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Replace usage of math_ops.maximum with math_ops.reduce_max when getting max length from SparseTensors.

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PiperOrigin-RevId: 170748309
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A. Unique TensorFlower 2017-10-02 13:52:58 -07:00 committed by TensorFlower Gardener
parent f08c961c97
commit 75cac0a5d5
2 changed files with 46 additions and 1 deletions
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45
tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py
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@ -30,6 +30,7 @@ from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@ -527,6 +528,50 @@ class PaddingTest(test.TestCase):
self.assertTrue(
math_ops.reduce_all(math_ops.equal(val, padded_seq[key])).eval())
def testPaddingOnlySparse(self):
ind1 = np.array([[0], [2]])
val1 = np.array([3, 4])
shape1 = np.array([4])
ind2 = np.array([[1], [2]])
val2 = np.array([9, 12])
shape2 = np.array([5])
with ops.Graph().as_default() as g, self.test_session(graph=g):
sp_tensor1 = sparse_tensor.SparseTensor(
indices=array_ops.constant(ind1, dtypes.int64),
values=array_ops.constant(val1, dtypes.int64),
dense_shape=array_ops.constant(shape1, dtypes.int64))
sp_tensor2 = sparse_tensor.SparseTensor(
indices=array_ops.constant(ind2, dtypes.int64),
values=array_ops.constant(val2, dtypes.int64),
dense_shape=array_ops.constant(shape2, dtypes.int64))
sp_tensor1_expected = sparse_tensor.SparseTensor(
indices=sp_tensor1.indices,
values=sp_tensor1.values,
dense_shape=[8])
sp_tensor2_expected = sparse_tensor.SparseTensor(
indices=sp_tensor2.indices,
values=sp_tensor2.values,
dense_shape=[8])
sequences = {
"key_1": sp_tensor1,
"key_2": sp_tensor2,
}
_, padded_seq = sqss._padding(sequences, 4)
expected_padded_seq = {
"key_1": sp_tensor1_expected,
"key_2": sp_tensor2_expected,
}
for key, val in expected_padded_seq.items():
self.assertAllEqual(
sparse_ops.sparse_tensor_to_dense(val).eval(),
sparse_ops.sparse_tensor_to_dense(padded_seq[key]).eval())
class SparseTensorReConstructionTest(test.TestCase):

2
tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py
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@ -1596,7 +1596,7 @@ def _padding(sequences, num_unroll):
else: # Only have SparseTensors
sparse_lengths = [value.dense_shape[0] for value in sequences_dict.values()
if isinstance(value, sparse_tensor.SparseTensor)]
length = math_ops.maximum(sparse_lengths)
length = math_ops.reduce_max(math_ops.to_int32(sparse_lengths))
unroll = array_ops.constant(num_unroll)
padded_length = length + ((unroll - (length % unroll)) % unroll)