class LSTMNet(torch.nn.Module): def __init__(self, num_hiddens, num_outputs): super(LSTMNet, self).__init__() #nn.Conv1d(1,16,2), #nn.Sigmoid(), # nn.MaxPool1d(2), #nn.Conv1d(1,32,2), self.hidden_size = num_hiddens # RNN 层，这里的 batch_first 指定传入的是 (批大小，序列长度，序列每个位置的大小) # 如果不指定其为 True，传入顺序应当是 (序列长度，批大小，序列每个位置的大小) input_size= num_inputs.view(len(input_x), 1, -1)//24 self.rnn = torch.nn.LSTM(input_size, hidden_size=num_hiddens,batch_first=True) # 线性层 self.dense = torch.nn.Linear(self.hidden_size*24, 256) self.dense2 = torch.nn.Linear(256,num_outputs) # dropout 层，这里的参数指 dropout 的概率 self.dropout = torch.nn.Dropout(0.3) self.dropout2 = torch.nn.Dropout(0.5) # ReLU 层 self.relu = torch.nn.ReLU() # 前向传播函数，这是一个拼接的过程，使用大量变量是为了避免混淆，不做过多讲解 def forward(self, x): # x shape: (batch_size, 24, 307) # LSTM 层会传出其参数，这里用 _ 将其舍弃 h, _ = self.rnn(x) # LSTM 层会传出 (batch_size, 24, num_hiddens) 个参数，故需要 reshape 后丢入全连接层 h_r = h.reshape(-1,self.hidden_size*24) h_d = self.dropout(h_r) y = self.dense(h_d) drop_y = self.dropout2(y) a = self.relu(drop_y) y2 = self.dense2(a) return y2

时间: 2024-04-19 16:30:13 浏览: 237
null
阅读全文

相关推荐

zip

RNN.zip_RNN_python_深度学习

class RNNModel(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(RNNModel, self).__init__() self.rnn = nn.RNN(input_size, hidden_size) self.fc = nn.Linear(hidden_size, ...
zip

pytorch classification的.py_pytorch_pytorch分类_MNISTpytorch_

class ConvNet(nn.Module): def __init__(self): super(ConvNet, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(16), nn.ReLU(), ...
zip

pytorch_mnist-python源码.zip

class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = ...

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。...

# 导入库 import torch # 语义分割损失函数 class CrossEntropyLoss2d(torch.nn.Module): def __init__(self, weight=None): super().__init__() self.Crossloss = torch.nn.NLLLoss(weight) def forward(self, outputs, targets): # 请在此添加你的代码计算loss # ********** Begin *********# loss = # ********** End **********# return loss

class CrossEntropyLoss2d(nn.Module): def __init__(self, weight=None, ignore_index=-100, reduction='mean'): super(CrossEntropyLoss2d, self).__init__() self.loss = nn.CrossEntropyLoss(weight=weight, ...

import torch import torch.nn as nn from scipy.interpolate import BSpline class KANLayer(nn.Module): def __init__(self, input_dim, output_dim, degree=3): super().__init__() self.input_dim = input_dim self.output_dim = output_dim self.degree = degree # 初始化样条控制点参数 self.ctrl_pts = nn.Parameter(torch.randn(output_dim, input_dim, degree + 1)) self.bias = nn.Parameter(torch.zeros(output_dim)) def forward(self, x): outputs = [] for i in range(self.output_dim): # 为每个输出神经元计算样条激活 neuron_output = 0 for j in range(self.input_dim): # 创建B样条基函数 t = torch.linspace(0, 1, self.degree + 2) basis = BSpline(t, self.ctrl_pts[i,j], self.degree, extrapolate=False) neuron_output += basis(x[:,j]) # 输入特征通过样条函数 outputs.append(neuron_output + self.bias[i]) return torch.stack(outputs, dim=1) class KANClassifier(nn.Module): def __init__(self, input_size, num_classes): super().__init__() self.kan1 = KANLayer(input_size, 64) self.kan2 = KANLayer(64, 32) self.fc = nn.Linear(32, num_classes) def forward(self, x): x = torch.sigmoid(self.kan1(x)) # 添加非线性 x = torch.relu(self.kan2(x)) return self.fc(x) 对代码进行注释，要求细致到每一步

def __init__(self, input_dim, output_dim, activation=nn.ReLU()): """ 自定义可激活网络层（Kolmogorov-Arnold Network Layer） :param input_dim: 输入特征维度 :param output_dim: 输出特征维度 :param ...

在执行：class MLP(torch.nn.Module): def __init__(self, weight_decay=0.01): super(MLP, self).__init__() self.fc1 = torch.nn.Linear(178, 100) self.relu = torch.nn.ReLU() self.fc2 = torch.nn.Linear(100, 50) self.fc3 = torch.nn.Linear(50, 5) self.dropout = torch.nn.Dropout(p=0.1) self.weight_decay = weight_decay def forward(self, x): x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) return x def regularization_loss(self): reg_loss = torch.tensor(0.).to(device) for name, param in self.named_parameters(): if 'weight' in name: reg_loss += self.weight_decay * torch.norm(param) return reg_lossmodel = MLP() criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(inputs.to(device)) loss = criterion(outputs, labels.to(device)) loss += model.regularization_loss() loss.backward() optimizer.step()如何将其中测试集的loss函数收敛

到最小值？ 首先，需要检查训练集和测试集的loss值是否有明显的差距，如果有，则需要增加模型复杂度或者增加训练集的数量。其次，可以尝试调整learning rate、weight decay等超参数，使模型更容易收敛。...

为以下代码写注释：class TransformerClassifier(torch.nn.Module): def __init__(self, num_labels): super().__init__() self.bert = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=num_labels) # print(self.bert.config.hidden_size) #768 self.dropout = torch.nn.Dropout(0.1) self.classifier1 = torch.nn.Linear(640, 256) self.classifier2 = torch.nn.Linear(256, num_labels) self.regress1 = torch.nn.Linear(640, 256) self.regress2 = torch.nn.Linear(256, 2) self.regress3 = torch.nn.Linear(640, 256) self.regress4 = torch.nn.Linear(256, 2) # self.regress3 = torch.nn.Linear(64, 1) # self.regress3 = torch.nn.Linear(640, 256) # self.regress4 = torch.nn.Linear(256, 1) # self.soft1 = torch.nn.Softmax(dim=1) def forward(self, input_ids, attention_mask, token_type_ids): # outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) # pooled_output = outputs.logits # # pooled_output = self.dropout(pooled_output) # # logits = self.classifier(pooled_output) outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) logits = outputs.logits clas = F.relu(self.classifier1(logits)) clas = self.classifier2(clas) death = F.relu(self.regress1(logits)) # xingqi = F.relu(self.regress2(xingqi)) death = self.regress2(death) life = F.relu(self.regress3(logits)) # xingqi = F.relu(self.regress2(xingqi)) life = self.regress4(life) # fakuan = F.relu(self.regress3(logits)) # fakuan = self.regress4(fakuan) # print(logits.shape) # logits = self.soft1(logits) # print(logits) # print(logits.shape) return clas,death,life

class TransformerClassifier(torch.nn.Module): # 定义一个名为TransformerClassifier的继承自nn.Module的类 def __init__(self, num_labels):　# 初始化函数，num_labels为标签数 super().__init__() # 调用父类...

