Narrow down argmin/argmax contract to always return the smallest index for ties
Currently we get this behavior consistently across TF/XLA:CPU/XLA:GPU/XLA:TPU, and it also matches Numpy semantics.
PiperOrigin-RevId: 312528188
Change-Id: I16901ff67052182fe374235f8c7521cbdf047779
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 31994c1..06132cc 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -256,7 +256,7 @@
def argmax_v2(input, axis=None, output_type=dtypes.int64, name=None):
"""Returns the index with the largest value across axes of a tensor.
- Note that in case of ties the identity of the return value is not guaranteed.
+ In case of identity returns the smallest index.
For example:
@@ -269,6 +269,9 @@
<tf.Tensor: shape=(5,), dtype=int64, numpy=array([2, 2, 0, 2, 2])>
>>> tf.math.argmax(B, 1)
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([2, 2, 1])>
+ >>> C = tf.constant([0, 0, 0, 0])
+ >>> tf.math.argmax(C) # Returns smallest index in case of ties
+ <tf.Tensor: shape=(), dtype=int64, numpy=0>
Args:
input: A `Tensor`.
@@ -307,7 +310,7 @@
def argmin_v2(input, axis=None, output_type=dtypes.int64, name=None):
"""Returns the index with the smallest value across axes of a tensor.
- Note that in case of ties the identity of the return value is not guaranteed.
+ Returns the smallest index in case of ties.
Args:
input: A `Tensor`. Must be one of the following types: `float32`, `float64`,
