#!/usr/bin/env python3
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"""
s_write_unit.py:
- Implementation of the :py:class:`WriteUnit` for the ``S-MAC`` network (simplified MAC).
- Cf https://arxiv.org/abs/1803.03067 for the reference MAC paper (Hudson and Manning, ICLR 2018).
"""
__author__ = "Vincent Marois & T.S. Jayram"
from torch.nn import Module
from miprometheus.models.mac.utils_mac import linear
[docs]class WriteUnit(Module):
"""
Implementation of the :py:class:`WriteUnit` for the ``S-MAC`` model.
.. note::
This implementation is part of a simplified version of the MAC network, where modifications regarding \
the different units have been done to reduce the number of linear layers (and thus number of parameters).
This is part of a submission to the ViGIL workshop for NIPS 2018. Feel free to use this model and refer to it \
with the following BibTex:
::
@article{marois2018transfer,
title={On transfer learning using a MAC model variant},
author={Marois, Vincent and Jayram, TS and Albouy, Vincent and Kornuta, Tomasz and Bouhadjar, Younes and Ozcan, Ahmet S},
journal={arXiv preprint arXiv:1811.06529},
year={2018}
}
"""
[docs] def __init__(self, dim):
"""
Constructor for the :py:class:`WriteUnit` of the ``S-MAC`` model.
:param dim: global 'd' hidden dimension.
:type dim: int
"""
# call base constructor
super(WriteUnit, self).__init__()
# linear layer to create the new memory state from the current read vector (coming from the read unit)
self.concat_layer = linear(dim, dim, bias=True)
[docs] def forward(self, read_vector):
"""
Forward pass of the :py:class:`WriteUnit` for the ``S-MAC`` model.
:param read_vector: current read vector (output of the :py:class:`ReadUnit`), shape `[batch_size x dim]`.
:type read_vector: :py:class:`torch.Tensor`
:return: current memory state, shape [batch_size x mem_dim] (:py:class:`torch.Tensor`).
"""
return self.concat_layer(read_vector)