from dataset import get_strange_symbol_loader,get_strange_symbols_test_data
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


class Net(nn.Module):
   def __init__(self):
    super().__init__()
    self.fc1 = nn.Linear(28*28,512)
    self.fc2 = nn.Linear(512,256)
    self.fc3 = nn.Linear(256,15)

def forward(self,x):
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)

    return F.softmax(x,dim=1)


if __name__ == '__main__':
   net = Net()
   train,test = get_strange_symbol_loader(batch_size=128)
   loss_function = nn.CrossEntropyLoss()
   optimizer = optim.Adam(net.parameters(),lr=1e-3)
   accuracy = []

   for epoch in range(30):   
       print("epoch",epoch)
       #Train
       for data in train:
           img,label = data  
           net.zero_grad()
           output = net(img.view(-1,28*28))
           loss = F.nll_loss(output,label)
           loss.backward()
           optimizer.step()
       #Test    
       correct,total = 0,0
       with torch.no_grad():
          for data in test:
               img,label = data
               output = net(img.view(-1,784))
               for idx,i in enumerate(output):
                   if torch.argmax(i) == label[idx]:
                       correct += 1
                       total += 1
       accuracy.append(round(correct/total,3))
       print("accuracy: ",accuracy)

这是我用PyTorch建立的神经网络，它基于Sentdex。我正在使用由功能get_strange_symbol_loader(batch_size=128)导入的大学课程管理员提供的数据集。

当我运行这段代码时，它告诉我每个时代的准确性应该是1.0。但是，在包含纪元的for循环迭代之后运行#Test块会产生一些更实际的结果。为什么会这样？

我的目标是将测试精度与历时数作图，以在模型开始过度拟合之前找到模型的最佳历时数。