一、下载数据集
python
train_data = torchvision.datasets.CIFAR10(root="datasets",train=True,transform=torchvision.transforms.ToTensor(),download=True)
train_data = torchvision.datasets.CIFAR10(root="datasets",train=False,transform=torchvision.transforms.ToTensor(),download=True)二、数据大小
python
train_data_len = len(train_data)
test_data_len = len(test_data)
print(train_data_len)
print(test_data_len)三、数据加载
python
train = DataLoader(train_data,batch_size=64)
test = DataLoader(test_data,batch_size=64)四、模型定义
python
class Mary(nn.Module):
def __init__(self):
super(Mary,self).__init__()
self.model1 = nn.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五、定义对象
python
Yorelee = Mary()六、损失函数
python
loss_fn = nn.CrossEntropyLoss()七、优化器
python
learning_rate = 0.01
optimizer = torch.optim.SGD(Yorelee.parameters(),lr=learning_rate)八、训练过程
python
epoch = 10
for i in range(epoch):
print("***********第{}轮训练***********".format(i+1))
index = 1
for data in train:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("({}) ".format(index),loss.item())
index += 1九、训练过程+测试过程
python
# 训练过程
epoch = 10
for i in range(epoch):
print("***********第{}轮训练***********".format(i+1))
index = 1
for data in train:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("({}) ".format(index),loss.item())
index += 1
#测试过程
total_loss = 0
with torch.no_grad():
for data in test:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
total_loss += loss
print("测试： ",total_loss.item())十、Tensorboard 可视化
python
# Tensorboard 可视化
writer = SummaryWriter("logs")
# 训练过程
epoch = 10
for i in range(epoch):
print("***********第{}轮训练***********".format(i+1))
index = 1
for data in train:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("({}) ".format(index),loss.item())
if index % 100 == 0:
writer.add_scalar("train_loss",loss,index)
index += 1
#测试过程
total_loss = 0
with torch.no_grad():
for data in test:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
total_loss += loss
print("测试： ",total_loss.item())
writer.add_scalar("test_loss", total_loss, i)
writer.close()十一、对于分类任务
可以用准确率来衡量，而不是用 total_loss 来衡量效果
先来举个简单的例子：
代码如下：
python
import torch
output = torch.tensor([[0.1,0.2],
[0.3,0.4]])
output = torch.argmax(output,dim=1)
preds = torch.tensor([0,1])
print((output==preds).sum())输出：
python
tensor(1)十二、对测试过程的重新改进
python
# Tensorboard 可视化
writer = SummaryWriter("logs")
# 训练过程
epoch = 10
for i in range(epoch):
print("***********第{}轮训练***********".format(i+1))
index = 1
for data in train:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("({}) ".format(index),loss.item())
if index % 100 == 0:
writer.add_scalar("train_loss",loss,index)
index += 1
#测试过程
total_loss = 0
with torch.no_grad():
find_true = 0
for data in test:
imgs,targets = data
output = Yorelee(imgs)
# loss = loss_fn(output,targets)
# total_loss += loss
output = torch.argmax(output,dim=1)
find_true += (output == targets).sum()
print("测试： ",find_true/test_data_len.item())
writer.add_scalar("test_loss", find_true/test_data_len, i)十三、总结
model.py
python
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class Mary(nn.Module):
def __init__(self):
super(Mary,self).__init__()
self.model1 = nn.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
if __name__ == '__main__':
input = torch.ones((64,3,32,32))
Yorelee = Mary()
output = Yorelee(input)
print(output.shape)train.py
python
import torch
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import *
# 数据集
train_data = torchvision.datasets.CIFAR10(root="datasets",train=True,transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10(root="datasets",train=False,transform=torchvision.transforms.ToTensor(),download=True)
# 数据大小
train_data_len = len(train_data)
test_data_len = len(test_data)
print(train_data_len)
print(test_data_len)
# 数据加载
train = DataLoader(train_data,batch_size=64)
test = DataLoader(test_data,batch_size=64)
# 定义对象
Yorelee = Mary()
# 损失函数
loss_fn = nn.CrossEntropyLoss()
# 优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(Yorelee.parameters(),lr=learning_rate)
# Tensorboard 可视化
writer = SummaryWriter("logs")
# 训练过程
epoch = 10
for i in range(epoch):
print("***********第{}轮训练***********".format(i+1))
index = 1
for data in train:
imgs,targets = data
output = Yorelee(imgs)
loss = loss_fn(output,targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("({}) ".format(index),loss.item())
if index % 100 == 0:
writer.add_scalar("train_loss",loss,index)
index += 1
#测试过程
total_loss = 0
with torch.no_grad():
find_true = 0
for data in test:
imgs,targets = data
output = Yorelee(imgs)
# loss = loss_fn(output,targets)
# total_loss += loss
output = torch.argmax(output,dim=1)
find_true += (output == targets).sum()
print("测试： ",find_true/test_data_len.item())
writer.add_scalar("test_loss", find_true/test_data_len, i)
writer.close()十四、XX.train() 和 XX.eval() 的说明
