tensorflow.python.ops.histogram_ops 源代码

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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# pylint: disable=g-short-docstring-punctuation
"""Histograms.
"""

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 gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util.tf_export import tf_export


[文档]@tf_export('histogram_fixed_width_bins') def histogram_fixed_width_bins(values, value_range, nbins=100, dtype=dtypes.int32, name=None): """Bins the given values for use in a histogram. Given the tensor `values`, this operation returns a rank 1 `Tensor` representing the indices of a histogram into which each element of `values` would be binned. 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 `Tensor` holding the indices of the binned values whose shape matches `values`. Raises: TypeError: If any unsupported dtype is provided. tf.errors.InvalidArgumentError: If value_range does not satisfy value_range[0] < value_range[1]. 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.compat.v1.get_default_session() as sess: indices = tf.histogram_fixed_width_bins(new_values, value_range, nbins=5) variables.global_variables_initializer().run() sess.run(indices) # [0, 0, 1, 2, 4, 4] ``` """ with ops.name_scope(name, 'histogram_fixed_width_bins', [values, value_range, nbins]): values = ops.convert_to_tensor(values, name='values') shape = array_ops.shape(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) return array_ops.reshape(indices, shape)
[文档]@tf_export('histogram_fixed_width') 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. Raises: TypeError: If any unsupported dtype is provided. tf.errors.InvalidArgumentError: If value_range does not satisfy value_range[0] < value_range[1]. 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.compat.v1.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 name: # pylint: disable=protected-access return gen_math_ops._histogram_fixed_width( values, value_range, nbins, dtype=dtype, name=name)
# pylint: enable=protected-access