import torch import torch.nn as nn class RNNCell(nn.Module): def __init__(self, input_dim, hidden_dim): super().__init__() self.W_hx = nn.Linear(input_dim, hidden_dim) self.W_hh = nn.Linear(hidden_dim, hidden_dim) def forward(self, x, h_prev): h_next = torch.tanh(self.W_hx(x) + self.W_hh(h_prev)) return h_next

import torch from torch import nn class MLP(nn.Module): def init(self, input_dim, num_class, hidden_dim) -> None: super().init() self.hidden_dim = hidden_dim self.mlp = nn.Sequential(*[ nn.Linear(input_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, num_class) ]) def forward(self, x): return self.mlp(x)

其输入特征维度为 input_dim，输出类别数为 num_class，隐藏层维度为 hidden_dim。其中，nn.Linear 表示全连接层，nn.ReLU 表示激活函数 ReLU。forward() 方法定义了模型的前向传播过程，即输入特征经过多个全连接层...

详细解释这段代码import torch from torch import nn from einops.layers.torch import Rearrange class Transformer(nn.Module): def init(self, input_dim, num_class, hidden_dim) -> None: super().init() self.d_model = hidden_dim self.hidden_dim = 21 * self.d_model self.transformer = nn.Sequential( nn.Linear(input_dim, self.hidden_dim), Rearrange("b (n c) -> b n c", c=self.d_model), nn.TransformerEncoder( nn.TransformerEncoderLayer( d_model=self.d_model, nhead=4, dim_feedforward=self.d_model * 2, dropout=0.1, batch_first=True ), 4, torch.nn.LayerNorm(self.d_model), ), Rearrange("b n c -> b (n c)"), nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU(), nn.Linear(self.hidden_dim, num_class), ) def forward(self,x): return self.transformer(x)

具体来说，模型的输入是一个大小为input_dim的向量，输出是一个大小为num_class的向量，表示预测的类别概率。模型的主要组成部分是一个TransformerEncoder，它是由多个TransformerEncoderLayer组成的序列。每个...

import torch.nn as nn class ThreeLayerNet(nn.Module): def init(self, input_dim, hidden1_dim, hidden2_dim, output_dim): super().init() self.layer = nn.Sequential( nn.Linear(input_dim, hidden1_dim), nn.ReLU(), nn.Linear(hidden1_dim, hidden2_dim), nn.ReLU(), nn.Linear(hidden2_dim, output_dim) ) def forward(self, x): return self.layer(x)这是哪个语言的代码

它定义了一个三层的全连接神经网络（包括两个隐藏层和一个输出层），其中输入维度为input_dim，第一个隐藏层维度为hidden1_dim，第二个隐藏层维度为hidden2_dim，输出维度为output_dim。nn.Sequential是...

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

self.weights = nn.Parameter(torch.Tensor(output_dim, input_dim)) # 权重参数 [output_dim, input_dim] self.bias = nn.Parameter(torch.Tensor(output_dim)) # 偏置参数 [output_dim] # 初始化参数（使用...

class LSTMCell(nn.Module): def init(self, input_dim, hidden_dim): super().init() self.input_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) self.forget_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) self.cell_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) self.output_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) def forward(self, x, h_c_prev): h_prev, c_prev = h_c_prev combined = torch.cat([h_prev, x], dim=-1) f_t = torch.sigmoid(self.forget_gate(combined)) i_t = torch.sigmoid(self.input_gate(combined)) c_hat = torch.tanh(self.cell_gate(combined)) o_t = torch.sigmoid(self.output_gate(combined)) c_t = f_t * c_prev + i_t * c_hat h_t = o_t * torch.tanh(c_t) return (h_t, c_t) 分析代码并且教会我具体如何使用

self.weight = nn.Parameter(torch.Tensor(4 * hidden_dim, self.input_hidden_dim)) self.bias = nn.Parameter(torch.Tensor(4 * hidden_dim)) # 初始化参数 nn.init.xavier_uniform_(self.weight) nn.init....