运行以下Python代码：import torchimport torch.nn as nnimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.utils.data import DataLoaderfrom torch.autograd import Variableclass Generator(nn.Module): def __init__(self, input_dim, output_dim, num_filters): super(Generator, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters), nn.ReLU(), nn.Linear(num_filters, num_filters*2), nn.ReLU(), nn.Linear(num_filters*2, num_filters*4), nn.ReLU(), nn.Linear(num_filters*4, output_dim), nn.Tanh() ) def forward(self, x): x = self.net(x) return xclass Discriminator(nn.Module): def __init__(self, input_dim, num_filters): super(Discriminator, self).__init__() self.input_dim = input_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters*4), nn.LeakyReLU(0.2), nn.Linear(num_filters*4, num_filters*2), nn.LeakyReLU(0.2), nn.Linear(num_filters*2, num_filters), nn.LeakyReLU(0.2), nn.Linear(num_filters, 1), nn.Sigmoid() ) def forward(self, x): x = self.net(x) return xclass ConditionalGAN(object): def __init__(self, input_dim, output_dim, num_filters, learning_rate): self.generator = Generator(input_dim, output_dim, num_filters) self.discriminator = Discriminator(input_dim+1, num_filters) self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate) self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate) def train(self, data_loader, num_epochs): for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(data_loader): # Train discriminator with real data real_inputs = Variable(inputs) real_labels = Variable(labels) real_labels = real_labels.view(real_labels.size(0), 1) real_inputs = torch.cat((real_inputs, real_labels), 1) real_outputs = self.discriminator(real_inputs) real_loss = nn.BCELoss()(real_outputs, torch.ones(real_outputs.size())) # Train discriminator with fake data noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0, 10)) fake_labels = fake_labels.view(fake_labels.size(0), 1) fake_inputs = self.generator(torch.cat((noise, fake_labels.float()), 1)) fake_inputs = torch.cat((fake_inputs, fake_labels), 1) fake_outputs = self.discriminator(fake_inputs) fake_loss = nn.BCELoss()(fake_outputs, torch.zeros(fake_outputs.size())) # Backpropagate and update weights for discriminator discriminator_loss = real_loss + fake_loss self.discriminator.zero_grad() discriminator_loss.backward() self.optimizer_D.step() # Train generator noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0,

def __init__(self, input_dim, num_filters): super(Discriminator, self).__init__() self.input_dim = input_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_...

class BertClassifier(nn.Module): def __init__(self, bert_model, num_classes): super(BertClassifier, self).__init__() self.bert = bert_model self.fc = nn.Linear(self.bert.config.hidden_size, num_classes) def forward(self, input_ids, attention_mask): outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) cls_token_output = outputs.last_hidden_state[:, 0, :] # CLS token logits = self.fc(cls_token_output) return logits代码解析

class BertClassifier(nn.Module): def __init__(self, bert_model, num_classes): super(BertClassifier, self).__init__() # 预训练BERT模型 self.bert = bert_model # 分类层：将BERT输出维度映射到类别数 ...

import torch import torch.nn as nn class CBAM(nn.Module): def __init__(self, channels, reduction=16): super(CBAM, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc = nn.Sequential( nn.Linear(channels, channels // reduction), nn.ReLU(inplace=True), nn.Linear(channels // reduction, channels) ) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc(self.avg_pool(x).view(x.size(0), -1)) max_out = self.fc(self.max_pool(x).view(x.size(0), -1)) out = avg_out + max_out channel_att = self.sigmoid(out).unsqueeze(2).unsqueeze(3) x = x * channel_att return x class ResNet50_CBAM(nn.Module): def __init__(self, num_classes=1000, torchvision=None): super(ResNet50_CBAM, self).__init__() self.base = torchvision.models.resnet50(pretrained=True) self.cbam1 = CBAM(256) self.cbam2 = CBAM(512) self.cbam3 = CBAM(1024) self.cbam4 = CBAM(2048) self.fc = ArcFace(2048, num_classes) def forward(self, x): x = self.base.conv1(x) x = self.base.bn1(x) x = self.base.relu(x) x = self.base.maxpool(x) x = self.base.layer1(x) x = self.cbam1(x) x = self.base.layer2(x) x = self.cbam2(x) x = self.base.layer3(x) x = self.cbam3(x) x = self.base.layer4(x) x = self.cbam4(x) x = self.base.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Parameter class ArcFace(nn.Module): def __init__(self, in_features, out_features, s=30.0, m=0.50): super(ArcFace, self).__init__() self.in_features = in_features self.out_features = out_features self.s = s self.m = m self.weight = Parameter(torch.FloatTensor(out_features, in_features)) nn.init.xavier_uniform_(self.weight) def forward(self, input, label=None): cosine = F.linear(F.normalize(input), F.normalize(self.weight)) if label is None: return cosine * self.s phi = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7)) one_hot = torch.zeros(cosine.size(), device=input.device) one_hot.scatter_(1, label.view(-1, 1), 1) output = (one_hot * (phi + self.m) + (1.0 - one_hot) * phi).cos() output *= self.s return output import torch import torch.nn as nn class CBAM(nn.Module): def __init__(self, channels, reduction=16): super(CBAM, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc = nn.Sequential( nn.Linear(channels, channels // reduction), nn.ReLU(inplace=True), nn.Linear(channels // reduction, channels) ) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc(self.avg_pool(x).view(x.size(0), -1)) max_out = self.fc(self.max_pool(x).view(x.size(0), -1)) out = avg_out + max_out channel_att = self.sigmoid(out).unsqueeze(2).unsqueeze(3) x = x * channel_att return x class ResNet50_CBAM(nn.Module): def __init__(self, num_classes=1000, torchvision=None): super(ResNet50_CBAM, self).__init__() self.base = torchvision.models.resnet50(pretrained=True) self.cbam1 = CBAM(256) self.cbam2 = CBAM(512) self.cbam3 = CBAM(1024) self.cbam4 = CBAM(2048) self.fc = ArcFace(2048, num_classes) def forward(self, x): x = self.base.conv1(x) x = self.base.bn1(x) x = self.base.relu(x) x = self.base.maxpool(x) x = self.base.layer1(x) x = self.cbam1(x) x = self.base.layer2(x) x = self.cbam2(x) x = self.base.layer3(x) x = self.cbam3(x) x = self.base.layer4(x) x = self.cbam4(x) x = self.base.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Parameter class ArcFace(nn.Module): def __init__(self, in_features, out_features, s=30.0, m=0.50): super(ArcFace, self).__init__() self.in_features = in_features self.out_features = out_features self.s = s self.m = m self.weight = Parameter(torch.FloatTensor(out_features, in_features)) nn.init.xavier_uniform_(self.weight) def forward(self, input, label=None): cosine = F.linear(F.normalize(input), F.normalize(self.weight)) if label is None: return cosine * self.s phi = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7)) one_hot = torch.zeros(cosine.size(), device=input.device) one_hot.scatter_(1, label.view(-1, 1), 1) output = (one_hot * (phi + self.m) + (1.0 - one_hot) * phi).cos() output *= self.s return output

def __init__(self, num_classes=1000): super().__init__() self.base = torchvision.models.resnet50(pretrained=True) # 移除torchvision参数 # 修复2：添加标签参数支持 def forward(self, x, label=None):...

