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e78dad3593
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24439 many literatures mentioned BPR is useful for improving recommendation quality. Add a BPR loss so that we can train TTSN with it. Would like to see if it can improve retrieval models. reference: https://arxiv.org/pdf/1205.2618.pdf Reviewed By: dragonxlwang Differential Revision: D16812513 fbshipit-source-id: 74488c714a37ccd10e0666d225751a845019eb94
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
## @package bpr_loss
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# Module caffe2.python.layers.bpr_loss
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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)
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from caffe2.python.layers.tags import (
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Tags
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)
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import numpy as np
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# ref: https://arxiv.org/pdf/1205.2618.pdf
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class BPRLoss(ModelLayer):
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def __init__(self, model, input_record, name='bpr_loss', **kwargs):
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super(BPRLoss, self).__init__(model, name, input_record, **kwargs)
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assert schema.is_schema_subset(
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schema.Struct(
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('pos_prediction', schema.Scalar()),
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('neg_prediction', schema.List(np.float32)),
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),
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input_record
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)
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self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
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self.output_schema = schema.Scalar(
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np.float32,
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self.get_next_blob_reference('output'))
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def add_ops(self, net):
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# formula:
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# loss = - SUM(Ln(Sigmoid(Simlarity(u, pos) - Simlarity(u, neg))))
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neg_score = self.input_record.neg_prediction['values']()
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pos_score = net.LengthsTile(
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[
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self.input_record.pos_prediction(),
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self.input_record.neg_prediction['lengths']()
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],
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net.NextScopedBlob('pos_score_repeated')
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)
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# https://www.tensorflow.org/api_docs/python/tf/math/log_sigmoid
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softplus = net.Softplus([net.Sub([neg_score, pos_score])])
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net.ReduceFrontSum(softplus, self.output_schema.field_blobs())
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