class NHLM(nn.Module): def init(self, input_dim=2, hidden_dim=10, output_dim=2): super().init() self.layers = nn.Sequential( nn.Linear(input_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, output_dim) ) def forward(self, x): return self.layers(x)是什么意思

def __init__(self, input_dim, hidden_dim): super().__init__() self.fc1 = nn.Linear(input_dim, hidden_dim) # 全连接层定义 self.act = nn.ReLU() # 激活函数定义 def forward(self, x): x = self.fc1(x)...

class SimpleRNN(nn.Module): def init(self, vocab_size, embed_dim, hidden_dim): super().init() self.embedding = nn.Embedding( num_embeddings=vocab_size + 1, # +1 for padding index 0 embedding_dim=embed_dim ) self.rnn = nn.RNN( input_size=embed_dim, hidden_size=hidden_dim, batch_first=True ) self.fc = nn.Linear(hidden_dim, 1) def forward(self, x): embedded = self.embedding(x) _, hidden = self.rnn(embedded) return torch.sigmoid(self.fc(hidden.squeeze(0)))为我的代码添加一个flatten层

return torch.sigmoid(self.fc(hidden.squeeze(0))) - **当前逻辑**：仅使用最后一个时间步的隐藏状态做预测 - **输出维度**：(batch_size, 1)（二分类结果） --- ### ️ Flatten层的两种添加方案 #### ...

将下面代码进行修改： class BaseModule(nn.Module): def init(self, embed_dim): super().init() self.norm = nn.LayerNorm(embed_dim) def forward_with_residual(self, x, sublayer): return x + sublayer(self.norm(x)) class LinearAttention(BaseModule): def init(self, embed_dim, num_heads): super().init(embed_dim) self.attention = nn.MultiheadAttention(embed_dim, num_heads) def forward(self, x): attn_output, _ = self.attention(x, x, x) return self.forward_with_residual(x, lambda _: attn_output) class DepthwiseSeparableFFN(BaseModule): def init(self, embed_dim, expansion_ratio=4): super().init(embed_dim) expanded_dim = embed_dim * expansion_ratio self.ffn = nn.Sequential( nn.Linear(embed_dim, expanded_dim), nn.GELU(), nn.Linear(expanded_dim, embed_dim) ) def forward(self, x): return self.forward_with_residual(x, self.ffn) class EfficientTransformerLayer(nn.Module): def init(self, embed_dim, num_heads, expansion_ratio=4): super().init() self.attention = LinearAttention(embed_dim, num_heads) # 注意力子层 self.ffn = DepthwiseSeparableFFN(embed_dim, expansion_ratio) # 前馈子层 def forward(self, x): x = self.attention(x) # 先通过注意力层 x = self.ffn(x) # 再通过前馈网络层 return x class EfficientTransformer(nn.Module): def init(self, num_layers, embed_dim, num_heads, expansion_ratio=4): super().init() self.layers = nn.ModuleList([ EfficientTransformerLayer(embed_dim, num_heads, expansion_ratio) for _ in range(num_layers) ]) def forward(self, x): for layer in self.layers: x = layer(x) return x 不使用层归一化，而是换成BN，同时减少sigmoid的使用

def __init__(self, input_dim, hidden_dim, output_dim): super(ModifiedNet, self).__init__() # 全连接层 self.fc1 = nn.Linear(input_dim, hidden_dim) # 归一化层：将LayerNorm替换为BatchNorm1d self....

