def loop_fn(i):
return linalg_ops.log_matrix_determinant(array_ops.gather(
x, i))
_AddTest(MatrixUnaryFunctorGradientTest, 'MatrixInverseGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_inverse,
dtype, shape))
_AddTest(MatrixUnaryFunctorGradientTest, 'MatrixExponentialGradient',
name,
_GetMatrixUnaryFunctorGradientTest(
linalg_impl.matrix_exponential, dtype, shape))
_AddTest(
MatrixUnaryFunctorGradientTest, 'MatrixDeterminantGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_determinant,
dtype, shape))
_AddTest(
MatrixUnaryFunctorGradientTest, 'LogMatrixDeterminantGradient',
name,
_GetMatrixUnaryFunctorGradientTest(
lambda x: linalg_ops.log_matrix_determinant(x)[1],
dtype, shape))
# Tests for gradients of matrix_solve_ls
for dtype in np.float32, np.float64:
for rows in 2, 5, 10:
for cols in 2, 5, 10:
for l2_regularization in 1e-6, 0.001, 1.0:
shape = (rows, cols)
name = '%s_%s_%s' % (dtype.__name__, '_'.join(map(str, shape)),
l2_regularization)
_AddTest(
MatrixBinaryFunctorGradientTest,
'MatrixSolveLsGradient',
name,
# pylint: disable=long-lambda,g-long-lambda
_AddTest(MatrixUnaryFunctorGradientTest, 'MatrixInverseGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_inverse,
dtype, shape))
_AddTest(MatrixUnaryFunctorGradientTest, 'MatrixExponentialGradient',
name,
_GetMatrixUnaryFunctorGradientTest(
linalg_impl.matrix_exponential, dtype, shape))
_AddTest(
MatrixUnaryFunctorGradientTest, 'MatrixDeterminantGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_determinant,
dtype, shape))
_AddTest(
MatrixUnaryFunctorGradientTest, 'LogMatrixDeterminantGradient',
name,
_GetMatrixUnaryFunctorGradientTest(
lambda x: linalg_ops.log_matrix_determinant(x)[1],
dtype, shape))
# The numerical Jacobian is consistently invalid for these four shapes
# because the matrix square root of the perturbed input doesn't exist
if shape in {(2, 5, 5), (3, 5, 5), (3, 10, 10), (3, 2, 5, 5)}:
# Alternative shape that consistently produces a valid numerical Jacobian
shape = extra + (size + 1, size + 1)
name = '%s_%s' % (dtype.__name__, '_'.join(map(str, shape)))
_AddTest(
MatrixUnaryFunctorGradientTest, 'MatrixSquareRootGradient', name,
_GetMatrixUnaryFunctorGradientTest(linalg_ops.matrix_square_root,
dtype, shape))
# Tests for gradients of matrix_solve_ls
for dtype in np.float32, np.float64:
def loop_fn(i): return linalg_ops.log_matrix_determinant(array_ops.gather(x, i))