#增加 多头注意力机制 import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader, TensorDataset import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from TCN.tcn import TemporalConvNet,Chomp1d,TemporalBlock import matplotlib.pyplot as plt import time # 配置参数 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") SEQ_LENGTH = 120 BATCH_SIZE = 128 # 减小批次以适应注意力计算 EPOCHS = 100 LEARNING_RATE = 5e-5 # 调整学习率 SPLIT_RATIO = 0.8 # 多头时间注意力模块 class MultiHeadTemporalAttention(nn.Module): def __init__(self, embed_size, heads=4): super().__init__() self.embed_size = embed_size self.heads = heads self.head_dim = embed_size // heads self.query = nn.Linear(embed_size, embed_size) self.key = nn.Linear(embed_size, embed_size) self.value = nn.Linear(embed_size, embed_size) self.fc_out = nn.Linear(embed_size, embed_size) def forward(self, x): batch_size, seq_len, _ = x.shape Q = self.query(x).view(batch_size, seq_len, self.heads, self.head_dim).permute(0, 2, 1, 3) K = self.key(x).view(batch_size, seq_len, self.heads, self.head_dim).permute(0, 2, 1, 3) V = self.value(x).view(batch_size, seq_len, self.heads, self.head_dim).permute(0, 2, 1, 3) energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / (self.head_dim ** 0.5) attention = F.softmax(energy, dim=-1) out = torch.matmul(attention, V) out = out.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_len, self.embed_size) return self.fc_out(out) # 带注意力的时序块 class AttentiveTemporalBlock(nn.Module): def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2): super().__init__() self.conv1 = nn.utils.weight_norm(nn.Conv1d( n_inputs, n_outputs, kernel_size, stride=stride, padding=p针对TCN模型改进的 多头注意力机制 的原理是什么 然后使用多头注意力机制进行改进TCN的步骤及流程是什么

def __init__(self, d_model, num_heads): super().__init__() self.d_head = d_model // num_heads self.Wq = nn.Linear(d_model, d_model) self.Wk = nn.Linear(d_model, d_model) self.Wv = nn.Linear(d_...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from sklearn.preprocessing import StandardScaler from torch.utils.data import Dataset, DataLoader # 加载数据集 data = pd.read_csv('pfyh.csv') df = pd.DataFrame(data) dataset = df.iloc[:, 2:].to_numpy() df.head() # 可视化数据 # 简单数据可视化 plt.plot(df.iloc[:, 2]) plt.title("Data Visualization") plt.show() # 提取特征和标签 X = np.array(dataset[:, :-1]) y = np.array(dataset[:, -1]) # 数据标准化和归一化 scaler = StandardScaler() X = scaler.fit_transform(X) y = y / 1000 # 划分训练集和测试集（90%训练，10%测试） split_index = int(len(X) * 0.9) X_train, X_test = X[:split_index], X[split_index:] y_train, y_test = y[:split_index], y[split_index:] # 自定义PyTorch数据集类 class TimeSeriesDataset(Dataset): def __init__(self, x, y, sequence_length): self.x = x self.y = y self.sequence_length = sequence_length def __len__(self): return len(self.x) - self.sequence_length def __getitem__(self, idx): return ( torch.tensor(self.x[idx:idx + self.sequence_length], dtype=torch.float), torch.tensor(self.y[idx + self.sequence_length], dtype=torch.float) ) # 创建数据集和数据加载器 sequence_length = 14 train_dataset = TimeSeriesDataset(X_train, y_train, sequence_length) train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) test_dataset = TimeSeriesDataset(X_test, y_test, sequence_length) test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False) # 定义LSTM模型 class LSTMModel(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size): super(LSTMModel, self).__init__() self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) self.init_weights() def forward(self, x): out, _ = self.lstm(x) out = self.fc(out[:, -1, :]) return out def init_weights(self): torch.manual_seed(42)

def __init__(self, input_size=1, hidden_size=50, output_size=1, num_layers=2, dropout=0.2): super().__init__() self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=...

以下代码多次计算损失的值始终不变? class QABasedOnAttentionModel(nn.Module): def __init__(self, vocab_size, embed_size, hidden_size, topk): super(QABasedOnAttentionModel, self).__init__() self.topk = topk self.embedding = nn.Embedding(vocab_size, embed_size) self.encoder = nn.GRU(embed_size, hidden_size, batch_first=True) self.attention = nn.Linear(hidden_size, 1) self.decoder = nn.Linear(hidden_size, vocab_size) def forward(self, input_question, input_answer): question_embed = self.embedding(input_question) answer_embed = self.embedding(input_answer) _, question_hidden = self.encoder(question_embed) answer_outputs, _ = self.encoder(answer_embed, question_hidden) attention_weights = self.attention(answer_outputs).squeeze(dim=-1) attention_weights = torch.softmax(attention_weights, dim=1) context_vector = torch.bmm(attention_weights.unsqueeze(dim=1), answer_outputs).squeeze(dim=1) logits = self.decoder(context_vector) top_100_values, _ = torch.topk(logits, self.topk, dim=1) mask = torch.zeros_like(logits, requires_grad=True) # 设置 requires_grad=True score = [] for i in range(logits.size(0)): top_100_indices = torch.argsort(logits[i])[-self.topk:] mask_i = mask[i].clone() # 创建副本 mask_i[top_100_indices] = 1.0 score.append(mask_i.clone()) # 创建副本并赋值回 mask score = torch.stack(score) return score

import torch.nn as nn import torch.optim as optim # 定义模型和损失函数 model = QABasedOnAttentionModel(vocab_size, embed_size, hidden_size, topk) criterion = nn.CrossEntropyLoss() # 定义优化器 ...

mport torch import torch.nn as nn class CBAM(nn.Module): def __init__(self, channels, reduction=16): super(CBAM, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc = nn.Sequential( nn.Linear(channels, channels // reduction), nn.ReLU(inplace=True), nn.Linear(channels // reduction, channels) ) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc(self.avg_pool(x).view(x.size(0), -1)) max_out = self.fc(self.max_pool(x).view(x.size(0), -1)) out = avg_out + max_out channel_att = self.sigmoid(out).unsqueeze(2).unsqueeze(3) x = x * channel_att return x class ResNet50_CBAM(nn.Module): def __init__(self, num_classes=1000): super(ResNet50_CBAM, self).__init__() self.base = torchvision.models.resnet50(pretrained=True) self.cbam1 = CBAM(256) self.cbam2 = CBAM(512) self.cbam3 = CBAM(1024) self.cbam4 = CBAM(2048) self.fc = ArcFace(2048, num_classes) def forward(self, x): x = self.base.conv1(x) x = self.base.bn1(x) x = self.base.relu(x) x = self.base.maxpool(x) x = self.base.layer1(x) x = self.cbam1(x) x = self.base.layer2(x) x = self.cbam2(x) x = self.base.layer3(x) x = self.cbam3(x) x = self.base.layer4(x) x = self.cbam4(x) x = self.base.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Parameter class ArcFace(nn.Module): def __init__(self, in_features, out_features, s=30.0, m=0.50): super(ArcFace, self).__init__() self.in_features = in_features self.out_features = out_features self.s = s self.m = m self.weight = Parameter(torch.FloatTensor(out_features, in_features)) nn.init.xavier_uniform_(self.weight) def forward(self, input, label=None): cosine = F.linear(F.normalize(input), F.normalize(self.weight)) if label is None: return cosine * self.s phi = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7)) one_hot = torch.zeros(cosine.size(), device=input.device) one_hot.scatter_(1, label.view(-1, 1), 1) output = (one_hot * (phi + self.m) + (1.0 - one_hot) * phi).cos() output *= self.s return output我想要让这段代码在不该的情况下有输出结果

def __init__(self, num_classes=1000): super(ResNet50_CBAM, self).__init__() # 加载预训练的ResNet50 self.base = models.resnet50(pretrained=True) # 添加CBAM模块到不同层 self.cbam1 = CBAM(256) ...

