【猫狗数据集】使用学习率衰减策略并边训练边测试

2019/04/10 10:10
阅读数 25

数据集下载地址:

链接:https://pan.baidu.com/s/1l1AnBgkAAEhh0vI5_loWKw
提取码:2xq4

创建数据集:https://www.cnblogs.com/xiximayou/p/12398285.html

读取数据集:https://www.cnblogs.com/xiximayou/p/12422827.html

进行训练:https://www.cnblogs.com/xiximayou/p/12448300.html

保存模型并继续进行训练:https://www.cnblogs.com/xiximayou/p/12452624.html

加载保存的模型并测试:https://www.cnblogs.com/xiximayou/p/12459499.html

划分验证集并边训练边验证:https://www.cnblogs.com/xiximayou/p/12464738.html

epoch、batchsize、step之间的关系:https://www.cnblogs.com/xiximayou/p/12405485.html

 

一个合适的学习率对网络的训练至关重要。学习率太大,会导致梯度在最优解处来回震荡,甚至无法收敛。学习率太小,将导致网络的收敛速度较为缓慢。一般而言,都会先采取较大的学习率进行训练,然后在训练的过程中不断衰减学习率。而学习率衰减的方式有很多,这里我们就只使用简单的方式。

上一节划分了验证集,这节我们要边训练边测试,同时要保存训练的最后一个epoch模型,以及保存测试准确率最高的那个模型。

首先是学习率衰减策略,这里展示两种方式:

scheduler = optim.lr_scheduler.StepLR(optimizer, 80, 0.1)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,[80,160],0.1)

第一种方式是每个80个epoch就将学习率衰减为原来的0.1倍。

第二种方式是在第80和第160个epoch时将学习率衰减为原来的0.1倍

比如说第1个epoch的学习率为0.1,那么在1-80epoch期间都会使用该学习率,在81-160期间使用0.1×0.1=0.01学习率,在161及以后使用0.01×0.1=0.001学习率

一般而言,会在1/3和2/3处进行学习率衰减,比如有200个epoch,那么在70、140个epoch上进行学习率衰减。不过也需要视情况而定。

接下来,我们将学习率衰减策略加入到main.py中:

main.py

import sys
sys.path.append("/content/drive/My Drive/colab notebooks")
from utils import rdata
from model import resnet
import torch.nn as nn
import torch
import numpy as np
import torchvision
import train
import torch.optim as optim

np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

batch_size=128
train_loader,val_loader,test_loader=rdata.load_dataset(batch_size)

model =torchvision.models.resnet18(pretrained=False)
model.fc = nn.Linear(model.fc.in_features,2,bias=False)
model.cuda()


#定义训练的epochs
num_epochs=6
#定义学习率
learning_rate=0.01
#定义损失函数
criterion=nn.CrossEntropyLoss()
#定义优化方法,简单起见,就是用带动量的随机梯度下降
optimizer = torch.optim.SGD(params=model.parameters(), lr=0.1, momentum=0.9,
                          weight_decay=1*1e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [2,4], 0.1)
print("训练集有:",len(train_loader.dataset))
#print("验证集有:",len(val_loader.dataset))
print("测试集有:",len(test_loader.dataset))
def main():
  trainer=train.Trainer(criterion,optimizer,model)
  trainer.loop(num_epochs,train_loader,val_loader,test_loader,scheduler)
  
main()

这里我们只训练6个epoch,在第2和第4个epoch进行学习率衰减策略。

train.py

import torch
class Trainer:
  def __init__(self,criterion,optimizer,model):
    self.criterion=criterion
    self.optimizer=optimizer
    self.model=model
  def get_lr(self):
    for param_group in self.optimizer.param_groups:
        return param_group['lr']
  def loop(self,num_epochs,train_loader,val_loader,test_loader,scheduler=None,acc1=0.0):
    self.acc1=acc1
    for epoch in range(1,num_epochs+1):
      lr=self.get_lr()
      print("epoch:{},lr:{}".format(epoch,lr))
      self.train(train_loader,epoch,num_epochs)
      #self.val(val_loader,epoch,num_epochs)
      self.test(test_loader,epoch,num_epochs)
      if scheduler is not None:
        scheduler.step()

  def train(self,dataloader,epoch,num_epochs):
    self.model.train()
    with torch.enable_grad():
      self._iteration_train(dataloader,epoch,num_epochs)

  def val(self,dataloader,epoch,num_epochs):
    self.model.eval()
    with torch.no_grad():
      self._iteration_val(dataloader,epoch,num_epochs)
  def test(self,dataloader,epoch,num_epochs):
    self.model.eval()
    with torch.no_grad():
      self._iteration_test(dataloader,epoch,num_epochs)

  def _iteration_train(self,dataloader,epoch,num_epochs):
    total_step=len(dataloader)
    tot_loss = 0.0
    correct = 0
    for i ,(images, labels) in enumerate(dataloader):
      images = images.cuda()
      labels = labels.cuda()

      # Forward pass
      outputs = self.model(images)
      _, preds = torch.max(outputs.data,1)
      loss = self.criterion(outputs, labels)

