1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
|
// 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 TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_TARGET_H_
#define TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_TARGET_H_
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_types.h"
namespace tensorflow {
namespace tensorforest {
typedef TTypes<float, 1>::UnalignedConstTensor SingleDimStorageType;
// Base class for classes that hold labels and weights. Mostly for testing
// purposes, because it's inconvenient to construct nasty Eigen::things.
class InputTarget {
public:
virtual ~InputTarget() {}
virtual int32 GetTargetAsClassIndex(int example_index,
int target_index) const = 0;
virtual float GetTargetWeight(int example_index) const = 0;
virtual float GetTargetAsContinuous(int example_index,
int target_index) const = 0;
};
template <typename T>
class StoredInputTarget : public InputTarget {
protected:
// Takes ownership of t and w with a std::unique_ptr.
StoredInputTarget(const T* t, const T* w, int num_targets)
: target_(t), weight_(w), num_targets_(num_targets) {}
const std::unique_ptr<const T> target_;
const std::unique_ptr<const T> weight_;
int num_targets_;
};
// Holds labels/targets and weights. Assumes that tensors are passed as
// t.unaligned_flat<float>(). For multi-output, specifying the number of
// outputs will correctly index the flattened data.
class TensorInputTarget : public StoredInputTarget<SingleDimStorageType> {
public:
TensorInputTarget(const Tensor& target, const Tensor& weight, int num_targets)
: StoredInputTarget(
new SingleDimStorageType(target.unaligned_flat<float>()),
new SingleDimStorageType(weight.unaligned_flat<float>()),
num_targets),
original_tensor_(target) {}
int32 GetTargetAsClassIndex(int example_index,
int target_index) const override {
return static_cast<int32>(
GetTargetAsContinuous(example_index, target_index));
}
float GetTargetWeight(int example_index) const override {
const size_t num_weights = weight_->size();
return num_weights > 0 && example_index < num_weights
? (*weight_)(example_index)
: 1.0;
}
float GetTargetAsContinuous(int example_index,
int target_index) const override {
QCHECK_LT(target_index, num_targets_);
return (*target_)(example_index * num_targets_ + target_index);
}
const Tensor& original_tensor() const {
return original_tensor_;
}
protected:
Tensor original_tensor_;
};
} // namespace tensorforest
} // namespace tensorflow
#endif // TENSORFLOW_CONTRIB_TENSOR_FOREST_KERNELS_V4_INPUT_TARGET_H_
|
