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// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_STATS_GRADIENT_STATS_H_
#define THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_STATS_GRADIENT_STATS_H_
#include <math.h>
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_util.h"
namespace tensorflow {
namespace boosted_trees {
namespace learner {
namespace stochastic {
const double kEps = 1e-6;
// A data structure for accumulating a Tensor value.
struct TensorStat {
TensorStat() {}
explicit TensorStat(const float v) : t(DT_FLOAT, TensorShape({1})) {
t.flat<float>()(0) = v;
}
explicit TensorStat(const Tensor& rt) : t(tensor::DeepCopy(rt)) {}
TensorStat(const TensorStat& ts) : t(tensor::DeepCopy(ts.t)) {}
TensorStat& operator+=(const TensorStat& other) {
if (t.NumElements() == 0) {
t = tensor::DeepCopy(other.t);
return (*this);
}
CHECK(t.shape() == other.t.shape())
<< "My shape = " << t.shape().DebugString()
<< " Other shape = " << other.t.shape().DebugString();
auto me_flat = t.unaligned_flat<float>();
auto other_flat = other.t.unaligned_flat<float>();
for (int i = 0; i < me_flat.size(); i++) {
me_flat(i) += other_flat(i);
}
return (*this);
}
TensorStat& operator-=(const TensorStat& other) {
if (other.t.NumElements() == 0) {
return (*this);
}
CHECK(t.shape() == other.t.shape())
<< "My shape = " << t.shape().DebugString()
<< " Other shape = " << other.t.shape().DebugString();
auto me_flat = t.unaligned_flat<float>();
auto other_flat = other.t.unaligned_flat<float>();
for (int i = 0; i < me_flat.size(); i++) {
me_flat(i) -= other_flat(i);
}
return (*this);
}
TensorStat& operator*=(float value) {
auto me_flat = t.unaligned_flat<float>();
for (size_t i = 0; i < me_flat.size(); i++) {
me_flat(i) *= value;
}
return (*this);
}
bool IsZero() const {
auto me_flat = t.unaligned_flat<float>();
for (int i = 0; i < me_flat.size(); i++) {
if (me_flat(i) != 0.0f) {
return false;
}
}
return true;
}
// Checks if the L^2 magnitude of the tensor is less than eps.
bool IsAlmostZero(const float eps = kEps) const {
auto me_flat = t.unaligned_flat<float>();
double s = 0.0;
for (int i = 0; i < me_flat.size(); i++) {
s += me_flat(i) * me_flat(i);
if (s > eps * eps) {
return false;
}
}
return true;
}
float Magnitude() const {
auto me_flat = t.unaligned_flat<float>();
double s = 0.0;
for (int i = 0; i < me_flat.size(); i++) {
s += me_flat(i) * me_flat(i);
}
return sqrt(s);
}
string DebugString() const { return t.DebugString(); }
Tensor t;
};
// GradientStats holds first and second order gradient stats.
struct GradientStats {
GradientStats() {}
// Legacy constructor for tests
GradientStats(float g, float h) : first(g), second(h) {}
GradientStats(const Tensor& g, const Tensor& h) : first(g), second(h) {}
GradientStats(const Tensor& g, const Tensor& h, int64 example_index)
: first(g.Slice(example_index, example_index + 1)),
second(h.Slice(example_index, example_index + 1)) {}
GradientStats& operator+=(const GradientStats& other) {
first += other.first;
second += other.second;
return (*this);
}
GradientStats& operator*=(float value) {
first *= value;
second *= value;
return (*this);
}
GradientStats& operator-=(const GradientStats& other) {
first -= other.first;
second -= other.second;
return (*this);
}
bool IsZero() const { return first.IsZero() && second.IsZero(); }
bool IsAlmostZero(const float eps = kEps) const {
return first.IsAlmostZero(eps) && second.IsAlmostZero(eps);
}
float Magnitude() const { return second.Magnitude(); }
string DebugString() const {
return "First = " + first.DebugString() +
" Second = " + second.DebugString();
}
TensorStat first;
TensorStat second;
};
struct GradientStatsAccumulator {
void operator()(const GradientStats& from, GradientStats* to) const {
(*to) += from;
}
};
inline GradientStats operator+(const GradientStats& a, const GradientStats& b) {
GradientStats ret(a);
ret += b;
return ret;
}
inline GradientStats operator-(const GradientStats& a, const GradientStats& b) {
GradientStats ret(a);
ret -= b;
return ret;
}
// Helper macro to check gradient stats approximate equality.
#define EXPECT_GRADIENT_STATS_EQ(val1, val2) \
EXPECT_TRUE((val1 - val2).IsAlmostZero());
} // namespace stochastic
} // namespace learner
} // namespace boosted_trees
} // namespace tensorflow
#endif // THIRD_PARTY_TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_LEARNER_COMMON_STATS_GRADIENT_STATS_H_
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