# 位置编码 class PositionalEncoding(nn.Module): def __init__(self, embed_size, max_length, device): super(PositionalEncoding, self).__init__() self.embed_size = embed_size self.device = device pe = torch.zeros(max_length, embed_size, device=device) position = torch.arange(0, max_length, dtype=torch.float, device=device).unsqueeze(1) div_term = torch.pow(10000.0, torch.arange(0, embed_size, 2).float() / embed_size).to(device) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) self.register_buffer('pe', pe.unsqueeze(0)) def forward(self, x): return x + self.pe[:, :x.size(1), :].to(x.device) # 多头注意力机制 class MultiHeadAttention(nn.Module): def __init__(self, embed_size, heads): super(MultiHeadAttention, self).__init__() self.embed_size = embed_size self.heads = heads self.head_dim = embed_size // heads assert (self.head_dim * heads == embed_size), "Embedding size needs to be divisible by heads" self.values = nn.Linear(embed_size, embed_size, bias=False) self.keys = nn.Linear(embed_size, embed_size, bias=False) self.queries = nn.Linear(embed_size, embed_size, bias=False) self.fc_out = nn.Linear(embed_size, embed_size) def forward(self, values, keys, query, mask): N = query.shape[0] value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1] values = self.values(values) keys = self.keys(keys) queries = self.queries(query) values = values.reshape(N, value_len, self.heads, self.head_dim) keys = keys.reshape(N, key_len, self.heads, self.head_dim) queries = queries.reshape(N, query_len, self.heads, self.head_dim) energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys]) if mask is not None: energy = energy.masked_fill(mask == 0, float("-1e20")) attention = torch.softmax(energy / (self.head_dim ** (1/2)), dim=3) out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape( N, query_len, self.heads * self.head_dim ) out = self.fc_out(out) return out, attention # 返回注意力权重 # 其他模型组件 class PositionwiseFeedForward(nn.Module): def __init__(self, embed_size, forward_expansion): super(PositionwiseFeedForward, self).__init__() self.fc1 = nn.Linear(embed_size, forward_expansion * embed_size) self.fc2 = nn.Linear(forward_expansion * embed_size, embed_size) self.relu = nn.ReLU() def forward(self, x): return self.fc2(self.relu(self.fc1(x))) class EncoderLayer(nn.Module): def __init__(self, embed_size, heads, forward_expansion, dropout, device, pre_norm=True): super(EncoderLayer, self).__init__() self.self_attn = MultiHeadAttention(embed_size, heads) self.feed_forward = PositionwiseFeedForward(embed_size, forward_expansion) # 前层归一化 self.norm1 = nn.LayerNorm(embed_size) self.norm2 = nn.LayerNorm(embed_size) self.dropout = nn.Dropout(dropout) self.pre_norm = pre_norm def forward(self, x, mask): # 前层归一化：先归一化再计算注意力 if self.pre_norm: x_norm = self.norm1(x) attn_output, _ = self.self_attn(x_norm, x_norm, x_norm, mask) x = x + self.dropout(attn_output) x_norm = self.norm2(x) ff_output = self.feed_forward(x_norm) x = x + self.dropout(ff_output) else: # 后层归一化（原始实现） attn_output, _ = self.self_attn(x, x, x, mask) x = self.norm1(x + self.dropout(attn_output)) ff_output = self.feed_forward(x) x = self.norm2(x + self.dropout(ff_output)) return x class DecoderLayer(nn.Module): def __init__(self, embed_size, heads, forward_expansion, dropout, device, pre_norm=True): super(DecoderLayer, self).__init__() self.self_attn = MultiHeadAttention(embed_size, heads) self.encoder_attn = MultiHeadAttention(embed_size, heads) self.feed_forward = PositionwiseFeedForward(embed_size, forward_expansion) # 前层归一化 self.norm1 = nn.LayerNorm(embed_size) self.norm2 = nn.LayerNorm(embed_size) self.norm3 = nn.LayerNorm(embed_size) self.dropout = nn.Dropout(dropout) self.pre_norm = pre_norm def forward(self, x, encoder_output, src_mask, trg_mask): if self.pre_norm: # 自注意力层 x_norm = self.norm1(x) attn_output, _ = self.self_attn(x_norm, x_norm, x_norm, trg_mask) x = x + self.dropout(attn_output) # 编码器注意力层 x_norm = self.norm2(x) encoder_attn_output, attention_weights = self.encoder_attn( encoder_output, encoder_output, x_norm, src_mask ) x = x + self.dropout(encoder_attn_output) # 前馈网络层 x_norm = self.norm3(x) ff_output = self.feed_forward(x_norm) x = x + self.dropout(ff_output) else: # 后层归一化（原始实现） attn_output, _ = self.self_attn(x, x, x, trg_mask) x = self.norm1(x + self.dropout(attn_output)) encoder_attn_output, attention_weights = self.encoder_attn( encoder_output, encoder_output, x, src_mask ) x = self.norm2(x + self.dropout(encoder_attn_output)) ff_output = self.feed_forward(x) x = self.norm3(x + self.dropout(ff_output)) return x, attention_weights # 返回注意力权重 class Encoder(nn.Module): def __init__( self, src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length, pre_norm=True ): super(Encoder, self).__init__() self.embed_size = embed_size self.device = device self.word_embedding = nn.Embedding(src_vocab_size, embed_size) self.position_embedding = PositionalEncoding(embed_size, max_length, device) self.layers = nn.ModuleList( [EncoderLayer(embed_size, heads, forward_expansion, dropout, device, pre_norm) for _ in range(num_layers)] ) self.dropout = nn.Dropout(dropout) def forward(self, x, mask): N, seq_length = x.shape x = self.word_embedding(x) x = self.position_embedding(x) out = self.dropout(x) for layer in self.layers: out = layer(out, mask) return out class Decoder(nn.Module): def __init__( self, trg_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length, pre_norm=True ): super(Decoder, self).__init__() self.device = device self.word_embedding = nn.Embedding(trg_vocab_size, embed_size) self.position_embedding = PositionalEncoding(embed_size, max_length, device) self.layers = nn.ModuleList( [ DecoderLayer( embed_size, heads, forward_expansion, dropout, device, pre_norm ) for _ in range(num_layers) ] ) self.fc_out = nn.Linear(embed_size, trg_vocab_size) self.dropout = nn.Dropout(dropout) def forward(self, x, encoder_output, src_mask, trg_mask): N, seq_length = x.shape x = self.word_embedding(x) x = self.position_embedding(x) out = self.dropout(x) attention_weights = [] # 用于存储各层的注意力权重 for layer in self.layers: out, attn_weights = layer(out, encoder_output, src_mask, trg_mask) attention_weights.append(attn_weights) out = self.fc_out(out) return out, attention_weights # 返回注意力权重 def create_trg_mask(trg, pad_idx): pad_mask = (trg != pad_idx).unsqueeze(1).unsqueeze(2) seq_len = trg.shape[1] look_ahead_mask = torch.tril(torch.ones(seq_len, seq_len, device=trg.device)).bool() return pad_mask & look_ahead_mask class Transformer(nn.Module): def __init__( self, src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, embed_size=512, num_layers=6, forward_expansion=4, heads=8, dropout=0.1, device="cpu", max_length=100, pre_norm=True ): super(Transformer, self).__init__() self.encoder = Encoder( src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length, pre_norm ) self.decoder = Decoder( trg_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length, pre_norm ) self.src_pad_idx = src_pad_idx self.trg_pad_idx = trg_pad_idx self.device = device def make_src_mask(self, src): src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2) return src_mask.to(self.device) def make_trg_mask(self, trg): N, trg_len = trg.shape pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2) look_ahead_mask = torch.tril( torch.ones((trg_len, trg_len), device=self.device) ).bool().unsqueeze(0).unsqueeze(0) return pad_mask & look_ahead_mask def forward(self, src, trg): src_mask = self.make_src_mask(src) trg_mask = self.make_trg_mask(trg) encoder_src = self.encoder(src, src_mask) out, attention_weights = self.decoder(trg, encoder_src, src_mask, trg_mask) return out, attention_weights # 返回注意力权重 def init_transformer_model(english_vocab, chinese_vocab, max_seq_length, device, pre_norm=True): src_pad_idx = english_vocab[''] trg_pad_idx = chinese_vocab[''] model = Transformer( src_vocab_size=len(english_vocab), trg_vocab_size=len(chinese_vocab), src_pad_idx=src_pad_idx, trg_pad_idx=trg_pad_idx, embed_size=512, num_layers=6, heads=8, dropout=0.1, device=device, max_length=max_seq_length, pre_norm=pre_norm ).to(device) return model 那我这个代码呢

