【Pytorch】搭建网络模型的实战

对CIFAR10数据集进行分类，根据图片内容识别这是哪一类

CIFAR10 model structure

• 输入input:3通道的32 x 32 图片
• 卷积操作的通道数不变 那是因为经过了padding操作
• 最大池化 不改变channel数
• 输出是一个一维向量 十种类别

搭建网络

第一次卷积操作之后，图片大小不变，经过了padding操作 padding = 2

from torch import nn
from  torch.nn import Conv2d,MaxPool2d,Flatten,Linearclass Tudui(nn.Module):def __init__(self):super(Tudui,self).__init__()#  第一层 卷积层  填充操作 保证图片大小不变self.conv1 = Conv2d(3,32,5,padding = 5)#  最大池化 操作self.maxpool1 = MaxPool2d(2)# 第二层卷积  通道数不变self.conv2 = Conv2d(32,32,5,padding=2)#  x最大池化 操作self.maxpool2 = MaxPool2d(2)#  第三层卷积操作self.conv3 = Conv2d(32,64,5,padding=2)# 最大池化操作self.maxpool3 = MaxPool2d(2)# 将数据进行展平 64 * 4 * 4 = 1024 一维向量# 64 x 1024  这里的64是batch_sizeself.flatten = Flatten()#  将展平之后的向量 输入全连接层# 输入1024  输出64self.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 xtudui = Tudui()print(tudui)input  = torch.ones((64,3,32,32))
output = tudui.forward(input)print(output.shape)

使用Sequential进行搭建网络模型

将上面的代码使用sequential进行置换

from torch import nn
from  torch.nn import Conv2d,MaxPool2d,Flatten,Linear,Sequential
import torchclass Tudui(nn.Module):def __init__(self):super(Tudui,self).__init__()# #  第一层 卷积层  填充操作 保证图片大小不变# self.conv1 = Conv2d(3,32,5,padding = 5)# #  最大池化 操作# self.maxpool1 = MaxPool2d(2)# # 第二层卷积  通道数不变# self.conv2 = Conv2d(32,32,5,padding=2)# #  x最大池化 操作# self.maxpool2 = MaxPool2d(2)# #  第三层卷积操作# self.conv3 = Conv2d(32,64,5,padding=2)# # 最大池化操作# self.maxpool3 = MaxPool2d(2)# # 将数据进行展平 64 * 4 * 4 = 1024 一维向量# # 64 x 1024  这里的64是batch_size# self.flatten = Flatten()# #  将展平之后的向量 输入全连接层# # 输入1024  输出64# self.linear1 = Linear(1024,64)# self.linear2 = Linear(64,10)self.model1 = Sequential(Conv2d(3,32,5,padding=2),MaxPool2d(2),Conv2d(32,32,5,padding=2),MaxPool2d(2),Conv2d(32,64,5,padding=2),MaxPool2d(2),Flatten(),Linear(1024,64),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)x = self.model1(x)return xtudui = Tudui()
print(tudui)input  = torch.ones((64,3,32,32))
output = tudui.forward(input)
print(output.shape)

使用tensorboard查看网络结构

from torch import nn
# from tensorboardX import SummaryWriter
from  torch.nn import Conv2d,MaxPool2d,Flatten,Linear,Sequential
import torchfrom torch.utils.tensorboard import SummaryWriterclass Tudui(nn.Module):def __init__(self):super(Tudui,self).__init__()self.model1 = Sequential(Conv2d(3,32,5,padding=2),MaxPool2d(2),Conv2d(32,32,5,padding=2),MaxPool2d(2),Conv2d(32,64,5,padding=2),MaxPool2d(2),Flatten(),Linear(1024,64),Linear(64,10))def forward(self,x):x = self.model1(x)return xtudui = Tudui()print(tudui)input  = torch.ones((64,3,32,32))
output = tudui.forward(input)#  输出是 64 x 10 64代表batch_size
print(output.shape)writer = SummaryWriter("../logs_seq")
writer.add_graph(tudui,input)
writer.close()