import torch import torch.nn as nn import torch.nn.init as init from TransformerBlock import MultiheadAttention from .NeuralNetwork import NeuralNetwork import torch.nn.functional as F from .GAT import GATConv import torch_geometric.utils as utils class Attention(nn.Module): def init(self, in_features, hidden_size): super(Attention, self).init() self.linear1 = nn.Linear(in_features*2, hidden_size) self.linear2 = nn.Linear(hidden_size, 1) self.activation = nn.ReLU() self.dropout = nn.Dropout(0.5) self.reset_parameters() def reset_parameters(self): init.xavier_normal_(self.linear1.weight) init.xavier_normal_(self.linear2.weight) def forward(self, K, V, mask = None): ''' :param K: (batch_size, d) :param V: (batch_size, hist_len, d) :return: (batch_size, d) ''' K = K.unsqueeze(dim=1).expand(V.size()) fusion = torch.cat([K, V], dim=-1) fc1 = self.activation(self.linear1(fusion)) score = self.linear2(fc1) if mask is not None: mask = mask.unsqueeze(dim=-1) score = score.masked_fill(mask, -2 ** 32 + 1) alpha = F.softmax(score, dim=1) alpha = self.dropout(alpha) att = (alpha * V).sum(dim=1) return att class GLAN(NeuralNetwork): def init(self, config, graph): super(GLAN, self).init() self.config = config embedding_weights = config['embedding_weights'] V, D = embedding_weights.shape maxlen = config['maxlen'] dropout_rate = config['dropout'] alpha = 0.4 self.graph = graph self.word_embedding = nn.Embedding(V, D, padding_idx=0, _weight=torch.from_numpy(embedding_weights)) self.user_tweet_embedding = nn.Embedding(graph.num_nodes, 300, padding_idx=0) self.mh_attention = MultiheadAttention(input_size=300, output_size=300) self.linear_fuse = nn.Lin

def __init__(self, input_dim): super().__init__() self.shared_attention = MultiHeadAttention(d_model=512, num_heads=8) self.task1_head = nn.Linear(512, 10) self.task2_head = nn.Linear(512, 5) ...

import torch import torch.nn as nn from torchsummary import summary class Model(nn.Module): def init(self): super(Model, self).init() self.l1 = nn.Linear(3, 3) self.l2 = nn.Linear(3, 2) self.out = nn.Linear(2, 2) def forward(self, x): x = torch.sigmoid(self.l1(x)) x = torch.relu(self.l2(x)) x = self.out(x) out = torch.softmax(x, dim=-1) return out model = Model() x = torch.randn(5, 3) out = model(x) print(out.size()) 解释每段代码的含义

PyTorch代码示例：pythonimporttorchimporttorch.nnasnn#定义一个简单的多层感知机模型classSimpleMLP(nn.Module):def__init__(self,input_size,hidden_size,output_size):super(SimpleMLP,self).__init__()self....

import torch import torch.nn as nn class TimeSeriesCNN(nn.Module): def init(self, input_dim, hidden_dim, output_dim): super(TimeSeriesCNN, self).init() self.conv1 = nn.Conv1d(input_dim, hidden_dim, kernel_size=3) self.conv2 = nn.Conv1d(hidden_dim, hidden_dim, kernel_size=3) self.pool = nn.MaxPool1d(kernel_size=2) self.relu = nn.ReLU() self.fc1 = nn.Linear(hidden_dim * 4, hidden_dim) self.fc2 = nn.Linear(hidden_dim, output_dim) def forward(self, x): x = self.conv1(x) x = self.relu(x) x = self.pool(x) x = self.conv2(x) x = self.relu(x) x = self.pool(x) x = torch.flatten(x, start_dim=1) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) return x # 定义输入数据和标签 input_dim = 1 # 输入维度（时间序列的特征数） hidden_dim = 16 # 隐藏层维度 output_dim = 1 # 输出维度（预测的目标） seq_length = 10 # 时间序列的长度 # 创建模型实例 model = TimeSeriesCNN(input_dim, hidden_dim, output_dim) # 创建输入数据（batch_size=1） input_data = torch.randn(1, input_dim, seq_length) # 运行模型进行预测 output = model(input_data) # 打印预测结果 print(output)

如果条件1和条件2都需要满足才能执行一段代码，可以使用逻辑运算符&&，将两个条件连接起来，如下所示： if (条件1 && 条件2) { // 执行代码 } 这段代码会在条件1和条件2都满足时执行。...

