1 files changed, 65 insertions, 0 deletions
diff --git a/tensorflow/python/ops/histogram_ops.py b/tensorflow/python/ops/histogram_ops.py
index d46084a41f..b2de2e5015 100644
--- a/tensorflow/python/ops/histogram_ops.py
+++ b/tensorflow/python/ops/histogram_ops.py
@@ -17,6 +17,7 @@
 
 Please see @{$python/histogram_ops} guide.
 
+@@histogram_fixed_width_bins
 @@histogram_fixed_width
 """
 
@@ -33,6 +34,70 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
+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`.
+
+  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:
+    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]
+  ```
+  """
+  with ops.name_scope(name, 'histogram_fixed_width_bins',
+                      [values, value_range, nbins]) as scope:
+    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)
+
+
 @tf_export('histogram_fixed_width')
 def histogram_fixed_width(values,
                           value_range,