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
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
|
# Copyright 2016 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.
# ==============================================================================
"""Implementation of kernel-methods-related loss operations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops.losses import losses
def sparse_multiclass_hinge_loss(
labels,
logits,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS):
"""Adds Ops for computing the multiclass hinge loss.
The implementation is based on the following paper:
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
by Crammer and Singer.
link: http://jmlr.csail.mit.edu/papers/volume2/crammer01a/crammer01a.pdf
This is a generalization of standard (binary) hinge loss. For a given instance
with correct label c*, the loss is given by:
loss = max_{c != c*} logits_c - logits_{c*} + 1.
or equivalently
loss = max_c { logits_c - logits_{c*} + I_{c != c*} }
where I_{c != c*} = 1 if c != c* and 0 otherwise.
Args:
labels: `Tensor` of shape [batch_size] or [batch_size, 1]. Corresponds to
the ground truth. Each entry must be an index in `[0, num_classes)`.
logits: `Tensor` of shape [batch_size, num_classes] corresponding to the
unscaled logits. Its dtype should be either `float32` or `float64`.
weights: Optional (python) scalar or `Tensor`. If a non-scalar `Tensor`, its
rank should be either 1 ([batch_size]) or 2 ([batch_size, 1]).
scope: The scope for the operations performed in computing the loss.
loss_collection: collection to which the loss will be added.
reduction: Type of reduction to apply to loss.
Returns:
Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
shape as `labels`; otherwise, it is a scalar.
Raises:
ValueError: If `logits`, `labels` or `weights` have invalid or inconsistent
shapes.
ValueError: If `labels` tensor has invalid dtype.
"""
with ops.name_scope(scope, 'sparse_multiclass_hinge_loss', (logits,
labels)) as scope:
# Check logits Tensor has valid rank.
logits_rank = logits.get_shape().ndims
if logits_rank != 2:
raise ValueError(
'logits should have rank 2 ([batch_size, num_classes]). Given rank is'
' {}'.format(logits_rank))
logits_shape = array_ops.shape(logits)
batch_size, num_classes = logits_shape[0], logits_shape[1]
logits = math_ops.to_float(logits)
# Check labels have valid type.
if labels.dtype != dtypes.int32 and labels.dtype != dtypes.int64:
raise ValueError(
'Invalid dtype for labels: {}. Acceptable dtypes: int32 and int64'.
format(labels.dtype))
# Check labels and weights have valid ranks and are consistent.
labels_rank = labels.get_shape().ndims
if labels_rank not in [1, 2]:
raise ValueError(
'labels should have rank 1 ([batch_size]) or 2 ([batch_size, 1]). '
'Given rank is {}'.format(labels_rank))
with ops.control_dependencies([
check_ops.assert_less(labels, math_ops.cast(num_classes, labels.dtype))
]):
labels = array_ops.reshape(labels, shape=[-1])
weights = ops.convert_to_tensor(weights)
weights_rank = weights.get_shape().ndims
if weights_rank not in [0, 1, 2]:
raise ValueError(
'non-scalar weights should have rank 1 ([batch_size]) or 2 '
'([batch_size, 1]). Given rank is {}'.format(labels_rank))
if weights_rank > 0:
weights = array_ops.reshape(weights, shape=[-1])
# Check weights and labels have the same number of elements.
weights.get_shape().assert_is_compatible_with(labels.get_shape())
# Compute the logits tensor corresponding to the correct class per instance.
example_indices = array_ops.reshape(
math_ops.range(batch_size), shape=[batch_size, 1])
indices = array_ops.concat(
[
example_indices,
array_ops.reshape(
math_ops.cast(labels, example_indices.dtype),
shape=[batch_size, 1])
],
axis=1)
label_logits = array_ops.reshape(
array_ops.gather_nd(params=logits, indices=indices),
shape=[batch_size, 1])
one_cold_labels = array_ops.one_hot(
indices=labels, depth=num_classes, on_value=0.0, off_value=1.0)
margin = logits - label_logits + one_cold_labels
margin = nn_ops.relu(margin)
loss = math_ops.reduce_max(margin, axis=1)
return losses.compute_weighted_loss(
loss, weights, scope, loss_collection, reduction=reduction)
|