def __init__(self, d_model, num_heads): super().__init__() self.W_q = nn.Linear(d_model, d_model) # 合并投影矩阵 self.W_k = nn.Linear(d_model, d_model) self.W_v = nn.Linear(d_model, d_model) ...

import torch import torch.nn as nn import numpy as np from torch.utils.data import DataLoader, TensorDataset import pandas as pd class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(RNN, self).__init__() self.hidden_size = hidden_size self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(1, x.size(0), self.hidden_size) out, _ = self.rnn(x, h0) out = self.fc(out[:, -1, :]) return out input_size = 1 hidden_size = 32 output_size = 1 model = RNN(input_size, hidden_size, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) df = pd.read_csv('beijing_wangjing_125_new.csv') congestion_index = df['idx'].values congestion_index_tensor= torch.tensor(congestion_index, dtype=torch.float32) print(congestion_index_tensor) for epoch in range(100): outputs = model(congestion_index_tensor) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step()为什么会说张量维度不匹配，应该如何修改

labels_tensor = torch.tensor(labels, dtype=torch.float32) 然后，你可以在训练循环中使用这个标签张量来计算损失： python for epoch in range(100): outputs = model(congestion_index_tensor) loss ...

import torch from torch.utils.data import DataLoader import torchvision.transforms as transforms import torchvision.datasets as datasets # 定义图像变换 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) # 加载训练集和测试集 train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True) test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True) # 创建数据加载器 train_loader = DataLoader(dataset=train_dataset, batch_size=100, shuffle=True) test_loader = DataLoader(dataset=test_dataset, batch_size=100, shuffle=False) import torch.nn as nn class ImageToPatches(nn.Module): def __init__(self, patch_size=7, embedding_dim=64): super(ImageToPatches, self).__init__() self.patch_size = patch_size self.projection = nn.Conv2d(1, embedding_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): # x: [batch_size, 1, 28, 28] patches = self.projection(x) # [batch_size, embedding_dim, 4, 4] b, e, h, w = patches.shape patches = patches.flatten(2).transpose(1, 2) # [batch_size, num_patches, embedding_dim] return patches class PositionalEmbedding(nn.Module): def __init__(self, num_patches, embedding_dim): super(PositionalEmbedding, self).__init__() self.position_embeddings = nn.Parameter(torch.randn(1, num_patches, embedding_dim)) def forward(self, x): return x + self.position_embeddings class TransformerModel(nn.Module): def __init__(self, num_classes=10, embedding_dim=64, num_heads=8, num_layers=3): super(TransformerModel, self).__init__() self.patch_embedding = ImageToPatches(patch_size=7, embedding_dim=embedding_dim) num_patches = (28 // 7) ** 2 # 4x4=16 patches per image self.position_embedding = PositionalEmbedding(num_patches, embedding_dim) encoder_layer = nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=num_heads) self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) self.classifier = nn.Linear(embedding_dim * num_patches, num_classes) def forward(self, x): x = self.patch_embedding(x) x = self.position_embedding(x) x = self.transformer_encoder(x) x = x.flatten(1) x = self.classifier(x) return x import torch.optim as optim # 初始化模型、损失函数和优化器 model = TransformerModel() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练模型 for epoch in range(12): # 训练 12 轮 for images, labels in train_loader: outputs = model(images) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() print(f"Epoch {epoch+1}, Loss: {loss.item()}") correct = 0 total = 0 with torch.no_grad(): for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print(f"Test Accuracy: {100 * correct / total}%") 逐行分析上述代码，要详细解释

- super().__init__()调用父类nn.Module的初始化方法。 python self.linear1 = nn.Linear(d_model, d_ff) self.dropout = nn.Dropout(dropout) self.relu = nn.ReLU() self.linear2 = nn.Linear(d_ff, d...

