115 lines
4.2 KiB
C++
115 lines
4.2 KiB
C++
/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
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#define TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
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#include <limits>
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#include "tensorflow/core/kernels/loss.h"
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#include "tensorflow/core/lib/core/errors.h"
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#include "tensorflow/core/lib/core/status.h"
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namespace tensorflow {
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class SmoothHingeLossUpdater : public DualLossUpdater {
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public:
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// Computes the updated dual variable (corresponding) to a single example. The
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// updated dual value maximizes the objective function of the dual
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// optimization problem associated with smooth hinge loss. The computations
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// are detailed in readme.md.
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double ComputeUpdatedDual(const int num_partitions, const double label,
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const double example_weight,
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const double current_dual, const double wx,
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const double weighted_example_norm) const final {
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// Intuitively there are 3 cases:
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// a. new optimal value of the dual variable falls within the admissible
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// range [0, 1]. In this case we set new dual to this value.
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// b. new optimal value is < 0. Then, because of convexity, the optimal
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// valid value for new dual = 0
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// c. new optimal value > 1.0. Then new optimal value should be set to 1.0.
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const double candidate_optimal_dual =
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current_dual +
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(label - wx - gamma * current_dual) /
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(num_partitions * example_weight * weighted_example_norm + gamma);
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if (label * candidate_optimal_dual < 0) {
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return 0.0;
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}
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if (label * candidate_optimal_dual > 1.0) {
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return label;
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}
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return candidate_optimal_dual;
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}
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double ComputeDualLoss(const double current_dual, const double example_label,
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const double example_weight) const final {
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// For binary classification, there are 2 conjugate functions, one per
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// label value (-1 and 1).
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const double y_alpha = current_dual * example_label; // y \alpha
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if (y_alpha < 0 || y_alpha > 1.0) {
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return std::numeric_limits<double>::max();
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}
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return (-y_alpha + 0.5 * gamma * current_dual * current_dual) *
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example_weight;
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}
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double ComputePrimalLoss(const double wx, const double example_label,
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const double example_weight) const final {
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const double y_wx = example_label * wx;
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if (y_wx >= 1) return 0;
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if (y_wx <= 1 - gamma) return (1 - y_wx - gamma / 2) * example_weight;
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return (1 - y_wx) * (1 - y_wx) * example_weight * 0.5 / gamma;
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}
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// Converts binary example labels from 0.0 or 1.0 to -1.0 or 1.0 respectively
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// as expected by smooth hinge loss.
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Status ConvertLabel(float* const example_label) const final {
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if (*example_label == 0.0) {
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*example_label = -1;
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return Status::OK();
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}
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if (*example_label == 1.0) {
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return Status::OK();
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}
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return errors::InvalidArgument(
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"Only labels of 0.0 or 1.0 are supported right now. "
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"Found example with label: ",
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*example_label);
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}
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double PrimalLossDerivative(const double wx, const double label,
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const double example_weight) const final {
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if (label * wx >= 1) {
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return 0;
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}
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if (label * wx <= 1 - gamma) {
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return -label;
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}
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return (wx - label) / gamma;
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}
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double SmoothnessConstant() const final { return gamma; }
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private:
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// Smoothness constant of smooth hinge loss
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// TODO(sibyl-Aix6ihai): expose this parameter
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const double gamma = 1;
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};
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} // namespace tensorflow
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#endif // TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
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// TENSORFLOW_KERNELS_SMOOTH_HINGE_LOSS_H_
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