class GaussSwish(nn.Module): def init(self, c=1): super(GaussSwish, self).init() self.c = c def forward(self, x): return x * torch.sigmoid(x - self.c) class MLP(nn.Module): def init( self, input_dim=3, hidden_dim=64, num_layers=8, dropout=0.1 # 添加Dropout ): super(MLP, self).init() layers = [] # 输入层 layers.append(nn.Linear(input_dim, hidden_dim)) layers.append(nn.BatchNorm1d(hidden_dim)) layers.append(nn.Dropout(dropout)) # 中间隐藏层（实现激活函数过渡） total_hidden = num_layers - 2 # 总隐藏层数 for layer_idx in range(total_hidden): # 线性层 + 归一化 layers.append(nn.Linear(hidden_dim, hidden_dim)) layers.append(nn.BatchNorm1d(hidden_dim)) # 渐进式激活函数选择 if layer_idx < total_hidden // 3: act = nn.Tanh() #elif layer_idx < 2 * (total_hidden // 3): # act = nn.Hardswish()#nn.SiLU() # Swish else: act = GaussSwish() layers.append(act) layers.append(nn.Dropout(dropout)) # 输出层 layers.append(nn.Linear(hidden_dim, 1)) self.net = nn.Sequential(*layers) self._init_weights() def _init_weights(self): """Xavier初始化""" for m in self.modules(): if isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): return self.net(x)更改网络结构为这个后，在gpu的训练变慢

class MLP(nn.Module): def forward(self, x): return checkpoint_sequential(self.net, len(self.net)//2, x) # 分段保存计算图 - 通过牺牲部分计算时间换取显存优化 $\boxed{6. \text{结构调优}}$ $\...

