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 22:30:13 浏览: 16
这是一个使用LSTM网络进行序列预测的模型,输入是一个形状为(batch_size, 24, 307)的张量。在模型的初始化方法中,定义了LSTM层、线性层、dropout层和ReLU层。在前向传播方法中,首先将输入张量传入LSTM层得到输出h,然后将h重新reshape成形状为(batch_size, num_hiddens*24)的张量,再经过线性层、dropout层和ReLU层得到最终的预测结果y2。
相关问题
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。这些分类器都是基于输入特征进行分类的,并且使用不同的方法来实现分类功能。此外,该模型还包含了一个MLP网络,用于将输入特征映射到更高维的特征空间中。该模型的输入特征维度为41,输出类别数为5。
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
注释如下:
```
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__() # 调用父类的构造函数
self.bert = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=num_labels) # 用预训练的中文Bert模型初始化self.bert, 输出维度为num_labels
# print(self.bert.config.hidden_size) #768
self.dropout = torch.nn.Dropout(0.1) # 定义dropout层,丢弃概率为0.1
self.classifier1 = torch.nn.Linear(640, 256) # 定义一个全连接层,输入维度为640,输出维度为256
self.classifier2 = torch.nn.Linear(256, num_labels) # 定义一个全连接层,输入维度为256,输出维度为num_labels
self.regress1 = torch.nn.Linear(640, 256) # 定义一个全连接层,输入维度为640,输出维度为256
self.regress2 = torch.nn.Linear(256, 2) # 定义一个全连接层,输入维度为256,输出维度为2
self.regress3 = torch.nn.Linear(640, 256) # 定义一个全连接层,输入维度为640,输出维度为256
self.regress4 = torch.nn.Linear(256, 2) # 定义一个全连接层,输入维度为256,输出维度为2
def forward(self, input_ids, attention_mask, token_type_ids): # 前向传播函数,输入参数分别为input_ids、attention_mask、token_type_ids
outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) # 将输入传入self.bert中,得到输出
logits = outputs.logits # 从输出中得到logits
clas = F.relu(self.classifier1(logits)) # 将logits输入到self.classifier1中,经过relu函数后得到clas
clas = self.classifier2(clas) # 将clas输入到self.classifier2中,得到分类结果
death = F.relu(self.regress1(logits)) # 将logits输入到self.regress1中,经过relu函数后得到death
death = self.regress2(death) # 将death输入到self.regress2中,得到死亡概率
life = F.relu(self.regress3(logits)) # 将logits输入到self.regress3中,经过relu函数后得到life
life = self.regress4(life) # 将life输入到self.regress4中,得到生存概率
return clas, death, life # 返回分类结果、死亡概率、生存概率