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Add regularization modules for LSTM baseline (#156)

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* Add Regularization Modules for LSTM

* Update Reuters Trainer and Evalueator for regularization

* Remove unnecessary comments

* Comply with PEP8

* Comply import order with PEP8

* Fix typos in README.md

* Comply with PEP8

* Add BSD 3-Clause Licence

* Remove deprecated call to Variable for PyTorch 0.4

* Update dataset selection in main

* Remove block comments
This commit is contained in:
Ashutosh-Adhikari
2018-11-06 10:48:28 -05:00
committed by Ralph Tang
parent f0a5c370bc
commit 91ed6261db
11 changed files with 548 additions and 10 deletions
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lstm_regularization/embed_regularize.py
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"""
BSD 3-Clause License
Copyright (c) 2017,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import numpy as np
import torch
def embedded_dropout(embed, words, dropout=0.1, scale=None):
if dropout:
mask = embed.weight.data.new().resize_((embed.weight.size(0), 1)).bernoulli_(1 - dropout).expand_as(embed.weight) / (1 - dropout)
masked_embed_weight = mask * embed.weight
else:
masked_embed_weight = embed.weight
if scale:
masked_embed_weight = scale.expand_as(masked_embed_weight) * masked_embed_weight
padding_idx = embed.padding_idx
if padding_idx is None:
padding_idx = -1
X = torch.nn.functional.embedding(words, masked_embed_weight,
padding_idx, embed.max_norm, embed.norm_type,
embed.scale_grad_by_freq, embed.sparse
)
return X