network殘差網絡。Kaiming He 的《Deep Residual Learning for Image Recognition》獲得了CVPR最佳論文。他提出的深度殘差網絡在2015年可以說是洗刷了圖像方面的各大比賽,以絕對優勢取得了多個比賽的冠軍。而且它在保證網絡精度的前提下,將網絡的深度達到了152層,後來又進一步加到1000的深度。
class ResidualBlock(nn.Module): # 我們定義網絡時一般是繼承的torch.nn.Module創建新的子類
def __init__(self, inchannel, outchannel, stride=1):
super(ResidualBlock, self).__init__()
#torch.nn.Sequential是一個Sequential容器,模塊將按照構造函數中傳遞的順序添加到模塊中。
self.left = nn.Sequential(
nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
# 添加第一個卷積層,調用了nn裏面的Conv2d()
nn.BatchNorm2d(outchannel), # 進行數據的歸一化處理
nn.ReLU(inplace=True), # 修正線性單元,是一種人工神經網絡中常用的激活函數
nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(outchannel)
)
self.shortcut = nn.Sequential()
if stride != 1 or inchannel != outchannel:
self.shortcut = nn.Sequential(
nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(outchannel)
)
# 便於之後的聯合,要判斷Y = self.left(X)的形狀是否與X相同
def forward(self, x): # 將兩個模塊的特徵進行結合,並使用ReLU激活函數得到最終的特徵。
out = self.left(x)
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, ResidualBlock, num_classes=10):
super(ResNet, self).__init__()
self.inchannel = 64
self.conv1 = nn.Sequential( # 用3個3x3的卷積核代替7x7的卷積核,減少模型參數
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
self.fc = nn.Linear(512, num_classes)
def make_layer(self, block, channels, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1) #第一個ResidualBlock的步幅由make_layer的函數參數stride指定
# ,後續的num_blocks-1個ResidualBlock步幅是1
layers = []
for stride in strides:
layers.append(block(self.inchannel, channels, stride))
self.inchannel = channels
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
def ResNet18():
return ResNet(ResidualBlock)
訓練和測試
# 定義是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 超參數設置
EPOCH = 20 #遍歷數據集次數
pre_epoch = 0 # 定義已經遍歷數據集的次數
LR = 0.1 #學習率
# 模型定義-ResNet
net = ResNet18().to(device)
# 定義損失函數和優化方式
criterion = nn.CrossEntropyLoss() #損失函數爲交叉熵,多用於多分類問題
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4)
#優化方式爲mini-batch momentum-SGD,並採用L2正則化(權重衰減)
# 訓練
if __name__ == "__main__":
print("Start Training, Resnet-18!")
num_iters = 0
for epoch in range(pre_epoch, EPOCH):
print('\nEpoch: %d' % (epoch + 1))
net.train()
sum_loss = 0.0
correct = 0.0
total = 0
for i, data in enumerate(trainloader, 0):
#用於將一個可遍歷的數據對象(如列表、元組或字符串)組合爲一個索引序列,同時列出數據和數據下標,
#下標起始位置爲0,返回 enumerate(枚舉) 對象。
num_iters += 1
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad() # 清空梯度
# forward + backward
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
sum_loss += loss.item() * labels.size(0)
_, predicted = torch.max(outputs, 1) #選出每一列中最大的值作爲預測結果
total += labels.size(0)
correct += (predicted == labels).sum().item()
# 每20個batch打印一次loss和準確率
if (i + 1) % 20 == 0:
print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '
% (epoch + 1, num_iters, sum_loss / (i + 1), 100. * correct / total))
print("Training Finished, TotalEPOCH=%d" % EPOCH)
以上感謝伯禹學習平臺和DWhale組織