PyTorch之LeNet-5：利用PyTorch實(shí)現(xiàn)最經(jīng)典的LeNet-5卷積神經(jīng)網(wǎng)絡(luò)對(duì)手寫數(shù)字圖片識(shí)別CNN
#PyTorch：利用PyTorch實(shí)現(xiàn)最經(jīng)典的LeNet卷積神經(jīng)網(wǎng)絡(luò)對(duì)手寫數(shù)字進(jìn)行識(shí)別CNN——Jason niu
import torch
import torch.nn as nn
import torch.optim as optim
class LeNet(nn.Module):
def __init__(self):
super(LeNet,self).__init__()
#Conv1 和 Conv2：卷積層，每個(gè)層輸出在卷積核（小尺寸的權(quán)重張量）和同樣尺寸輸入?yún)^(qū)域之間的點(diǎn)積；
self.conv1 = nn.Conv2d(1,10,kernel_size=5)
self.conv2 = nn.Conv2d(10,20,kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320,50)
self.fc2 = nn.Linear(50,10)
def forward(self,x):
x = F.relu(F.max_pool2d(self.conv1(x),2)) #使用 max 運(yùn)算執(zhí)行特定區(qū)域的下采樣（通常 2x2 像素）；
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)),2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x)) #修正線性單元函數(shù)，使用逐元素的激活函數(shù) max(0,x)；
x = F.dropout(x, training=self.training) #Dropout2D隨機(jī)將輸入張量的所有通道設(shè)為零。當(dāng)特征圖具備強(qiáng)相關(guān)時(shí)，dropout2D 提升特征圖之間的獨(dú)立性；
x = self.fc2(x)
return F.log_softmax(x, dim=1) #將 Log(Softmax(x)) 函數(shù)應(yīng)用到 n 維輸入張量，以使輸出在 0 到 1 之間。
#創(chuàng)建 LeNet 類后，創(chuàng)建對(duì)象并移至 GPU
model = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr = 0.005, momentum = 0.9) #要訓(xùn)練該模型，我們需要使用帶動(dòng)量的 SGD，學(xué)習(xí)率為 0.01，momentum 為 0.5。
import os
from torch.autograd import Variable
import torch.nn.functional as F
cuda_gpu = torch.cuda.is_available()
def train(model, epoch, criterion, optimizer, data_loader):
model.train()
for batch_idx, (data, target) in enumerate(data_loader):
if cuda_gpu:
data, target = data.cuda(), target.cuda()
model.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
optimizer.zero_grad()
loss = criterion(output, target)
loss.backward()
optimizer.step()
if (batch_idx+1) % 400 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, (batch_idx+1) * len(data), len(data_loader.dataset),
100. * (batch_idx+1) / len(data_loader), loss.data[0]))
from torchvision import datasets, transforms
batch_num_size = 64
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data',train=True, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_num_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data',train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_num_size, shuffle=True)
def test(model, epoch, criterion, data_loader):
model.eval()
test_loss = 0
correct = 0
for data, target in data_loader:
if cuda_gpu:
data, target = data.cuda(), target.cuda()
model.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
test_loss += criterion(output, target).data[0]
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
test_loss /= len(data_loader) # loss function already averages over batch size
acc = correct / len(data_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(data_loader.dataset), 100. * acc))
return (acc, test_loss)
epochs = 5 #僅僅需要 5 個(gè) epoch（一個(gè) epoch 意味著你使用整個(gè)訓(xùn)練數(shù)據(jù)集來更新訓(xùn)練模型的權(quán)重），就可以訓(xùn)練出一個(gè)相當(dāng)準(zhǔn)確的 LeNet 模型。
#這段代碼檢查可以確定文件中是否已有預(yù)訓(xùn)練好的模型。有則加載；無則訓(xùn)練一個(gè)并保存至磁盤。
if (os.path.isfile('pretrained/MNIST_net.t7')):
print ('Loading model')
model.load_state_dict(torch.load('pretrained/MNIST_net.t7', map_location=lambda storage, loc: storage))
acc, loss = test(model, 1, criterion, test_loader)
else:
print ('Training model') #打印出該模型的信息。打印函數(shù)顯示所有層（如 Dropout 被實(shí)現(xiàn)為一個(gè)單獨(dú)的層）及其名稱和參數(shù)。
for epoch in range(1, epochs + 1):
train(model, epoch, criterion, optimizer, train_loader)
acc, loss = test(model, 1, criterion, test_loader)
torch.save(model.state_dict(), 'pretrained/MNIST_net.t7')
print (type(t.cpu().data))#以使用 .cpu() 方法將張量移至 CPU（或確保它在那里）。
#或當(dāng) GPU 可用時(shí)（torch.cuda. 可用），使用 .cuda() 方法將張量移至 GPU。你可以看到張量是否在 GPU 上，其類型為 torch.cuda.FloatTensor。
#如果張量在 CPU 上，則其類型為 torch.FloatTensor。
if torch.cuda.is_available():
print ("Cuda is available")
print (type(t.cuda().data))
else:
print ("Cuda is NOT available")
if torch.cuda.is_available():
try:
print(t.data.numpy())
except RuntimeError as e:
"you can't transform a GPU tensor to a numpy nd array, you have to copy your weight tendor to cpu and then get the numpy array"
print(type(t.cpu().data.numpy()))
print(t.cpu().data.numpy().shape)
print(t.cpu().data.numpy())
data = model.conv1.weight.cpu().data.numpy()
print (data.shape)
print (data[:, 0].shape)
kernel_num = data.shape[0]
fig, axes = plt.subplots(ncols=kernel_num, figsize=(2*kernel_num, 2))
for col in range(kernel_num):
axes[col].imshow(data[col, 0, :, :], cmap=plt.cm.gray)
plt.show()
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LeNet-5 is our latest convolutional network designed for handwritten and machine-printed character recognition.
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