143 lines
5.3 KiB
Python
143 lines
5.3 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# pylint: disable=g-short-docstring-punctuation
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"""Histograms.
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Please see @{$python/histogram_ops} guide.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import clip_ops
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from tensorflow.python.ops import gen_math_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('histogram_fixed_width_bins')
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def histogram_fixed_width_bins(values,
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value_range,
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nbins=100,
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dtype=dtypes.int32,
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name=None):
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"""Bins the given values for use in a histogram.
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Given the tensor `values`, this operation returns a rank 1 `Tensor`
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representing the indices of a histogram into which each element
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of `values` would be binned. The bins are equal width and
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determined by the arguments `value_range` and `nbins`.
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Args:
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values: Numeric `Tensor`.
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value_range: Shape [2] `Tensor` of same `dtype` as `values`.
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values <= value_range[0] will be mapped to hist[0],
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values >= value_range[1] will be mapped to hist[-1].
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nbins: Scalar `int32 Tensor`. Number of histogram bins.
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dtype: dtype for returned histogram.
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name: A name for this operation (defaults to 'histogram_fixed_width').
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Returns:
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A `Tensor` holding the indices of the binned values whose shape matches
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`values`.
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Examples:
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```python
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# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
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nbins = 5
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value_range = [0.0, 5.0]
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new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
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with tf.get_default_session() as sess:
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indices = tf.histogram_fixed_width_bins(new_values, value_range, nbins=5)
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variables.global_variables_initializer().run()
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sess.run(indices) => [0, 0, 1, 2, 4]
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```
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"""
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with ops.name_scope(name, 'histogram_fixed_width_bins',
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[values, value_range, nbins]):
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values = ops.convert_to_tensor(values, name='values')
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shape = array_ops.shape(values)
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values = array_ops.reshape(values, [-1])
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value_range = ops.convert_to_tensor(value_range, name='value_range')
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nbins = ops.convert_to_tensor(nbins, dtype=dtypes.int32, name='nbins')
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nbins_float = math_ops.cast(nbins, values.dtype)
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# Map tensor values that fall within value_range to [0, 1].
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scaled_values = math_ops.truediv(
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values - value_range[0],
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value_range[1] - value_range[0],
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name='scaled_values')
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# map tensor values within the open interval value_range to {0,.., nbins-1},
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# values outside the open interval will be zero or less, or nbins or more.
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indices = math_ops.floor(nbins_float * scaled_values, name='indices')
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# Clip edge cases (e.g. value = value_range[1]) or "outliers."
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indices = math_ops.cast(
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clip_ops.clip_by_value(indices, 0, nbins_float - 1), dtypes.int32)
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return array_ops.reshape(indices, shape)
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@tf_export('histogram_fixed_width')
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def histogram_fixed_width(values,
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value_range,
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nbins=100,
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dtype=dtypes.int32,
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name=None):
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"""Return histogram of values.
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Given the tensor `values`, this operation returns a rank 1 histogram counting
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the number of entries in `values` that fell into every bin. The bins are
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equal width and determined by the arguments `value_range` and `nbins`.
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Args:
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values: Numeric `Tensor`.
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value_range: Shape [2] `Tensor` of same `dtype` as `values`.
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values <= value_range[0] will be mapped to hist[0],
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values >= value_range[1] will be mapped to hist[-1].
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nbins: Scalar `int32 Tensor`. Number of histogram bins.
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dtype: dtype for returned histogram.
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name: A name for this operation (defaults to 'histogram_fixed_width').
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Returns:
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A 1-D `Tensor` holding histogram of values.
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Examples:
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```python
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# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
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nbins = 5
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value_range = [0.0, 5.0]
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new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
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with tf.get_default_session() as sess:
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hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
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variables.global_variables_initializer().run()
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sess.run(hist) => [2, 1, 1, 0, 2]
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```
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"""
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with ops.name_scope(name, 'histogram_fixed_width',
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[values, value_range, nbins]) as name:
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# pylint: disable=protected-access
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return gen_math_ops._histogram_fixed_width(
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values, value_range, nbins, dtype=dtype, name=name)
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# pylint: enable=protected-access
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