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)