def test_shape_tuple():
tensor = np.ones([3, 5, 2])
np_result = numpy_backend.NumPyBackend().shape_tuple(tensor)
sh_result = shell_backend.ShellBackend().shape_tuple(tensor)
assert np_result == sh_result
def test_eye_dtype(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.eye(N=4, M=4, dtype=dtype)
assert a.dtype == dtype
def test_random_uniform_dtype(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.random_uniform((4, 4), dtype=dtype, seed=10)
assert a.dtype == dtype
def test_eye(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.eye(N=4, M=5, dtype=dtype)
np.testing.assert_allclose(np.eye(N=4, M=5, dtype=dtype), a)
def test_randn(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.randn((4, 4), dtype=dtype, seed=10)
assert a.shape == (4, 4)
def test_trace(): backend = numpy_backend.NumPyBackend() a = backend.convert_to_tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])) actual = backend.trace(a) np.testing.assert_allclose(actual, 6)
def test_convert_bad_test():
backend = numpy_backend.NumPyBackend()
with pytest.raises(TypeError):
backend.convert_to_tensor(tf.ones((2, 2)))
def test_zeros_dtype(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
dtype_2 = np.float32
a = backend.zeros((4, 4), dtype=dtype_2)
assert a.dtype == dtype_2
def test_randn_dtype(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
dtype_2 = np.float32
a = backend.randn((4, 4), dtype=dtype_2)
assert a.dtype == dtype_2
def test_randn_non_zero_imag(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
a = backend.randn((4, 4))
assert np.linalg.norm(np.imag(a)) != 0.0
def test_eye_dtype(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
dtype_2 = np.float32
a = backend.eye(N=4, M=4, dtype=dtype_2)
assert a.dtype == dtype_2
def test_randn(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
a = backend.randn((4, 4))
assert a.shape == (4, 4)
def test_ones(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
a = backend.ones((4, 4))
np.testing.assert_allclose(np.ones((4, 4), dtype=dtype), a)
def test_zeros_dtype_2(dtype):
dtype = np.float32
backend = numpy_backend.NumPyBackend(dtype=dtype)
a = backend.zeros((4, 4))
assert a.dtype == dtype
def test_shape_tuple(): backend = numpy_backend.NumPyBackend() a = backend.convert_to_tensor(np.ones([2, 3, 4])) actual = backend.shape_tuple(a) assert actual == (2, 3, 4)
def test_ones_dtype_2(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
a = backend.ones((4, 4))
assert a.dtype == dtype
def test_prod(): backend = numpy_backend.NumPyBackend() a = backend.convert_to_tensor(2 * np.ones([1, 2, 3, 4])) actual = np.array(backend.prod(a)) assert actual == 2**24
def test_randn_dtype_2(dtype):
backend = numpy_backend.NumPyBackend(dtype=dtype)
a = backend.randn((4, 4))
assert a.dtype == dtype
def test_sqrt():
backend = numpy_backend.NumPyBackend()
a = backend.convert_to_tensor(np.array([4., 9.]))
actual = backend.sqrt(a)
expected = np.array([2, 3])
np.testing.assert_allclose(expected, actual)
def test_backend_dtype_exception():
backend = numpy_backend.NumPyBackend(dtype=np.float32)
tensor = np.random.rand(2, 2, 2)
with pytest.raises(TypeError):
_ = backend.convert_to_tensor(tensor)
def test_norm():
backend = numpy_backend.NumPyBackend()
a = backend.convert_to_tensor(np.ones((2, 2)))
assert backend.norm(a) == 2
def test_reshape(): backend = numpy_backend.NumPyBackend() a = backend.convert_to_tensor(np.ones((2, 3, 4))) actual = backend.shape_tuple(backend.reshape(a, np.array((6, 4, 1)))) assert actual == (6, 4, 1)
def test_zeros(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.zeros((4, 4), dtype=dtype)
np.testing.assert_allclose(np.zeros((4, 4), dtype=dtype), a)
def test_multiply(a, b, expected): backend = numpy_backend.NumPyBackend() tensor1 = backend.convert_to_tensor(a) tensor2 = backend.convert_to_tensor(b) np.testing.assert_allclose(backend.multiply(tensor1, tensor2), expected)
def test_random_uniform_non_zero_imag(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.random_uniform((4, 4), dtype=dtype, seed=10)
assert np.linalg.norm(np.imag(a)) != 0.0
def test_index_update(dtype): backend = numpy_backend.NumPyBackend() tensor = backend.randn((4, 2, 3), dtype=dtype, seed=10) out = backend.index_update(tensor, tensor > 0.1, 0) tensor[tensor > 0.1] = 0.0 np.testing.assert_allclose(tensor, out)
def test_zeros_dtype(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.zeros((4, 4), dtype=dtype)
assert a.dtype == dtype
def test_matrix_inv_raises(dtype):
backend = numpy_backend.NumPyBackend()
matrix = backend.randn((4, 4, 4), dtype=dtype, seed=10)
with pytest.raises(ValueError):
backend.inv(matrix)
def test_randn_seed(dtype):
backend = numpy_backend.NumPyBackend()
a = backend.randn((4, 4), seed=10, dtype=dtype)
b = backend.randn((4, 4), seed=10, dtype=dtype)
np.testing.assert_allclose(a, b)
def test_svd_decomposition():
tensor = np.ones([2, 3, 4, 5, 6])
np_res = numpy_backend.NumPyBackend().svd_decomposition(tensor, 3)
sh_res = shell_backend.ShellBackend().svd_decomposition(tensor, 3)
for x, y in zip(np_res, sh_res):
assert x.shape == y.shape
