def gru_features(self, x, predict=False): x_input = x out = None out_lis = [] out_weight_list = [] if ( self.model_type == 'AdaRNN') else None for i in range(self.num_layers): out, _ = self.features[i](x_input.float()) x_input = out out_lis.append(out) if self.model_type == 'AdaRNN' and predict == False: out_gate = self.process_gate_weight(x_input, i) out_weight_list.append(out_gate) return out, out_lis, out_weight_list
时间: 2024-04-12 09:33:14 浏览: 22
这是一个用于提取特征的函数。该函数接受输入数据x,并根据模型的参数进行GRU层的计算。函数会返回GRU层的输出以及中间层的输出列表和权重列表(仅适用于AdaRNN模型)。
函数首先将输入数据赋值给x_input变量,然后使用循环遍历模型的每一层。在每一层中,函数会将x_input输入到GRU层中,并将输出保存到out变量中。然后,将out添加到out_lis列表中,以便在后续的操作中使用。
如果模型的类型是AdaRNN,并且predict参数为False(即非预测阶段),函数会调用process_gate_weight函数处理x_input并将结果添加到out_weight_list中。
最后,函数会返回out(GRU层的输出)、out_lis(中间层的输出列表)和out_weight_list(权重列表)作为结果。
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 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 forward(self, x): h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device) out, _ = self.gru(x, h0) out = self.fc(out[:, -1, :]) return out
这是一个使用双向GRU和全连接层的神经网络模型。该模型的输入大小为input_size,输出大小为output_size。它有num_layers层双向GRU,每层的隐藏状态大小为hidden_size。在GRU层之后,它使用全连接层将GRU的输出转换为所需的输出大小。该模型还使用了dropout来减少过拟合。在forward函数中,它首先将输入x传递给双向GRU,然后将最后一个时间步的输出传递给全连接层以获取最终输出。