From efb8b10982c05f270c7c7623a79ab269fe00f960 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Mon, 31 Aug 2020 00:02:50 +0530
Subject: [PATCH 01/20] updated contrastive.py

---
 tensorflow_addons/losses/contrastive.py | 35 ++++++++++++++++++-------
 1 file changed, 25 insertions(+), 10 deletions(-)

diff --git a/tensorflow_addons/losses/contrastive.py b/tensorflow_addons/losses/contrastive.py
index c6a9e9f826..28f394dfb5 100644
--- a/tensorflow_addons/losses/contrastive.py
+++ b/tensorflow_addons/losses/contrastive.py
@@ -15,10 +15,9 @@
 """Implements contrastive loss."""
 
 import tensorflow as tf
-
+from typeguard import typechecked
 from tensorflow_addons.utils.keras_utils import LossFunctionWrapper
 from tensorflow_addons.utils.types import TensorLike, Number
-from typeguard import typechecked
 
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
@@ -36,10 +35,18 @@ def contrastive_loss(
     `a` and `b` with shape `[batch_size, hidden_size]` can be computed
     as follows:
 
-    ```python
-    # y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)
-    y_pred = tf.linalg.norm(a - b, axis=1)
-    ```
+    >>> a = tf.constant([[1, 2],
+    ...                 [3, 4],
+    ...                 [5, 6]], dtype=tf.float16)
+    >>> b = tf.constant([[5, 9],
+    ...                 [3, 6],
+    ...                 [1, 8]], dtype=tf.float16)
+    >>> y_pred = tf.linalg.norm(a - b, axis=1)
+    <tf.Tensor: shape=(3,), dtype=float16, numpy=
+    array([8.06 , 2.   , 4.473], dtype=float16)>
+    <y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)>
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
 
     See: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
 
@@ -79,10 +86,18 @@ class ContrastiveLoss(LossFunctionWrapper):
     `a` and `b` with shape `[batch_size, hidden_size]` can be computed
     as follows:
 
-    ```python
-    # y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)
-    y_pred = tf.linalg.norm(a - b, axis=1)
-    ```
+    >>> a = tf.constant([[1, 2],
+    ...                 [3, 4],
+    ...                 [5, 6]], dtype=tf.float16)
+    >>> b = tf.constant([[5, 9],
+    ...                 [3, 6],
+    ...                 [1, 8]], dtype=tf.float16)
+    >>> y_pred = tf.linalg.norm(a - b, axis=1)
+    <tf.Tensor: shape=(3,), dtype=float16, numpy=
+    array([8.06 , 2.   , 4.473], dtype=float16)>
+    <y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)>
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
 
     Args:
       margin: `Float`, margin term in the loss definition.

From ed45e85ce3636464bc7a3d9c8473602d8bb3467f Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Mon, 31 Aug 2020 00:03:11 +0530
Subject: [PATCH 02/20] updated focal_loss.py

---
 tensorflow_addons/losses/focal_loss.py | 24 +++++++++---------------
 1 file changed, 9 insertions(+), 15 deletions(-)

diff --git a/tensorflow_addons/losses/focal_loss.py b/tensorflow_addons/losses/focal_loss.py
index 8f29951277..bf98643dff 100644
--- a/tensorflow_addons/losses/focal_loss.py
+++ b/tensorflow_addons/losses/focal_loss.py
@@ -16,10 +16,9 @@
 
 import tensorflow as tf
 import tensorflow.keras.backend as K
-
+from typeguard import typechecked
 from tensorflow_addons.utils.keras_utils import LossFunctionWrapper
 from tensorflow_addons.utils.types import FloatTensorLike, TensorLike
-from typeguard import typechecked
 
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
@@ -37,22 +36,17 @@ class SigmoidFocalCrossEntropy(LossFunctionWrapper):
 
     Usage:
 
-    ```python
-    fl = tfa.losses.SigmoidFocalCrossEntropy()
-    loss = fl(
-      y_true = [[1.0], [1.0], [0.0]],
-      y_pred = [[0.97], [0.91], [0.03]])
-    print('Loss: ', loss.numpy())  # Loss: [6.8532745e-06,
-                                            1.9097870e-04,
-                                            2.0559824e-05]
-    ```
+    >>> fl = tfa.losses.SigmoidFocalCrossEntropy()
+    >>> loss = fl(
+    ...    y_true = [[1.0], [1.0], [0.0]],
+    ...    y_pred = [[0.97], [0.91], [0.03]])
+    >>> print('Loss: ', loss.numpy())
+    <Loss:  [6.8532745e-06 1.9097870e-04 2.0559824e-05]>
 
     Usage with `tf.keras` API:
 
-    ```python
-    model = tf.keras.Model(inputs, outputs)
-    model.compile('sgd', loss=tfa.losses.SigmoidFocalCrossEntropy())
-    ```
+    >>> model = tf.keras.Model()
+    >>> model.compile('sgd', loss=tfa.losses.SigmoidFocalCrossEntropy())
 
     Args:
       alpha: balancing factor, default value is 0.25.

