class RestNet18(nn.Module): def __init__(self): super(RestNet18, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = nn.Sequential(RestNetBasicBlock(64, 64, 1), RestNetBasicBlock(64, 64, 1)) self.layer2 = nn.Sequential(RestNetDownBlock(64, 128, [2, 1]), RestNetBasicBlock(128, 128, 1)) self.layer3 = nn.Sequential(RestNetDownBlock(128, 256, [2, 1]), RestNetBasicBlock(256, 256, 1)) self.layer4 = nn.Sequential(RestNetDownBlock(256, 512, [2, 1]), RestNetBasicBlock(512, 512, 1)) self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) self.fc = nn.Linear(512, 10) ———————————————— 逐行解释

class NormedLinear(nn.Module): def init(self, feat_dim, num_classes): super().init() self.weight = nn.Parameter(torch.Tensor(feat_dim, num_classes)) self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5) def forward(self, x): return F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0)) class LearnableWeightScalingLinear(nn.Module): def init(self, feat_dim, num_classes, use_norm=False): super().init() self.classifier = NormedLinear(feat_dim, num_classes) if use_norm else nn.Linear(feat_dim, num_classes) self.learned_norm = nn.Parameter(torch.ones(1, num_classes)) def forward(self, x): return self.classifier(x) * self.learned_norm class DisAlignLinear(nn.Module): def init(self, feat_dim, num_classes, use_norm=False): super().init() self.classifier = NormedLinear(feat_dim, num_classes) if use_norm else nn.Linear(feat_dim, num_classes) self.learned_magnitude = nn.Parameter(torch.ones(1, num_classes)) self.learned_margin = nn.Parameter(torch.zeros(1, num_classes)) self.confidence_layer = nn.Linear(feat_dim, 1) torch.nn.init.constant_(self.confidence_layer.weight, 0.1) def forward(self, x): output = self.classifier(x) confidence = self.confidence_layer(x).sigmoid() return (1 + confidence * self.learned_magnitude) * output + confidence * self.learned_margin class MLP_ConClassfier(nn.Module): def init(self): super(MLP_ConClassfier, self).init() self.num_inputs, self.num_hiddens_1, self.num_hiddens_2, self.num_hiddens_3, self.num_outputs \ = 41, 512, 128, 32, 5 self.num_proj_hidden = 32 self.mlp_conclassfier = nn.Sequential( nn.Linear(self.num_inputs, self.num_hiddens_1), nn.ReLU(), nn.Linear(self.num_hiddens_1, self.num_hiddens_2), nn.ReLU(), nn.Linear(self.num_hiddens_2, self.num_hiddens_3), ) self.fc1 = torch.nn.Linear(self.num_hiddens_3, self.num_proj_hidden) self.fc2 = torch.nn.Linear(self.num_proj_hidden, self.num_hiddens_3) self.linearclassfier = nn.Linear(self.num_hiddens_3, self.num_outputs) self.NormedLinearclassfier = NormedLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs) self.DisAlignLinearclassfier = DisAlignLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs, use_norm=True) self.LearnableWeightScalingLinearclassfier = LearnableWeightScalingLinear(feat_dim=self.num_hiddens_3, num_classes=self.num_outputs, use_norm=True)

这段代码定义了一个名为MLP_ConClassfier的神经网络模型，它包含了多个子模块，包括三个不同的分类器：NormedLinearclassfier、DisAlignLinearclassfier和LearnableWeightScalingLinearclassfier。...

class Identity(nn.Module): def init(self): super().init() def forward(self, x): return x是什么意思

1. Identity 类继承自 nn.Module，表示这是一个 PyTorch 模块。 2. 在 __init__ 方法中，调用父类的 __init__ 方法进行初始化。 3. forward 方法定义了模块的前向传播逻辑。它接收一个输入张量 x，并...

class CenteredLayer(nn.Module): def init(self): super().init() def forward(self, X): return X - X.mean()

在__init__()方法中，这个层的初始化操作并不需要做什么，所以它只调用了父类nn.Module的构造函数来初始化一些必要的属性。在forward()方法中，这个层实现了它的前向传播逻辑。它接收一个输入张量X，然后...

class Model(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(1, 20, 5)

self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(20, 50, 5) self.fc1 = nn.Linear(4*4*50, 500) self.fc2 = nn.Linear(500, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self....

class View(nn.Module): def init(self, shape): super().init() self.shape = shape, def forward(self, x): return x.view(*self.shape)

在 __init__ 方法中，它继承了 nn.Module 类，并且定义了一个 shape 属性。在 forward 方法中，它对输入的 x 应用了 view 方法，并将 shape 属性作为参数传入。这个模型的作用是将输入的张量的形状调整为 shape 属性...

class NeuralNetwork(nn.Module): def init(self): super(NeuralNetwork, self).init() self.flatten = nn.Flatten() self.linear_relu_stack = nn.Sequential( nn.Linear(in_features=28 * 28, out_features=512),

对于这段代码，它定义了一个名为 NeuralNetwork 的类，该类是 nn.Module 的子类。在 __init__ 方法中，它初始化了类中的两个属性：flatten 和 linear_relu_stack。 flatten 是一个 nn.Flatten 实例，用于将输入的二...

class Train(nn.Module): def init(self,args,dataset): super(Train, self).init() self.args = args self.D = dataset self.entity_vec = nn.Embedding(self.D.entity_num,args.emb_dim) self.concept_vec = nn.Embedding(self.D.concept_num,args.emb_dim+1) self.relation_vec = nn.Embedding(self.D.relation_num,args.emb_dim) self.optimizer = torch.optim.SGD(self.parameters(),lr=args.lr) nn.init.normal_(self.entity_vec.weight.data, 0.0, 1.0 / args.emb_dim) nn.init.normal_(self.relation_vec.weight.data, 0.0, 1.0 / args.emb_dim) nn.init.normal_(self.concept_vec.weight.data[:, :-1], 0.0, 1.0 / args.emb_dim) nn.init.uniform_(self.concept_vec.weight.data[:, -1], 0.0, 1.0)

使用 nn.Embedding 可以将实体、概念和关系映射到低维空间中的向量表示。其中，entity_vec 和 relation_vec 的权重矩阵使用正态分布进行初始化，而 concept_vec 的权重矩阵的前 n-1 列也使用正态分布进行初始化，...

class CSPDarknet(nn.Module): def init(self, base_channels, base_depth, phi, pretrained): super().init()

它继承了 nn.Module 类，并实现了 __init__() 方法。构造函数中有四个参数： - base_channels：基础通道数，用于控制模型的宽度。 - base_depth：基础深度，用于控制模型的深度。 - phi：缩放因子，用于控制模型的...

class Encoder(nn.Module): def init(self,encoder_embedding_num,encoder_hidden_num,en_corpus_len): super().init() self.embedding = nn.Embedding(en_corpus_len,encoder_embedding_num) self.lstm = nn.LSTM(encoder_embedding_num,encoder_hidden_num,batch_first=True) def forward(self,en_index): en_embedding = self.embedding(en_index) _,encoder_hidden =self.lstm(en_embedding) return encoder_hidden解释每行代码的含义

- class Encoder(nn.Module): 定义一个名为Encoder的类，继承自nn.Module。 - def __init__(self,encoder_embedding_num,encoder_hidden_num,en_corpus_len): 定义Encoder类的初始化函数，传入三个参数：encoder...

class Mlp(nn.Module): def init(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().init() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop)是什么意思

- super().__init__()：调用父类（nn.Module）的初始化函数。 - out_features = out_features or in_features和hidden_features = hidden_features or in_features：如果未指定输出特征和隐藏特征的数量，...

class LayerScale(nn.Module): def init(self, dim, init_values=1e-5, inplace=False): super().init() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x): return x.mul_(self.gamma) if self.inplace else x * self.gamma

它接受三个参数：dim表示输入张量的维度，init_values表示初始化缩放因子的值，默认为1e-5，inplace表示是否原地操作，默认为False。在该函数中，创建了一个可学习的参数gamma，它是一个大小为dim的张量，...

class MolecularGCN(nn.Module): def init(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None): super(MolecularGCN, self).init() self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)是什么

这是一个名为 MolecularGCN 的 PyTorch 模块，它是一个分子图卷积神经网络，用于分子结构的学习和预测。它的输入特征维度为 in_...其中，init_transform 是一个线性变换层，用于将输入特征转换为指定维度的特征表示。

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class Tudui(nn.Module): def __init__(self): super(Tudui ,self).__init__()

class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__()

class Encoder(nn.Module): def __init__(self): super(Encoder, self).__init__()

class Net(nn.Module): def__init__(self): super(Net,self).__init__()

class Model(nn.Module): def __init__(self): super(Model, self).__init__()

class Identity(nn.Module): def __init__(self): super().__init__() def forward(self, x): return x是什么意思

class CenteredLayer(nn.Module): def __init__(self): super().__init__() def forward(self, X): return X - X.mean()

class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5)

class View(nn.Module): def __init__(self, shape): super().__init__() self.shape = shape, def forward(self, x): return x.view(*self.shape)

class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, self).__init__() self.flatten = nn.Flatten() self.linear_relu_stack = nn.Sequential( nn.Linear(in_features=28 * 28, out_features=512),

class CSPDarknet(nn.Module): def __init__(self, base_channels, base_depth, phi, pretrained): super().__init__()

class LayerScale(nn.Module): def __init__(self, dim, init_values=1e-5, inplace=False): super().__init__() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x): return x.mul_(self.gamma) if self.inplace else x * self.gamma

class MolecularGCN(nn.Module): def __init__(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None): super(MolecularGCN, self).__init__() self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)是什么

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class Tudui(nn.Module): def init(self): super(Tudui ,self).init()

class LeNet(nn.Module): def init(self): super(LeNet, self).init()

class Encoder(nn.Module): def init(self): super(Encoder, self).init()

class Net(nn.Module): definit(self): super(Net,self).init()

class Model(nn.Module): def init(self): super(Model, self).init()

class Identity(nn.Module): def init(self): super().init() def forward(self, x): return x是什么意思

class CenteredLayer(nn.Module): def init(self): super().init() def forward(self, X): return X - X.mean()

class Model(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(1, 20, 5)

class View(nn.Module): def init(self, shape): super().init() self.shape = shape, def forward(self, x): return x.view(*self.shape)

class NeuralNetwork(nn.Module): def init(self): super(NeuralNetwork, self).init() self.flatten = nn.Flatten() self.linear_relu_stack = nn.Sequential( nn.Linear(in_features=28 * 28, out_features=512),

class CSPDarknet(nn.Module): def init(self, base_channels, base_depth, phi, pretrained): super().init()

class LayerScale(nn.Module): def init(self, dim, init_values=1e-5, inplace=False): super().init() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x): return x.mul_(self.gamma) if self.inplace else x * self.gamma

class MolecularGCN(nn.Module): def init(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None): super(MolecularGCN, self).init() self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)是什么

数学建模学习资料姜启源数学模型课件 M04 数学规划模型共85页.pptx