def testMatmulError(self):
with self.assertRaisesRegex(ValueError, r''):
np_math_ops.matmul(
np_array_ops.ones([], np.int32), np_array_ops.ones([2, 3], np.int32))
with self.assertRaisesRegex(ValueError, r''):
np_math_ops.matmul(
np_array_ops.ones([2, 3], np.int32), np_array_ops.ones([], np.int32))
def testSize(self):
def run_test(arr, axis=None):
onp_arr = np.array(arr)
self.assertEqual(np_array_ops.size(arr, axis), np.size(onp_arr, axis))
run_test(np_array_ops.array([1]))
run_test(np_array_ops.array([1, 2, 3, 4, 5]))
run_test(np_array_ops.ones((2, 3, 2)))
run_test(np_array_ops.ones((3, 2)))
run_test(np_array_ops.zeros((5, 6, 7)))
run_test(1)
run_test(np_array_ops.ones((3, 2, 1)))
run_test(constant_op.constant(5))
run_test(constant_op.constant([1, 1, 1]))
self.assertRaises(NotImplementedError, np_array_ops.size, np.ones((2, 2)),
1)
@def_function.function(input_signature=[
tensor_spec.TensorSpec(dtype=dtypes.float64, shape=None)])
def f(arr):
arr = np_array_ops.asarray(arr)
return np_array_ops.size(arr)
self.assertEqual(f(np_array_ops.ones((3, 2))).numpy(), 6)
def testOnes(self):
for s in self.all_shapes:
actual = np_array_ops.ones(s)
expected = np.ones(s)
msg = 'shape: {}'.format(s)
self.match(actual, expected, msg)
for s, t in itertools.product(self.all_shapes, self.all_types):
actual = np_array_ops.ones(s, t)
expected = np.ones(s, t)
msg = 'shape: {}, dtype: {}'.format(s, t)
self.match(actual, expected, msg)
def testIndexedSlices(self):
dtype = dtypes.int64
iss = indexed_slices.IndexedSlices(
values=np_array_ops.ones([2, 3], dtype=dtype),
indices=constant_op.constant([1, 9]),
dense_shape=[10, 3])
a = np_array_ops.array(iss, copy=False)
expected = array_ops.scatter_nd([[1], [9]],
array_ops.ones([2, 3], dtype=dtype),
[10, 3])
self.assertAllEqual(expected, a)
