關于 Pytorch 幾種定義網(wǎng)絡的方法

來源：知乎—ppgod

地址：https://zhuanlan.zhihu.com/p/80308275

import torchimport torch.nn as nnfrom torch.autograd import Variablefrom collections import OrderedDictclass Net(nn.Module):    def __init__(self):        super(Net, self).__init__()        self.fc1 = nn.Linear(10,10)        self.relu1 = nn.ReLU(inplace=True)        self.fc2 = nn.Linear(10,2)    def forward(self,x):        x = self.fc1(x)        x = self.relu1(x)        x = self.fc2(x)        return x

這是最簡單的定義一個網(wǎng)絡的方法，但是當網(wǎng)絡層數(shù)過多的時候，這么寫未免太麻煩，于是Pytorch還有第二種定義網(wǎng)絡的方法nn.ModuleList()

class Net(nn.Module):    def __init__(self):        super(Net, self).__init__()        self.base = nn.ModuleList([nn.Linear(10,10), nn.ReLU(), nn.Linear(10,2)])    def forward(self,x):        x = self.base(x)        return x

base = [nn.Linear(10,10) for i in range(5)]net = nn.ModuleList(base)

class Net(nn.Module):    def __init__(self):        super(Net, self).__init__()        self.base = nn.Sequential(nn.Linear(10,10), nn.ReLU(), nn.Linear(10,2))    def forward(self,x):        x = self.base(x)        return x

class MultiLayerNN5(nn.Module):    def __init__(self):        super(MultiLayerNN5, self).__init__()        self.base = nn.Sequential(OrderedDict([            ('0', BasicConv(1, 16, 5, 1, 2)),            ('1', BasicConv(16, 32, 5, 1, 2)),        ]))        self.fc1 = nn.Linear(32 * 7 * 7, 10)
    def forward(self, x):        x = self.base(x)        x = x.view(x.size(0), -1)        x = self.fc1(x)        return x

class MultiLayerNN4(nn.Module):    def __init__(self):        super(MultiLayerNN4, self).__init__()        self.base = nn.Sequential()        self.base.add_module('0', BasicConv(1, 16, 5, 1, 2))        self.base.add_module('1', BasicConv(16, 32, 5, 1, 2))        self.fc1 = nn.Linear(32 * 7 * 7, 10)
    def forward(self, x):        x = self.base(x)        x = x.view(x.size(0),-1)        x = self.fc1(x)

tt = [nn.Linear(10,10), nn.Linear(10,2)]n_1 = nn.Sequential(*tt)n_2 = nn.ModuleList(tt)x = torch.rand([1,10,10])x = Variable(x)n_1(x)n_2(x)#會出現(xiàn)NotImplementedError

class DenseLayer(nn.Sequential):    def __init__(self):        super(DenseLayer, self).__init__()        self.add_module("conv1", nn.Conv2d(1, 1, 1, 1, 0))        self.add_module("conv2", nn.Conv2d(1, 1, 1, 1, 0))
    def forward(self, x):        new_features = super(DenseLayer, self).forward(x)        return torch.cat([x, new_features], 1)#這個寫法和下面的是一樣的class DenLayer1(nn.Module):    def __init__(self):        super(DenLayer1, self).__init__()        convs = [nn.Conv2d(1, 1, 1, 1, 0), nn.Conv2d(1, 1, 1, 1, 0)]        self.conv = nn.Sequential(*convs)    def forward(self, x):        return torch.cat([x, self.conv(x)], 1)net = DenLayer1()x = torch.Tensor([[[[1, 2], [3, 4]]]])print(x)x = Variable(x)print(net(x))

猜您喜歡：

等你著陸！【GAN生成對抗網(wǎng)絡】知識星球！  
CVPR 2021 | GAN的說話人驅動、3D人臉論文匯總
CVPR 2021 | 圖像轉換 今如何？幾篇GAN論文
【CVPR 2021】通過GAN提升人臉識別的遺留難題
CVPR 2021生成對抗網(wǎng)絡GAN部分論文匯總
經(jīng)典GAN不得不讀：StyleGAN
最新最全20篇！基于 StyleGAN 改進或應用相關論文
超100篇！CVPR 2020最全GAN論文梳理匯總！
附下載 | 《Python進階》中文版
附下載 | 經(jīng)典《Think Python》中文版
附下載 | 《Pytorch模型訓練實用教程》
附下載 | 最新2020李沐《動手學深度學習》
附下載 | 《可解釋的機器學習》中文版
附下載 |《TensorFlow 2.0 深度學習算法實戰(zhàn)》
附下載 | 超100篇！CVPR 2020最全GAN論文梳理匯總！
附下載 |《計算機視覺中的數(shù)學方法》分享