在Pytorch中创建具有动态隐藏层数的前馈神经网络模型

为什么这两个代码段不相等： 第1部分：创建具有2个图层的模型

class FNNModule(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim1, hidden_dim2, non_linear_function):
        super().__init__()
        self.hidden1 = nn.Linear(input_dim, hidden_dim1)
        self.hidden2 = nn.Linear(hidden_dim1, hidden_dim2)       
        self.non_linear_function = non_linear_function()
        self.final_linear = nn.Linear(hidden_dim2, output_dim)

    def forward(self, x):
        out = self.hidden1(x)
        out = self.non_linear_function(out)
        out = self.hidden2(x)
        out = self.non_linear_function(out)        
        out = self.final_linear(out)
        return out

第二部分：创建相同的模型，但更改代码，其中隐藏的图层是一个变量：

class FNNModuleVar(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim_array = [], non_linear_function_array=[]):
        super().__init__()
        self.linear_functions = []
        self.non_linear_functions = [x() for x in non_linear_function_array]
        self.hidden_layers = len(hidden_dim_array)
        for l in range(self.hidden_layers):
            self.linear_functions.append(nn.Linear(input_dim, hidden_dim_array[l]))
            input_dim = hidden_dim_array[l]
        self.final_linear = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        out = x
        for i in range(self.hidden_layers):
            out = self.linear_functions[i](out)
            out = self.non_linear_functions[i](out)
        out = self.final_linear(x)
        return out
modelVar = FNNModuleVar(input_dim, output_dim, [100, 50], [nn.Tanh, nn.Tanh])
model = FNNModule(input_dim, output_dim, 100, 50, nn.Tanh)

当我尝试遍历modelVar.parameters和model.parameters时，我发现我有非常不同的模型

---

我在modelVar中做错了什么？

这些模块的调用方式与您期望的调用方式相同，只是模块不可见而已。为了使它们可见，您可以将它们包装在nn.ModuleList中，如下所示：

class FNNModuleVar(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim_array = [], non_linear_function_array=[]):
        super().__init__()
        self.linear_functions = []
        self.non_linear_functions = [x() for x in non_linear_function_array]
        self.hidden_layers = len(hidden_dim_array)
        for l in range(self.hidden_layers):
            self.linear_functions.append(nn.Linear(input_dim, hidden_dim_array[l]))
            input_dim = hidden_dim_array[l]
        self.linear_functions = nn.ModuleList(self.linear_functions)
        self.final_linear = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        out = x
        for i in range(self.hidden_layers):
            out = self.linear_functions[i](out)
            out = self.non_linear_functions[i](out)
        out = self.final_linear(out)
        return out

现在打印模型将产生：

FNNModule(
  (hidden1): Linear(in_features=50, out_features=100, bias=True)
  (hidden2): Linear(in_features=100, out_features=50, bias=True)
  (non_linear_function): Tanh()
  (final_linear): Linear(in_features=50, out_features=100, bias=True)
)
FNNModuleVar(
  (linear_functions): ModuleList(
    (0): Linear(in_features=50, out_features=100, bias=True)
    (1): Linear(in_features=100, out_features=50, bias=True)
  )
  (final_linear): Linear(in_features=50, out_features=100, bias=True)
)

更多详细信息：

请格式化您的代码。有关更多详细信息，请参阅。