fix documentation and alphabetically sort modules

ashpy/__init__.py

@@ -22,6 +22,7 @@
 from . import models
 from . import trainers
 from . import ashtypes
+from . import keras
 from .modes import LogEvalMode
 
 __version__ = "1.0.2"

ashpy/keras/__init__.py

@@ -0,0 +1,29 @@
+# Copyright 2019 Zuru Tech HK Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Custom extensions of standard Keras components.
+
+.. currentmodule:: ashpy.keras
+
+.. rubric:: Modules
+
+.. autosummary::
+    :nosignatures:
+    :toctree: keras
+    :template: autosummary/submodule.rst
+
+    losses
+
+"""

ashpy/keras/losses.py

@@ -54,7 +54,8 @@ def call(self, x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
 
 
 class DMinMax(tf.keras.losses.Loss):
-    r"""Implementation of MinMax Discriminator loss as :py:class:`tf.keras.losses.Loss`.
+    r"""
+    Implementation of MinMax Discriminator loss as :py:class:`tf.keras.losses.Loss`.
 
     .. math::
          L_{D} =  - \frac{1}{2} E [\log(D(x)) + \log (1 - D(G(z))]
@@ -116,7 +117,8 @@ class DLeastSquare(tf.keras.losses.Loss):
     """Discriminator Least Square Loss as :py:class:`tf.keras.losses.Loss`."""
 
     def __init__(self) -> None:
-        """Least square Loss for Discriminator.
+        r"""
+        Least square Loss for Discriminator.
 
         Reference: Least Squares Generative Adversarial Networks [1]_ .
 

ashpy/models/convolutional/__init__.py

@@ -70,7 +70,7 @@
 
     unet.UNet
     unet.SUNet
-    unet.FunctionalUNet
+    unet.FUNet
 
 ----
 

ashpy/models/convolutional/unet.py

@@ -23,7 +23,7 @@
 from ashpy.layers import Attention, InstanceNormalization
 from ashpy.models.convolutional.interfaces import Conv2DInterface
 
-__ALL__ = ["UNet", "SUNet", "FunctionalUNet"]
+__ALL__ = ["UNet", "SUNet", "FUNet"]
 
 
 class UNet(Conv2DInterface):

docs/source/api.rst

@@ -5,11 +5,13 @@ API Reference
    :nosignatures:
    :toctree: _autosummary
 
-   ashpy.models
+   ashpy.ashtypes
    ashpy.contexts
-   ashpy.trainers
+   ashpy.keras
    ashpy.layers
    ashpy.losses
    ashpy.metrics
+   ashpy.models
    ashpy.modes
-   ashpy.ashtypes
+   ashpy.trainers
+