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jax version: 0.4.38.
jax
0.4.38
Logs:
> self.assertArraysAllClose(scipy_val, jaxopt_val) tests/lbfgs_test.py:422: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ jaxopt/_src/test_util.py:292: in assertArraysAllClose _assert_numpy_allclose(x, y, atol=atol, rtol=rtol, err_msg=err_msg) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = np.float64(695.4757505059242), b = Array(636.7615, dtype=float32) atol = 1e-06, rtol = 1e-06, err_msg = '' def _assert_numpy_allclose(a, b, atol=None, rtol=None, err_msg=''): if a.dtype == b.dtype == float0: np.testing.assert_array_equal(a, b, err_msg=err_msg) return #a = a.astype(np.float32) if a.dtype == _dtypes.bfloat16 else a #b = b.astype(np.float32) if b.dtype == _dtypes.bfloat16 else b kw = {} if atol: kw["atol"] = atol if rtol: kw["rtol"] = rtol with onp.errstate(invalid='ignore'): # TODO(phawkins): surprisingly, assert_allclose sometimes reports invalid # value errors. It should not do that. > onp.testing.assert_allclose(a, b, **kw, err_msg=err_msg) E AssertionError: E Not equal to tolerance rtol=1e-06, atol=1e-06 E E Mismatched elements: 1 / 1 (100%) E Max absolute difference among violations: 58.7142759 E Max relative difference among violations: 0.09220764 E ACTUAL: array(695.475751) E DESIRED: array(636.7615, dtype=float32) jaxopt/_src/test_util.py:262: AssertionError
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jax
version:0.4.38
.Logs:
The text was updated successfully, but these errors were encountered: