方法一：直接在epoch過程中求取準確率

簡介：此段代碼是LeNet5中截取的。

def train_model(model,train_loader):

optimizer = torch.optim.Adam(model.parameters())

loss_func = nn.CrossEntropyLoss()

EPOCHS = 5

for epoch in range(EPOCHS):

correct = 0

for batch_idx,(X_batch,y_batch) in enumerate(train_loader):

optimizer.zero_grad()

#這里是只取訓練數據的意思嗎，X_batch和y_batch是怎么分開的?

#答：X_batch和y_batch是一一對應的，只不過順序打亂了，參考torch.utils.data.ipynb

output = model(X_batch.float()) #X_batch.float()是什么意思

loss = loss_func(output,y_batch)

loss.backward()

optimizer.step()

# Total correct predictions

#第一個1代表取每行的最大值，第二個1代表只取最大值的索引

#這兩行代碼是求準確率的地方

predicted = torch.max(output.data,1)[1]

correct += (predicted == y_batch).sum()

#print(correct)

if batch_idx % 100 == 0:

print('Epoch :{}[{}/{}({:.0f}%)]\t Loss:{:.6f}\t Accuracy:{:.3f}'.format(epoch,batch_idx * len(X_batch),len(train_loader.dataset),100.*batch_idx / len(train_loader),loss.data.item(),float(correct*100)/float(BATCH_SIZE)*(batch_idx+1)))

if __name__ == '__main__':

myModel = LeNet5()

print(myModel)

train_model(myModel,train_loader)

evaluate(myModel,test_loader,BATCH_SIZE)

方法二：構建函數，然后在epoch中調用該函數

簡介：此段代碼是對Titanic(泰坦尼克號）數據分析截取。

epochs = 10

log_step_freq = 30

dfhistory = pd.DataFrame(columns = ['epoch','loss',metric_name,'val_loss','val_'+metric_name])

print('Start Training...')

nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')

print('========='*8 + '%s'%nowtime)

for epoch in range(1,epochs+1):

#1.訓練循環

net.train()

loss_sum = 0.0

metric_sum = 0.0

step = 1

for step,(features,labels) in enumerate(dl_train,1):

#梯度清零

optimizer.zero_grad()

#正向傳播求損失

predictions = net(features)

loss = loss_func(predictions,labels)

metric = metric_func(predictions,labels)

#反向傳播求梯度

loss.backward()

optimizer.step()

#打印batch級別日志

loss_sum += loss.item()

metric_sum += metric.item()

if step%log_step_freq == 0:

print(('[Step = %d] loss: %.3f,' + metric_name+': %.3f %%')%(step,loss_sum/step,100*metric_sum/step))

#2,驗證循環

net.eval()

val_loss_sum = 0.0

val_metric_sum = 0.0

val_step =1

for val_step,(features,labels) in enumerate(dl_valid,1):

#關閉梯度計算

with torch.no_grad():

pred = net(features)

val_loss = loss_func(pred,labels)

val_metric = metric_func(labels,pred)

val_loss_sum += val_loss.item()

val_metric_sum += val_metric.item()

#3,記錄日志

info = (epoch,loss_sum/step,100*metric_sum/step,

val_loss_sum/val_step,100*val_metric_sum/val_step)

dfhistory.loc[epoch-1] = info

#打印epoch級別日志

print(('\nEPOCH = %d,loss = %.3f,' + metric_name+\

'=%.3f %%,val_loss = %.3f'+' val_'+metric_name+'= %.3f %%')%info)

nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')

print('\n'+'=========='*8 + '%s'%nowtime)

print('Finishing Training...')

總結

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