参数的多个值

Question

我正在尝试转换此 code 通过 pysyft 引用传递它

像这样：

class SyNet(sy.Module):
  def __init__(self,embedding_size, num_numerical_cols, output_size, layers, p ,torch_ref):
    super(SyNet, self ).__init__(  embedding_size, num_numerical_cols , output_size , layers , p=0.4  ,torch_ref=torch_ref  )
    self.all_embeddings=self.torch_ref.nn.ModuleList([nn.Embedding(ni, nf) for ni, nf in embedding_size])
    self.embedding_dropout=self.torch_ref.nn.Dropout(p)
    self.batch_norm_num=self.torch_ref.nn.BatchNorm1d(num_numerical_cols)

    all_layers= []
    num_categorical_cols = sum((nf for ni, nf in embedding_size))
    input_size = num_categorical_cols + num_numerical_cols

    for i in layers:
      all_layers.append(self.torch_ref.nn.Linear(input_size,i))
      all_layers.append(self.torch_ref.nn.ReLU(inplace=True))
      all_layers.append(self.torch_ref.nn.BatchNorm1d(i))
      all_layers.append(self.torch_ref.nn.Dropout(p))
      input_size = i

    all_layers.append(self.torch_ref.nn.Linear(layers[-1], output_size))

    self.layers = self.torch_ref.nn.Sequential(*all_layers)

  def forward(self, x_categorical, x_numerical):
    embeddings= []
    for i,e in enumerate(self.all_embeddings):
      embeddings.append(e(x_categorical[:,i]))

    x_numerical = self.batch_norm_num(x_numerical)
    x = self.torch_ref.cat([x, x_numerical], 1)
    x = self.layers(x)
    return x

但是当我尝试创建模型的实例时

model = SyNet( categorical_embedding_sizes, numerical_data.shape[1], 2, [200,100,50], p=0.4 ,torch_ref= th)

我遇到类型错误

类型错误：参数有多个值 'torch_ref'

我试图更改参数的顺序，但我收到关于位置参数的错误。你能帮帮我吗，我在类和函数 (oop)

方面不是很有经验

提前致谢！

Answer 1

正在为 Module 查看 PySyft source code。 class 父级的构造函数只接受一个参数：torch_ref.

因此您应该调用超级构造函数：

super(SyNet, self).__init__(torch_ref=torch_ref) # line 3

正在从调用中删除除 torch_ref 之外的所有参数。

参数的多个值

Multiple values for argument

python

pytorch

pysyft