PyTorch正向传播在相同样本上返回不同的logit

考虑以下针对MNIST的LeNet模型

import torch
from torch import nn
import torch.nn.functional as F

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)
        self.ceriation = nn.CrossEntropyLoss()
    def forward(self, x):
        x = self.conv1(x)
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(x)
        x = x.view(-1, 4*4*50)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

现在，我使用此模型对一批样品进行单步前进，例如

network=LeNet()
optimizer = torch.optim.SGD(self.network.parameters(), lr=0.001, momentum=0.9)
device = torch.device("cpu")
network.to(device)
network.train()
optimizer.zero_grad()
# X_batch= ... some batch of 50 samples pulled from a train_loader defined as
# torch.manual_seed(42)
# training_set = datasets.MNIST('./mnist_data', train=True, download=False, 
#                               transform=transforms.Compose([
#                                   transforms.ToTensor(),
#                                   transforms.Normalize((0.1307,), (0.3081,))]))
# train_loader = torch.utils.data.DataLoader(training_set, 
#                                            batch_size=50, 
#                                            shuffle=False)
logits = network(X_batch)

请注意，shuffle=False并download=False为因为数据集的装载机已经下载了，我不想洗牌。我的问题是，如果我两次运行此代码，我将获得不同的值，logits而且我不明白为什么，因为其他所有内容似乎都没有改变。为了进行额外检查，我还提取X_batch了一个numpy数组，并验证了这批样本与先前执行的样本完全相同。我用numpy.array_equal()功能进行此检查。

除非存在精度问题，否则我真的无法弄清楚这里缺少什么。