--- title: MLP keywords: fastai sidebar: home_sidebar summary: "Multi-layer Perceptron for Recommendations." description: "Multi-layer Perceptron for Recommendations." nb_path: "nbs/models/models.mlp.ipynb" ---
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class MLP[source]

MLP(args, num_users, num_items) :: 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 Args:
    factor_num = 4
    layers = [8,4,2]
args = Args()

model = MLP(args, num_users=5, num_items=5)
model.forward(torch.tensor([0,1]), torch.tensor([1,3]))
tensor([[0.4265],
        [0.4298]], grad_fn=<SigmoidBackward0>)
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