Merge branch 'develop' of github.com:baidu/Paddle into feature/serialize_deserialize_in_parameters

demo/image_classification/api_v2_train.py

@@ -66,7 +66,7 @@ def main():
                 sys.stdout.flush()
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(
-                reader=paddle.reader.batched(
+                reader=paddle.batch(
                     paddle.dataset.cifar.test10(), batch_size=128),
                 reader_dict={'image': 0,
                              'label': 1})
@@ -77,7 +77,7 @@ def main():
                                  parameters=parameters,
                                  update_equation=momentum_optimizer)
     trainer.train(
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.shuffle(
                 paddle.dataset.cifar.train10(), buf_size=50000),
             batch_size=128),

demo/mnist/api_train_v2.py

@@ -111,7 +111,7 @@ def main():
                           result.metrics['classification_error_evaluator']))
 
     trainer.train(
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.shuffle(
                 paddle.dataset.mnist.train(), buf_size=8192),
             batch_size=128),
@@ -128,7 +128,7 @@ def main():
     probs = paddle.infer(
         output=predict,
         parameters=parameters,
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.firstn(
                 paddle.reader.map_readers(lambda item: (item[0], ),
                                           paddle.dataset.mnist.test()),

demo/seqToseq/api_train_v2.py

@@ -72,31 +72,35 @@ def main():
     # define network topology
     cost = seqToseq_net_v2(source_dict_dim, target_dict_dim)
     parameters = paddle.parameters.create(cost)
-    optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
-
-    def event_handler(event):
-        if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 10 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
 
+    # define optimize method and trainer
+    optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
     trainer = paddle.trainer.SGD(cost=cost,
                                  parameters=parameters,
                                  update_equation=optimizer)
 
+    # define data reader
     reader_dict = {
         'source_language_word': 0,
         'target_language_word': 1,
         'target_language_next_word': 2
     }
 
-    trn_reader = paddle.reader.batched(
+    wmt14_reader = paddle.reader.batched(
         paddle.reader.shuffle(
             train_reader("data/pre-wmt14/train/train"), buf_size=8192),
         batch_size=5)
 
+    # define event_handler callback
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 10 == 0:
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+
+    # start to train
     trainer.train(
-        reader=trn_reader,
+        reader=wmt14_reader,
         event_handler=event_handler,
         num_passes=10000,
         reader_dict=reader_dict)

demo/seqToseq/seqToseq_net_v2.py

-import paddle.v2.activation as activation
-import paddle.v2.attr as attr
-import paddle.v2.data_type as data_type
-import paddle.v2.layer as layer
-import paddle.v2.networks as networks
+import paddle.v2 as paddle
 
 
 def seqToseq_net_v2(source_dict_dim, target_dict_dim):
@@ -12,64 +8,70 @@ def seqToseq_net_v2(source_dict_dim, target_dict_dim):
     encoder_size = 512  # dimension of hidden unit in GRU Encoder network
 
     #### Encoder
-    src_word_id = layer.data(
+    src_word_id = paddle.layer.data(
         name='source_language_word',
-        type=data_type.integer_value_sequence(source_dict_dim))
-    src_embedding = layer.embedding(
+        type=paddle.data_type.integer_value_sequence(source_dict_dim))
+    src_embedding = paddle.layer.embedding(
         input=src_word_id,
         size=word_vector_dim,
-        param_attr=attr.ParamAttr(name='_source_language_embedding'))
-    src_forward = networks.simple_gru(input=src_embedding, size=encoder_size)
-    src_backward = networks.simple_gru(
+        param_attr=paddle.attr.ParamAttr(name='_source_language_embedding'))
+    src_forward = paddle.networks.simple_gru(
+        input=src_embedding, size=encoder_size)
+    src_backward = paddle.networks.simple_gru(
         input=src_embedding, size=encoder_size, reverse=True)
-    encoded_vector = layer.concat(input=[src_forward, src_backward])
+    encoded_vector = paddle.layer.concat(input=[src_forward, src_backward])
 
     #### Decoder
-    with layer.mixed(size=decoder_size) as encoded_proj:
-        encoded_proj += layer.full_matrix_projection(input=encoded_vector)
+    with paddle.layer.mixed(size=decoder_size) as encoded_proj:
+        encoded_proj += paddle.layer.full_matrix_projection(
+            input=encoded_vector)
 
-    backward_first = layer.first_seq(input=src_backward)
+    backward_first = paddle.layer.first_seq(input=src_backward)
 
-    with layer.mixed(size=decoder_size, act=activation.Tanh()) as decoder_boot:
-        decoder_boot += layer.full_matrix_projection(input=backward_first)
+    with paddle.layer.mixed(
+            size=decoder_size, act=paddle.activation.Tanh()) as decoder_boot:
+        decoder_boot += paddle.layer.full_matrix_projection(
+            input=backward_first)
 
     def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
 
-        decoder_mem = layer.memory(
+        decoder_mem = paddle.layer.memory(
             name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
 
-        context = networks.simple_attention(
+        context = paddle.networks.simple_attention(
             encoded_sequence=enc_vec,
             encoded_proj=enc_proj,
             decoder_state=decoder_mem)
 
-        with layer.mixed(size=decoder_size * 3) as decoder_inputs:
-            decoder_inputs += layer.full_matrix_projection(input=context)
-            decoder_inputs += layer.full_matrix_projection(input=current_word)
+        with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
+            decoder_inputs += paddle.layer.full_matrix_projection(input=context)
+            decoder_inputs += paddle.layer.full_matrix_projection(
+                input=current_word)
 
-        gru_step = layer.gru_step(
+        gru_step = paddle.layer.gru_step(
             name='gru_decoder',
             input=decoder_inputs,
             output_mem=decoder_mem,
             size=decoder_size)
 
-        with layer.mixed(
-                size=target_dict_dim, bias_attr=True,
-                act=activation.Softmax()) as out:
-            out += layer.full_matrix_projection(input=gru_step)
+        with paddle.layer.mixed(
+                size=target_dict_dim,
+                bias_attr=True,
+                act=paddle.activation.Softmax()) as out:
+            out += paddle.layer.full_matrix_projection(input=gru_step)
         return out
 
     decoder_group_name = "decoder_group"
-    group_input1 = layer.StaticInputV2(input=encoded_vector, is_seq=True)
-    group_input2 = layer.StaticInputV2(input=encoded_proj, is_seq=True)
+    group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
+    group_input2 = paddle.layer.StaticInputV2(input=encoded_proj, is_seq=True)
     group_inputs = [group_input1, group_input2]
 
-    trg_embedding = layer.embedding(
-        input=layer.data(
+    trg_embedding = paddle.layer.embedding(
+        input=paddle.layer.data(
             name='target_language_word',
-            type=data_type.integer_value_sequence(target_dict_dim)),
+            type=paddle.data_type.integer_value_sequence(target_dict_dim)),
         size=word_vector_dim,
-        param_attr=attr.ParamAttr(name='_target_language_embedding'))
+        param_attr=paddle.attr.ParamAttr(name='_target_language_embedding'))
     group_inputs.append(trg_embedding)
 
     # For decoder equipped with attention mechanism, in training,
@@ -77,14 +79,14 @@ def seqToseq_net_v2(source_dict_dim, target_dict_dim):
     # while encoded source sequence is accessed to as an unbounded memory.
     # Here, the StaticInput defines a read-only memory
     # for the recurrent_group.
-    decoder = layer.recurrent_group(
+    decoder = paddle.layer.recurrent_group(
         name=decoder_group_name,
         step=gru_decoder_with_attention,
         input=group_inputs)
 
-    lbl = layer.data(
+    lbl = paddle.layer.data(
         name='target_language_next_word',
-        type=data_type.integer_value_sequence(target_dict_dim))
-    cost = layer.classification_cost(input=decoder, label=lbl)
+        type=paddle.data_type.integer_value_sequence(target_dict_dim))
+    cost = paddle.layer.classification_cost(input=decoder, label=lbl)
 
