Deep learning PyTorch不会使初始权重随机

我创建了一个神经网络，它获取两个灰度图像14x14像素，描绘一个数字（来自MNIST数据库），如果第一个数字小于或等于第二个数字，则返回1，否则返回0代码运行，但每次初始权重都相同。它们应该是随机的 通过在Net类中使用下面的代码行强制初始权重为随机，这没有帮助

torch.nn.init.normal_(self.layer1.weight, mean=0.0, std=0.01)

以下是“main.py”文件的代码：

下面是“dlc_practical_monogue.py”的代码：

同样在Linux上，我总是得到相同的初始权重

---

请帮我一个忙好吗？

如果我在这里说错了，请纠正我，但每次运行时，只有第一层的重量应该相同。问题是，当您导入

dlc\u practical\u monogue.py时，其中包含以下内容：

if args.seed >= 0:
    torch.manual_seed(args.seed)

如果种子大于等于0（默认值为0），则会激发。
这应仅初始化第一层，使其在每次运行时具有相同的权重。检查情况是否如此。
解决方案是从“dlv\u practical\u prologue.py”中删除以下行：

我无法重现这种行为。无论是否使用
nn.init
行，每次实例化新的
Net（）
时，我都会获得不同的初始权重。在Windows 10上运行时，我总是获得相同的初始权重。同样在Linux上，我总是得到相同的权重。但我刚刚意识到，在Linux上，初始权重与Windows 10不同。如果我在Net类之前写入print（torch.rand（1）），我总是得到相同的print值，这意味着随机种子总是相同的。奇怪不幸的是，每次我运行程序时，每层的权重都是相同的。我通过在main的开头添加torch.manual_seed（time.time（））解决了这个问题，但是在我必须完成的作业中，我不允许使用库“time”，所以即使您从dlc_practical_monogue.py中删除这个“``if args.seed>=0:torch.manual_seed（args.seed）``，并且如果您没有包含任何种子操作，每次跑步的重量都一样吗？有趣的是，我删除了``if args.seed>=0:torch.manual_seed（args.seed）``现在它工作得很好。谢谢！
import os; os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import torch
from torchvision import datasets

import argparse
import os

import urllib

######################################################################

parser = argparse.ArgumentParser(description='DLC prologue file for practical sessions.')

parser.add_argument('--full',
                    action='store_true', default=False,
                    help = 'Use the full set, can take ages (default False)')

parser.add_argument('--tiny',
                    action='store_true', default=False,
                    help = 'Use a very small set for quick checks (default False)')

parser.add_argument('--seed',
                    type = int, default = 0,
                    help = 'Random seed (default 0, < 0 is no seeding)')

parser.add_argument('--cifar',
                    action='store_true', default=False,
                    help = 'Use the CIFAR data-set and not MNIST (default False)')

parser.add_argument('--data_dir',
                    type = str, default = None,
                    help = 'Where are the PyTorch data located (default $PYTORCH_DATA_DIR or \'./data\')')

# Timur's fix
parser.add_argument('-f', '--file',
                    help = 'quick hack for jupyter')

args = parser.parse_args()

if args.seed >= 0:
    torch.manual_seed(args.seed)

######################################################################
# The data

def convert_to_one_hot_labels(input, target):
    tmp = input.new_zeros(target.size(0), target.max() + 1)
    tmp.scatter_(1, target.view(-1, 1), 1.0)
    return tmp

def load_data(cifar = None, one_hot_labels = False, normalize = False, flatten = True):

    if args.data_dir is not None:
        data_dir = args.data_dir
    else:
        data_dir = os.environ.get('PYTORCH_DATA_DIR')
        if data_dir is None:
            data_dir = './data'

    if args.cifar or (cifar is not None and cifar):
        print('* Using CIFAR')
        cifar_train_set = datasets.CIFAR10(data_dir + '/cifar10/', train = True, download = True)
        cifar_test_set = datasets.CIFAR10(data_dir + '/cifar10/', train = False, download = True)

        train_input = torch.from_numpy(cifar_train_set.data)
        train_input = train_input.transpose(3, 1).transpose(2, 3).float()
        train_target = torch.tensor(cifar_train_set.targets, dtype = torch.int64)

        test_input = torch.from_numpy(cifar_test_set.data).float()
        test_input = test_input.transpose(3, 1).transpose(2, 3).float()
        test_target = torch.tensor(cifar_test_set.targets, dtype = torch.int64)

    else:
        print('* Using MNIST')

