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
|
# Copyright 2015 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.
# ==============================================================================
# pylint: disable=g-short-docstring-punctuation
"""Histograms.
Please see @{$python/histogram_ops} guide.
@@histogram_fixed_width
"""
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 clip_ops
from tensorflow.python.ops import math_ops
def histogram_fixed_width(values,
value_range,
nbins=100,
dtype=dtypes.int32,
name=None):
"""Return histogram of values.
Given the tensor `values`, this operation returns a rank 1 histogram counting
the number of entries in `values` that fell into every bin. The bins are
equal width and determined by the arguments `value_range` and `nbins`.
Args:
values: Numeric `Tensor`.
value_range: Shape [2] `Tensor` of same `dtype` as `values`.
values <= value_range[0] will be mapped to hist[0],
values >= value_range[1] will be mapped to hist[-1].
nbins: Scalar `int32 Tensor`. Number of histogram bins.
dtype: dtype for returned histogram.
name: A name for this operation (defaults to 'histogram_fixed_width').
Returns:
A 1-D `Tensor` holding histogram of values.
Examples:
```python
# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with tf.get_default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.global_variables_initializer().run()
sess.run(hist) => [2, 1, 1, 0, 2]
```
"""
with ops.name_scope(name, 'histogram_fixed_width',
[values, value_range, nbins]) as scope:
values = ops.convert_to_tensor(values, name='values')
values = array_ops.reshape(values, [-1])
value_range = ops.convert_to_tensor(value_range, name='value_range')
nbins = ops.convert_to_tensor(nbins, dtype=dtypes.int32, name='nbins')
nbins_float = math_ops.cast(nbins, values.dtype)
# Map tensor values that fall within value_range to [0, 1].
scaled_values = math_ops.truediv(values - value_range[0],
value_range[1] - value_range[0],
name='scaled_values')
# map tensor values within the open interval value_range to {0,.., nbins-1},
# values outside the open interval will be zero or less, or nbins or more.
indices = math_ops.floor(nbins_float * scaled_values, name='indices')
# Clip edge cases (e.g. value = value_range[1]) or "outliers."
indices = math_ops.cast(
clip_ops.clip_by_value(indices, 0, nbins_float - 1), dtypes.int32)
# TODO(langmore) This creates an array of ones to add up and place in the
# bins. This is inefficient, so replace when a better Op is available.
return math_ops.unsorted_segment_sum(
array_ops.ones_like(indices, dtype=dtype),
indices,
nbins,
name=scope)
|
