1/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#ifndef TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
17#define TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
18
19#include <limits>
20
21#include "tensorflow/core/kernels/loss.h"
22#include "tensorflow/core/lib/core/errors.h"
23#include "tensorflow/core/lib/core/status.h"
24
25namespace tensorflow {
26
27class SmoothHingeLossUpdater : public DualLossUpdater {
28 public:
29 // Computes the updated dual variable (corresponding) to a single example. The
30 // updated dual value maximizes the objective function of the dual
31 // optimization problem associated with smooth hinge loss. The computations
32 // are detailed in readme.md.
33 double ComputeUpdatedDual(const int num_partitions, const double label,
34 const double example_weight,
35 const double current_dual, const double wx,
36 const double weighted_example_norm) const final {
37 // Intuitively there are 3 cases:
38 // a. new optimal value of the dual variable falls within the admissible
39 // range [0, 1]. In this case we set new dual to this value.
40 // b. new optimal value is < 0. Then, because of convexity, the optimal
41 // valid value for new dual = 0
42 // c. new optimal value > 1.0. Then new optimal value should be set to 1.0.
43 const double candidate_optimal_dual =
44 current_dual +
45 (label - wx - gamma * current_dual) /
46 (num_partitions * example_weight * weighted_example_norm + gamma);
47 if (label * candidate_optimal_dual < 0) {
48 return 0.0;
49 }
50 if (label * candidate_optimal_dual > 1.0) {
51 return label;
52 }
53 return candidate_optimal_dual;
54 }
55
56 double ComputeDualLoss(const double current_dual, const double example_label,
57 const double example_weight) const final {
58 // For binary classification, there are 2 conjugate functions, one per
59 // label value (-1 and 1).
60 const double y_alpha = current_dual * example_label; // y \alpha
61 if (y_alpha < 0 || y_alpha > 1.0) {
62 return std::numeric_limits<double>::max();
63 }
64 return (-y_alpha + 0.5 * gamma * current_dual * current_dual) *
65 example_weight;
66 }
67
68 double ComputePrimalLoss(const double wx, const double example_label,
69 const double example_weight) const final {
70 const double y_wx = example_label * wx;
71 if (y_wx >= 1) return 0;
72 if (y_wx <= 1 - gamma) return (1 - y_wx - gamma / 2) * example_weight;
73 return (1 - y_wx) * (1 - y_wx) * example_weight * 0.5 / gamma;
74 }
75
76 // Converts binary example labels from 0.0 or 1.0 to -1.0 or 1.0 respectively
77 // as expected by smooth hinge loss.
78 Status ConvertLabel(float* const example_label) const final {
79 if (*example_label == 0.0) {
80 *example_label = -1;
81 return OkStatus();
82 }
83 if (*example_label == 1.0) {
84 return OkStatus();
85 }
86 return errors::InvalidArgument(
87 "Only labels of 0.0 or 1.0 are supported right now. "
88 "Found example with label: ",
89 *example_label);
90 }
91
92 double PrimalLossDerivative(const double wx, const double label,
93 const double example_weight) const final {
94 if (label * wx >= 1) {
95 return 0;
96 }
97 if (label * wx <= 1 - gamma) {
98 return -label;
99 }
100 return (wx - label) / gamma;
101 }
102
103 double SmoothnessConstant() const final { return gamma; }
104
105 private:
106 // Smoothness constant of smooth hinge loss
107 // TODO(sibyl-Aix6ihai): expose this parameter
108 const double gamma = 1;
109};
110
111} // namespace tensorflow
112
113#endif // TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
114// TENSORFLOW_KERNELS_SMOOTH_HINGE_LOSS_H_
115