预训练的意思是用 torchvision 里写好的 alexnet （修改最后一层），不是指导入训练好的，尝试用 quickstart 里的代码训练 cifar10 ，但是网上普遍查到的实验数据，准确率大概在 80%，78%左右，我迭代到收敛也只能得到 70%的准确率，这个差异产生的原因是啥呢？

完整代码：

from utils import * from pipeit import * import os,sys,time,pickle,random import matplotlib.pyplot as plt import numpy as np  import torch from torch import nn from torchvision import datasets, models from torch.utils.data import Dataset, DataLoader, TensorDataset from torchvision.transforms import ToTensor, Lambda, Resize, Compose, InterpolationMode  device = "cuda" if torch.cuda.is_available() else "cpu" print("Using {} device".format(device)) torch.backends.cudnn.benchmark=True  # Download training data from open datasets. training_data = datasets.CIFAR10(     root=".\data\cifar10",     train=True,     download=True,     transform=Compose([         Resize((64, 64), InterpolationMode.BICUBIC),         ToTensor()     ]) )  # Download test data from open datasets. test_data = datasets.CIFAR10(     root=".\data\cifar10",     train=False,     download=True,     transform=Compose([         Resize((64, 64), InterpolationMode.BICUBIC),         ToTensor()     ]) )  def imshow(training_data):     labels_map = {         0: "plane",         1: "car",         2: "bird",         3: "cat",         4: "deer",         5: "dog",         6: "frog",         7: "horse",         8: "ship",         9: "truck",     }     cols, rows = 3, 3     figure = plt.figure(figsize=(8,8))     for i in range(1, cols * rows + 1):         sample_idx = torch.randint(len(training_data), size=(1,)).item()         img, label = training_data[sample_idx]         img = img.swapaxes(0,1)         img = img.swapaxes(1,2)         figure.add_subplot(rows, cols, i)         plt.title(labels_map[label])         plt.axis("off")         plt.imshow(img)     plt.show()  # imshow(training_data)  def train_loop(dataloader, net, loss_fn, optimizer):     size = len(dataloader)     train_loss = 0     for batch_idx, (X, tag) in enumerate(dataloader):         X, tag = X.to(device), tag.to(device)         pred = net(X)         loss = loss_fn(pred, tag)         train_loss += loss.item()          # Back propagation         optimizer.zero_grad()         loss.backward()         optimizer.step()     train_loss /= size      return train_loss  def test_loop(dataloader, model, loss_fn):     size = len(dataloader.dataset)     num_batches = len(dataloader)     test_loss, correct = 0, 0      with torch.no_grad():         for X, y in dataloader:             X, y = X.to(device), y.to(device)             pred = model(X)             test_loss += loss_fn(pred, y).item()             correct += (pred.argmax(1) == y).type(torch.float).sum().item()      test_loss /= num_batches     correct /= size     return test_loss, correct  net = models.alexnet().to(device) net.classifier[6] = nn.Linear(4096, 10).to(device)  learning_rate = 0.01 batch_size = 128 weight_decay = 0  train_dataloader = DataLoader(training_data, batch_size = batch_size) test_dataloader = DataLoader(test_data, batch_size = batch_size)  loss_fn = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(net.parameters(), lr = learning_rate)  epochs = 50 for t in range(epochs):     print(f"Epoch {t+1}n-------------------------------")     st_time = time.time()     train_loss = train_loop(train_dataloader, net, loss_fn, optimizer)     test_loss, correct = test_loop(test_dataloader, net, loss_fn)     print(f"Train loss: {train_loss:>8f}, Test loss: {test_loss:>8f}, Accuracy: {(100*correct):>0.1f}%, Epoch time: {time.time() - st_time:.2f}sn") print("Done!") torch.save(net.state_dict(), 'alexnet-pre1.model') 

最后收敛时的数据在这样：

Epoch 52 ------------------------------- Train loss: 0.399347, Test loss: 0.970927, Accuracy: 70.3%, Epoch time: 17.20s