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## @package bpr_loss
# Module caffe2.python.layers.bpr_loss
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
# ref: https://arxiv.org/pdf/1205.2618.pdf
class BPRLoss(ModelLayer):
def __init__(self, model, input_record, name='bpr_loss', **kwargs):
super(BPRLoss, self).__init__(model, name, input_record, **kwargs)
assert schema.is_schema_subset(
schema.Struct(
('pos_prediction', schema.Scalar()),
('neg_prediction', schema.List(np.float32)),
),
input_record
)
self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
self.output_schema = schema.Scalar(
np.float32,
self.get_next_blob_reference('output'))
def add_ops(self, net):
# formula:
# loss = - SUM(Ln(Sigmoid(Simlarity(u, pos) - Simlarity(u, neg))))
neg_score = self.input_record.neg_prediction['values']()
pos_score = net.LengthsTile(
[
self.input_record.pos_prediction(),
self.input_record.neg_prediction['lengths']()
],
net.NextScopedBlob('pos_score_repeated')
)
# https://www.tensorflow.org/api_docs/python/tf/math/log_sigmoid
softplus = net.Softplus([net.Sub([neg_score, pos_score])])
net.ReduceFrontSum(softplus, self.output_schema.field_blobs())
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