【Pytorch】搭建网络模型的实战
- CIFAR10 model structure
- 搭建网络
- 使用Sequential进行搭建网络模型
- 使用tensorboard查看网络结构
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,Linear
class 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)
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
tudui = 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 torch
class 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 x
tudui = 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 torch
from torch.utils.tensorboard import SummaryWriter
class 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 x
tudui = 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()
