[sphinx] Fixed warnings

bob/learn/tensorflow/layers/Maxout.py

@@ -16,54 +16,32 @@ from tensorflow.python.layers import base
 
 
 def maxout(inputs, num_units, axis=-1, name=None):
-    """Adds a maxout op from https://arxiv.org/abs/1302.4389
-
-      "Maxout Networks"
+    return MaxOut(num_units=num_units, axis=axis, name=name)(inputs)
 
-      Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua
-      Bengio
 
-    Usually the operation is performed in the filter/channel dimension. This can also be
-    used after fully-connected layers to reduce number of features.
+class MaxOut(base.Layer):
 
-    Args:
-      inputs: Tensor input
-      num_units: Specifies how many features will remain after maxout in the `axis` dimension (usually channel).
-      This must be multiple of number of `axis`.
-      axis: The dimension where max pooling will be performed. Default is the
-        last dimension.
-      name: Optional scope for name_scope.
-    Returns:
-      A `Tensor` representing the results of the pooling operation.
-    Raises:
-      ValueError: if num_units is not multiple of number of features.
     """
+     Adds a maxout op from 
 
-    return MaxOut(num_units=num_units, axis=axis, name=name)(inputs)
+    "Maxout Networks"
 
+    Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua
+    Bengio
 
-class MaxOut(base.Layer):
-    """Adds a maxout op from https://arxiv.org/abs/1302.4389
+    Usually the operation is performed in the filter/channel dimension. This can also be
+    used after fully-connected layers to reduce number of features.
 
-      "Maxout Networks"
+    **Parameters**
+    inputs: Tensor input
 
-      Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua
-      Bengio
+    num_units: Specifies how many features will remain after maxout in the `axis` dimension (usually channel).
+    This must be multiple of number of `axis`.
 
-    Usually the operation is performed in the filter/channel dimension. This can also be
-    used after fully-connected layers to reduce number of features.
+    axis: The dimension where max pooling will be performed. Default is the
+      last dimension.
 
-    Args:
-      inputs: Tensor input
-      num_units: Specifies how many features will remain after maxout in the `axis` dimension (usually channel).
-      This must be multiple of number of `axis`.
-      axis: The dimension where max pooling will be performed. Default is the
-        last dimension.
-      name: Optional scope for name_scope.
-    Returns:
-      A `Tensor` representing the results of the pooling operation.
-    Raises:
-      ValueError: if num_units is not multiple of number of features.
+    name: Optional scope for name_scope.
     """
 
     def __init__(self,
@@ -100,5 +78,5 @@ class MaxOut(base.Layer):
         shape[self.axis] = self.num_units
         outputs.set_shape(shape)
 
-
         return outputs
+

bob/learn/tensorflow/layers/__init__.py

 from .Layer import Layer
 from .Conv1D import Conv1D
-from .Maxout import maxout
+from .Maxout import maxout, maxout
 
 
 # gets sphinx autodoc done right - don't remove it
@@ -20,6 +20,7 @@ def __appropriate__(*args):
 __appropriate__(
     Layer,
     Conv1D,
+    maxout,
     Maxout
     )
 __all__ = [_ for _ in dir() if not _.startswith('_')]

doc/nitpick-exceptions.txt

-py:class list
\ No newline at end of file
+py:class list

doc/references.rst

@@ -6,4 +6,4 @@
 ===========
 
 
-.. [facenet_2015] Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
+.. [facenet2015] Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

doc/user_guide.rst

@@ -187,7 +187,7 @@ or :py:class:`bob.learn.tensorflow.datashuffler.TripletWithSelectionDisk`.
 How the data is sampled ?
 `````````````````````````
 
-The paper [facenet_2015]_ introduced a new strategy to select triplets to train triplet networks (this is better described
+The paper [facenet2015]_ introduced a new strategy to select triplets to train triplet networks (this is better described
 here :py:class:`bob.learn.tensorflow.datashuffler.TripletWithSelectionDisk` and :py:class:`bob.learn.tensorflow.datashuffler.TripletWithFastSelectionDisk`).
 This triplet selection relies in the current state of the network and are extensions of `bob.learn.tensorflow.datashuffler.OnlineSampling`.