From a898b40eb9297c71ee84b2eb4b8b31daf213de35 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Mon, 31 Aug 2020 00:03:27 +0530
Subject: [PATCH 03/20] updated giou_loss.py

---
 tensorflow_addons/losses/giou_loss.py | 28 +++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/tensorflow_addons/losses/giou_loss.py b/tensorflow_addons/losses/giou_loss.py
index e789fcacb1..dc9ee5ec60 100644
--- a/tensorflow_addons/losses/giou_loss.py
+++ b/tensorflow_addons/losses/giou_loss.py
@@ -14,11 +14,11 @@
 # ==============================================================================
 """Implements GIoU loss."""
 
+from typing import Optional
 import tensorflow as tf
+from typeguard import typechecked
 from tensorflow_addons.utils.keras_utils import LossFunctionWrapper
 from tensorflow_addons.utils.types import TensorLike
-from typing import Optional
-from typeguard import typechecked
 
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
@@ -33,20 +33,18 @@ class GIoULoss(LossFunctionWrapper):
 
     Usage:
 
-    ```python
-    gl = tfa.losses.GIoULoss()
-    boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
-    boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]])
-    loss = gl(boxes1, boxes2)
-    print('Loss: ', loss.numpy())  # Loss: [1.07500000298023224, 1.9333333373069763]
+    >>> gl = tfa.losses.GIoULoss()
+    >>> boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
+    >>> boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]])
+    >>> loss = gl(boxes1, boxes2)
+    >>> print('Loss: ', loss.numpy())
+    <Loss: [1.07500000298023224, 1.9333333373069763]>
     ```
 
     Usage with `tf.keras` API:
 
-    ```python
-    model = tf.keras.Model(inputs, outputs)
-    model.compile('sgd', loss=tfa.losses.GIoULoss())
-    ```
+    >>> model = tf.keras.Model()
+    >>> model.compile('sgd', loss=tfa.losses.GIoULoss())
 
     Args:
       mode: one of ['giou', 'iou'], decided to calculate GIoU or IoU loss.
@@ -63,7 +61,8 @@ def __init__(
 
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
-def giou_loss(y_true: TensorLike, y_pred: TensorLike, mode: str = "giou") -> tf.Tensor:
+def giou_loss(y_true: TensorLike, y_pred: TensorLike,
+    mode: str = "giou") -> tf.Tensor:
     """Implements the GIoU loss function.
 
     GIoU loss was first introduced in the
@@ -93,7 +92,8 @@ def giou_loss(y_true: TensorLike, y_pred: TensorLike, mode: str = "giou") -> tf.
     return 1 - giou
 
 
-def _calculate_giou(b1: TensorLike, b2: TensorLike, mode: str = "giou") -> tf.Tensor:
+def _calculate_giou(b1: TensorLike, b2: TensorLike,
+    mode: str = "giou") -> tf.Tensor:
     """
     Args:
         b1: bounding box. The coordinates of the each bounding box in boxes are

From 1e66c19785751dde027b9dfeb50c772aeb52590a Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Mon, 31 Aug 2020 00:03:41 +0530
Subject: [PATCH 04/20] updated kappa_loss.py

---
 tensorflow_addons/losses/kappa_loss.py | 37 +++++++++++++-------------
 1 file changed, 18 insertions(+), 19 deletions(-)

diff --git a/tensorflow_addons/losses/kappa_loss.py b/tensorflow_addons/losses/kappa_loss.py
index c041cb8b01..7982f7bd62 100644
--- a/tensorflow_addons/losses/kappa_loss.py
+++ b/tensorflow_addons/losses/kappa_loss.py
@@ -37,24 +37,23 @@ class WeightedKappaLoss(tf.keras.losses.Loss):
 
     Usage:
 
