experiments/STT-tensorflow
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Work around the following bug in python 3.8

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OverflowError: Python int too large to convert to C long

On windows, sizeof(long) is 4 bytes. Therefore, the large integers seem to be a problem when backed by some numpy types:
https://stackoverflow.com/questions/38314118/overflowerror-python-int-too-large-to-convert-to-c-long-on-windows-but-not-ma

PiperOrigin-RevId: 301407257
Change-Id: Ic0f7379eee360ff053283742f6495c1974b2c5e4
This commit is contained in:
Gunhan Gulsoy 2020-03-17 10:50:24 -07:00 committed by TensorFlower Gardener
parent 73278044ba
commit bdb77d1e2e
2 changed files with 2 additions and 2 deletions
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2
tensorflow/compiler/tests/categorical_op_test.py
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@ -187,7 +187,7 @@ class CategoricalTest(xla_test.XLATestCase):
0, 0,
seed=seed_t, seed=seed_t,
output_dtype=dtypes.int32) output_dtype=dtypes.int32)
y = sess.run(x, {seed_t: [0x12345678, 0xabcdef12]}) y = sess.run(x, {seed_t: [0x12345678, 0xabcdef1]})
self.assertEqual(y.shape, (42, 0)) self.assertEqual(y.shape, (42, 0))

2
tensorflow/compiler/tests/stateless_random_ops_test.py
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@ -101,7 +101,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase):
seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
x = stateless.stateless_random_normal( x = stateless.stateless_random_normal(
shape=[10000], seed=seed_t, dtype=dtype) shape=[10000], seed=seed_t, dtype=dtype)
y = sess.run(x, {seed_t: [0x12345678, 0xabcdef12]}) y = sess.run(x, {seed_t: [0x12345678, 0xabcdef1]})
self.assertTrue(np.all(np.isfinite(y))) self.assertTrue(np.all(np.isfinite(y)))
def testDistributionOfStatelessRandomNormal(self): def testDistributionOfStatelessRandomNormal(self):