Complete documentation for v2.

Author: reyoung

doc/api/index_cn.rst

@@ -1,2 +1,26 @@
 API
-===
+===
+
+模型配置 API
+------------
+
+..  toctree::
+    :maxdepth: 1
+
+    v2/model_configs.rst
+
+数据 API
+--------
+
+..  toctree::
+    :maxdepth: 1
+
+    v2/data.rst
+
+训练 API
+--------
+
+..	toctree::
+	:maxdepth: 1
+
+	v2/run_logic.rst

doc/api/index_en.rst

@@ -16,3 +16,11 @@ Data API
     :maxdepth: 1
 
     v2/data.rst
+
+Train API
+---------
+
+..	toctree::
+	:maxdepth: 1
+
+	v2/run_logic.rst

doc/api/v2/data.rst

@@ -2,5 +2,80 @@
 DataTypes
 #########
 
-..	automodule:: paddle.v2.data_type
-	:members:
+..  automodule:: paddle.v2.data_type
+    :members:
+
+##########
+DataFeeder
+##########
+
+..  automodule:: paddle.v2.data_feeder
+    :members:
+
+######
+Reader
+######
+
+..  automodule:: paddle.v2.reader
+    :members:
+
+..  automodule:: paddle.v2.reader.creator
+    :members:
+
+#######
+Dataset
+#######
+
+..  automodule:: paddle.v2.dataset
+    :members:
+
+
+mnist
++++++
+
+..  automodule:: paddle.v2.dataset.mnist
+    :members:
+
+
+cifar
++++++
+
+..  automodule:: paddle.v2.dataset.cifar
+    :members:
+
+conll05
++++++++
+
+..  automodule:: paddle.v2.dataset.conll05
+    :members:
+
+imdb
+++++
+
+..  automodule:: paddle.v2.dataset.imdb
+    :members:
+
+imikolov
+++++++++
+
+..  automodule:: paddle.v2.dataset.imikolov
+    :members:
+
+movielens
++++++++++
+
+..  automodule:: paddle.v2.dataset.movielens
+    :members:
+
+sentiment
++++++++++
+
+..  automodule:: paddle.v2.dataset.sentiment
+    :members:
+
+uci_housing
++++++++++++
+
+..  automodule:: paddle.v2.dataset.uci_housing
+    :members:
+

doc/api/v2/model_configs.rst

@@ -33,3 +33,10 @@ Networks
 
 ..	automodule:: paddle.v2.networks
 	:members:
+
+==========
+Optimizers
+==========
+
+..	automodule:: paddle.v2.optimizers
+	:members:

doc/api/v2/run_logic.rst

@@ -0,0 +1,22 @@
+==========
+Parameters
+==========
+
+..  automodule:: paddle.v2.parameters
+    :members:
+
+
+=======
+Trainer
+=======
+
+..	automodule:: paddle.v2.trainer
+	:members:
+
+
+=====
+Event
+=====
+
+..	automodule:: paddle.v2.event
+	:members:

doc/design/reader/README.md

@@ -23,19 +23,19 @@ An example implementation for single item data reader creator:
 
 ```python
 def reader_creator_random_image(width, height):
-	def reader():
-		while True:
-			yield numpy.random.uniform(-1, 1, size=width*height)
-	return reader
+    def reader():
+        while True:
+            yield numpy.random.uniform(-1, 1, size=width*height)
+    return reader
 ```
 
 An example implementation for multiple item data reader creator:
 ```python
-def reader_creator_random_imageand_label(widht, height, label):
-	def reader():
-		while True:
-			yield numpy.random.uniform(-1, 1, size=width*height), label
-	return reader
+def reader_creator_random_image_and_label(width, height, label):
+    def reader():
+        while True:
+            yield numpy.random.uniform(-1, 1, size=width*height), label
+    return reader
 ```
 
 ## Batch Reader Interface
@@ -74,11 +74,11 @@ mnist_train_batch_reader = paddle.batch(mnist_train, 128)
 Also easy to create custom batch reader:
 ```python
 def custom_batch_reader():
-	while True:
-		batch = []
-		for i in xrange(128):
-			batch.append((numpy.random.uniform(-1, 1, 28*28),)) # note that it's a tuple being appended.
-		yield batch
+    while True:
+        batch = []
+        for i in xrange(128):
+            batch.append((numpy.random.uniform(-1, 1, 28*28),)) # note that it's a tuple being appended.
+        yield batch
 
 mnist_random_image_batch_reader = custom_batch_reader
 ```
@@ -123,16 +123,16 @@ We can do:
 
 ```python
 def reader_creator_random_image(width, height):
-	def reader():
-		while True:
-			yield numpy.random.uniform(-1, 1, size=width*height)
-	return reader
+    def reader():
+        while True:
+            yield numpy.random.uniform(-1, 1, size=width*height)
+    return reader
 
 def reader_creator_bool(t):
-	def reader:
-		while True:
-			yield t
-	return reader
+    def reader:
+        while True:
+            yield t
+    return reader
 
 true_reader = reader_creator_bool(True)
 false_reader = reader_creator_bool(False)
@@ -172,18 +172,18 @@ We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["imag
 
