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| author |  Akshay Modi <nareshmodi@google.com> | 2018-06-18 11:48:36 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-06-18 11:55:03 -0700 |
| commit | 148b4381fd0259cae441e459ec8ebe2c5d557722 (patch) | |
| tree | c66c96ea6c60c63385b528dce195af802b8acf3b /tensorflow/contrib/distributions | |
| parent | fc03fbff3dd7a58fa4f16226df4ada1f21f8b53f (diff) | |
Automated g4 rollback of changelist 201011811
PiperOrigin-RevId: 201033171
Diffstat (limited to 'tensorflow/contrib/distributions')
| -rw-r--r-- | tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py | 28 |
1 files changed, 10 insertions, 18 deletions
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py index 795f1993ba..45760a29ee 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py @@ -151,24 +151,16 @@ class SinhArcsinhBijectorTest(test.TestCase): self.assertAllClose(y, bijector.forward(x).eval(), rtol=1e-4, atol=0.) self.assertAllClose(x, bijector.inverse(y).eval(), rtol=1e-4, atol=0.) - # On IBM PPC systems, longdouble (np.float128) is same as double except that it can have more precision. - # Type double being of 8 bytes, can't hold square of max of float64 (which is also 8 bytes) and - # below test fails due to overflow error giving inf. So this check avoids that error by skipping square - # calculation and corresponding assert. - - if np.amax(y) <= np.sqrt(np.finfo(np.float128).max) and \ - np.fabs(np.amin(y)) <= np.sqrt(np.fabs(np.finfo(np.float128).min)): - - # Do the numpy calculation in float128 to avoid inf/nan. - y_float128 = np.float128(y) - self.assertAllClose( - np.log(np.cosh( - np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( - y_float128**2 + 1)) - - np.log(tailweight), - bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), - rtol=1e-4, - atol=0.) + # Do the numpy calculation in float128 to avoid inf/nan. + y_float128 = np.float128(y) + self.assertAllClose( + np.log(np.cosh( + np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( + y_float128**2 + 1)) - + np.log(tailweight), + bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), + rtol=1e-4, + atol=0.) self.assertAllClose( -bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), bijector.forward_log_det_jacobian(x, event_ndims=0).eval(), |