-    ```python
-    kappa_loss = WeightedKappaLoss(num_classes=4)
-    y_true = tf.constant([[0, 0, 1, 0], [0, 1, 0, 0],
-                          [1, 0, 0, 0], [0, 0, 0, 1]])
-    y_pred = tf.constant([[0.1, 0.2, 0.6, 0.1], [0.1, 0.5, 0.3, 0.1],
-                          [0.8, 0.05, 0.05, 0.1], [0.01, 0.09, 0.1, 0.8]])
-    loss = kappa_loss(y_true, y_pred)
-    print('Loss: ', loss.numpy())  # Loss: -1.1611923
-    ```
+    >>> kappa_loss = tfa.losses.WeightedKappaLoss(num_classes=4)
+    >>> y_true = tf.constant([[0, 0, 1, 0], [0, 1, 0, 0],
+    ...                  [1, 0, 0, 0], [0, 0, 0, 1]])
+    >>> y_pred = tf.constant([[0.1, 0.2, 0.6, 0.1], [0.1, 0.5, 0.3, 0.1],
+    ...                  [0.8, 0.05, 0.05, 0.1], [0.01, 0.09, 0.1, 0.8]])
+    >>> loss = kappa_loss(y_true, y_pred)
+    >>> print('Loss: ', loss.numpy())
+    <Loss:  -1.1611925>
 
     Usage with `tf.keras` API:
-    ```python
-    # outputs should be softmax results
-    # if you want to weight the samples, just multiply the outputs
-    # by the sample weight.
-    model = tf.keras.Model(inputs, outputs)
-    model.compile('sgd', loss=tfa.losses.WeightedKappa(num_classes=4))
-    ```
+
+    >>> model = tf.keras.Model()
+    >>> model.compile('sgd', loss=tfa.losses.WeightedKappa(num_classes=4))
+    < outputs should be softmax results
+    if you want to weight the samples, just multiply the outputs
+    by the sample weight.>
+
     """
 
     @typechecked
@@ -111,8 +110,8 @@ def __init__(
             self.weight_mat = (col_mat - row_mat) ** 2
 
     def call(self, y_true, y_pred):
-        y_true = tf.cast(y_true, dtype=self.col_label_vec.dtype)
-        y_pred = tf.cast(y_pred, dtype=self.weight_mat.dtype)
+        y_true = tf.cast(y_true, self.col_label_vec.dtype)
+        y_pred = tf.cast(y_pred, self.weight_mat.dtype)
         batch_size = tf.shape(y_true)[0]
         cat_labels = tf.matmul(y_true, self.col_label_vec)
         cat_label_mat = tf.tile(cat_labels, [1, self.num_classes])
@@ -126,7 +125,7 @@ def call(self, y_true, y_pred):
         pred_dist = tf.reduce_sum(y_pred, axis=0, keepdims=True)
         w_pred_dist = tf.matmul(self.weight_mat, pred_dist, transpose_b=True)
         denominator = tf.reduce_sum(tf.matmul(label_dist, w_pred_dist))
-        denominator /= tf.cast(batch_size, dtype=denominator.dtype)
+        denominator /= tf.cast(batch_size, denominator.dtype)
         loss = tf.math.divide_no_nan(numerator, denominator)
         return tf.math.log(loss + self.epsilon)
 

From ed2292109fad7a5098cad036dc4c72c74bad27c3 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Mon, 31 Aug 2020 00:04:06 +0530
Subject: [PATCH 05/20] updated npairs.py

---
 tensorflow_addons/losses/npairs.py | 72 +++++++++++++++++++++++-------
 1 file changed, 55 insertions(+), 17 deletions(-)

diff --git a/tensorflow_addons/losses/npairs.py b/tensorflow_addons/losses/npairs.py
index 1693123505..094e4b0ff1 100644
--- a/tensorflow_addons/losses/npairs.py
+++ b/tensorflow_addons/losses/npairs.py
@@ -16,8 +16,8 @@
 
 import tensorflow as tf
 
-from tensorflow_addons.utils.types import TensorLike
 from typeguard import typechecked
+from tensorflow_addons.utils.types import TensorLike
 
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
@@ -33,10 +33,21 @@ def npairs_loss(y_true: TensorLike, y_pred: TensorLike) -> tf.Tensor:
     The similarity matrix `y_pred` between two embedding matrices `a` and `b`
     with shape `[batch_size, hidden_size]` can be computed as follows:
 
-    ```python
-    # y_pred = a * b^T
-    y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
-    ```
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
+    >>> a = tf.constant([[1, 2],
+    ...                 [3, 4],
+    ...                 [5, 6]], dtype=tf.float16)
+    >>> b = tf.constant([[5, 9],
+    ...                 [3, 6],
+    ...                 [1, 8]], dtype=tf.float16)
+    >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
+    array([[23., 15., 17.],
+       [51., 33., 35.],
+       [79., 51., 53.]], dtype=float16)>
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 
@@ -89,10 +100,19 @@ def npairs_multilabel_loss(y_true: TensorLike, y_pred: TensorLike) -> tf.Tensor:
     The similarity matrix `y_pred` between two embedding matrices `a` and `b`
     with shape `[batch_size, hidden_size]` can be computed as follows:
 
-    ```python
-    # y_pred = a * b^T
-    y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
-    ```
+    >>> a = tf.constant([[1, 2],
+    ...                 [3, 4],
+    ...                 [5, 6]], dtype=tf.float16)
+    >>> b = tf.constant([[5, 9],
+    ...                 [3, 6],
+    ...                 [1, 8]], dtype=tf.float16)
+    >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
+    array([[23., 15., 17.],
+       [51., 33., 35.],
+       [79., 51., 53.]], dtype=float16)>
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 
@@ -139,10 +159,19 @@ class NpairsLoss(tf.keras.losses.Loss):
     The similarity matrix `y_pred` between two embedding matrices `a` and `b`
     with shape `[batch_size, hidden_size]` can be computed as follows:
 
