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