Fid Docs : note -> notes, variables -> attributes, code ref --> refs

Author: zsdonghao

tensorlayer/files.py

@@ -220,12 +220,9 @@ def load_ptb_dataset(path='data/ptb/'):
     --------
     >>> train_data, valid_data, test_data, vocab_size = tl.files.load_ptb_dataset()
 
-    Code References
+    References
     ---------------
     - ``tensorflow.models.rnn.ptb import reader``
-
-    Download Links
-    ---------------
     - `Manual download <http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz>`_
     """
     print("Load or Download Penn TreeBank (PTB) dataset > {}".format(path))
@@ -1213,7 +1210,7 @@ def natural_keys(text):
     >>> l.sort() # that is what we dont want
     ... ['im03.jpg', 'im05', 'im1.jpg', 'im11.jpg', 'im21.jpg', 'im31.jpg']
 
-    Reference
+    References
     ----------
     alist.sort(key=natural_keys) sorts in human order
     http://nedbatchelder.com/blog/200712/human_sorting.html

tensorlayer/iterate.py

@@ -21,11 +21,6 @@ def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
     shuffle : boolean
         Indicating whether to use a shuffling queue, shuffle the dataset before return.
 
-    Hints
-    -------
-    - If you have two inputs, e.g. X1 (1000, 100) and X2 (1000, 80), you can ``np.hstack((X1, X2))
-    into (1000, 180) and feed into ``inputs``, then you can split a batch of X1 and X2.
-
     Examples
     --------
     >>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
@@ -41,6 +36,12 @@ def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
     ... (array([['e', 'e'],
     ...        ['f', 'f']],
     ...         dtype='<U1'), array([4, 5]))
+
+
+    Notes
+    -------
+    - If you have two inputs, e.g. X1 (1000, 100) and X2 (1000, 80), you can ``np.hstack((X1, X2))
+    into (1000, 180) and feed into ``inputs``, then you can split a batch of X1 and X2.
     """
     assert len(inputs) == len(targets)
     if shuffle:

tensorlayer/layers.py

@@ -410,7 +410,7 @@ class Word2vecEmbeddingInputlayer(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
-    Variables
+    Attributes
     --------------
     nce_cost : a tensor
         The NCE loss.
@@ -543,7 +543,7 @@ class EmbeddingInputlayer(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
-    Variables
+    Attributes
     ------------
     outputs : a tensor
         The outputs of embedding layer.
@@ -3777,7 +3777,7 @@ class RNNLayer(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
-    Variables
+    Attributes
     --------------
     outputs : a tensor
         The output of this RNN.
@@ -4044,7 +4044,7 @@ class BiRNNLayer(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
-    Variables
+    Attributes
     --------------
     outputs : a tensor
         The output of this RNN.
@@ -4634,7 +4634,7 @@ class BiDynamicRNNLayer(Layer):
         The number of hidden units in the layer.
     initializer : initializer
         The initializer for initializing the parameters.
-    sequence_length : a tensor, array or None
+    sequence_length : a tensor, array or None.
         The sequence length of each row of input data, see ``Advanced Ops for Dynamic RNN``.
             - If None, it uses ``retrieve_seq_length_op`` to compute the sequence_length, i.e. when the features of padding (on right hand side) are all zeros.
             - If using word embedding, you may need to compute the sequence_length from the ID array (the integer features before word embedding) by using ``retrieve_seq_length_op2`` or ``retrieve_seq_length_op``.
@@ -4659,7 +4659,7 @@ class BiDynamicRNNLayer(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
-    Variables
+    Attributes
     -----------------------
     outputs : a tensor
         The output of this RNN.
@@ -4902,7 +4902,7 @@ class Seq2Seq(Layer):
     name : a string or None
         An optional name to attach to this layer.
 
-    Variables
+    Attributes
     ------------
     outputs : a tensor
         The output of RNN decoder.

tensorlayer/nlp.py

@@ -201,7 +201,7 @@ class Vocabulary(object):
   end_word : Special word denoting sentence end.
   unk_word : Special word denoting unknown words.
 
-  Properties
+  Attributes
   ------------
   vocab : a dictionary from word to id.
   reverse_vocab : a list from id to word.
@@ -338,9 +338,9 @@ def create_vocab(sentences, word_counts_output_file, min_word_count=1):
     --------
     - tl.nlp.SimpleVocabulary object.
 
-    Mores
-    -----
-    - ``tl.nlp.build_vocab()``
+    Notes
+    -------
+    - See more ``tl.nlp.build_vocab()``
 
     Examples
     --------
@@ -428,7 +428,7 @@ def read_words(filename="nietzsche.txt", replace = ['\n', '<eos>']):
     The context in a list, split by space by default, and use ``<eos>`` to represent ``\\n``,
     e.g. ``[... 'how', 'useful', 'it', "'s" ... ]``.
 
-    Code References
+    References
     ---------------
     - `tensorflow.models.rnn.ptb.reader <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/rnn/ptb>`_
     """
@@ -520,7 +520,7 @@ def build_vocab(data):
     word_to_id : a dictionary
         mapping words to unique IDs. e.g. {'campbell': 2587, 'atlantic': 2247, 'aoun': 6746 .... }
 
-    Code References
+    References
     ---------------
     - `tensorflow.models.rnn.ptb.reader <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/rnn/ptb>`_
 
@@ -594,7 +594,7 @@ def build_words_dataset(words=[], vocabulary_size=50000, printable=True, unk_key
     >>> vocabulary_size = 50000
     >>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size)
 
-    Code References
+    References
     -----------------
     - `tensorflow/examples/tutorials/word2vec/word2vec_basic.py <https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/word2vec/word2vec_basic.py>`_
     """
@@ -653,7 +653,7 @@ def words_to_word_ids(data=[], word_to_id={}, unk_key = 'UNK'):
     >>> print(context)
     ... [b'hello', b'how', b'are', b'you']
 
-    Code References
+    References
     ---------------
     - `tensorflow.models.rnn.ptb.reader <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/rnn/ptb>`_
     """

tensorlayer/utils.py

@@ -67,11 +67,11 @@ def fit(sess, network, train_op, cost, X_train, y_train, x, y_, acc=None, batch_
     ...            X_val=X_val, y_val=y_val, eval_train=False,
     ...            tensorboard=True, tensorboard_weight_histograms=True, tensorboard_graph_vis=True)
 
-    Note
+    Notes
     --------
-        If tensorboard=True, the global_variables_initializer will be run inside the fit function
-        in order to initalize the automatically generated summary nodes used for tensorboard visualization,
-        thus tf.global_variables_initializer().run() before the fit() call will be undefined.
+    If tensorboard=True, the global_variables_initializer will be run inside the fit function
+    in order to initalize the automatically generated summary nodes used for tensorboard visualization,
+    thus tf.global_variables_initializer().run() before the fit() call will be undefined.
     """
     assert X_train.shape[0] >= batch_size, "Number of training examples should be bigger than the batch size"
 
@@ -130,7 +130,7 @@ def fit(sess, network, train_op, cost, X_train, y_train, x, y_, acc=None, batch_
                     result = sess.run(merged, feed_dict=feed_dict)
                     train_writer.add_summary(result, tensorboard_train_index)
                     tensorboard_train_index += 1
-                if (X_val is not None) and (y_val is not None):                      
+                if (X_val is not None) and (y_val is not None):
                         for X_val_a, y_val_a in iterate.minibatches(
                                         X_val, y_val, batch_size, shuffle=True):
                                 dp_dict = dict_to_one( network.all_drop )    # disable noise layers