 ```python
 def image_reader_creator(image_path, label_path, n):
-	def reader():
-		f = open(image_path)
-		l = open(label_path)
-		images = numpy.fromfile(
-			f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
-		images = images / 255.0 * 2.0 - 1.0
-		labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
-		for i in xrange(n):
-			yield images[i, :], labels[i] # a single entry of data is created each time
-		f.close()
-		l.close()
-	return reader
+    def reader():
+        f = open(image_path)
+        l = open(label_path)
+        images = numpy.fromfile(
+            f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
+        images = images / 255.0 * 2.0 - 1.0
+        labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
+        for i in xrange(n):
+            yield images[i, :], labels[i] # a single entry of data is created each time
+        f.close()
+        l.close()
+    return reader
 
 # images_reader_creator creates a reader
 reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
@@ -196,7 +196,7 @@ An example implementation of paddle.train could be:
 
 ```python
 def train(batch_reader, mapping, batch_size, total_pass):
-	for pass_idx in range(total_pass):
-		for mini_batch in batch_reader(): # this loop will never end in online learning.
-			do_forward_backward(mini_batch, mapping)
+    for pass_idx in range(total_pass):
+        for mini_batch in batch_reader(): # this loop will never end in online learning.
+            do_forward_backward(mini_batch, mapping)
 ```

python/paddle/v2/data_feeder.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from py_paddle import swig_paddle
 from py_paddle import DataProviderConverter
+
 import data_type
 
 __all__ = ['DataFeeder']
@@ -29,7 +29,10 @@ class DataFeeder(DataProviderConverter):
     to feed it to C++ interface.
     
     The example usage:
-    
+
+
+    ..  code-block:: python
+
         data_types = [('image', paddle.data_type.dense_vector(784)),
                       ('label', paddle.data_type.integer_value(10))]
         reader_dict = {'image':0, 'label':1}
@@ -43,20 +46,24 @@ class DataFeeder(DataProviderConverter):
         #                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample
         #                     ]
         arg = feeder(minibatch_data)
+
+    ..  note::
+
+        This module is for internal use only. Users should use the `reader`
+        interface.
+
+
+
+    :param data_types: A list to specify data name and type. Each item is
+                       a tuple of (data_name, data_type).
+
+    :type data_types: list
+    :param reader_dict: A dictionary to specify the position of each data
+                        in the input data.
+    :type reader_dict: dict
     """
 
     def __init__(self, data_types, reader_dict):
-        """
-        :param data_types: A list to specify data name and type. Each item is
-                           a tuple of (data_name, data_type). For example:
-                           [('image', paddle.data_type.dense_vector(784)),
-                            ('label', paddle.data_type.integer_value(10))]
-
-        :type data_types: A list of tuple
-        :param reader_dict: A dictionary to specify the position of each data
-                            in the input data.
-        :type reader_dict: dict()
-        """
         self.input_names = []
         input_types = []
         self.reader_dict = reader_dict
@@ -70,22 +77,12 @@ def convert(self, dat, argument=None):
         """
         :param dat: A list of mini-batch data. Each sample is a list or tuple
                     one feature or multiple features.
-                    for example:
-                    [ 
-                      ([0.2, 0.2], ), # first sample
-                      ([0.8, 0.3], ), # second sample
-                    ]
-                    or,
-                    [ 
-                      [[0.2, 0.2], ], # first sample
-                      [[0.8, 0.3], ], # second sample
-                    ]
-
-        :type dat: List
+
+        :type dat: list
         :param argument: An Arguments object contains this mini-batch data with
                          one or multiple features. The Arguments definition is
                          in the API.
-        :type argument: swig_paddle.Arguments
+        :type argument: py_paddle.swig_paddle.Arguments
         """
 
         def reorder_data(data):

python/paddle/v2/dataset/__init__.py

@@ -11,6 +11,9 @@
 # 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.
+"""
+Dataset package.
+"""
 
 import mnist
 import imikolov

python/paddle/v2/dataset/cifar.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 """
 CIFAR dataset: https://www.cs.toronto.edu/~kriz/cifar.html
+
+TODO(yuyang18): Complete the comments.
 """
 
 import cPickle

python/paddle/v2/dataset/conll05.py

@@ -16,15 +16,17 @@
 import gzip
 import itertools
 from common import download
-
-__all__ = ['test, get_dict', 'get_embedding']
 """
 Conll 2005 dataset.  Paddle semantic role labeling Book and demo use this
 dataset as an example. Because Conll 2005 is not free in public, the default
 downloaded URL is test set of Conll 2005 (which is public). Users can change
 URL and MD5 to their Conll dataset.
+
+TODO(yuyang18): Complete comments.
 """
 
+__all__ = ['test, get_dict', 'get_embedding']
+
 DATA_URL = 'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
 DATA_MD5 = '387719152ae52d60422c016e92a742fc'
 WORDDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt'