def test_gpu_matrix_inverse(self):
A = theano.tensor.fmatrix("A")
fn = theano.function([A], gpu_matrix_inverse(A), mode=mode_with_gpu)
N = 1000
A_val = rand(N, N).astype('float32')
A_val_inv = fn(A_val)
utt.assert_allclose(np.dot(A_val_inv, A_val), np.eye(N), atol=1e-3)
def test_gpu_matrix_inverse(self):
A = theano.tensor.fmatrix("A")
fn = theano.function([A], gpu_matrix_inverse(A), mode=mode_with_gpu)
N = 1000
A_val = rand(N, N).astype('float32')
A_val_inv = fn(A_val)
utt.assert_allclose(np.dot(A_val_inv, A_val), np.eye(N), atol=1e-3)
def test_gpu_matrix_inverse(self):
A = theano.tensor.fmatrix("A")
fn = theano.function([A], gpu_matrix_inverse(A), mode=mode_with_gpu)
N = 1000
test_rng = np.random.RandomState(seed=1)
# Copied from theano.tensor.tests.test_basic.rand.
A_val = test_rng.rand(N, N).astype('float32') * 2 - 1
A_val_inv = fn(A_val)
utt.assert_allclose(np.eye(N), np.dot(A_val_inv, A_val), atol=5e-3)
def test_gpu_matrix_inverse(self):
A = theano.tensor.fmatrix("A")
fn = theano.function([A], gpu_matrix_inverse(A), mode=mode_with_gpu)
N = 1000
test_rng = np.random.RandomState(seed=1)
# Copied from tests.tensor.utils.rand.
A_val = test_rng.rand(N, N).astype("float32") * 2 - 1
A_val_inv = fn(A_val)
utt.assert_allclose(np.eye(N), np.dot(A_val_inv, A_val), atol=1e-2)