LeCun 1998年 <<Gradient-Based Learning Applied to Document Recognition>>
加上下采样共7层
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(1, 6, 5),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.conv2 = nn.Sequential(
nn.Conv2d(6, 16, 5),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2)
)
self.fc1 = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.ReLU(inplace=True)
)
self.fc2 = nn.Sequential(
nn.Linear(120, 84),
nn.ReLU(inplace=True)
)
self.fc3 = nn.Linear(84, 10)
# 定义前向传播过程,输入为x
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
# nn.Linear()的输入输出都是维度为1的值,所以要把多维度的tensor展平成1维
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
inplace为True,将会改变输入的数据,否则不会改变原输入,只会产生新的输出