Trun nitpicky back on!

bob/learn/tensorflow/dataset/tfrecords.py

@@ -44,7 +44,7 @@ def dataset_to_tfrecord(dataset, output):
 
     Parameters
     ----------
-    dataset : tf.data.Dataset
+    dataset : ``tf.data.Dataset``
         The tf.data.Dataset that you want to write into a TFRecord file.
     output : str
         Path to the TFRecord file. Besides this file, a .json file is also created.
@@ -53,7 +53,7 @@ def dataset_to_tfrecord(dataset, output):
 
     Returns
     -------
-    tf.Operation
+    ``tf.Operation``
         A tf.Operation that, when run, writes contents of dataset to a file. When
         running in eager mode, calling this function will write the file. Otherwise, you
         have to call session.run() on the returned operation.
@@ -100,7 +100,7 @@ def dataset_from_tfrecord(tfrecord):
 
     Returns
     -------
-    tf.data.Dataset
+    ``tf.data.Dataset``
         A dataset that contains the data from the TFRecord file.
     """
     # these imports are needed so that eval can work
@@ -468,7 +468,8 @@ def batch_data_and_labels(
     """
   Dump in order batches from a list of tf-record files
 
-  **Parameters**
+  Parameters
+  ----------
 
      tfrecord_filenames:
         List containing the tf-record paths

bob/learn/tensorflow/loss/utils.py

@@ -15,17 +15,17 @@ def balanced_softmax_cross_entropy_loss_weights(labels, dtype="float32"):
 
     Parameters
     ----------
-    labels : tf.Tensor
+    labels : ``tf.Tensor``
         Labels of your current input. The shape must be [batch_size, n_classes]. If your
         labels are not one-hot encoded, you can use ``tf.one_hot`` to convert them first
         before giving them to this function.
-    dtype : dtype
+    dtype : ``tf.dtype``
         The dtype that weights will have. It should be float. Best is to provide
         logits.dtype as input.
 
     Returns
     -------
-    tf.Tensor
+    ``tf.Tensor``
         Computed weights that will cancel your dataset imbalance per batch.
 
     Examples
@@ -100,16 +100,16 @@ def balanced_sigmoid_cross_entropy_loss_weights(labels, dtype="float32"):
 
     Parameters
     ----------
-    labels : tf.Tensor
+    labels : ``tf.Tensor``
         Labels of your current input. The shape must be [batch_size] and values must be
         either 0 or 1.
-    dtype : dtype
+    dtype : ``tf.dtype``
         The dtype that weights will have. It should be float. Best is to provide
         logits.dtype as input.
 
     Returns
     -------
-    tf.Tensor
+    ``tf.Tensor``
         Computed weights that will cancel your dataset imbalance per batch.
 
     Examples

doc/conf.py

@@ -30,7 +30,7 @@ extensions = [
     ]
 
 # Be picky about warnings
-nitpicky = False
+nitpicky = True
 
 # Ignores stuff we can't easily resolve on other project's sphinx manuals
 nitpick_ignore = []

doc/py_api.rst

@@ -86,6 +86,7 @@ Detailed Information
 .. automodule:: bob.learn.tensorflow
 .. automodule:: bob.learn.tensorflow.estimators
 .. automodule:: bob.learn.tensorflow.dataset
+.. automodule:: bob.learn.tensorflow.dataset.generator
 .. automodule:: bob.learn.tensorflow.dataset.bio
 .. automodule:: bob.learn.tensorflow.dataset.image
 .. automodule:: bob.learn.tensorflow.dataset.siamese_image