--- title: FFM keywords: fastai sidebar: home_sidebar summary: "A pytorch implementation of Field-aware Factorization Machine." description: "A pytorch implementation of Field-aware Factorization Machine." nb_path: "nbs/models/models.ffm.ipynb" ---
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class FieldAwareFactorizationMachine[source]

FieldAwareFactorizationMachine(field_dims, embed_dim) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

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class FFM[source]

FFM(field_dims, embed_dim) :: Module

A pytorch implementation of Field-aware Factorization Machine. Reference: Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.

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References:-- Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.