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Merge pull request #26796 from abenmao:clip_by_value

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PiperOrigin-RevId: 239067830
This commit is contained in:
TensorFlower Gardener 2019-03-18 15:31:24 -07:00
commit 2e7bf1d595
2 changed files with 44 additions and 6 deletions
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34
tensorflow/python/kernel_tests/clip_ops_test.py
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@ -150,6 +150,40 @@ class ClipTest(test.TestCase):
self.assertAllClose(np_ans, tf_ans)
def _testClipIndexedSlicesByValue(self, values, indices, shape,
clip_value_min, clip_value_max, expected):
with self.session(use_gpu=True) as sess:
values = constant_op.constant(values)
indices = constant_op.constant(indices)
shape = constant_op.constant(shape)
# IndexedSlices mode
indixed_slices = ops.IndexedSlices(values, indices, shape)
clipped = clip_ops.clip_by_value(indixed_slices, clip_value_min,
clip_value_max)
# clipped should be IndexedSlices
self.assertIsInstance(clipped, ops.IndexedSlices)
self.assertAllClose(clipped.values, expected)
def testClipByValueWithIndexedSlicesClipped(self):
values = [[[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]],
[[0.0, 2.0, 0.0], [0.0, 0.0, -1.0]]]
indices = [2, 6]
shape = [10, 2, 3]
# [-2.0, 2.0]
self._testClipIndexedSlicesByValue(values, indices, shape, -2.0, 2.0,
[[[-2.0, 0.0, 0.0], [2.0, 0.0, 0.0]],
[[0.0, 2.0, 0.0], [0.0, 0.0, -1.0]]])
# [1.0, 2.0]
self._testClipIndexedSlicesByValue(values, indices, shape, 1.0, 2.0,
[[[1.0, 1.0, 1.0], [2.0, 1.0, 1.0]],
[[1.0, 2.0, 1.0], [1.0, 1.0, 1.0]]])
# [-2.0, -1.0]
self._testClipIndexedSlicesByValue(
values, indices, shape, -2.0, -1.0,
[[[-2.0, -1.0, -1.0], [-1.0, -1.0, -1.0]],
[[-1.0, -1.0, -1.0], [-1.0, -1.0, -1.0]]])
# ClipByNorm tests
def testClipByNormClipped(self):
# Norm clipping when clip_norm < 5

16
tensorflow/python/ops/clip_ops.py
View File

@ -50,7 +50,7 @@ def clip_by_value(t, clip_value_min, clip_value_max,
correct results.
Args:
t: A `Tensor`.
t: A `Tensor` or `IndexedSlices`.
clip_value_min: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
as `t`. The minimum value to clip by.
clip_value_max: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
@ -58,7 +58,7 @@ def clip_by_value(t, clip_value_min, clip_value_max,
name: A name for the operation (optional).
Returns:
A clipped `Tensor`.
A clipped `Tensor` or `IndexedSlices`.
Raises:
ValueError: If the clip tensors would trigger array broadcasting
@ -66,16 +66,20 @@ def clip_by_value(t, clip_value_min, clip_value_max,
"""
with ops.name_scope(name, "clip_by_value",
[t, clip_value_min, clip_value_max]) as name:
t = ops.convert_to_tensor(t, name="t")
values = ops.convert_to_tensor(
t.values if isinstance(t, ops.IndexedSlices) else t, name="t")
# Go through list of tensors, for each value in each tensor clip
t_min = math_ops.minimum(t, clip_value_max)
t_min = math_ops.minimum(values, clip_value_max)
# Assert that the shape is compatible with the initial shape,
# to prevent unintentional broadcasting.
_ = t.shape.merge_with(t_min.shape)
_ = values.shape.merge_with(t_min.shape)
t_max = math_ops.maximum(t_min, clip_value_min, name=name)
_ = t.shape.merge_with(t_max.shape)
_ = values.shape.merge_with(t_max.shape)
if isinstance(t, ops.IndexedSlices):
t_max = ops.IndexedSlices(t_max, t.indices, t.dense_shape)
return t_max
# TODO(scottzhu): switch to use new implmentation in 2 weeks.