def gcd(a, b): while b: a, b = b, a % b return a # Other types of layers can go here (e.g., nn.Linear, etc.) def _init_weights(module, name, scheme=''): if isinstance(module, nn.Conv2d) or isinstance(module, nn.Conv3d): if scheme == 'normal': nn.init.normal_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif scheme == 'trunc_normal': trunc_normal_tf_(module.weight, std=.02) if module.bias is not None: nn.init.zeros_(module.bias) elif scheme == 'xavier_normal': nn.init.xavier_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif scheme == 'kaiming_normal': nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu') if module.bias is not None: nn.init.zeros_(module.bias) else: # efficientnet like fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels fan_out //= module.groups nn.init.normal_(module.weight, 0, math.sqrt(2.0 / fan_out)) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.BatchNorm2d) or isinstance(module, nn.BatchNorm3d): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) elif isinstance(module, nn.LayerNorm): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) def act_layer(act, inplace=False, neg_slope=0.2, n_prelu=1): # activation layer act = act.lower() if act == 'relu': layer = nn.ReLU(inplace) elif act == 'relu6': layer = nn.ReLU6(inplace) elif act == 'leakyrelu': layer = nn.LeakyReLU(neg_slope, inplace) elif act == 'prelu': layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) elif act == 'gelu': layer = nn.GELU() elif act == 'hswish': layer = nn.Hardswish(inplace) else: raise NotImplementedError('activation layer [%s] is not found' % act) return layer def channel_shuffle(x, groups): batchsize, num_channels, height, width = x.data.size() channels_per_group = num_channels // groups # reshape x = x.view(batchsize, groups, channels_per_group, height, width) x = torch.transpose(x, 1, 2).contiguous() # flatten x = x.view(batchsize, -1, height, width) return x # Multi-scale depth-wise convolution (MSDC) class MSDC(nn.Module): def __init__(self, in_channels, kernel_sizes, stride, activation='relu6', dw_parallel=True): super(MSDC, self).__init__() self.in_channels = in_channels self.kernel_sizes = kernel_sizes self.activation = activation self.dw_parallel = dw_parallel self.dwconvs = nn.ModuleList([ nn.Sequential( nn.Conv2d(self.in_channels, self.in_channels, kernel_size, stride, kernel_size // 2, groups=self.in_channels, bias=False), nn.BatchNorm2d(self.in_channels), act_layer(self.activation, inplace=True) ) for kernel_size in self.kernel_sizes ]) self.init_weights('normal') def init_weights(self, scheme=''): named_apply(partial(_init_weights, scheme=scheme), self) def forward(self, x): # Apply the convolution layers in a loop outputs = [] for dwconv in self.dwconvs: dw_out = dwconv(x) outputs.append(dw_out) if self.dw_parallel == False: x = x + dw_out # You can return outputs based on what you intend to do with them return outputs class MSCB(nn.Module): """ Multi-scale convolution block (MSCB) """ def __init__(self, in_channels, out_channels, shortcut=False, stride=1, kernel_sizes=[1, 3, 5], expansion_factor=2, dw_parallel=True, activation='relu6'): super(MSCB, self).__init__() add = shortcut self.in_channels = in_channels self.out_channels = out_channels self.stride = stride self.kernel_sizes = kernel_sizes self.expansion_factor = expansion_factor self.dw_parallel = dw_parallel self.add = add self.activation = activation self.n_scales = len(self.kernel_sizes) # check stride value assert self.stride in [1, 2] # Skip connection if stride is 1 self.use_skip_connection = True if self.stride == 1 else False # expansion factor self.ex_channels = int(self.in_channels * self.expansion_factor) self.pconv1 = nn.Sequential( # pointwise convolution nn.Conv2d(self.in_channels, self.ex_channels, 1, 1, 0, bias=False), nn.BatchNorm2d(self.ex_channels), act_layer(self.activation, inplace=True) ) self.msdc = MSDC(self.ex_channels, self.kernel_sizes, self.stride, self.activation, dw_parallel=self.dw_parallel) if self.add == True: self.combined_channels = self.ex_channels * 1 else: self.combined_channels = self.ex_channels * self.n_scales self.pconv2 = nn.Sequential( # pointwise convolution nn.Conv2d(self.combined_channels, self.out_channels, 1, 1, 0, bias=False), nn.BatchNorm2d(self.out_channels), ) if self.use_skip_connection and (self.in_channels != self.out_channels): self.conv1x1 = nn.Conv2d(self.in_channels, self.out_channels, 1, 1, 0, bias=False) self.init_weights('normal') def init_weights(self, scheme=''): named_apply(partial(_init_weights, scheme=scheme), self) def forward(self, x): pout1 = self.pconv1(x) msdc_outs = self.msdc(pout1) if self.add == True: dout = 0 for dwout in msdc_outs: dout = dout + dwout else: dout = torch.cat(msdc_outs, dim=1) dout = channel_shuffle(dout, gcd(self.combined_channels, self.out_channels)) out = self.pconv2(dout) if self.use_skip_connection: if self.in_channels != self.out_channels: x = self.conv1x1(x) return x + out else: return out def autopad(k, p=None, d=1): # kernel, padding, dilation """Pad to 'same' shape outputs.""" if d > 1: k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad return p class Conv(nn.Module): """Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation).""" default_act = nn.SiLU() # default activation def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): """Initialize Conv layer with given arguments including activation.""" super().__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() def forward(self, x): """Apply convolution, batch normalization and activation to input tensor.""" return self.act(self.bn(self.conv(x))) def forward_fuse(self, x): """Perform transposed convolution of 2D data.""" return self.act(self.conv(x)) import torch from torch import nn from ultralytics.nn.modules.conv import Conv from torch.nn import functional as F class Channel_Att(nn.Module): def __init__(self, channels, t=16): super(Channel_Att, self).__init__() self.channels = channels self.bn2 = nn.BatchNorm2d(self.channels, affine=True) def forward(self, x): residual = x x = self.bn2(x) weight_bn = self.bn2.weight.data.abs() / torch.sum(self.bn2.weight.data.abs()) x = x.permute(0, 2, 3, 1).contiguous() x = torch.mul(weight_bn, x) x = x.permute(0, 3, 1, 2).contiguous() x = torch.sigmoid(x) * residual # return x class NAMAttention(nn.Module): def __init__(self, channels, shape, out_channels=None, no_spatial=True): super(NAMAttention, self).__init__() self.Channel_Att = Channel_Att(channels) def forward(self, x): x_out1 = self.Channel_Att(x) return x_out1 根据这些代码，参考Conv的结构，创建一个名为MSConv的模块，输入分为两个分支，第一个是MSDC模块到BatchNorm2d到SiLU，另一个是NAM注意力，注意力机制与其他三个模块并行，最后将SiLU的输出与NAM的输出合并为最终的输出。请编辑代码实现这个思路。注意NAM注意力机制的参数问题

super(SpatialAttention, self).__init__() self.bn = nn.BatchNorm2d(in_channels, affine=True) def forward(self, x): residual = x x = self.bn(x) # 提取gamma（缩放因子）和beta（偏移）？但是，他们...

阅读如下代码""" 编码器 """ from torch.nn.utils.rnn import pad_packed_sequence,pack_padded_sequence import torch.nn as nn import config import numpy as np class Encoder(nn.Module): def __init__(self): super(Encoder,self).__init__() self.embedding=nn.EmbeddingBag(num_embeddings=len(config.num_sequence), embedding_dim=config.embedding_dim, padding_idx=config.num_sequence.PAD ) self.gru=nn.GRU(input_size=config.embedding_dim, num_layers=config.num_layers, hidden_size=config.hidden_size, batch_first=True ) def forward(self,input,input_length): embeded=self.embedding(input) embeded=pack_padded_sequence(embeded,input_length,batch_first=True) out,hidden=self.gru(embeded) out,out_length=pad_packed_sequence(out,batch_first=True,padding_value=config.num_sequence.PAD) return out,hidden,out_length if __name__=='__main__': from dataset import train_data_loader encoder=Encoder() print(encoder) for input,target,input_length,target_length in train_data_loader: out,hidden,out_length=encoder(input,input_length) print(out.size()) print(hidden.size()) print(out_length) break

class GRUEncoder(nn.Module): def __init__(self, vocab_size, embed_dim, hidden_size, num_layers=2): super().__init__() self.embedding = nn.Embedding(vocab_size, embed_dim) self.gru = nn.GRU(embed_...

sklearn函数CountVectorizer()和TfidfVectorizer()计算方法介绍

sklearn函数CountVectorizer()和TfidfVectorizer()计算方法介绍 CountVectorizer()函数 CountVectorizer()函数只考虑每个单词出现的频率；然后构成一个特征矩阵，每一行表示一个训练文本的词频统计结果。其思想是，先根据所有训练文本，不考虑其出现顺序，只将训练文本中每个出现过的词汇单独视为一列特征，构成一个词汇表(vocabulary ...
pptx

系统集成项目管理概述管理资料.pptx

系统集成项目管理概述管理资料.pptx

大家在看

recommend-type

上海GBQ4.0-2349.rar

官网最后版广联达GBQ4.0上海专版，可以打开上海GBQ4.0文件；本文件只是安装包！！！
recommend-type

乳酸菌发酵植物蛋白质饮料的研究

俗话说国以民为本，民以食为天，食品的生产、安全、质量均需要相应的食品资料知识累积与制定，相信这一份...该文档为乳酸菌发酵植物蛋白质饮料的研究，是一份很不错的参考资料，具有较高参考价值，感兴趣的可以下载看看
recommend-type

