LeNet-5定义¶
LeNet-5
模型结构图所下所示:
- 输入层
INPUT
:32x32
大小 - 卷积层
C1
:6x28x28
大小,卷积核5x5
- 池化层
S2
:6x14x14
大小,滤波器2x2
- 卷积层
C3
:16x10x10
大小,卷积核5x5
- 池化层
S4
:16x5x5
大小,滤波器2x2
- 卷积层
C5
:120
大小,滤波器5x5
- 全连接层
F6
:84
大小 - 输出层
OUTPUT
:10
大小
类定义¶
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
"""
LeNet-5网络模型
输入图像大小为1x32x32
"""
def __init__(self):
super(LeNet, self).__init__()
# 卷积层
# 1 input image channel, 6 output channels, 5x5 square convolution
self.conv1 = nn.Conv2d(1, 6, (5, 5))
# 池化层
# Max pooling over a (2, 2) window
self.pool2 = nn.MaxPool2d(2, 2)
# If the size is a square you can only specify a single number
# 如果滤波器是正方形,可以只输入一个数值
self.conv3 = nn.Conv2d(6, 16, 5)
# If the size is a square you can only specify a single number
self.pool4 = nn.MaxPool2d(2)
# 全连接层
# an affine operation: y = Wx + b
self.fc5 = nn.Linear(16 * 5 * 5, 120)
self.fc6 = nn.Linear(120, 84)
self.fc7 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool2(F.relu(self.conv1(x)))
x = self.pool4(F.relu(self.conv3(x)))
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc5(x))
x = F.relu(self.fc6(x))
x = self.fc7(x)
return x
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
if __name__ == '__main__':
net = LeNet()
print(net)
结果如下:
LeNet(
(conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
(pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
(pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(fc5): Linear(in_features=400, out_features=120, bias=True)
(fc6): Linear(in_features=120, out_features=84, bias=True)
(fc7): Linear(in_features=84, out_features=10, bias=True)
)
调用¶
输入是一个4维向量,分别表示样本数量、通道数、高和宽
\left(N, C_{i n}, H_{i n}, W_{i n}\right)
输出是一个2维向量,分别表示样本数量和分类结果
\left(N, C_{o u t}\right)
if __name__ == '__main__':
net = LeNet()
# print(net)
inp = torch.randn(2, 1, 32, 32)
print(inp.size())
out = net.forward(inp)
print(out)
print(out.size())
torch.Size([2, 1, 32, 32])
tensor([[ 0.0248, -0.0205, 0.0697, 0.0797, 0.0734, -0.0455, -0.0684, -0.0488,
0.1245, -0.1140],
[ 0.0130, -0.0355, 0.0659, 0.0751, 0.0736, -0.0455, -0.0391, -0.0383,
0.1408, -0.1173]], grad_fn=<AddmmBackward>)
torch.Size([2, 10])