     return cost

demo/word2vec/train_v2.py

+import math
+
+import paddle.v2 as paddle
+
+dictsize = 1953
+embsize = 32
+hiddensize = 256
+N = 5
+
+
+def wordemb(inlayer):
+    wordemb = paddle.layer.table_projection(
+        input=inlayer,
+        size=embsize,
+        param_attr=paddle.attr.Param(
+            name="_proj",
+            initial_std=0.001,
+            learning_rate=1,
+            l2_rate=0, ))
+    return wordemb
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+    firstword = paddle.layer.data(
+        name="firstw", type=paddle.data_type.integer_value(dict_size))
+    secondword = paddle.layer.data(
+        name="secondw", type=paddle.data_type.integer_value(dict_size))
+    thirdword = paddle.layer.data(
+        name="thirdw", type=paddle.data_type.integer_value(dict_size))
+    fourthword = paddle.layer.data(
+        name="fourthw", type=paddle.data_type.integer_value(dict_size))
+    nextword = paddle.layer.data(
+        name="fifthw", type=paddle.data_type.integer_value(dict_size))
+
+    Efirst = wordemb(firstword)
+    Esecond = wordemb(secondword)
+    Ethird = wordemb(thirdword)
+    Efourth = wordemb(fourthword)
+
+    contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
+    hidden1 = paddle.layer.fc(input=contextemb,
+                              size=hiddensize,
+                              act=paddle.activation.Sigmoid(),
+                              layer_attr=paddle.attr.Extra(drop_rate=0.5),
+                              bias_attr=paddle.attr.Param(learning_rate=2),
+                              param_attr=paddle.attr.Param(
+                                  initial_std=1. / math.sqrt(embsize * 8),
+                                  learning_rate=1))
+    predictword = paddle.layer.fc(input=hidden1,
+                                  size=dict_size,
+                                  bias_attr=paddle.attr.Param(learning_rate=2),
+                                  act=paddle.activation.Softmax())
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                result = trainer.test(
+                    paddle.batch(
+                        paddle.dataset.imikolov.test(word_dict, N), 32))
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
+    cost = paddle.layer.classification_cost(input=predictword, label=nextword)
+    parameters = paddle.parameters.create(cost)
+    adam_optimizer = paddle.optimizer.Adam(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+    trainer.train(
+        paddle.batch(paddle.dataset.imikolov.train(word_dict, N), 32),
+        num_passes=30,
+        event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    main()

doc/api/index_cn.rst

 API
-===
\ No newline at end of file
+===
+
+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
\ No newline at end of file

doc/api/index_en.rst

@@ -7,4 +7,20 @@ Model Config API
 ..  toctree::
     :maxdepth: 1
 
-    v2/model_configs.rst
\ No newline at end of file
+    v2/model_configs.rst
+
+Data API
+--------
+
+..  toctree::
+    :maxdepth: 1
+
+    v2/data.rst
+
+Train API
+---------
+
+..	toctree::
+	:maxdepth: 1
+
+	v2/run_logic.rst
\ No newline at end of file

doc/api/v2/data.rst

+================
+Data Related API
+================
+
+
+#########
+DataTypes
+#########
+
+..  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:
+
+#########
+minibatch
+#########
+
+..  automodule:: paddle.v2.minibatch
+    :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

+#########################
+Configuration Related API
+#########################
+
 ======
 Layers
 ======
 
 ..  automodule:: paddle.v2.layer
     :members:
+
+
+==========
+Attributes
+==========
+
+..	automodule:: paddle.v2.attr
+	:members:
+
+===========
+Activations
+===========
+
+..	automodule:: paddle.v2.activation
+	:members:
+
+========
+Poolings
+========
+
+..	automodule:: paddle.v2.pooling
+	:members:
+
+========
+Networks
+========
+
+..	automodule:: paddle.v2.networks
+	:members:
+
+==========
+Optimizers
+==========
+
+..	automodule:: paddle.v2.optimizer
+	:members:

