训练过程
代码设计
#PyTorch：利用PyTorch实现最经典的LeNet卷积神经网络对手写数字进行识别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：卷积层，每个层输出在卷积核（小尺寸的权重张量）和同样尺寸输入区域之间的点积；
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 运算执行特定区域的下采样（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)) #修正线性单元函数，使用逐元素的激活函数 max(0,x)；
x = F.dropout(x, training=self.training) #Dropout2D随机将输入张量的所有通道设为零。当特征图具备强相关时，dropout2D 提升特征图之间的独立性；
x = self.fc2(x)
return F.log_softmax(x, dim=1) #将 Log(Softmax(x)) 函数应用到 n 维输入张量，以使输出在 0 到 1 之间。
#创建 LeNet 类后，创建对象并移至 GPU
model = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr = 0.005, momentum = 0.9) #要训练该模型，我们需要使用带动量的 SGD，学习率为 0.01，momentum 为 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 #仅仅需要 5 个 epoch（一个 epoch 意味着你使用整个训练数据集来更新训练模型的权重），就可以训练出一个相当准确的 LeNet 模型。
#这段代码检查可以确定文件中是否已有预训练好的模型。有则加载；无则训练一个并保存至磁盘。
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') #打印出该模型的信息。打印函数显示所有层（如 Dropout 被实现为一个单独的层）及其名称和参数。
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() 方法将张量移至 CPU（或确保它在那里）。
#或当 GPU 可用时（torch.cuda. 可用），使用 .cuda() 方法将张量移至 GPU。你可以看到张量是否在 GPU 上，其类型为 torch.cuda.FloatTensor。
#如果张量在 CPU 上，则其类型为 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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