Adding histogram_ops module containing one Op: histogram_fixed_width, which updates a histogram Variable with new_values.

Change: 112728652

3 files changed, 175 insertions, 0 deletions

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index de7c6d2453..5ade3d8c17 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -699,6 +699,7 @@ py_library(
         "ops/gen_string_ops.py",
         "ops/gen_summary_ops.py",
         "ops/gradients.py",
+        "ops/histogram_ops.py",
         "ops/image_grad.py",
         "ops/image_ops.py",
         "ops/init_ops.py",
diff --git a/tensorflow/python/ops/histogram_ops.py b/tensorflow/python/ops/histogram_ops.py
new file mode 100644
index 0000000000..f15d1c5cad
--- /dev/null
+++ b/tensorflow/python/ops/histogram_ops.py
@@ -0,0 +1,94 @@
+# Copyright 2015 Google Inc. 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.
+# ==============================================================================
+"""Operations for 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 math_ops
+from tensorflow.python.ops import state_ops
+
+
+def histogram_fixed_width(hist,
+                          new_values,
+                          value_range,
+                          use_locking=False,
+                          name='histogram_fixed_width'):
+  """Update histogram Variable with new values.
+
+  This Op fills histogram with counts of values falling within fixed-width,
+  half-open bins.
+
+  Args:
+    hist:  1-D mutable `Tensor`, e.g. a `Variable`.
+    new_values:  Numeric `Tensor`.
+    value_range:  Shape [2] `Tensor`.  new_values <= value_range[0] will be
+      mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
+      Must be same dtype as new_values.
+    use_locking:  Boolean.
+      If `True`, use locking during the operation (optional).
+    name:  A name for this operation (optional).
+
+  Returns:
+    An op that updates `hist` with `new_values` when evaluated.
+
+  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.default_session() as sess:
+    hist = variables.Variable(array_ops.zeros(nbins, dtype=tf.int32))
+    hist_update = histogram_ops.histogram_fixed_width(hist, new_values,
+                                                      value_range)
+    variables.initialize_all_variables().run()
+    sess.run(hist_update) => [2, 1, 1, 0, 2]
+  ```
+  """
+  with ops.op_scope([hist, new_values, value_range], name) as scope:
+    new_values = ops.convert_to_tensor(new_values, name='new_values')
+    value_range = ops.convert_to_tensor(value_range, name='value_range')
+    dtype = hist.dtype
+
+    # Map tensor values that fall within value_range to [0, 1].
+    scaled_values = math_ops.truediv(new_values - value_range[0],
+                                     value_range[1] - value_range[0],
+                                     name='scaled_values')
+    nbins = math_ops.cast(hist.get_shape()[0], scaled_values.dtype)
+
+    # 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 * 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 - 1), dtypes.int32)
+
+    # Dummy vector to scatter.
+    # TODO(langmore) Replace non-ideal creation of large dummy vector once an
+    # alternative to scatter is available.
+    updates = array_ops.ones(indices.get_shape()[0], dtype=dtype)
+    return state_ops.scatter_add(hist,
+                                 indices,
+                                 updates,
+                                 use_locking=use_locking,
+                                 name=scope)
diff --git a/tensorflow/python/ops/histogram_ops_test.py b/tensorflow/python/ops/histogram_ops_test.py
new file mode 100644
index 0000000000..3b5ca3c748
--- /dev/null
+++ b/tensorflow/python/ops/histogram_ops_test.py
@@ -0,0 +1,80 @@
+# Copyright 2015 Google Inc. 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.
+# ==============================================================================
+
+"""Tests for tensorflow.ops.histogram_ops."""
+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 test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import histogram_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+
+
+class HistogramFixedWidthTest(test_util.TensorFlowTestCase):
+
+  def test_one_update_on_constant_input(self):
+    # 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]
+    expected_bin_counts = [2, 1, 1, 0, 2]
+    with self.test_session() as sess:
+      hist = variables.Variable(array_ops.zeros(nbins, dtype=dtypes.int32))
+      hist_update = histogram_ops.histogram_fixed_width(hist, new_values,
+                                                        value_range)
+      variables.initialize_all_variables().run()
+      self.assertTrue(hist.dtype.is_compatible_with(hist_update.dtype))
+      updated_hist_array = sess.run(hist_update)
+
+      # The new updated_hist_array is returned by the updating op.
+      self.assertAllClose(expected_bin_counts, updated_hist_array)
+
+      # hist should contain updated values, but eval() should not change it.
+      self.assertAllClose(expected_bin_counts, hist.eval())
+      self.assertAllClose(expected_bin_counts, hist.eval())
+
+  def test_two_updates_on_constant_input(self):
+    # Bins will be:
+    #   (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
+    nbins = 5
+    value_range = [0.0, 5.0]
+    new_values_1 = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
+    new_values_2 = [1.5, 4.5, 4.5, 4.5, 0.0, 0.0]
+    expected_bin_counts_1 = [2, 1, 1, 0, 2]
+    expected_bin_counts_2 = [4, 2, 1, 0, 5]
+    with self.test_session() as sess:
+      hist = variables.Variable(array_ops.zeros(nbins, dtype=dtypes.int32))
+      new_values = array_ops.placeholder(dtypes.float32, shape=[6])
+      hist_update = histogram_ops.histogram_fixed_width(hist, new_values,
+                                                        value_range)
+      variables.initialize_all_variables().run()
+      updated_hist_array = sess.run(hist_update,
+                                    feed_dict={new_values: new_values_1})
+      self.assertAllClose(expected_bin_counts_1, updated_hist_array)
+      self.assertAllClose(expected_bin_counts_1, hist.eval())
+
+      updated_hist_array = sess.run(hist_update,
+                                    feed_dict={new_values: new_values_2})
+      self.assertAllClose(expected_bin_counts_2, updated_hist_array)
+      self.assertAllClose(expected_bin_counts_2, hist.eval())
+
+
+if __name__ == '__main__':
+  googletest.main()