#!/usr/bin/env python3
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"""
output_unit.py: Implementation of the ``OutputUnit`` for the MAC network. Cf https://arxiv.org/abs/1803.03067 \
for the reference paper.
"""
__author__ = "Vincent Marois"
import torch
from torch.nn import Module
from miprometheus.models.mac.utils_mac import linear
[docs]class OutputUnit(Module):
"""
Implementation of the ``OutputUnit`` of the MAC network.
"""
[docs] def __init__(self, dim, nb_classes):
"""
Constructor for the ``OutputUnit``.
:param dim: global 'd' dimension.
:type dim: int
:param nb_classes: number of classes to consider (classification problem).
:type nb_classes: int
"""
# call base constructor
super(OutputUnit, self).__init__()
# define the 2-layers MLP & specify weights initialization
self.classifier = torch.nn.Sequential(linear(dim * 3, dim, bias=True),
torch.nn.ELU(),
linear(dim, nb_classes, bias=True))
torch.nn.init.kaiming_uniform_(self.classifier[0].weight)
[docs] def forward(self, mem_state, question_encodings):
"""
Forward pass of the ``OutputUnit``.
:param mem_state: final memory state, shape [batch_size x dim]
:type mem_state: torch.tensor
:param question_encodings: questions encodings, shape [batch_size x (2*dim)]
:type question_encodings: torch.tensor
:return: probability distribution over the classes, [batch_size x nb_classes]
"""
# cat memory state & questions encodings
concat = torch.cat([mem_state, question_encodings], dim=1)
# get logits
logits = self.classifier(concat)
return logits