        ######################################################################
        # import torchvision

        # raw_folder = data_dir + '/mnist/raw/'
        # resources = [
            # ("https://fleuret.org/dlc/data/train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
            # ("https://fleuret.org/dlc/data/train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"),
            # ("https://fleuret.org/dlc/data/t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"),
            # ("https://fleuret.org/dlc/data/t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c")
        # ]

        # os.makedirs(raw_folder, exist_ok=True)

        # # download files
        # for url, md5 in resources:
            # filename = url.rpartition('/')[2]
            # torchvision.datasets.utils.download_and_extract_archive(url, download_root=raw_folder, filename=filename, md5=md5)
        ######################################################################

        mnist_train_set = datasets.MNIST(data_dir + '/mnist/', train = True, download = True)
        mnist_test_set = datasets.MNIST(data_dir + '/mnist/', train = False, download = True)

        train_input = mnist_train_set.data.view(-1, 1, 28, 28).float()
        train_target = mnist_train_set.targets
        test_input = mnist_test_set.data.view(-1, 1, 28, 28).float()
        test_target = mnist_test_set.targets

    if flatten:
        train_input = train_input.clone().reshape(train_input.size(0), -1)
        test_input = test_input.clone().reshape(test_input.size(0), -1)

    if args.full:
        if args.tiny:
            raise ValueError('Cannot have both --full and --tiny')
    else:
        if args.tiny:
            print('** Reduce the data-set to the tiny setup')
            train_input = train_input.narrow(0, 0, 500)
            train_target = train_target.narrow(0, 0, 500)
            test_input = test_input.narrow(0, 0, 100)
            test_target = test_target.narrow(0, 0, 100)
        else:
            print('** Reduce the data-set (use --full for the full thing)')
            train_input = train_input.narrow(0, 0, 1000)
            train_target = train_target.narrow(0, 0, 1000)
            test_input = test_input.narrow(0, 0, 1000)
            test_target = test_target.narrow(0, 0, 1000)

    print('** Use {:d} train and {:d} test samples'.format(train_input.size(0), test_input.size(0)))

    if one_hot_labels:
        train_target = convert_to_one_hot_labels(train_input, train_target)
        test_target = convert_to_one_hot_labels(test_input, test_target)

    if normalize:
        mu, std = train_input.mean(), train_input.std()
        train_input.sub_(mu).div_(std)
        test_input.sub_(mu).div_(std)

    return train_input, train_target, test_input, test_target

######################################################################

def mnist_to_pairs(nb, input, target):
    input = torch.functional.F.avg_pool2d(input, kernel_size = 2)
    a = torch.randperm(input.size(0))
    a = a[:2 * nb].view(nb, 2)
    input = torch.cat((input[a[:, 0]], input[a[:, 1]]), 1)
    classes = target[a]
    target = (classes[:, 0] <= classes[:, 1]).long()
    return input, target, classes

######################################################################

def generate_pair_sets(nb):
    if args.data_dir is not None:
        data_dir = args.data_dir
    else:
        data_dir = os.environ.get('PYTORCH_DATA_DIR')
        if data_dir is None:
            data_dir = './data'

    train_set = datasets.MNIST(data_dir + '/mnist/', train = True, download = True)
    train_input = train_set.data.view(-1, 1, 28, 28).float()
    train_target = train_set.targets

    test_set = datasets.MNIST(data_dir + '/mnist/', train = False, download = True)
    test_input = test_set.data.view(-1, 1, 28, 28).float()
    test_target = test_set.targets

    return mnist_to_pairs(nb, train_input, train_target) + \
           mnist_to_pairs(nb, test_input, test_target)

######################################################################

import os; os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

if args.seed >= 0:
    torch.manual_seed(args.seed)

if args.seed >= 0:
    torch.manual_seed(args.seed)