神经网络_Sequential使用

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nn.Sequential

nn.Sequential是一个有序的容器，用于搭建神经网络的模块被按照被传入构造器的顺序添加到nn.Sequential()容器中。

import torch.nn  as nn
from collections import OrderedDict
# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))
print(model)

Sequential(
  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (relu1): ReLU()
  (conv2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
  (relu2): ReLU()
)

搭建小实战

还是以 $CIFAR-10 model$为例

1. 输入图像是3通道的32×32的
2. 先后经过卷积层(5×5的卷积核)
3. 最大池化层(2×2的池化核)
4. 卷积层(5×5的卷积核)
5. 最大池化层(2×2的池化核)
6. 卷积层(5×5的卷积核)
7. 最大池化层(2×2的池化核)
8. 拉直(flatten)
9. 全连接层的处理，
10. 最后输出的大小为10

基于以上的介绍，后续将利用Pytorch构建模型，实现 $CIFAR-10 \quad model \quad structure$

参数说明：in_channels: int、out_channels: int,kernel_size: Union由input、特征图以及卷积核即可看出，而stride、padding需要通过公式计算得到。

特得到的具体的特征图尺寸的计算公式如下:

inputs : 3@32x32，3通道32x32的图片,5*5的kernel --> 特征图(Feature maps) : 32@32x32

即经过32个3@5x5的卷积层，输出尺寸没有变化（有x个卷积核即由x个卷积核，卷积核的通道数与输入的通道数相等）

由上述的计算公式来计算出 $stride$和 $padding$

卷积层中的stride默认为1

池化层中的stride默认为kernel_size的大小

import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

class BS(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=3,
                               out_channels=32,
                               kernel_size=5,
                               stride=1,
                               padding=2)  #stride和padding计算得到
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(in_channels=32,
                               out_channels=32,
                               kernel_size=5,
                               stride=1,
                               padding=2)
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)
        self.conv3 = nn.Conv2d(in_channels=32,
                               out_channels=64,
                               kernel_size=5,
                               padding=2)
        self.maxpool3 = nn.MaxPool2d(kernel_size=2)
        self.flatten = nn.Flatten()  #变为63*4*4=1024
        self.linear1 = nn.Linear(in_features=1024, out_features=64)
        self.linear2 = nn.Linear(in_features=64, out_features=10)
        
        
    def forward(self,x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x
    
bs = BS()
bs

BS(
  (conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear1): Linear(in_features=1024, out_features=64, bias=True)
  (linear2): Linear(in_features=64, out_features=10, bias=True)
)

利用Sequential优化代码，并在tensorboard显示

.add_graph函数用于将PyTorch模型图添加到TensorBoard中。通过这个函数，您可以以可视化的方式展示模型的计算图，使其他人更容易理解您的模型结构和工作流程。

add_graph(model, input_to_model, strip_default_attributes=True)

• model：要添加的PyTorch模型。
• input_to_model：用于生成模型图的输入数据。
• strip_default_attributes：是否删除模型中的默认属性，默认为True。

class BS(nn.Module):

    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Conv2d(in_channels=3,
                               out_channels=32,
                               kernel_size=5,
                               stride=1,
                               padding=2),  #stride和padding计算得到
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(in_channels=32,
                                   out_channels=32,
                                   kernel_size=5,
                                   stride=1,
                                   padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(in_channels=32,
                                   out_channels=64,
                                   kernel_size=5,
                                   padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Flatten(),  #变为64*4*4=1024
            nn.Linear(in_features=1024, out_features=64),
            nn.Linear(in_features=64, out_features=10),
        )
    
    def forward(self,x):
        x = self.model(x)
        return x
    
bs = BS()
print(bs)

BS(
  (model): Sequential(
    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=1024, out_features=64, bias=True)
    (8): Linear(in_features=64, out_features=10, bias=True)
  )
)

# 在tensorboard中显示
input_ = torch.ones((64,3,32,32))
writer = SummaryWriter(".logs")
writer.add_graph(bs, input_)  # 定义的模型，数据
writer.close()

利用tensorboard可视化网络结构graph如下