P21_神经网络——搭建小实战和Sequential的使用
21.1打开pytorch官网
1.神经网络-搭建小实战和Sequential的使用
（1）Sequential的使用：将网络结构放入其中即可，可以简化代码。
（2）一个对CIFAR10进行分类的模型
（3）模型的实现(下文)
21.2打开pycharm实现模型
1.第一步的Convolution
（1）在第一步的Convolution：in_channel是3，out_channel是32，kernel_size是5×5
（2）Conv2d的参数：
点击查看代码
class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)
其中in_channel、out_channel和kernel_size已知，至于stride|padding|dilation是否要设置还是默认，要根据计算得知；
（3）根据Conv2d的计算公式和上图的对CIFAR10进行分类的模型来计算：
①NCHW是指一种张量数据的存储格式，具体表示为N（批大小）-C（通道数）-H（高度）-W（宽度）
②在公式中可知：
Hin=32，Hout=32，dilation默认是1，stride默认是1，padding默认是0
③则32=（32+2*padding-dilation×(5-1)-1)/stride +1
设置dilation为默认值1，stride设置为默认值1，则代入得：32=(32+2×padding-1×(5-1)-1)/1+1即padding=2
④即：
self.conv1 = Conv2d(in_channels=3,out_channels=32,kernel_size=5,padding=2)#stride和dilation默认
2.第二步的Maxpooling
self.maxpool1 = MaxPool2d(kernel_size=2)
3.第三步的Convolution
self.conv2 = Conv2d(32,32,5,padding=2)
4.第四步的Maxpooling
self.maxpool2 = MaxPool2d(kernel_size=2)
5.第五步的Convolution
self.conv3 = Conv2d(32,64,5, padding=2)
6.第六步的Maxpooling
self.maxpool3 = MaxPool2d(kernel_size=2)
7.第七步的Flatten
self.flatten = Flatten()
8.第八步的linear
self.linear1 = Linear(1024,64)
9.第九步的linear
self.linear2 = Linear(64,10)
linear示意图：
10.定义forward
点击查看代码
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
11.搭建网络，实例化并print输出
点击查看代码
dyl_seq = DYL_seq()
print(dyl_seq)
得到：
点击查看代码
x DYL_seq( (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))
12.创建输入，使用ones指定我们想要创建的形状大小
创建一个假想的输入：
torch.ones(64,3,32,32) #64个batch_size，3个通道，32×32的尺寸
点击查看代码
#torch.ones可以指定我们想要创建的数的形状大小
input = torch.ones(64,3,32,32) #64个batch_size，3个通道，32×32的尺寸
output = dyl_seq(input)
print(output.shape)
其中64表示64个batch_size，可以想象成64张图片
21.3使用Sequential实现模型
1.原始繁琐的代码：
点击查看代码
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class DYL_seq(nn.Module):
def __init__(self):
super(DYL_seq, self).__init__()
self.conv1 = Conv2d(in_channels=3,out_channels=32,kernel_size=5,padding=2)#stride和dilation默认
self.maxpool1 = MaxPool2d(kernel_size=2)
self.conv2 = Conv2d(32,32,5,padding=2)
self.maxpool2 = MaxPool2d(kernel_size=2)
self.conv3 = Conv2d(32,64,5, padding=2)
self.maxpool3 = MaxPool2d(kernel_size=2)
self.flatten = Flatten()
self.linear1 = Linear(1024,64)
self.linear2 = Linear(64, 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
dyl_seq = DYL_seq()
print(dyl_seq)
#创建一个假想的输入
#torch.ones可以指定我们想要创建的数的形状大小
input = torch.ones(64,3,32,32) #64个batch_size，3个通道，32×32的尺寸
output = dyl_seq(input)
print(output.shape)
输出结果如下：
点击查看代码
DYL_seq(
(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)
)
torch.Size([64, 10])
2.使用Sequential对模型重新进行实现
点击查看代码
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
class DYL_seq(nn.Module):
def __init__(self):
super(DYL_seq, self).__init__()
self.model1 = Sequential(
Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),
MaxPool2d(kernel_size=2),
Conv2d(32, 32, 5, padding=2),
MaxPool2d(kernel_size=2),
Conv2d(32, 64, 5, padding=2),
MaxPool2d(kernel_size=2),
Flatten(),
Linear(1024, 64),
Linear(64, 10)
)
def forward(self,x):
x = self.model1(x)
return x
dyl_seq = DYL_seq()
print(dyl_seq)
#创建一个假想的输入
#torch.ones可以指定我们想要创建的数的形状大小
input = torch.ones(64,3,32,32) #64个batch_size，3个通道，32×32的尺寸
output = dyl_seq(input)
print(output.shape)
实现了如上同样的效果：
点击查看代码
xxxxxxxxxx DYL_seq( (model1): 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) ))torch.Size([64, 10])
可见，代码变得简洁很多
21.4使用tensorboard进行可视化
tensorboard --logdir=dyl_seq
双击dyl_seq就会得到中间有一个Sequential，再双击Sequential就可以看到各网络层：
然后在网络层继续双击，就可以看到：
每个箭头上显示的我们送到该网络层的尺寸的大小，设置的各参数：
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