fix docstr issue and add example

Author: autoih

tensorflow_addons/metrics/cohens_kappa.py

@@ -34,25 +34,37 @@ class CohenKappa(Metric):
     A score of 0 means agreement by chance.
     Note: As of now, this implementation considers all labels
     while calculating the Cohen's Kappa score.
+
     Usage:
-    ```python
-    actuals = np.array([4, 4, 3, 4, 2, 4, 1, 1], dtype=np.int32)
-    preds = np.array([4, 4, 3, 4, 4, 2, 1, 1], dtype=np.int32)
-    weights = np.array([1, 1, 2, 5, 10, 2, 3, 3], dtype=np.int32)
-    m = tfa.metrics.CohenKappa(num_classes=5)
-    m.update_state(actuals, preds)
-    print('Final result: ', m.result().numpy()) # Result: 0.61904764
-    # To use this with weights, sample_weight argument can be used.
-    m = tfa.metrics.CohenKappa(num_classes=5)
-    m.update_state(actuals, preds, sample_weight=weights)
-    print('Final result: ', m.result().numpy()) # Result: 0.37209308
-    ```
+    
+    >>> actuals = np.array([4, 4, 3, 4, 2, 4, 1, 1], dtype=np.int32)
+    >>> preds = np.array([4, 4, 3, 4, 4, 2, 1, 1], dtype=np.int32)
+    >>> weights = np.array([1, 1, 2, 5, 10, 2, 3, 3], dtype=np.int32)
+    >>> m = tfa.metrics.CohenKappa(num_classes=5, sparse_labels=True)
+    >>> m.update_state(actuals, preds)
+    <tf.Tensor: shape=(5, 5), dtype=float32, numpy=
+    array([[0., 0., 0., 0., 0.],
+           [0., 2., 0., 0., 0.],
+           [0., 0., 0., 0., 1.],
+           [0., 0., 0., 1., 0.],
+           [0., 0., 1., 0., 3.]], dtype=float32)>
+    >>> m.result().numpy()
+    0.61904764
+    >>> m = tfa.metrics.CohenKappa(num_classes=5, sparse_labels=True)
+    >>> m.update_state(actuals, preds, sample_weight=weights)
+    <tf.Tensor: shape=(5, 5), dtype=float32, numpy=
+    array([[ 0.,  0.,  0.,  0.,  0.],
+           [ 0.,  6.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0., 10.],
+           [ 0.,  0.,  0.,  2.,  0.],
+           [ 0.,  0.,  2.,  0.,  7.]], dtype=float32)>
+    >>> m.result().numpy()
+    0.37209308
+
     Usage with tf.keras API:
-    ```python
     model = tf.keras.models.Model(inputs, outputs)
     model.add_metric(tfa.metrics.CohenKappa(num_classes=5)(outputs))
     model.compile('sgd', loss='mse')
-    ```
     """
 
     @typechecked

tensorflow_addons/metrics/multilabel_confusion_matrix.py

@@ -46,6 +46,36 @@ class MultiLabelConfusionMatrix(Metric):
     - false negatives for class i in M(1,0)
     - true positives for class i in M(1,1)
 
+    Usage:
+    
+    >>> y_true = tf.constant([[1, 0, 1], [0, 1, 0]], dtype=tf.int32)	
+    >>> y_pred = tf.constant([[1, 0, 0],[0, 1, 1]], dtype=tf.int32)
+    >>> output1 = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)	
+    >>> output1.update_state(y_true, y_pred)
+    >>> output1.result()
+    <tf.Tensor: shape=(3, 2, 2), dtype=float32, numpy=
+    array([[[1., 0.],
+            [0., 1.]],
+    <BLANKLINE>
+           [[1., 0.],
+            [0., 1.]],
+    <BLANKLINE>
+           [[0., 1.],
+            [1., 0.]]], dtype=float32)>
+    >>> y_true = tf.constant([[1, 0, 0], [0, 1, 0]], dtype=tf.int32)	
+    >>> y_pred = tf.constant([[1, 0, 0],[0, 0, 1]], dtype=tf.int32)	
+    >>> output2 = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)	
+    >>> output2.update_state(y_true, y_pred)	
+    >>> output2.result()
+    <tf.Tensor: shape=(3, 2, 2), dtype=float32, numpy=
+    array([[[1., 0.],
+            [0., 1.]],
+    <BLANKLINE>
+           [[1., 0.],
+            [1., 0.]],
+    <BLANKLINE>
+           [[1., 1.],
+            [0., 0.]]], dtype=float32)>
     """
 
     @typechecked