FactorizationMachine implementation and paper are different

[JavanTang]

class FactorizationMachine(torch.nn.Module):

    def __init__(self, reduce_sum=True):
        super().__init__()
        self.reduce_sum = reduce_sum

    def forward(self, x):
        """
        :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
        """
        square_of_sum = torch.sum(x, dim=1) ** 2
        sum_of_square = torch.sum(x ** 2, dim=1)
        ix = square_of_sum - sum_of_square
        if self.reduce_sum:
            ix = torch.sum(ix, dim=1, keepdim=True)
        return 0.5 * ix

What happened to v parameters?

[yfreedomliTHU]

class FactorizationMachine(torch.nn.Module):

    def __init__(self, reduce_sum=True):
        super().__init__()
        self.reduce_sum = reduce_sum

    def forward(self, x):
        """
        :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
        """
        square_of_sum = torch.sum(x, dim=1) ** 2
        sum_of_square = torch.sum(x ** 2, dim=1)
        ix = square_of_sum - sum_of_square
        if self.reduce_sum:
            ix = torch.sum(ix, dim=1, keepdim=True)
        return 0.5 * ix

What happened to v parameters?

class FactorizationMachineModel(torch.nn.Module):
"""
A pytorch implementation of Factorization Machine.
Reference:
S Rendle, Factorization Machines, 2010.
"""

def __init__(self, field_dims, embed_dim):
    super().__init__()
    self.embedding = FeaturesEmbedding(field_dims, embed_dim)
    self.linear = FeaturesLinear(field_dims)
    self.fm = FactorizationMachine(reduce_sum=True)

def forward(self, x):
    """
    :param x: Long tensor of size ``(batch_size, num_fields)``
    """
    x = self.linear(x) + self.fm(self.embedding(x))
    return torch.sigmoid(x.squeeze(1))

@JavanTang, The implementation of FM is in fm.py, which is actually class FactorizationMachineModel. And Parameters V is implemented by FeaturesEmbedding.

[JavanTang]

I see, thank you