doc/api/v2/run_logic.rst

+###########
+Trainer API
+###########
+
+==========
+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)
 ```

doc/howto/usage/k8s/k8s_distributed_cn.md

@@ -43,22 +43,55 @@ docker push  [YOUR_REPO]/paddle:mypaddle
 
 注意上述命令中`[YOUR_REPO]`表示读者所使用的Docker镜像仓库地址，读者需要替换成自己使用的仓库地址。下文使用`[YOUR_REPO]/paddle:mypaddle`这个地址来表示此步骤所构建出的镜像。
 
-### 上传训练文件
+### 准备训练数据
 
-本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到volume所在的共享存储（使用不同分布式存储会有不同的挂载方式，需要要先挂载这个目录，然后拷贝数据）。完成后volume中的文件内容大致如下：
+这里我们通过在Kubernetes集群上启动一个Job来下载并切割数据，也可以通过修改[k8s_train](./src/k8s_train/README.md)的内容来定制image.
 
-```bash
-[root@paddle-kubernetes-node0 mfs]# tree -d
+在启动Job之前，需要根据不同的分布式存储来绑定一个[persistentVolumeClaim](https://kubernetes.io/docs/user-guide/persistent-volumes/),生成的数据将会存储在这个volume下.
+
+```yaml
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: paddle-data
+spec:
+  template:
+    metadata:
+      name: pi
+    spec:
+      hostNetwork: true
+      containers:
+      - name: paddle-data
+        image: paddledev/paddle-tutorial:k8s_data
+        imagePullPolicy: Always
+        volumeMounts:
+        - mountPath: "/mnt"
+          name: nfs
+        env:
+        - name: OUT_DIR
+          value: /home/work/mfs/paddle-cluster-job
+        - name: SPLIT_COUNT
+          value: "3"
+      volumes:
+        - name: nfs
+          persistentVolumeClaim:
+            claimName: mfs
+      restartPolicy: Never
+```
+
+完成后volume中的文件内容大致如下：
+```base
+[root@paddle-kubernetes-node0 nfsdir]$ tree -d
 .
-└── paddle-cluster-job
-    ├── data
-    │   ├── 0
-    │   │
-    │   ├── 1
-    │   │
-    │   └── 2
-    ├── output
-    └── recommendation
+`-- paddle-cluster-job
+    |-- 0
+    |   `-- data
+    |-- 1
+    |   `-- data
+    |-- 2
+    |   `-- data
+    |-- output
+    |-- quick_start
 ```
 
 目录中paddle-cluster-job是本次训练对应的job name，本次训练要求有3个PaddlePaddle节点，在paddle-cluster-job/data目录中存放切分好的数据，文件夹0，1，2分别代表3个节点的trainer_id。recommendation文件夹内存放训练文件，output文件夹存放训练结果与日志。
@@ -118,15 +151,16 @@ spec:
 
 `env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内。
 
-`JOB_PATH`表示共享存储挂载的路径，`JOB_NAME`表示job名字，`TRAIN_CONFIG_DIR`表示本次训练文件所在目录，这三个变量组合就可以找到本次训练需要的文件路径。
-
-`CONF_PADDLE_NIC`表示`paddle pserver`进程需要的`--nics`参数，即网卡名
-
-`CONF_PADDLE_PORT`表示`paddle pserver`的`--port`参数，`CONF_PADDLE_PORTS_NUM`则表示稠密更新的端口数量，也就是`--ports_num`参数。
-
-`CONF_PADDLE_PORTS_NUM_SPARSE`表示稀疏更新的端口数量，也就是`--ports_num_for_sparse`参数。
-
-`CONF_PADDLE_GRADIENT_NUM`表示训练节点数量，即`--num_gradient_servers`参数
+环境变量 | 说明
+--- | ---
+JOB_PATH | 共享存储挂在的路径
+JOB_NAME | Job的名字
+TRAIN_CONFIG_DIR | 本次训练文件所在目录，与JOB_PATH,JOB_NAME组合可以找到本次训练需要的文件路径
+CONF_PADDLE_NIC | `paddle pserver`进程需要的`--nics`参数，即网卡名
+CONF_PADDLE_PORT | `paddle paserver`的`--port`参数
+CONF_PADDLE_PORTS_NUM | 稠密更新的端口数量，即`--ports_num`参数
+CONF_PADDLE_PORTS_NUM_SPARSE | 稀疏更新的端口数量，即`--ports_num_for_sparse`参数
+CONF_PADDLE_GRADIENT_NUM | 训练节点数量，即`--num_gradient_servers参数`
 
