seq2seq models documentation cleaned up

Author: stephenhky

docs/codes.rst

@@ -40,6 +40,11 @@ Module `shorttext.classifiers.topic.SkLearnClassification`
 Supervised Classification using Word Embedding
 ----------------------------------------------
 
+Module `shorttext.generators.seq2seq.s2skeras`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. automodule:: shorttext.generators.seq2seq.s2skeras
+   :members:
 
 
 Module `shorttext.classifiers.embed.sumvec.VarNNSumEmbedVecClassification`
@@ -125,4 +130,5 @@ Module `shorttext.spell`
 
 
 
+
 Home: :doc:`index`

docs/tutorial_charbaseseq2seq.rst

@@ -16,6 +16,10 @@ To use it, create an instance of the class :class:`shorttext.generators.Sentence
 
 The above code is the same as :doc:`tutorial_charbaseonehot` .
 
+.. automodule:: shorttext.generators.charbase.char2vec
+   :members: initSentenceToCharVecEncoder
+
+
 Training
 --------
 
@@ -30,6 +34,10 @@ And then train this neural network model:
 
 This model takes several hours to train on a laptop.
 
+
+.. autoclass:: shorttext.generators.seq2seq.charbaseS2S.CharBasedSeq2SeqGenerator
+   :members:
+
 Decoding
 --------
 
@@ -51,6 +59,10 @@ And can be loaded by:
 
 >>> seq2seqer2 = shorttext.generators.seq2seq.charbaseS2S.loadCharBasedSeq2SeqGenerator('/path/to/norvigtxt_iter5model.bin')
 
+.. automodule:: shorttext.generators.seq2seq.charbaseS2S
+   :members: loadCharBasedSeq2SeqGenerator
+
+
 Reference
 ---------
 

shorttext/generators/seq2seq/charbaseS2S.py

@@ -12,7 +12,6 @@
 charbases2s_suffices = kerasseq2seq_suffices + ['_dictionary.dict', '_charbases2s.json']
 
 
-# @cio.compactio({'classifier': 'charbases2s'}, 'charbases2s', charbases2s_suffices)
 class CharBasedSeq2SeqGenerator(cio.CompactIOMachine):
     """ Class implementing character-based sequence-to-sequence (seq2seq) learning model.
 

shorttext/generators/seq2seq/s2skeras.py

@@ -12,7 +12,7 @@
 
 kerasseq2seq_suffices = ['.h5', '.json', '_s2s_hyperparam.json', '_encoder.h5', '_encoder.json', '_decoder.h5', '_decoder.json']
 
-# @cio.compactio({'classifier': 'kerasseq2seq'}, 'kerasseq2seq', kerasseq2seq_suffices)
+
 class Seq2SeqWithKeras(cio.CompactIOMachine):
     """ Class implementing sequence-to-sequence (seq2seq) learning with keras.