-    ```python
-    # y_pred = a * b^T
-    y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
-    ```
+    >>> a = tf.constant([[1, 2],
+    ...                 [3, 4],
+    ...                 [5, 6]], dtype=tf.float16)
+    >>> b = tf.constant([[5, 9],
+    ...                 [3, 6],
+    ...                 [1, 8]], dtype=tf.float16)
+    >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
+    array([[23., 15., 17.],
+       [51., 33., 35.],
+       [79., 51., 53.]], dtype=float16)>
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 
@@ -184,10 +213,19 @@ class NpairsMultilabelLoss(tf.keras.losses.Loss):
     The similarity matrix `y_pred` between two embedding matrices `a` and `b`
     with shape `[batch_size, hidden_size]` can be computed as follows:
 
-    ```python
-    # y_pred = a * b^T
-    y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
-    ```
+    >>> a = tf.constant([[1, 2],
+    ...                 [3, 4],
+    ...                 [5, 6]], dtype=tf.float16)
+    >>> b = tf.constant([[5, 9],
+    ...                 [3, 6],
+    ...                 [1, 8]], dtype=tf.float16)
+    >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
+    array([[23., 15., 17.],
+       [51., 33., 35.],
+       [79., 51., 53.]], dtype=float16)>
+    <Note: constants a & b have been used purely for
+    example purposes and have no significant value>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 

From 3e34a9bdfe92ff05fd8d9dfc4656f4550fe146cc Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Mon, 31 Aug 2020 00:04:22 +0530
Subject: [PATCH 06/20] updated quantiles.py

---
 tensorflow_addons/losses/quantiles.py | 31 +++++++++++----------------
 1 file changed, 12 insertions(+), 19 deletions(-)

diff --git a/tensorflow_addons/losses/quantiles.py b/tensorflow_addons/losses/quantiles.py
index 2aad531294..037e37c2d9 100644
--- a/tensorflow_addons/losses/quantiles.py
+++ b/tensorflow_addons/losses/quantiles.py
@@ -35,14 +35,11 @@ def pinball_loss(
     See: https://en.wikipedia.org/wiki/Quantile_regression
 
     Usage:
-    ```python
-    loss = pinball_loss([0., 0., 1., 1.], [1., 1., 1., 0.], tau=.1)
 
-    # loss = max(0.1 * (y_true - y_pred), (0.1 - 1) * (y_true - y_pred))
-    #      = (0.9 + 0.9 + 0 + 0.1) / 4
-
-    print('Loss: ', loss.numpy())  # Loss: 0.475
-    ```
+    >>> loss = tfa.losses.pinball_loss([0., 0., 1., 1.],
+    ... [1., 1., 1., 0.], tau=.1)
+    >>> print('Loss: ', loss.numpy())
+    <Loss:  0.475>
 
     Args:
       y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`
@@ -84,22 +81,18 @@ class PinballLoss(LossFunctionWrapper):
     See: https://en.wikipedia.org/wiki/Quantile_regression
 
     Usage:
-    ```python
-    pinball = tfa.losses.PinballLoss(tau=.1)
-    loss = pinball([0., 0., 1., 1.], [1., 1., 1., 0.])
-
-    # loss = max(0.1 * (y_true - y_pred), (0.1 - 1) * (y_true - y_pred))
-    #      = (0.9 + 0.9 + 0 + 0.1) / 4
 
-    print('Loss: ', loss.numpy())  # Loss: 0.475
-    ```
+    >>> pinball = tfa.losses.PinballLoss(tau=.1)
+    <tensorflow_addons.losses.quantiles.PinballLoss object at 0x102f49df0>
+    >>> loss = pinball([0., 0., 1., 1.], [1., 1., 1., 0.])
+    >>> print('Loss: ', loss.numpy())
+    <Loss:  0.475>
 
     Usage with the `tf.keras` API:
 
-    ```python
-    model = tf.keras.Model(inputs, outputs)
-    model.compile('sgd', loss=tfa.losses.PinballLoss(tau=.1))
-    ```
+    >>> model = tf.keras.Model()
+    >>> model.compile('sgd', loss=tfa.losses.PinballLoss(tau=.1))
+    <tensorflow.python.keras.engine.training.Model object at 0x103040310>
 
     Args:
       tau: (Optional) Float in [0, 1] or a tensor taking values in [0, 1] and

From 0e6a5c99e8e7471477137ee896cec502fd424cbc Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 10:35:03 +0530
Subject: [PATCH 07/20] reformatting giou_loss.py

---
 tensorflow_addons/losses/giou_loss.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/tensorflow_addons/losses/giou_loss.py b/tensorflow_addons/losses/giou_loss.py
index dc9ee5ec60..10856e712f 100644
--- a/tensorflow_addons/losses/giou_loss.py
+++ b/tensorflow_addons/losses/giou_loss.py
@@ -61,8 +61,7 @@ def __init__(
 
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
-def giou_loss(y_true: TensorLike, y_pred: TensorLike,
-    mode: str = "giou") -> tf.Tensor:
+def giou_loss(y_true: TensorLike, y_pred: TensorLike, mode: str = "giou") -> tf.Tensor:
     """Implements the GIoU loss function.
 
     GIoU loss was first introduced in the
@@ -92,8 +91,7 @@ def giou_loss(y_true: TensorLike, y_pred: TensorLike,
     return 1 - giou
 
 
-def _calculate_giou(b1: TensorLike, b2: TensorLike,
-    mode: str = "giou") -> tf.Tensor:
+def _calculate_giou(b1: TensorLike, b2: TensorLike, mode: str = "giou") -> tf.Tensor:
     """
     Args:
         b1: bounding box. The coordinates of the each bounding box in boxes are

From 16df78319439982f41a92fb518e0e13d1da5676e Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 18:02:09 +0530
Subject: [PATCH 08/20] updated testable docs contrastive.py

---
 tensorflow_addons/losses/contrastive.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/tensorflow_addons/losses/contrastive.py b/tensorflow_addons/losses/contrastive.py
index 28f394dfb5..54f63d0591 100644
--- a/tensorflow_addons/losses/contrastive.py
+++ b/tensorflow_addons/losses/contrastive.py
@@ -42,11 +42,12 @@ def contrastive_loss(
     ...                 [3, 6],
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.linalg.norm(a - b, axis=1)
+    >>> y_pred
     <tf.Tensor: shape=(3,), dtype=float16, numpy=
     array([8.06 , 2.   , 4.473], dtype=float16)>
-    <y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)>
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
+
+    <... Note: constants a & b have been used purely for
+    example purposes and have no significant value ...>
 
     See: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
 
@@ -93,11 +94,12 @@ class ContrastiveLoss(LossFunctionWrapper):
     ...                 [3, 6],
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.linalg.norm(a - b, axis=1)
+    >>> y_pred
     <tf.Tensor: shape=(3,), dtype=float16, numpy=
     array([8.06 , 2.   , 4.473], dtype=float16)>
-    <y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)>
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
+
+    <... Note: constants a & b have been used purely for
+    example purposes and have no significant value ...>
 
     Args:
       margin: `Float`, margin term in the loss definition.

From 4d57abdb0018fc5cb787e3ce4032b6c5e3b60f16 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 18:03:11 +0530
Subject: [PATCH 09/20] updated doctests focal_loss.py

---
 tensorflow_addons/losses/focal_loss.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow_addons/losses/focal_loss.py b/tensorflow_addons/losses/focal_loss.py
index bf98643dff..3e1ad2863b 100644
--- a/tensorflow_addons/losses/focal_loss.py
+++ b/tensorflow_addons/losses/focal_loss.py
@@ -40,8 +40,9 @@ class SigmoidFocalCrossEntropy(LossFunctionWrapper):
     >>> loss = fl(
     ...    y_true = [[1.0], [1.0], [0.0]],
     ...    y_pred = [[0.97], [0.91], [0.03]])
-    >>> print('Loss: ', loss.numpy())
-    <Loss:  [6.8532745e-06 1.9097870e-04 2.0559824e-05]>
+    >>>loss
+    <tf.Tensor: shape=(3,), dtype=float32, numpy=
+    array([6.8532745e-06, 1.9097870e-04, 2.0559824e-05], dtype=float32)>
 
     Usage with `tf.keras` API:
 

From d7d6a167b9449956cda06df82777d427e5a87052 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 18:04:03 +0530
Subject: [PATCH 10/20] updated testdocs giou_loss.py

---
 tensorflow_addons/losses/giou_loss.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tensorflow_addons/losses/giou_loss.py b/tensorflow_addons/losses/giou_loss.py
index 10856e712f..6f508609bb 100644
--- a/tensorflow_addons/losses/giou_loss.py
+++ b/tensorflow_addons/losses/giou_loss.py
@@ -37,9 +37,8 @@ class GIoULoss(LossFunctionWrapper):
     >>> boxes1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
     >>> boxes2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0]])
     >>> loss = gl(boxes1, boxes2)
-    >>> print('Loss: ', loss.numpy())
-    <Loss: [1.07500000298023224, 1.9333333373069763]>
-    ```
+    >>> loss
+    <tf.Tensor: shape=(), dtype=float32, numpy=1.5041667>
 
     Usage with `tf.keras` API:
 

From 3bef6eef16d3cabbfa7e33ef167c78831c36b0c6 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 18:04:49 +0530
Subject: [PATCH 11/20] updated testdocs kappa_loss.py

---
 tensorflow_addons/losses/kappa_loss.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/tensorflow_addons/losses/kappa_loss.py b/tensorflow_addons/losses/kappa_loss.py
index 7982f7bd62..862cc5212c 100644
--- a/tensorflow_addons/losses/kappa_loss.py
+++ b/tensorflow_addons/losses/kappa_loss.py
@@ -43,16 +43,17 @@ class WeightedKappaLoss(tf.keras.losses.Loss):
     >>> y_pred = tf.constant([[0.1, 0.2, 0.6, 0.1], [0.1, 0.5, 0.3, 0.1],
     ...                  [0.8, 0.05, 0.05, 0.1], [0.01, 0.09, 0.1, 0.8]])
     >>> loss = kappa_loss(y_true, y_pred)
-    >>> print('Loss: ', loss.numpy())
-    <Loss:  -1.1611925>
+    >>> loss
+    <tf.Tensor: shape=(), dtype=float32, numpy=-1.1611925>
 
     Usage with `tf.keras` API:
 
     >>> model = tf.keras.Model()
-    >>> model.compile('sgd', loss=tfa.losses.WeightedKappa(num_classes=4))
-    < outputs should be softmax results
+    >>> model.compile('sgd', loss=tfa.losses.WeightedKappaLoss(num_classes=4))
+
+    <... outputs should be softmax results
     if you want to weight the samples, just multiply the outputs
-    by the sample weight.>
+    by the sample weight ...>
 
     """
 

From 7fb5bfb7147cd7cac7e059b0fa0dc7d4a7c9b5b0 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 18:05:30 +0530
Subject: [PATCH 12/20] updated testdocs npairs.py

---
 tensorflow_addons/losses/npairs.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/tensorflow_addons/losses/npairs.py b/tensorflow_addons/losses/npairs.py
index 094e4b0ff1..a761e6de48 100644
--- a/tensorflow_addons/losses/npairs.py
+++ b/tensorflow_addons/losses/npairs.py
@@ -33,8 +33,6 @@ def npairs_loss(y_true: TensorLike, y_pred: TensorLike) -> tf.Tensor:
     The similarity matrix `y_pred` between two embedding matrices `a` and `b`
     with shape `[batch_size, hidden_size]` can be computed as follows:
 
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
     >>> a = tf.constant([[1, 2],
     ...                 [3, 4],
     ...                 [5, 6]], dtype=tf.float16)
@@ -42,12 +40,14 @@ def npairs_loss(y_true: TensorLike, y_pred: TensorLike) -> tf.Tensor:
     ...                 [3, 6],
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    >>> y_pred
     <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
     array([[23., 15., 17.],
        [51., 33., 35.],
        [79., 51., 53.]], dtype=float16)>
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
+
+    <... Note: constants a & b have been used purely for
+    example purposes and have no significant value ...>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 
@@ -107,12 +107,14 @@ def npairs_multilabel_loss(y_true: TensorLike, y_pred: TensorLike) -> tf.Tensor:
     ...                 [3, 6],
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    >>> y_pred
     <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
     array([[23., 15., 17.],
        [51., 33., 35.],
        [79., 51., 53.]], dtype=float16)>
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
+
+    <... Note: constants a & b have been used purely for
+    example purposes and have no significant value ...>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 
@@ -166,12 +168,14 @@ class NpairsLoss(tf.keras.losses.Loss):
     ...                 [3, 6],
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    >>> y_pred
     <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
     array([[23., 15., 17.],
        [51., 33., 35.],
        [79., 51., 53.]], dtype=float16)>
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
+
+    <... Note: constants a & b have been used purely for
+    example purposes and have no significant value ...>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 
@@ -220,12 +224,14 @@ class NpairsMultilabelLoss(tf.keras.losses.Loss):
     ...                 [3, 6],
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
+    >>> y_pred
     <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
     array([[23., 15., 17.],
        [51., 33., 35.],
        [79., 51., 53.]], dtype=float16)>
-    <Note: constants a & b have been used purely for
-    example purposes and have no significant value>
+
+    <... Note: constants a & b have been used purely for
+    example purposes and have no significant value ...>
 
     See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
 

From 91dd450ce12b121e35b690ab5041a9dcbac3c3f6 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Tue, 1 Sep 2020 18:07:08 +0530
Subject: [PATCH 13/20] updated testdocs quantiles.py

---
 tensorflow_addons/losses/quantiles.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/tensorflow_addons/losses/quantiles.py b/tensorflow_addons/losses/quantiles.py
index 037e37c2d9..513ffd2398 100644
--- a/tensorflow_addons/losses/quantiles.py
+++ b/tensorflow_addons/losses/quantiles.py
@@ -38,8 +38,8 @@ def pinball_loss(
 
     >>> loss = tfa.losses.pinball_loss([0., 0., 1., 1.],
     ... [1., 1., 1., 0.], tau=.1)
-    >>> print('Loss: ', loss.numpy())
-    <Loss:  0.475>
+    >>> loss
+    <tf.Tensor: shape=(), dtype=float32, numpy=0.475>
 
     Args:
       y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`
@@ -83,16 +83,14 @@ class PinballLoss(LossFunctionWrapper):
     Usage:
 
     >>> pinball = tfa.losses.PinballLoss(tau=.1)
-    <tensorflow_addons.losses.quantiles.PinballLoss object at 0x102f49df0>
     >>> loss = pinball([0., 0., 1., 1.], [1., 1., 1., 0.])
-    >>> print('Loss: ', loss.numpy())
-    <Loss:  0.475>
+    >>> loss
+    <tf.Tensor: shape=(), dtype=float32, numpy=0.475>
 
     Usage with the `tf.keras` API:
 
     >>> model = tf.keras.Model()
     >>> model.compile('sgd', loss=tfa.losses.PinballLoss(tau=.1))
-    <tensorflow.python.keras.engine.training.Model object at 0x103040310>
 
     Args:
       tau: (Optional) Float in [0, 1] or a tensor taking values in [0, 1] and

From ff0846aadd8d26f086fee81ce29f2e89afaad46e Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Wed, 2 Sep 2020 12:06:47 +0530
Subject: [PATCH 14/20] minor changes to formatting

---
 tensorflow_addons/losses/focal_loss.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow_addons/losses/focal_loss.py b/tensorflow_addons/losses/focal_loss.py
index 3e1ad2863b..916144e916 100644
--- a/tensorflow_addons/losses/focal_loss.py
+++ b/tensorflow_addons/losses/focal_loss.py
@@ -40,7 +40,7 @@ class SigmoidFocalCrossEntropy(LossFunctionWrapper):
     >>> loss = fl(
     ...    y_true = [[1.0], [1.0], [0.0]],
     ...    y_pred = [[0.97], [0.91], [0.03]])
-    >>>loss
+    >>> loss
     <tf.Tensor: shape=(3,), dtype=float32, numpy=
     array([6.8532745e-06, 1.9097870e-04, 2.0559824e-05], dtype=float32)>
 

From 51d8eb6786559a106a318eb0767c7c53927d494f Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Wed, 2 Sep 2020 21:07:05 +0530
Subject: [PATCH 15/20] fixing multi-line error

---
 tensorflow_addons/losses/contrastive.py | 14 ++++++--------
 tensorflow_addons/losses/focal_loss.py  |  7 +++----
 2 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/tensorflow_addons/losses/contrastive.py b/tensorflow_addons/losses/contrastive.py
index 54f63d0591..aa6a50421c 100644
--- a/tensorflow_addons/losses/contrastive.py
+++ b/tensorflow_addons/losses/contrastive.py
@@ -43,8 +43,8 @@ def contrastive_loss(
     ...                 [1, 8]], dtype=tf.float16)
     >>> y_pred = tf.linalg.norm(a - b, axis=1)
     >>> y_pred
-    <tf.Tensor: shape=(3,), dtype=float16, numpy=
-    array([8.06 , 2.   , 4.473], dtype=float16)>
+    <tf.Tensor: shape=(3,), dtype=float16, numpy=array([8.06 , 2.   , 4.473],
+    dtype=float16)>
 
     <... Note: constants a & b have been used purely for
     example purposes and have no significant value ...>
@@ -88,15 +88,13 @@ class ContrastiveLoss(LossFunctionWrapper):
     as follows:
 
     >>> a = tf.constant([[1, 2],
-    ...                 [3, 4],
-    ...                 [5, 6]], dtype=tf.float16)
+    ...                 [3, 4],[5, 6]], dtype=tf.float16)
     >>> b = tf.constant([[5, 9],
-    ...                 [3, 6],
-    ...                 [1, 8]], dtype=tf.float16)
+    ...                 [3, 6],[1, 8]], dtype=tf.float16)
     >>> y_pred = tf.linalg.norm(a - b, axis=1)
     >>> y_pred
-    <tf.Tensor: shape=(3,), dtype=float16, numpy=
-    array([8.06 , 2.   , 4.473], dtype=float16)>
+    <tf.Tensor: shape=(3,), dtype=float16, numpy=array([8.06 , 2.   , 4.473],
+    dtype=float16)>
 
     <... Note: constants a & b have been used purely for
     example purposes and have no significant value ...>
diff --git a/tensorflow_addons/losses/focal_loss.py b/tensorflow_addons/losses/focal_loss.py
index 916144e916..2e2028651b 100644
--- a/tensorflow_addons/losses/focal_loss.py
+++ b/tensorflow_addons/losses/focal_loss.py
@@ -38,11 +38,10 @@ class SigmoidFocalCrossEntropy(LossFunctionWrapper):
 
     >>> fl = tfa.losses.SigmoidFocalCrossEntropy()
     >>> loss = fl(
-    ...    y_true = [[1.0], [1.0], [0.0]],
-    ...    y_pred = [[0.97], [0.91], [0.03]])
+    ...     y_true = [[1.0], [1.0], [0.0]],y_pred = [[0.97], [0.91], [0.03]])
     >>> loss
-    <tf.Tensor: shape=(3,), dtype=float32, numpy=
-    array([6.8532745e-06, 1.9097870e-04, 2.0559824e-05], dtype=float32)>
+    <tf.Tensor: shape=(3,), dtype=float32, numpy=array([6.8532745e-06, 1.9097870e-04, 2.0559824e-05],
+    dtype=float32)>
 
     Usage with `tf.keras` API:
 

From 4d5a3bc120f54e284a68c2f67a3df9080d1caf52 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Fri, 4 Sep 2020 08:18:11 +0530
Subject: [PATCH 16/20] adding empty line inbetween imports

---
 tensorflow_addons/losses/contrastive.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow_addons/losses/contrastive.py b/tensorflow_addons/losses/contrastive.py
index aa6a50421c..7f26d51ee9 100644
--- a/tensorflow_addons/losses/contrastive.py
+++ b/tensorflow_addons/losses/contrastive.py
@@ -16,6 +16,7 @@
 
 import tensorflow as tf
 from typeguard import typechecked
+
 from tensorflow_addons.utils.keras_utils import LossFunctionWrapper
 from tensorflow_addons.utils.types import TensorLike, Number
 

From 0fdb5fadd2532dcfb98695b9cd43b63b4c2d8de8 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Fri, 4 Sep 2020 08:19:48 +0530
Subject: [PATCH 17/20] adding empty line inbetween imports

---
 tensorflow_addons/losses/focal_loss.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow_addons/losses/focal_loss.py b/tensorflow_addons/losses/focal_loss.py
index 2e2028651b..cf74dc7ccf 100644
--- a/tensorflow_addons/losses/focal_loss.py
+++ b/tensorflow_addons/losses/focal_loss.py
@@ -17,6 +17,7 @@
 import tensorflow as tf
 import tensorflow.keras.backend as K
 from typeguard import typechecked
+
 from tensorflow_addons.utils.keras_utils import LossFunctionWrapper
 from tensorflow_addons.utils.types import FloatTensorLike, TensorLike
 

From ad89d2746b6f0948c54fb52c66e4d637d884c4cb Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Fri, 4 Sep 2020 08:20:39 +0530
Subject: [PATCH 18/20] reformatting imports

---
 tensorflow_addons/losses/giou_loss.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow_addons/losses/giou_loss.py b/tensorflow_addons/losses/giou_loss.py
index 6f508609bb..b6e2ad6568 100644
--- a/tensorflow_addons/losses/giou_loss.py
+++ b/tensorflow_addons/losses/giou_loss.py
@@ -15,8 +15,10 @@
 """Implements GIoU loss."""
 
 from typing import Optional
+
 import tensorflow as tf
 from typeguard import typechecked
+
 from tensorflow_addons.utils.keras_utils import LossFunctionWrapper
 from tensorflow_addons.utils.types import TensorLike
 

From 92b99f15d98a5918e154d5a3780ce4c6b4213c1c Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Fri, 4 Sep 2020 08:21:38 +0530
Subject: [PATCH 19/20] reverting code formatting changes

---
 tensorflow_addons/losses/kappa_loss.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow_addons/losses/kappa_loss.py b/tensorflow_addons/losses/kappa_loss.py
index 862cc5212c..717267d2e3 100644
--- a/tensorflow_addons/losses/kappa_loss.py
+++ b/tensorflow_addons/losses/kappa_loss.py
@@ -111,8 +111,8 @@ def __init__(
             self.weight_mat = (col_mat - row_mat) ** 2
 
     def call(self, y_true, y_pred):
-        y_true = tf.cast(y_true, self.col_label_vec.dtype)
-        y_pred = tf.cast(y_pred, self.weight_mat.dtype)
+        y_true = tf.cast(y_true, dtype=self.col_label_vec.dtype)
+        y_pred = tf.cast(y_pred, dtype=self.weight_mat.dtype)
         batch_size = tf.shape(y_true)[0]
         cat_labels = tf.matmul(y_true, self.col_label_vec)
         cat_label_mat = tf.tile(cat_labels, [1, self.num_classes])
@@ -126,7 +126,7 @@ def call(self, y_true, y_pred):
         pred_dist = tf.reduce_sum(y_pred, axis=0, keepdims=True)
         w_pred_dist = tf.matmul(self.weight_mat, pred_dist, transpose_b=True)
         denominator = tf.reduce_sum(tf.matmul(label_dist, w_pred_dist))
-        denominator /= tf.cast(batch_size, denominator.dtype)
+        denominator /= tf.cast(batch_size, dtype=denominator.dtype)
         loss = tf.math.divide_no_nan(numerator, denominator)
         return tf.math.log(loss + self.epsilon)
 

From 1f7e9cb64204b6caf13f6df9da818c8bd0eafba8 Mon Sep 17 00:00:00 2001
From: Harsh188 <hiharshith18@gmail.com>
Date: Fri, 4 Sep 2020 08:22:58 +0530
Subject: [PATCH 20/20] reformatting imports

---
 tensorflow_addons/losses/npairs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow_addons/losses/npairs.py b/tensorflow_addons/losses/npairs.py
index a761e6de48..c7acc64992 100644
--- a/tensorflow_addons/losses/npairs.py
+++ b/tensorflow_addons/losses/npairs.py
@@ -15,8 +15,8 @@
 """Implements npairs loss."""
 
 import tensorflow as tf
-
 from typeguard import typechecked
+
 from tensorflow_addons.utils.types import TensorLike