 这些参数的具体描述，读者可以查看[这里](http://www.paddlepaddle.org/doc/ui/cmd_argument/detail_introduction.html#parameter-server-and-distributed-communication)。
 

python/paddle/trainer/PyDataProvider2.py

@@ -45,6 +45,23 @@ class CacheType(object):
 
 
 class InputType(object):
+    """
+    InputType is the base class for paddle input types.
+
+    ..  note::
+
+        this is a base class, and should never be used by user.
+
+    :param dim: dimension of input. If the input is an integer, it means the
+                value range. Otherwise, it means the size of layer.
+    :type dim: int
+    :param seq_type: sequence type of input. 0 means it is not a sequence. 1
+                     means it is a variable length sequence. 2 means it is a
+                     nested sequence.
+    :type seq_type: int
+    :param type: data type of input.
+    :type type: int
+    """
     __slots__ = ['dim', 'seq_type', 'type']
 
     def __init__(self, dim, seq_type, tp):
@@ -54,20 +71,61 @@ class InputType(object):
 
 
 def dense_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
+    """
+    Dense Vector. It means the input feature is dense float vector. For example,
+    if the input is an image with 28*28 pixels, the input of Paddle neural
+    network should be a dense vector with dimension 784.
+
+    :param dim: dimension of this vector.
+    :type dim: int
+    :param seq_type: sequence type of input.
+    :type seq_type: int
+    :return: An input type object.
+    :rtype: InputType
+    """
     return InputType(dim, seq_type, DataType.Dense)
 
 
 def sparse_non_value_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
+    """
+    Sparse binary vector. It means the input feature is a sparse vector and the
+    every element in this vector is either zero or one.
+
+    :param dim: dimension of this vector.
+    :type dim: int
+    :param seq_type: sequence type of this input.
+    :type seq_type: int
+    :return: An input type object.
+    :rtype: InputType
+    """
     return InputType(dim, seq_type, DataType.SparseNonValue)
 
 
 def sparse_value_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
+    """
+    Sparse vector. It means the input feature is a sparse vector. Most of the
+    elements in this vector are zero, others could be any float value.
+
+    :param dim: dimension of this vector.
+    :type dim: int
+    :param seq_type: sequence type of this input.
+    :type seq_type: int
+    :return: An input type object.
+    :rtype: InputType
+    """
     return InputType(dim, seq_type, DataType.SparseValue)
 
 
 def index_slot(value_range, seq_type=SequenceType.NO_SEQUENCE):
-    """Data type of integer.
+    """
+    Data type of integer.
+
+    :param seq_type: sequence type of this input.
+    :type seq_type: int
     :param value_range: range of this integer.
+    :type value_range: int
+    :return: An input type object
+    :rtype: InputType
     """
     return InputType(value_range, seq_type, DataType.Index)
 
@@ -76,10 +134,17 @@ dense_vector = dense_slot
 sparse_binary_vector = sparse_non_value_slot
 sparse_vector = sparse_value_slot
 integer_value = index_slot
-integer_value.__doc__ = index_slot.__doc__
 
 
 def dense_vector_sequence(dim):
+    """
+    Data type of a sequence of dense vector.
+
+    :param dim: dimension of dense vector.
+    :type dim: int
+    :return: An input type object
+    :rtype: InputType
+    """
     return dense_vector(dim, seq_type=SequenceType.SEQUENCE)
 
 
@@ -88,6 +153,15 @@ def dense_vector_sub_sequence(dim):
 
 
 def sparse_binary_vector_sequence(dim):
+    """
+    Data type of a sequence of sparse vector, which every element is either zero
+     or one.
+
+    :param dim: dimension of sparse vector.
+    :type dim: int
+    :return: An input type object
+    :rtype: InputType
+    """
     return sparse_binary_vector(dim, seq_type=SequenceType.SEQUENCE)
 
 
@@ -96,6 +170,15 @@ def sparse_binary_vector_sub_sequence(dim):
 
 
 def sparse_vector_sequence(dim):
+    """
+    Data type of a sequence of sparse vector, which most elements are zero,
+    others could be any float value.
+
+    :param dim: dimension of sparse vector.
+    :type dim: int
+    :return: An input type object
+    :rtype: InputType
+    """
     return sparse_vector(dim, seq_type=SequenceType.SEQUENCE)
 
 
@@ -104,8 +187,11 @@ def sparse_vector_sub_sequence(dim):
 
 
 def integer_value_sequence(value_range):
-    """Data type of a sequence of integer.
+    """
+    Data type of a sequence of integer.
+
     :param value_range: range of each element.
+    :type value_range: int
     """
     return integer_value(value_range, seq_type=SequenceType.SEQUENCE)
 
