Pytorch 什么'；用toy MNIST示例说明这两种推理代码之间的区别

我发现这两个代码之间有不同的精度值。 我认为这些代码相同，但给出的值不同。 为什么这两个代码的结果不同

[代码1]

def mytest(model, data_loader):
    with torch.no_grad():
        corrected_sample = 0
        for x_test, y_test in data_loader:
            x_test, y_test = x_test.to(device), y_test.to(device)
            x_test = x_test.view(-1,28*28).float()
            prediction = model(x_test)
            #predicted_class = prediction.max(1)[1]
            predicted_class = torch.argmax(prediction, 1)
            corrected_sample += (predicted_class==y_test).float().sum().item()
    accuracy = corrected_sample / len(data_loader.dataset)
    print("Accuracy : {}".format(accuracy))

data_loader_test = torch.utils.data.DataLoader(mnist_test, batch_size=1, shuffle=False)
mytest(model, data_loader_test)

with torch.no_grad():
    x_test = mnist_test.test_data.view(-1,28*28).float().to(device)
    y_test = mnist_test.test_labels.to(device)

    prediction = model(x_test)
    correct_prediction = torch.argmax(prediction,1) == y_test
    accuracy = correct_prediction.float().mean()
    print("Accuracy ", accuracy.item())

准确度：0.92232

[代码2]

def mytest(model, data_loader):
    with torch.no_grad():
        corrected_sample = 0
        for x_test, y_test in data_loader:
            x_test, y_test = x_test.to(device), y_test.to(device)
            x_test = x_test.view(-1,28*28).float()
            prediction = model(x_test)
            #predicted_class = prediction.max(1)[1]
            predicted_class = torch.argmax(prediction, 1)
            corrected_sample += (predicted_class==y_test).float().sum().item()
    accuracy = corrected_sample / len(data_loader.dataset)
    print("Accuracy : {}".format(accuracy))

data_loader_test = torch.utils.data.DataLoader(mnist_test, batch_size=1, shuffle=False)
mytest(model, data_loader_test)

with torch.no_grad():
    x_test = mnist_test.test_data.view(-1,28*28).float().to(device)
    y_test = mnist_test.test_labels.to(device)

    prediction = model(x_test)
    correct_prediction = torch.argmax(prediction,1) == y_test
    accuracy = correct_prediction.float().mean()
    print("Accuracy ", accuracy.item())

精度0.882699664306641

---

mnist\u test.test\u data的形状是什么？您的模型是否处于评估模式？mnist_test.test_数据的形状为[10000,28,28]。在尝试eval模式时，“model.eval（）”并不重要。模型只是“nn.Linear（784,10，bias=True）”。你能给我们看一下完整的代码吗？（应该足够简短）我在这里没有发现任何错误。