我可以使用此代码计算每个历元后的精度。但是，我想在最后计算每个类的精度。我该怎么做呢？我有两个文件夹train和瓦尔。每个文件夹有7个文件夹，7个不同的类。train文件夹用于训练。否则val文件夹用于测试
def train_model(model, criterion, optimizer, lr_scheduler, num_epochs=25):
since = time.time()
best_model = model
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
mode='train'
optimizer = lr_scheduler(optimizer, epoch)
model.train() # Set model to training mode
else:
model.eval()
mode='val'
running_loss = 0.0
running_corrects = 0
counter=0
# Iterate over data.
for data in dset_loaders[phase]:
inputs, labels = data
print(inputs.size())
# wrap them in Variable
if use_gpu:
try:
inputs, labels = Variable(inputs.float().cuda()),
Variable(labels.long().cuda())
except:
print(inputs,labels)
else:
inputs, labels = Variable(inputs), Variable(labels)
# Set gradient to zero to delete history of computations in previous epoch. Track operations so that differentiation can be done automatically.
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
# print('loss done')
# Just so that you can keep track that something's happening and don't feel like the program isn't running.
# if counter%10==0:
# print("Reached iteration ",counter)
counter+=1
# backward + optimize only if in training phase
if phase == 'train':
# print('loss backward')
loss.backward()
# print('done loss backward')
optimizer.step()
# print('done optim')
# print evaluation statistics
try:
# running_loss += loss.data[0]
running_loss += loss.item()
# print(labels.data)
# print(preds)
running_corrects += torch.sum(preds == labels.data)
# print('running correct =',running_corrects)
except:
print('unexpected error, could not calculate loss or do a sum.')
print('trying epoch loss')
epoch_loss = running_loss / dset_sizes[phase]
epoch_acc = running_corrects.item() / float(dset_sizes[phase])
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val':
if USE_TENSORBOARD:
foo.add_scalar_value('epoch_loss',epoch_loss,step=epoch)
foo.add_scalar_value('epoch_acc',epoch_acc,step=epoch)
if epoch_acc > best_acc:
best_acc = epoch_acc
best_model = copy.deepcopy(model)
print('new best accuracy = ',best_acc)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
print('returning and looping back')
return best_model
def exp_lr_scheduler(optimizer, epoch, init_lr=BASE_LR, lr_decay_epoch=EPOCH_DECAY):
"""Decay learning rate by a factor of DECAY_WEIGHT every lr_decay_epoch epochs."""
lr = init_lr * (DECAY_WEIGHT**(epoch // lr_decay_epoch))
if epoch % lr_decay_epoch == 0:
print('LR is set to {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return optimizer
4条答案
按热度按时间ryhaxcpt1#
计算总体精度相当简单：
要按类计算它，需要多几行代码：
mbyulnm02#
您也可以考虑使用sklearn classification_report来详细报告多类分类模型的性能。它可以为您提供所有类的精度、召回率和f1-score等参数，然后提供总体的宏和加权平均值。
您可以使用此代码片段来完成此操作。
8e2ybdfx3#
在试图了解我的CNN的问题时偶然发现了这个问题。已经使用了维克托的解决方案。也想检查哪些类没有得到适当的培训，以及哪些类被错误地归类为其他类
此处代码为https://github.com/alexcpn/cnn_lenet_pytorch/blob/main/cnn/model_accuracy.py
以下片段
输出
稍后将使用此数据以适当的图更新答案
zfciruhq4#
一个比我之前的答案更准确的方法，首先创建一个混淆矩阵，然后从中进行推断;也将有助于培训的其他分析
请注意，这使用了下面的TorchTensor语义
输出量