@@ -115,7 +201,6 @@ def integer_value_sub_sequence(dim):
 
 
 integer_sequence = integer_value_sequence
-integer_sequence.__doc__ = integer_value_sequence.__doc__
 
 
 class SingleSlotWrapper(object):

python/paddle/trainer_config_helpers/layers.py

@@ -795,17 +795,16 @@ def data_layer(name, size, height=None, width=None, layer_attr=None):
 
     ..  code-block:: python
 
-        data = data_layer(name="input",
-                          size=1000)
+        data = data_layer(name="input", size=1000)
 
     :param name: Name of this data layer.
     :type name: basestring
     :param size: Size of this data layer.
     :type size: int
     :param height: Height of this data layer, used for image
-    :type size: int|None
+    :type height: int|None
     :param width: Width of this data layer, used for image
-    :type size: int|None
+    :type width: int|None
     :param layer_attr: Extra Layer Attribute.
     :type layer_attr: ExtraLayerAttribute.
     :return: LayerOutput object.

python/paddle/v2/__init__.py

@@ -28,6 +28,7 @@ import pooling
 import inference
 import networks
 import py_paddle.swig_paddle as api
+import minibatch
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
@@ -45,3 +46,4 @@ def init(**kwargs):
 
 
 infer = inference.infer
+batch = minibatch.batch

python/paddle/v2/activation.py

@@ -12,26 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.trainer_config_helpers.activations import *
+import paddle.trainer_config_helpers.activations
+import copy
 
-__all__ = [
-    "Base", "Tanh", "Sigmoid", "Softmax", "Identity", "Linear",
-    'SequenceSoftmax', "Exp", "Relu", "BRelu", "SoftRelu", "STanh", "Abs",
-    "Square", "Log"
-]
+__all__ = []
 
-Base = BaseActivation
-Tanh = TanhActivation
-Sigmoid = SigmoidActivation
-Softmax = SoftmaxActivation
-SequenceSoftmax = SequenceSoftmaxActivation
-Identity = IdentityActivation
-Linear = Identity
-Relu = ReluActivation
-BRelu = BReluActivation
-SoftRelu = SoftReluActivation
-STanh = STanhActivation
-Abs = AbsActivation
-Square = SquareActivation
-Exp = ExpActivation
-Log = LogActivation
+suffix = 'Activation'
+for act in paddle.trainer_config_helpers.activations.__all__:
+    new_name = act[:-len(suffix)]
+    globals()[new_name] = copy.copy(
+        getattr(paddle.trainer_config_helpers.activations, act))
+    globals()[new_name].__name__ = new_name
+    __all__.append(new_name)

python/paddle/v2/attr.py

@@ -12,12 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.trainer_config_helpers.attrs import *
+import paddle.trainer_config_helpers.attrs
 
 __all__ = [
     "Param",
     "Extra",
 ]
 
-Param = ParameterAttribute
-Extra = ExtraLayerAttribute
+Param = paddle.trainer_config_helpers.attrs.ParameterAttribute
+Extra = paddle.trainer_config_helpers.attrs.ExtraLayerAttribute
+
+for each in paddle.trainer_config_helpers.attrs.__all__:
+    globals()[each] = getattr(paddle.trainer_config_helpers.attrs, each)
+    __all__.append(each)

python/paddle/v2/config_base.py

@@ -13,12 +13,55 @@
 # limitations under the License.
 
 import collections
-
+import re
 from paddle.trainer_config_helpers.default_decorators import wrap_name_default
 import paddle.trainer_config_helpers as conf_helps
 
