【Pytorch实现】——LeNet网络
import torch
from torch import nn
from d2l import torch as d2l
# 继承nn.Module类，实现__init__和forward
class Reshape(nn.Module):
def forward(self,x):
# 将图片形状变成BxCxHxW
return x.reshape(-1,1,28,28)
# nn.Sequential可以包装任何继承nn.Module实现的实例
net = nn.Sequential(
Reshape(),
# F = 2 * P + 1 特征图尺寸不变
nn.Conv2d(1,6,kernel_size=5,padding=2),
nn.Sigmoid(),
# 平均池化 特征图尺寸减小为原来的一半
nn.AvgPool2d(kernel_size=2,stride=2),
nn.Conv2d(6,16,kernel_size=5),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2,stride=2),
# nn.Flatten操作仅保留第一个维度将其他维度打平
nn.Flatten(),
nn.Linear(16*5*5,120),
nn.Sigmoid(),
nn.Linear(120,84),
nn.Sigmoid(),
nn.Linear(84,10)
)
X = torch.randn(size=(4,1,28,28),dtype=torch.float32)
# nn.Sequential()包装后相当于将不同层存放到一个list中
# 迭代打印经过每一层后特征图尺寸变化（get)
for layer in net:
X = layer(X)
print(layer.__class__.__name__, 'output shape: t', X.shape)
Reshape output shape: torch.Size([4, 1, 28, 28])
Conv2d output shape: torch.Size([4, 6, 28, 28])
Sigmoid output shape: torch.Size([4, 6, 28, 28])
AvgPool2d output shape: torch.Size([4, 6, 14, 14])
Conv2d output shape: torch.Size([4, 16, 10, 10])
Sigmoid output shape: torch.Size([4, 16, 10, 10])
AvgPool2d output shape: torch.Size([4, 16, 5, 5])
Flatten output shape: torch.Size([4, 400])
Linear output shape: torch.Size([4, 120])
Sigmoid output shape: torch.Size([4, 120])
Linear output shape: torch.Size([4, 84])
Sigmoid output shape: torch.Size([4, 84])
Linear output shape: torch.Size([4, 10])
- 上面我们是手动打印每一层的名称和参数，其实Pytorch中提供了一种更加便捷的工具summary
import torch
from torch import nn
from d2l import torch as d2l
from torchsummary import summary
# 继承nn.Module类，实现__init__和forward
class Reshape(nn.Module):
def forward(self,x):
# 将图片形状变成BxCxHxW
return x.reshape(-1,1,28,28)
# nn.Sequential可以包装任何继承nn.Module实现的实例
net = nn.Sequential(
Reshape(),
# F = 2 * P + 1 特征图尺寸不变
nn.Conv2d(1,6,kernel_size=5,padding=2),
nn.Sigmoid(),
# 平均池化 特征图尺寸减小为原来的一半
nn.AvgPool2d(kernel_size=2,stride=2),
nn.Conv2d(6,16,kernel_size=5),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2,stride=2),
# nn.Flatten操作仅保留第一个维度将其他维度打平
nn.Flatten(),
nn.Linear(16*5*5,120),
nn.Sigmoid(),
nn.Linear(120,84),
nn.Sigmoid(),
nn.Linear(84,10)
)
X = torch.randn(size=(4,1,28,28),dtype=torch.float32)
summary(net,(4,1,28,28))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Reshape-1 [-1, 1, 28, 28] 0
Conv2d-2 [-1, 6, 28, 28] 156
Sigmoid-3 [-1, 6, 28, 28] 0
AvgPool2d-4 [-1, 6, 14, 14] 0
Conv2d-5 [-1, 16, 10, 10] 2,416
Sigmoid-6 [-1, 16, 10, 10] 0
AvgPool2d-7 [-1, 16, 5, 5] 0
Flatten-8 [-1, 400] 0
Linear-9 [-1, 120] 48,120
Sigmoid-10 [-1, 120] 0
Linear-11 [-1, 84] 10,164
Sigmoid-12 [-1, 84] 0
Linear-13 [-1, 10] 850
================================================================
Total params: 61,706
Trainable params: 61,706
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 0.12
Params size (MB): 0.24
Estimated Total Size (MB): 0.37
----------------------------------------------------------------
- 调用summary函数后输出内容包括：
- 名称Layer
- 输出特征图大小Output Shape （注意输出大小的Batch维度为-1）
- 参数量 Param
- 总参数量 Total params
- 可训练参数量 Trainable params
- 不可训练参数量 Non-trainable params