      # Backward and optimizer
      self.optimizer.zero_grad()
      loss.backward()
      self.optimizer.step()
      tot_loss += loss.data
      if (i+1) % 2 == 0:
          print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'
                .format(epoch, num_epochs, i+1, total_step, loss.item()))
      correct += torch.sum(preds == labels.data).to(torch.float32)
    ### Epoch info ####
    epoch_loss = tot_loss/len(dataloader.dataset)
    print('train loss: {:.4f}'.format(epoch_loss))
    epoch_acc = correct/len(dataloader.dataset)
    print('train acc: {:.4f}'.format(epoch_acc))
    if epoch==num_epochs:
      state = { 
        'model': self.model.state_dict(), 
        'optimizer':self.optimizer.state_dict(), 
        'epoch': epoch,
        'train_loss':epoch_loss,
        'train_acc':epoch_acc,
      }
      save_path="/content/drive/My Drive/colab notebooks/output/"   
      torch.save(state,save_path+"/resnet18_final"+".t7")
  def _iteration_val(self,dataloader,epoch,num_epochs):
    total_step=len(dataloader)
    tot_loss = 0.0
    correct = 0
    for i ,(images, labels) in enumerate(dataloader):
        images = images.cuda()
        labels = labels.cuda()

        # Forward pass
        outputs = self.model(images)
        _, preds = torch.max(outputs.data,1)
        loss = self.criterion(outputs, labels)
        tot_loss += loss.data
        correct += torch.sum(preds == labels.data).to(torch.float32)
        if (i+1) % 2 == 0:
            print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'
                  .format(1, 1, i+1, total_step, loss.item()))
    ### Epoch info ####
    epoch_loss = tot_loss/len(dataloader.dataset)
    print('val loss: {:.4f}'.format(epoch_loss))
    epoch_acc = correct/len(dataloader.dataset)
    print('val acc: {:.4f}'.format(epoch_acc))
  def _iteration_test(self,dataloader,epoch,num_epochs):
    total_step=len(dataloader)
    tot_loss = 0.0
    correct = 0
    for i ,(images, labels) in enumerate(dataloader):
        images = images.cuda()
        labels = labels.cuda()

        # Forward pass
        outputs = self.model(images)
        _, preds = torch.max(outputs.data,1)
        loss = self.criterion(outputs, labels)
        tot_loss += loss.data
        correct += torch.sum(preds == labels.data).to(torch.float32)
        if (i+1) % 2 == 0:
            print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'
                  .format(1, 1, i+1, total_step, loss.item()))
    ### Epoch info ####
    epoch_loss = tot_loss/len(dataloader.dataset)
    print('test loss: {:.4f}'.format(epoch_loss))
    epoch_acc = correct/len(dataloader.dataset)
    print('test acc: {:.4f}'.format(epoch_acc))
    if epoch_acc > self.acc1:
      state = {  
      "model": self.model.state_dict(),
      "optimizer": self.optimizer.state_dict(),
      "epoch": epoch,
      "epoch_loss": epoch_loss,
      "epoch_acc": epoch_acc,
      "acc1": self.acc1,
      }
      save_path="/content/drive/My Drive/colab notebooks/output/"
      print("在第{}个epoch取得最好的测试准确率,准确率为:{}".format(epoch,epoch_acc))   
      torch.save(state,save_path+"/resnet18_best"+".t7")
      self.acc1=max(self.acc1,epoch_acc)

我们首先增加了test()和_iteration_test()用于测试。

这里需要注意的是:

UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.

也就是说:

scheduler = ...
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

在pytorch1.1.0及之后,scheduler.step()这个要放在最后面了。我们定义了一个获取学习率的函数,在每一个epoch的时候打印学习率。我们同时要存储训练的最后一个epoch的模型,方便我们继续训练。存储测试准确率最高的模型,方便我们使用。

最终结果如下,省略了其中的每一个step:

训练集有: 18255
测试集有: 4750
epoch:1,lr:0.1
train loss: 0.0086
train acc: 0.5235
test loss: 0.0055
test acc: 0.5402
在第1个epoch取得最好的测试准确率,准确率为:0.5402105450630188
epoch:2,lr:0.1
train loss: 0.0054
train acc: 0.5562
test loss: 0.0055
test acc: 0.5478
在第2个epoch取得最好的测试准确率,准确率为:0.547789454460144
epoch:3,lr:0.010000000000000002
train loss: 0.0052
train acc: 0.6098
test loss: 0.0053
test acc: 0.6198
在第3个epoch取得最好的测试准确率,准确率为:0.6197894811630249
epoch:4,lr:0.010000000000000002
train loss: 0.0051
train acc: 0.6150
test loss: 0.0051
test acc: 0.6291
在第4个epoch取得最好的测试准确率,准确率为:0.6290526390075684
train loss: 0.0051
train acc: 0.6222
test loss: 0.0052
test acc: 0.6257
epoch:6,lr:0.0010000000000000002
train loss: 0.0051
train acc: 0.6224
test loss: 0.0052
test acc: 0.6295
在第6个epoch取得最好的测试准确率,准确率为:0.6294736862182617

很神奇,lr最后面居然不是0。对lr和准确率输出时可指定输出小数点后?位:{:.?f}

最后看下保存的模型:

的确是都有的。

 

下一节:可视化训练和测试过程。 

原文出处:https://www.cnblogs.com/xiximayou/p/12468010.html

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