基於Pytorch的cifar10分類網路模型
Pytorch作為新興的深度學習框架,目前的使用率正在逐步上升。相比TensorFlow,Pytorch的上手難度更低,同時Pytorch支援對圖的動態定義,並且能夠方便的將網路中的tensor格式資料與numpy格式資料進行轉換,使得其對某些特殊結構的網路定義起來更加方便,但是Pytorch對於分散式訓練之類的支援相對較差,同時沒有Tensorboard之類的工具對網路進行方便的視覺化。當然,Tensorflow能夠選擇Keras之類的框架,來大幅簡化網路架設工作。
Pytorch擁有一個不錯的官方教程
這裡也按照官網的步驟來,首先是通過torchvision庫匯入cifar10資料集:
import torch import torchvision import torchvision.transforms as transforms 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=2) 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=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
檢視圖片之類的操作就看官網教程好了,這裡省略掉。
第二步是定義卷積神經網路,官網使用的作為示例的網路如下:
class Net(nn.Module): def __init__(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(16 * 5 * 5, 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 net = Net()
這個網路是卷積+全連線層的形式,這種結構的網路效果其實不好,因為全連線層傳遞效率較低,同時會干擾到卷積層提取出的區域性特徵,並且也沒有用到BatchNorm和Dropout來防止過擬合的問題。現在流行的網路結構大多采用全卷積層的結構,下面的結構效果會好很多:
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, padding = 1)
self.conv2 = nn.Conv2d(64, 64, 3, padding = 1)
self.conv3 = nn.Conv2d(64, 128, 3, padding = 1)
self.conv4 = nn.Conv2d(128, 128, 3, padding = 1)
self.conv5 = nn.Conv2d(128, 256, 3, padding = 1)
self.conv6 = nn.Conv2d(256, 256, 3, padding = 1)
self.maxpool = nn.MaxPool2d(2, 2)
self.avgpool = nn.AvgPool2d(2, 2)
self.globalavgpool = nn.AvgPool2d(8, 8)
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.dropout50 = nn.Dropout(0.5)
self.dropout10 = nn.Dropout(0.1)
self.fc = nn.Linear(256, 10)
def forward(self, x):
x = self.bn1(F.relu(self.conv1(x)))
x = self.bn1(F.relu(self.conv2(x)))
x = self.maxpool(x)
x = self.dropout10(x)
x = self.bn2(F.relu(self.conv3(x)))
x = self.bn2(F.relu(self.conv4(x)))
x = self.avgpool(x)
x = self.dropout10(x)
x = self.bn3(F.relu(self.conv5(x)))
x = self.bn3(F.relu(self.conv6(x)))
x = self.globalavgpool(x)
x = self.dropout50(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
net = Net()
Pytorch也可以用nn.Sequential函式來很簡單的定義序列網路,和keras的Sequential差不多,但是pytorch需要給出每一層網路的輸入與輸出引數,這一點就不像keras那麼無腦。由於pytorch不像keras自帶GlobalAveragePooling,手寫一個怕自己忘記,其實這裡不加效果會更好,畢竟這相當於強行壓縮特徵資料之後再進行分類。
這裡再舉個nn.Sequential的基本栗子:
channel_1 = 32
channel_2 = 16
model = nn.Sequential(
nn.Conv2d(3, channel_1, 5, padding = 2),
nn.ReLU(),
nn.Conv2d(channel_1, channel_2, 3, padding = 1),
nn.ReLU(),
Flatten(),
nn.Linear(channel_2 * 32 * 32, 10),
)
第三步是定義損失函式和優化器,官網這裡用的是帶動量項的SGD,但是個人感覺Adam對複雜函式的優化效果會比SGD好,所以這裡用Adam來代替:
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
接下來,第四步就是訓練網路了。可以首先使用下列語句來自動判斷使用GPU還是CPU進行計算,不過一般而言,GPU和同檔次的CPU計算速度可以差到50~70倍……
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
下面就可以開始訓練了,這裡要注意訓練的資料也要.to(device):
for epoch in range(10):
running_loss = 0.
batch_size = 100
for i, data in enumerate(
torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2), 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print('[%d, %5d] loss: %.4f' %(epoch + 1, (i+1)*batch_size, loss.item()))
print('Finished Training')
當然,Pytorch不如Keras直接一個.fit來的方便,但是也不算麻煩。由於不用在一個session裡邊進行計算,靈活性還是比tensorflow和封裝的嚴嚴實實的keras高很多。
之後,可以用下面的語句儲存或讀取儲存好的模型:
torch.save(net, 'cifar10.pkl')
net = torch.load('cifar10.pkl')
在訓練完成之後,就可以用測試集檢視訓練結果:
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
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))
with torch.no_grad():
for data in testloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
這個網路模型在batch_size=100的條件下訓練10個epoch之後,測試集正確率大概在80%左右,對cifar10資料集而言還算可以啦。
原始碼放在github上,歡迎取用~地址:https://github.com/PolarisShi/cifar10