diff --git a/tensorflow_addons/metrics/cohens_kappa.py b/tensorflow_addons/metrics/cohens_kappa.py
index 3f9340e5f7..2b14c20310 100644
--- a/tensorflow_addons/metrics/cohens_kappa.py
+++ b/tensorflow_addons/metrics/cohens_kappa.py
@@ -93,7 +93,7 @@ def __init__(self,
             'conf_mtx',
             shape=(self.num_classes, self.num_classes),
             initializer=tf.keras.initializers.zeros,
-            dtype=tf.int64)
+            dtype=tf.float32)
 
     def update_state(self, y_true, y_pred, sample_weight=None):
         """Accumulates the confusion matrix condition statistics.
@@ -125,21 +125,21 @@ def update_state(self, y_true, y_pred, sample_weight=None):
             predictions=y_pred,
             num_classes=self.num_classes,
             weights=sample_weight,
-            dtype=tf.int64)
+            dtype=tf.float32)
 
         # update the values in the original confusion matrix
         return self.conf_mtx.assign_add(new_conf_mtx)
 
     def result(self):
         nb_ratings = tf.shape(self.conf_mtx)[0]
-        weight_mtx = tf.ones([nb_ratings, nb_ratings], dtype=tf.int64)
+        weight_mtx = tf.ones([nb_ratings, nb_ratings], dtype=tf.float32)
 
         # 2. Create a weight matrix
         if self.weightage is None:
-            diagonal = tf.zeros([nb_ratings], dtype=tf.int64)
+            diagonal = tf.zeros([nb_ratings], dtype=tf.float32)
             weight_mtx = tf.linalg.set_diag(weight_mtx, diagonal=diagonal)
         else:
-            weight_mtx += tf.cast(tf.range(nb_ratings), dtype=tf.int64)
+            weight_mtx += tf.cast(tf.range(nb_ratings), dtype=tf.float32)
             weight_mtx = tf.cast(weight_mtx, dtype=self.dtype)
 
             if self.weightage == 'linear':
@@ -167,8 +167,10 @@ def result(self):
         # 6. Calculate Kappa score
         numerator = tf.reduce_sum(conf_mtx * weight_mtx)
         denominator = tf.reduce_sum(out_prod * weight_mtx)
-        kp = 1 - (numerator / denominator)
-        return kp
+        return tf.cond(
+            tf.math.is_nan(denominator),
+            true_fn=lambda: 0.0,
+            false_fn=lambda: 1 - (numerator / denominator))
 
     def get_config(self):
         """Returns the serializable config of the metric."""
diff --git a/tensorflow_addons/metrics/cohens_kappa_test.py b/tensorflow_addons/metrics/cohens_kappa_test.py
index efb33e4fc0..40b0c9a0a1 100644
--- a/tensorflow_addons/metrics/cohens_kappa_test.py
+++ b/tensorflow_addons/metrics/cohens_kappa_test.py
@@ -56,6 +56,11 @@ def update_obj_states(self, obj1, obj2, obj3, actuals, preds, weights):
         self.evaluate(update_op2)
         self.evaluate(update_op3)
 
+    def reset_obj_states(self, obj1, obj2, obj3):
+        obj1.reset_states()
+        obj2.reset_states()
+        obj3.reset_states()
+
     def check_results(self, objs, values):
         obj1, obj2, obj3 = objs
         val1, val2, val3 = values
@@ -130,6 +135,16 @@ def test_kappa_with_sample_weights(self):
         self.check_results([kp_obj1, kp_obj2, kp_obj3],
                            [-0.25473321, -0.38992332, -0.60695344])
 
+    def test_kappa_reset_states(self):
+        # Initialize
+        kp_obj1, kp_obj2, kp_obj3 = self.initialize_vars()
+
+        # reset states
+        self.reset_obj_states(kp_obj1, kp_obj2, kp_obj3)
+
+        # check results
+        self.check_results([kp_obj1, kp_obj2, kp_obj3], [0.0, 0.0, 0.0])
+
     def test_large_values(self):
         y_true = [1] * 10000 + [0] * 20000 + [1] * 20000
         y_pred = [0] * 20000 + [1] * 30000