import numpy as np from resnet import resnet50 import torch import torch.nn as nn from densenet3D import DenseNet3D from vit3D import ViT class AttentionFusion(nn.Module): def init(self, input_dim, hidden_dim): super(AttentionFusion, self).init() # Attention layers self.attention = nn.Sequential( nn.Linear(input_dim * 2, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, 2), nn.Softmax(dim=1) ) def forward(self, feat1, feat2): combined_feat = torch.cat((feat1, feat2), dim=1) weights = self.attention(combined_feat) feat_fused = weights[:, 0:1] * feat1 + weights[:, 1:2] * feat2 return feat_fused class ImageFusionModel(nn.Module): def init(self, img_encoder = 'resnet50', device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')): super(ImageFusionModel, self).init() self.img_encoder = img_encoder if img_encoder == 'resnet50': self.DWI_ImgEncoder = resnet50().to(device) self.T2_ImgEncoder = resnet50().to(device) input_dim = 2048 elif img_encoder == 'vit': self.DWI_ImgEncoder = ViT( image_size = 224, # image size frames = 12, # number of frames image_patch_size = 4, # image patch size frame_patch_size = 2, # frame patch size num_classes=1024, mlp_dim = 2048, dim = 1024, depth = 6, heads = 8, dropout = 0.1, emb_dropout = 0.1 ) self.T2_ImgEncoder = ViT( image_size = 224, # image size frames = 12, # number of frames image_patch_size = 4, # image patch size frame_patch_size = 2, # frame patch size num_classes=1024, mlp_dim = 2048, dim = 1024, depth = 6, heads = 8, dropout = 0.1, emb_dropout = 0.1 ) input_dim = 1024 elif img_encoder == 'dense3D': self.DWI_ImgEncoder = DenseNet3D() self.T2_ImgEncoder = DenseNet3D() input_dim = 1024 # Attention layers self.ImgAttentionFusion = AttentionFusion(input_dim=input_dim, hidden_dim=256).to(device) def forward(self, DWI_ImgTensor, T2_ImgTensor): if self.img_encoder == 'resnet50': _, DWI_features = self.DWI_ImgEncoder(DWI_ImgTensor) _, T2_features = self.T2_ImgEncoder(T2_ImgTensor) else: DWI_features = self.DWI_ImgEncoder(DWI_ImgTensor) T2_features = self.T2_ImgEncoder(T2_ImgTensor) print (f"DWI_features", DWI_features.shape) fused_features = self.ImgAttentionFusion(DWI_features, T2_features) return fused_features class MLP(nn.Module): """ Standard fully-connected MLP with configurable hidden layers, batch norm, activation, and dropout. Args: input_dim (int): hidden_dims (list of int): output_dim (int): activation (callable or nn.Module): (e.g., nn.ReLU or nn.ReLU()) dropout (float): dropout rate batchnorm (bool): BatchNorm1d """ def init( self, input_dim, hidden_dims, output_dim, activation = nn.ReLU, dropout = 0.0, batchnorm = False, ): super().init() if isinstance(activation, type) and issubclass(activation, nn.Module): activation_layer = activation elif isinstance(activation, nn.Module): activation_layer = lambda: activation else: raise ValueError("activation must be an nn.Module class or instance") layers = [] in_dim = input_dim for h_dim in hidden_dims: layers.append(nn.Linear(in_dim, h_dim)) if batchnorm: layers.append(nn.BatchNorm1d(h_dim)) layers.append(activation_layer()) if dropout > 0: layers.append(nn.Dropout(dropout)) in_dim = h_dim layers.append(nn.Linear(in_dim, output_dim)) self.net = nn.Sequential(*layers) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x: shape (batch_size, input_dim) Returns: shape (batch_size, output_dim) """ return self.net(x) class End2EndModel(nn.Module): def init(self, img_encoder = 'resnet50', device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')): super(End2EndModel, self).init() self.img_encoder = img_encoder if img_encoder == 'resnet50': input_dim = 2048 elif img_encoder == 'vit': input_dim = 1024 elif img_encoder == 'dense3D': input_dim = 1024 self.ImageFusionLayer = ImageFusionModel(img_encoder=self.img_encoder, device=torch.device('cpu')) self.extractedImageFusedLayer = AttentionFusion(input_dim=2030, hidden_dim=2048) self.ImageMLP = MLP(input_dim=input_dim, hidden_dims=[1024], output_dim=512) self.ExtractedImageMLP = MLP(input_dim=2030, hidden_dims=[1024], output_dim=512) self.concat_fusion_layer = MLP(input_dim=512+512+16, hidden_dims=[512], output_dim=1) def forward(self, DWI_ImgTensor, T2_ImgTensor, DWI_feature_tensor, T2_feature_tensor, clinical_features): image_fused_features = self.ImageFusionLayer(DWI_ImgTensor=DWI_ImgTensor, T2_ImgTensor=T2_ImgTensor) extracted_fusion_features = self.extractedImageFusedLayer(feat1=DWI_feature_tensor, feat2=T2_feature_tensor) image_X = self.ImageMLP(image_fused_features) extracted_image_X = self.ExtractedImageMLP(extracted_fusion_features) x = torch.cat((image_X, extracted_image_X, clinical_features), dim=1) output = self.concat_fusion_layer(x) output = nn.Sigmoid()(output) return output, x 在该代码中增加交叉注意力机制模块，DWI_ImgTensor, T2_ImgTensor通过原有的fusion进行融合，DWI_feature_tensor, T2_feature_tensor不做原有的attention fusion ,其后续纳入同一个表格，20个特征，定义为omicsfestures，clinical_features改为3个特征，进一步的，将image_X 分别与omicsfestures和clinical_features进行cross attention 机制融合，其中image_X 作为Q，其余两个为K V。进一步的，将image_X ，cross attention后的omicsfestures，cross attention后的clinical_features进行contact，得到x

特征拼接融合pythonimporttorchimporttorch.nnasnnclassCrossModalFusion(nn.Module):def__init__(self,image_dim,omics_dim,clinical_dim,embed_dim,num_heads):super().__init__()#特征投影层（统一维度）self....