MPU9250-MPL-STM32F1

STM32F103C8T6 通过PC11（SDA），PC12（SCL）与MPU9250连接，用MPL姿态解算出姿态角
recommend-type

hfss 3D layout指导ppt.rar

PCB全链路参数提取_3D Layout 3D Layout中处理封装基板的金线结构
recommend-type

C# Winform使用DataGridView的VirtualMode虚拟模式

C# Winform使用DataGridView的VirtualMode虚拟模式。 有两种数据类型作为DataGridView的数据源，一种是DataTable，一种是List。有不明白的，欢迎加微信交流：VPAmway。

最新推荐

recommend-type

后端项目中常用的设计模式总结.doc

后端项目中常用的设计模式总结.doc
recommend-type

PKID查壳工具最新版发布，轻松识别安卓安装包加壳

根据提供的文件信息，我们可以详细解读以下知识点： ### PKiD(查壳)工具介绍 #### 标题分析 - **PKiD(查壳)**: 这是一个专门用于分析安卓安装包（APK文件）是否被加壳的应用程序。"查壳"是一种用于检测软件是否被保护层（即“壳”）包裹的技术术语。加壳是一种常见的软件保护手段，用于隐藏真实的代码逻辑，防止恶意逆向分析。 - **RAR格式文件**: 文件使用了RAR格式进行压缩，这是WinRAR软件用于文件压缩和解压缩的专有格式。 #### 描述分析 - **ApkScan-PKID查壳工具.zip**: 这指的是一款名为ApkScan的工具，它包含了PKID查壳功能。该工具被打包成ZIP格式，便于用户下载和使用。 - **安卓安装包**: 这是指Android平台的应用程序安装包，通常以APK作为文件扩展名。 - **加壳检测**: PKID查壳工具用于检测APK文件是否被加壳，加壳是一种常见的软件保护技术，用于加密和保护软件免遭逆向工程。 - **脱壳测试**: 如果检测到加壳，脱壳测试将用于尝试去除或绕过保护层，以便进行安全分析、调试或修改程序。 #### 标签分析 - **查壳**: 再次强调了工具的主要功能，即检测APK文件中的加壳情况。 - **最新版**: 表示这个文件是PKID查壳工具的最新版本。 - **PKID**: 这是工具的核心名称，代表着该软件的主要功能和用途。 #### 文件列表分析 - **PKiD(查壳).exe**: 这是一个可执行文件，说明PKID查壳工具是一个独立的应用程序，用户可以通过双击此文件直接运行程序，而无需安装。 ### 技术背景 #### 查壳工具的工作原理 查壳工具通常通过分析APK文件的头部信息、资源文件和代码段来检测加壳。它可能会检查PE文件格式的特定区域（APK基于DEX，但PE检查的概念相似），这些区域在加壳过程中可能会被特定的代码模式、字符串或签名标记。例如，某些壳会在文件头部加入特定的字符串，或者修改方法计数等信息。 #### 加壳技术 加壳技术通常用于防止软件被轻易反编译或逆向工程。它可以阻止潜在的窃取知识产权、绕过付费或防止代码分析等。加壳过程包括加密和压缩原始代码，然后在运行时解压和解密代码以供执行。 #### 脱壳技术 脱壳技术是指绕过或移除软件保护壳的过程。这通常需要对壳的工作原理有深入了解，并且需要一定的逆向工程技能。脱壳过程可能包括识别壳的签名、分析保护机制、修改代码和重定位等步骤。 #### 安全测试与逆向工程 查壳工具对于安全测试和逆向工程人员来说是非常重要的工具。它们可以帮助识别软件是否被加壳，从而决定是否需要进行脱壳操作以便深入分析软件的安全漏洞或版权问题。 #### 使用场景 - **安全测试**: 安全工程师在对移动应用程序进行安全评估时，需要确认是否有必要进行脱壳处理。 - **逆向工程**: 开发人员可能需要逆向工程某个应用，以更好地理解其工作原理或为了兼容性问题进行修改。 - **软件保护**: 对于软件开发者而言，了解常见的加壳和脱壳技术有助于更好地保护自己的应用免遭未授权的篡改和分析。 ### 结论 了解和掌握PKID查壳工具的使用对于任何需要对APK文件进行安全性分析的专业人士来说都是基础且关键的技能。该工具提供了一种快速判断软件是否加壳的方法，而且在发现壳的情况下，还可以推测出壳的来源公司，这对于后续的分析和处理提供了便利。然而，值得注意的是，进行脱壳操作往往需要对原始程序代码进行逆向工程，这在很多情况下可能涉及法律和道德问题，因此在使用此类工具时，必须确保其合法性和符合道德规范。
recommend-type

【PDF.js问题诊断手册】：快速定位与解决常见问题

# 摘要 PDF.js是一个广泛应用于Web环境中的PDF文件渲染库，其基本原理和应用概况将在本文第一章进行概述。第二章深入探讨PDF.js的问题诊断基础，包括架构分析、问题分类和日志分析技巧。随后，本文将具体讨论PDF.js在显示问题、加载与性能问题以及兼容性与安全问题的诊断与
recommend-type

grep -Ei

<think>首先，用户的问题是关于使用grep命令进行忽略大小写的扩展正则表达式搜索。用户指定了："grep -Ei ignore case and use extended regular expressions"。所以，我需要解释如何使用grep的-E和-i选项。 从系统指令中： - 所有行内数学表达式必须用$...$格式。 - 独立公式用$$...$$。 - 使用中文回答。 - 生成相关问题。 - 在回答中引用的段落末尾添加引用标识，如[^1]。 - 回答结构清晰，逐步解决问题。 参考引用： - 引用[1]提到使用-E选项进行扩展正则表达式，而不是基本正则表达式。这更清晰，因为反斜
recommend-type

一键关闭系统更新的工具介绍

从给定的文件信息中我们可以分析出几个相关的知识点，以下是详细说明： 【标题】“系统禁止更新工具.7z”暗示着这个压缩文件内包含的可能是一款软件工具，其主要功能是阻止或禁止操作系统的更新。这种工具可能针对的是Windows、Linux或者其他操作系统的自动更新功能。一般来说，用户可能出于稳定性考虑，希望控制更新时间，或者是因为特定的软件环境依赖于旧版本的系统兼容性，不希望系统自动更新导致兼容性问题。 【描述】“一健关闭系统更新”说明了该工具的使用方式非常简单直接。用户只需通过简单的操作，比如点击一个按钮或者执行一个命令，就能实现关闭系统自动更新的目的。这种一键式操作符合用户追求的易用性原则，使得不太精通系统操作的用户也能轻松控制更新设置。 【标签】“系统工具”表明这是一个与操作系统紧密相关的辅助工具。系统工具通常包括系统清理、性能优化、磁盘管理等多种功能，而本工具专注于管理系统更新，使其成为系统维护中的一环。 【压缩包子文件的文件名称列表】“系统禁止更新工具”是压缩包内的文件名。由于文件格式为“.7z”，这说明该工具采用了7-Zip压缩格式。7-Zip是一款开源且免费的压缩软件，支持非常高的压缩比，并且能够处理各种压缩文件格式，如ZIP、RAR等。它支持创建密码保护的压缩文件和分卷压缩，这在需要转移大量数据时特别有用。然而在这个上下文中，“系统禁止更新工具”文件名暗示了该压缩包内只包含了一个程序，即专门用于关闭系统更新的工具。 根据标题和描述，我们可以推测该工具可能的实现机制，例如： 1. 修改系统服务的配置：在Windows系统中，可以通过修改Windows Update服务的属性来禁用该服务，从而阻止系统自动下载和安装更新。 2. 修改注册表设置：通过编辑Windows注册表中的某些特定键值，可以关闭系统更新功能。这通常涉及到对HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate和HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU等路径下的设置进行修改。 3. 使用第三方软件：某些第三方工具提供了一键禁用系统更新的功能，通过修改操作系统的相关文件和配置来实现更新的禁用。这包括阻止Windows Update的运行、拦截更新下载等。 该工具的使用可能带来一些后果，比如系统安全风险的增加、系统漏洞得不到及时修复以及可能违反某些软件许可协议。用户在使用这类工具时应该清楚这些潜在的影响，并自行承担相应的风险。 总结来说，该“系统禁止更新工具”可以视为一个针对特定用户需求，尤其是企业用户或个人用户中对系统稳定性有较高要求的人群，提供的一个便捷的解决方案。它通过直接修改系统设置来禁止操作系统自动更新，但用户在使用此工具时需要谨慎，因为这可能会带来系统安全和稳定性的风险。
recommend-type

