I am trying to convert this code passing it with pysyft refference
like this :
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
But when I try to create a instance of the model
model = SyNet( categorical_embedding_sizes, numerical_data.shape[1], 2, [200,100,50], p=0.4 ,torch_ref= th)
I got a TypeError
TypeError: multiple values for argument ‘torch_ref’
I tried to change the order of the arguments but i got an error about positional arguments . Can you help me , I am not very experienced in classes and functions (oop)
Thank you in advance !
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Answer
Looking at PySyft source code for Module
. The constructor of your class parent only takes a single argument: torch_ref
.
You should therefore call the super constructor with:
super(SyNet, self).__init__(torch_ref=torch_ref) # line 3
removing all arguments but torch_ref
from the call.