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这是模型结构。为了方便超参数调整，我将几乎所有内容组合在一起。''' 类 MultiTaskDNN(nn.Module):

def __init__(self, n_tasks, 
             input_dim=1024, 
             output_dim=1, 
             hidden_dim=[1024, 100], 
             inits=['xavier_normal', 'kaiming_uniform'],
             act_function=['relu', 'leaky_relu'], 
             dropouts=[0.10, 0.25], 
             batch_norm=True):

    
    super(MultiTaskDNN, self).__init__()
    self.n_tasks = n_tasks
    self.input_dim = input_dim
    self.output_dim = output_dim
    self.hidden_dim = hidden_dim
    self.act_function = act_function
    self.batch_norm = batch_norm
    current_dim = input_dim
    self.layers = nn.ModuleList()
    self.dropouts = nn.ModuleList()
    self.bns = nn.ModuleList()
    
    for k, hdim in enumerate(hidden_dim):
        self.layers.append(nn.Linear(current_dim, hdim))
        self.bns.append(nn.BatchNorm1d(hdim, eps=2e-1))
        
        current_dim = hdim
        
        if inits[k] == 'xavier_normal':
            nn.init.xavier_normal_(self.layers[k].weight)
        elif inits[k] == 'kaiming_normal':
            nn.init.kaiming_normal_(self.layers[k].weight)
        elif inits[k] == 'xavier_uniform':
            nn.init.xavier_uniform_(self.layers[k].weight)
        elif inits[k] == 'kaiming_uniform':
            nn.init.kaiming_uniform_(self.layers[k].weight)
            
        self.dropouts.append(nn.Dropout(dropouts[k]))
    # n_targets
    self.heads = nn.ModuleList()
    for _ in range(self.n_tasks):
        self.heads.append(nn.Linear(current_dim, output_dim))


def forward(self, x):
    
    for k, layer in enumerate(self.layers):
        x = layer(x)
        if self.act_function[k] == 'sigmoid':
            x = torch.sigmoid(x)
        elif self.act_function[k] == 'relu':
            x = F.relu(x)
        elif self.act_function[k] == 'leaky_relu':
            x = F.leaky_relu(x)
        
        if self.batch_norm == True:
            x = self.bns[k](x)
            
        x = self.dropouts[k](x)
    
    outputs = []
    for head in self.heads:
        outputs.append(head(x)) 

    return outputs

'''

如果结构看起来正确，也请告诉我。在训练这个具有 10 个任务（头）的多任务模型之后。我只想预测任务 7，即头 7。我应该如何加载模型并进行预测？谢谢你。

model.state_dict()

MultiTaskDNN(
  (layers): ModuleList(
    (0): Linear(in_features=1024, out_features=128, bias=True)
    (1): Linear(in_features=128, out_features=128, bias=True)
  )
  (dropouts): ModuleList(
    (0): Dropout(p=0.25, inplace=False)
    (1): Dropout(p=0.25, inplace=False)
  )
  (bns): ModuleList(
    (0): BatchNorm1d(128, eps=0.2, momentum=0.1, affine=True, track_running_stats=True)
    (1): BatchNorm1d(128, eps=0.2, momentum=0.1, affine=True, track_running_stats=True)
  )
  (heads): ModuleList(
    (0): Linear(in_features=128, out_features=1, bias=True)
    (1): Linear(in_features=128, out_features=1, bias=True)
    (2): Linear(in_features=128, out_features=1, bias=True)
    (3): Linear(in_features=128, out_features=1, bias=True)
    (4): Linear(in_features=128, out_features=1, bias=True)
    (5): Linear(in_features=128, out_features=1, bias=True)
    (6): Linear(in_features=128, out_features=1, bias=True)
    (7): Linear(in_features=128, out_features=1, bias=True)
    (8): Linear(in_features=128, out_features=1, bias=True)
    (9): Linear(in_features=128, out_features=1, bias=True)
    (10): Linear(in_features=128, out_features=1, bias=True)
    )
  )
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