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"""Tests for tensorflow.ops.linalg_grad."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class MatrixInverseGradientTest(tf.test.TestCase):
pass # Filled in below
def _GetMatrixInverseGradientTest(dtype, shape):
def Test(self):
with self.test_session():
np.random.seed(1)
m = np.random.uniform(low=1.0, high=100.0, size=np.prod(shape)).reshape(
shape).astype(dtype)
a = tf.constant(m)
epsilon = np.finfo(dtype).eps
# Optimal stepsize for central difference is O(epsilon^{1/3}).
delta = epsilon ** (1.0 / 3.0)
tol = 1e-3
if len(shape) == 2:
ainv = tf.matrix_inverse(a)
else:
ainv = tf.batch_matrix_inverse(a)
theoretical, numerical = gc.ComputeGradient(a, shape, ainv, shape,
delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
return Test
if __name__ == "__main__":
# TODO(rmlarsen,irving): Reenable float32 once tolerances are fixed
# The test used to loop over (np.float, np.double), both of which are float64.
for dtype in np.float64,:
for size in 2, 3, 5, 10:
# We skip the rank 4, size 10 case: it is slow and conceptually covered
# by the other cases.
for extra in [(), (2,), (3,)] + [(3, 2)] * (size < 10):
shape = extra + (size, size)
name = '%s_%s' % (dtype.__name__, '_'.join(map(str, shape)))
setattr(MatrixInverseGradientTest, 'testMatrixInverseGradient_' + name,
_GetMatrixInverseGradientTest(dtype, shape))
tf.test.main()
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