【PC3000高级应用】：复杂故障快速诊断与解决

# 摘要 本文详细介绍了PC3000在数据恢复领域的应用及其重要性，阐述了其基本操作、界面理解以及在复杂故障诊断中的应用方法。深入剖析了PC3
recommend-type

我的数据是names(data) [1] "分组" "排序" "PACU" "V1" "V2" "V3" ；后四个数据分别代表四个时间点的测量值，请你用ART模型进行规范检验，我需要得到分组（即不同处理方法）的差异

<think>我们正在处理一个重复测量设计的数据分析问题，用户希望使用ART（Align Rank Transform）方法分析包含分组因素和多个时间点的数据。数据格式包括分组变量（group）、受试者编号（subject）以及四个时间点（V1-V3）的测量值。 ART分析步骤： 1. 数据准备：将宽格式数据转换为长格式（每个时间点一行） 2. 使用ARTool包进行对齐秩变换 3. 构建混合效应模型（包含分组、时间及其交互） 4. 对变换后的数据进行方差分析 5. 进行事后检验（如存在显著效应） 引用说明： - 用户提供的引用[1][2][3][4]与当前问题无关，因此忽略这些引用 -
recommend-type

鼎捷易飞ERPV9.0委外进货单批量导入解决方案

根据提供的文件信息，我们可以从标题、描述、标签以及压缩包文件列表中提取以下知识点： 1. 委外进货单批量导入程序及模版格式 标题提到的“委外进货单批量导入程序”指的是一个软件应用，其主要功能是允许用户批量地将委外进货数据导入到ERP系统中。批量导入通常是指在ERP系统中不需要逐条手动输入数据，而是通过预先定义好的模板，一次性将大量数据导入系统。这样的程序对于提高工作效率、减少重复性工作以及避免人为错误具有重要意义。 2. 鼎捷易飞ERPV9.0 描述中提到的“鼎捷易飞ERPV9.0”是一个特定版本的ERP系统，由鼎捷软件公司开发。ERP（Enterprise Resource Planning，企业资源计划）系统是一种用于整合企业内部所有资源信息，实现信息流、物流、资金流、工作流的高度集成和自动化管理的软件。ERPV9.0是该系列产品的版本号，表明该程序和文件模板是为这一特定版本的ERP系统设计。 3. .NET C#源代码 标题中的“.NET C#源代码”表示程序是使用.NET框架和C#语言开发的。.NET是微软公司开发的一个软件框架，用于构建和运行Windows应用程序。C#（读作“C Sharp”）是.NET框架下的一种编程语言，具有面向对象、类型安全和垃圾回收等特点。开发者可能提供了源代码，以便企业用户可以自行修改、调整以满足特定需求。 4. 使用方法和步骤 描述中详细说明了程序的使用方法： - 首先编辑模版格式数据，即将需要导入的数据按照特定的格式要求填写到模板中。 - 然后在程序中选择单别（可能指的是单据类型）和日期等条件。 - 点击“导入数据”按钮，程序将提示用户选择含有数据的模板文件。 - 程序会进行数据校验，以确保数据的正确性。校验规则是特定的，如果用户不需要特定的校验条件，可以在程序中直接删除这部分代码。 - 最后，数据校验无误后，程序可以生成相应的进货单据。 5. 自定义程序和模板 在标签中提到的“易飞ERP委外进货单导入程序”、“委外进货单导入程序”和“易飞ERP自定义程序”表明，所提供的程序可以根据用户的特定需求进行定制。同时，模板格式的使用，也意味着用户可以根据自己的业务需要和ERP系统的要求调整模板内容，以便更好地适应数据导入过程。 6. 压缩包子文件的文件名称列表 提供的压缩包包含两个文件： - “委外进货单批量导入格式.xls”指的是一个Excel格式的文件，这个文件应该是一个符合要求的导入模板，用户需要按照这个模板的格式来准备需要导入的数据。 - “委外入库导入(源代码).zip”是一个包含源代码的压缩文件，其中可能包含程序的完整源代码，也可能仅包含与导入功能相关的部分代码。 7. 文件列表和数据导入流程 通过文件列表可以理解整个数据导入流程： - 用户首先需要准备好数据，并将其按照“委外进货单批量导入格式.xls”模板中的要求填入相应的数据。 - 用户然后使用“委外入库导入(源代码).zip”中的程序，根据程序使用说明进行配置。 - 最后，用户通过程序界面导入整理好的Excel模板文件，程序会自动处理数据导入的过程，并进行必要的数据校验，最终生成所需的进货单据。 以上总结的知识点涵盖了该压缩包文件的内容、用途、技术细节以及操作步骤，为了解和使用该委外进货单批量导入程序提供了充分的信息。
recommend-type

硬盘数据恢复秘籍：PC3000流程全面解读

# 摘要 本文全面概述了硬盘数据恢复的各个方面，从基础理论到实际操作，再到行业规范和面临的挑战。首先介绍了硬盘数据恢复的基本概念和PC3000工具的工作原理，随后详细讨论了PC3000的硬件组成、软件功能及操作流程。接着，深入探讨了硬盘的基础知识、常见故障类型、故障识别方法以及数据恢复前的准备工作。通过实际案例演练，文章展示了PC3000在模拟故障和实际数据恢复中的应用，同时强调了高级数据恢复技巧
recommend-type

MRAM在PLC中起到的作用

<think>首先，用户的问题是基于之前的引用内容，特别是引用[1]，它讨论了MRAM在PLC中的应用。用户明确说：“我想了解MRAM在PLC中的作用”，并问“MRAM在可编程逻辑控制器（PLC）中的功能和应用”。 关键引用是引用[1]： - MRAM具有纳秒级高速写入、万亿次擦写寿命和宽工作温区。 - PLC是工业自动化系统的核心，需要快速读写和高擦写寿命。 - 传统方案使用SRAM和电池，但存在系统复杂、硬件成本高、数据丢失风险。 - MRAM可以替代这种方案，因为它支持连续20年毫秒级间隔的擦写操作。 - 在异常掉电时，MRAM能快速保存数据，无需备份电池。 - 具体例子：数字输入模块