import torch import torch.nn as nn import torch.nn.functional as F class CrossAttention(nn.Module): def init(self, embed_dim, hidden_dim, num_heads): super(CrossAttention, self).init() self.embed_dim = embed_dim self.hidden_dim = hidden_dim self.num_heads = num_heads self.query_proj = nn.Linear(embed_dim, hidden_dim * num_heads) self.key_proj = nn.Linear(embed_dim, hidden_dim * num_heads) self.value_proj = nn.Linear(embed_dim, hidden_dim * num_heads) self.out_proj = nn.Linear(hidden_dim * num_heads, embed_dim) def forward(self, query, context): """ query: (batch_size, query_len, embed_dim) context: (batch_size, context_len, embed_dim) """ batch_size, query_len, _ = query.size() context_len = context.size(1) # Project input embeddings query_proj = self.query_proj(query).view(batch_size, query_len, self.num_heads, self.hidden_dim) key_proj = self.key_proj(context).view(batch_size, context_len, self.num_heads, self.hidden_dim) value_proj = self.value_proj(context).view(batch_size, context_len, self.num_heads, self.hidden_dim) # Transpose to get dimensions (batch_size, num_heads, len, hidden_dim) query_proj = query_proj.permute(0, 2, 1, 3) key_proj = key_proj.permute(0, 2, 1, 3) value_proj = value_proj.permute(0, 2, 1, 3) # Compute attention scores scores = torch.matmul(query_proj, key_proj.transpose(-2, -1)) / (self.hidden_dim ** 0.5) attn_weights = F.softmax(scores, dim=-1) # Compute weighted context context = torch.matmul(attn_weights, value_proj) # Concatenate heads and project output context = context.permute(0, 2, 1, 3).contiguous().view(batch_size, query_len, -1) output = self.out_proj(context) return output, attn_weights # Example usage: embed_dim = 512 hidden_dim = 64 num_heads = 8 cross_attention = CrossAttention(embed_dim, hidden_dim, num_heads) # Dummy data batch_size = 2 query_len = 10 context_len = 20 query = torch.randn(batch_size, query_len, embed_dim) context = torch.randn(batch_size, context_len, embed_dim) output, attn_weights = cross_attention(query, context) print(output.size()) # Should be (batch_size, query_len, embed_dim) print(attn_weights.size()) # Should be (batch_size, num_heads, query_len, context_len)请解释该段代码

scores = torch.matmul(query_proj, key_proj.transpose(-2, -1)) / (self.hidden_dim ** 0.5) attn_weights = F.softmax(scores, dim=-1) 4. **Compute Weighted Context Vector**: 根据上述求得的概率加权...

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=...

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_...

class GaussSwish(nn.Module): def init(self, c=1): super(GaussSwish, self).init() self.c = c def forward(self, x): return x * torch.sigmoid(x - self.c) class MLP(nn.Module): def init( self, input_dim=3, hidden_dim=20, num_layers=10, dropout=0.1 # 添加Dropout ): super(MLP, self).init() layers = [] # 输入层 input_layer = nn.Linear(input_dim, hidden_dim) layers.append(input_layer) # 中间隐藏层（实现激活函数过渡） total_hidden = num_layers - 2 # 总隐藏层数 for layer_idx in range(total_hidden): linear_layer = nn.Linear(hidden_dim, hidden_dim) layers.append(linear_layer) # 渐进式激活函数选择 if layer_idx < total_hidden // 4: act = nn.Tanh() elif layer_idx <(total_hidden // 4)2: act = nn.Hardswish()#nn.SiLU() # Swish else: act = GaussSwish() # 绑定到线性层 linear_layer.act = act layers.append(act) #layers.append(nn.Dropout(dropout)) # 输出层 layers.append(nn.Linear(hidden_dim, 1)) self.net = nn.Sequential(layers) self._init_weights() def _init_weights(self): for m in self.modules(): if isinstance(m, nn.Linear): if hasattr(m, 'act'): if isinstance(m.act, nn.Tanh): nn.init.xavier_normal_(m.weight, gain=5/3) elif isinstance(m.act, nn.Hardswish): # 使用PyTorch支持的hardswish nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu') elif isinstance(m.act, GaussSwish): # 自定义Swish使用Xavier初始化 nn.init.xavier_normal_(m.weight, gain=1.676) else: nn.init.xavier_uniform_(m.weight) # 偏置项保持原逻辑 if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): return self.net(x)代码中神经网络的GaussSwish激活函数，公式更改为：-x⋅e^-((x^2)/2)+x/(1+e^-x)

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