class Model_Adp(nn.Module): def __init__(self, SE, args, window_size = 3, T = 12, N=None): super(Model_Adp, self).__init__() L = args.L K = args.K d = args.d D = K * d self.num_his = args.num_his self.SE = SE.to(device) emb_dim = SE.shape[1] self.STEmbedding = STEmbedding(D, emb_dim=emb_dim).to(device) self.STAttBlock_1 = nn.ModuleList([ST_Layer(K, d, T=T, window_size = window_size,N=N) for _ in range(L)]) self.STAttBlock_2 = nn.ModuleList([ST_Layer(K, d, T=T, window_size = window_size,N=N) for _ in range(L)]) self.transformAttention = TransformAttention(K, d) self.mlp_1 = CONVs(input_dims=[1, D], units=[D, D], activations=[F.relu, None]) self.mlp_2 = CONVs(input_dims=[D, D], units=[D, 1], activations=[F.relu, None]) def forward(self, X, TE): # input X = torch.unsqueeze(X, -1) X = self.mlp_1(X) # STE STE = self.STEmbedding(self.SE, TE) STE_his = STE[:, :self.num_his] STE_pred = STE[:, self.num_his:] # encoder for net in self.STAttBlock_1: X = net(X, STE_his) X = self.transformAttention(X, STE_his, STE_pred) # decoder for net in self.STAttBlock_2: X = net(X, STE_pred) X = self.mlp_2(X) del STE, STE_his, STE_pred return torch.squeeze(X, 3)

这段代码定义了一个名为Model_Adp的类，继承自nn.Module。在初始化函数中，需要传入SE、args、window_size、T和N等参数。该类包含多个属性和方法，其中包括一个STEmbedding、两个STAttBlock、一个TransformAttention、两个CONVs和一个forward方法。其中STEmbedding是对空间数据做embedding操作，STAttBlock是空间-时间数据的多头注意力机制，TransformAttention是将多头注意力输出转化为特征向量，CONVs是一些卷积神经网络。forward方法用来执行前向传播。具体实现可以参考代码中的实现。

相关问题

这段代码哪里错了 class my_BiGRU(torch.nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )

这段代码存在两个问题：

1.函数 `_setup_BiGRU_layers` 中的代码应该是在类的构造函数 `__init__` 中进行初始化，而不是在另外一个函数中进行初始化。

2.在调用 `my_BiGRU` 类时，参数名称有误，应该将 `apsule_out_features` 改为 `capsule_out_features`。

改正后的代码如下所示：

class my_BiGRU(torch.nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout, capsule_out_features):
        super(my_BiGRU, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.gru_dropout = gru_dropout
        
        self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True)
        self.fc = torch.nn.Linear(hidden_size * 2, output_size)
        
        self.BiGRU_layers = my_BiGRU(input_size=self.input_size,
                                     output_size=capsule_out_features,
                                     hidden_size=self.hidden_size,
                                     num_layers=self.num_layers,
                                     gru_dropout=self.gru_dropout,
                                    )

注意：这里假设 `capsule_out_dim` 和 `args` 都已经在代码中被定义好了。

class Residual(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, x, *args, **kwargs): return self.fn(x, *args, **kwargs) + x

这是一个使用PyTorch库编写的神经网络类。这个类叫做Residual，继承了nn.Module这个基类。它有一个构造函数__init__，这个函数接受一个参数fn，并将它赋值给self.fn。这个类还有一个forward函数，它接受输入x和其他可选的参数args和kwargs，并将它们传递给fn函数，然后返回fn的结果。