 
+class LayerType(type):
+    def __new__(cls, name, bases, attrs):
+        method_name = attrs.get('METHOD_NAME', None)
+        if method_name is not None:
+            method = getattr(conf_helps, method_name)
+            if method.__doc__ is not None:
+                mapper = attrs.get("__map_docstr__", None)
+                if mapper is not None:
+                    attrs['__doc__'] = LayerType.__map_docstr__(
+                        mapper(method.__doc__),
+                        method_name=method_name,
+                        name=name)
+                else:
+                    attrs['__doc__'] = LayerType.__map_docstr__(
+                        method.__doc__, method_name=method_name, name=name)
+        return super(LayerType, cls).__new__(cls, name, bases, attrs)
+
+    @staticmethod
+    def __map_docstr__(doc, name, method_name):
+        assert isinstance(doc, basestring)
+
+        # replace LayerOutput to paddle.v2.config_base.Layer
+        doc = doc.replace("LayerOutput", "paddle.v2.config_base.Layer")
+
+        doc = doc.replace('ParameterAttribute',
+                          'paddle.v2.attr.ParameterAttribute')
+
+        doc = re.sub(r'ExtraLayerAttribute[^\s]?',
+                     'paddle.v2.attr.ExtraAttribute', doc)
+
+        # xxx_layer to xxx
+        doc = re.sub(r"(?P<name>[a-z]+)_layer", r"\g<name>", doc)
+
+        # XxxxActivation to paddle.v2.Activation.Xxxx
+        doc = re.sub(r"(?P<name>[A-Z][a-zA-Z]+)Activation",
+                     r"paddle.v2.Activation.\g<name>", doc)
+
+        # TODO(yuyang18): Add more rules if needed.
+        return doc
+
+
 class Layer(object):
+    __metaclass__ = LayerType
+
     def __init__(self, name=None, parent_layers=None):
         assert isinstance(parent_layers, dict)
         self.name = name
@@ -80,6 +123,8 @@ def __convert_to_v2__(method_name, parent_names, is_default_name=True):
         wrapper = None
 
     class V2LayerImpl(Layer):
+        METHOD_NAME = method_name
+
         def __init__(self, **kwargs):
             parent_layers = dict()
             other_kwargs = dict()

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 @@ class DataFeeder(DataProviderConverter):
         """
         :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/data_type.py

@@ -12,11 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.trainer.PyDataProvider2 import \
-    InputType, DataType, dense_vector, sparse_binary_vector,\
-    sparse_vector, integer_value, integer_value_sequence
+import paddle.trainer.PyDataProvider2 as pydp2
 
-__all__ = [
-    'InputType', 'DataType', 'dense_vector', 'sparse_binary_vector',
-    'sparse_vector', 'integer_value', 'integer_value_sequence'
+import_list = [
+    nm for nm in dir(pydp2)
+    if '_' in nm and nm[0] != '_' and ('value' in nm or 'vector' in nm)
 ]
+import_list.extend(['InputType'])
+
+for nm in import_list:
+    globals()[nm] = getattr(pydp2, nm)
+
+__all__ = import_list

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 tarfile
 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'

python/paddle/v2/dataset/imdb.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 """
 IMDB dataset: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
+
+TODO(yuyang18): Complete comments.
 """
 
 import paddle.v2.dataset.common

python/paddle/v2/dataset/imikolov.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 """
 imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/
+
+Complete comments.
 """
 import paddle.v2.dataset.common
 import tarfile

python/paddle/v2/dataset/mnist.py

@@ -13,6 +13,9 @@
 # limitations under the License.
 """
 MNIST dataset.
+
+This module will download dataset from http://yann.lecun.com/exdb/mnist/ and
+parse train set and test set into paddle reader creators.
 """
 import paddle.v2.dataset.common
 import subprocess
@@ -72,6 +75,15 @@ def reader_creator(image_filename, label_filename, buffer_size):
 
 
 def train():
+    """
+    MNIST train set creator.
+
+    It returns a reader creator, each sample in the reader is image pixels in
+    [0, 1] and label in [0, 9].
+
+    :return: Train reader creator
+    :rtype: callable
+    """
     return reader_creator(
         paddle.v2.dataset.common.download(TRAIN_IMAGE_URL, 'mnist',
                                           TRAIN_IMAGE_MD5),
@@ -80,6 +92,15 @@ def train():
 
 
 def test():
+    """
+    MNIST test set cretor.
+
+    It returns a reader creator, each sample in the reader is image pixels in
+    [0, 1] and label in [0, 9].
+
+    :return: Test reader creator.
+    :rtype: callable
+    """
     return reader_creator(
         paddle.v2.dataset.common.download(TEST_IMAGE_URL, 'mnist',
                                           TEST_IMAGE_MD5),

python/paddle/v2/dataset/movielens.py

@@ -11,6 +11,11 @@
 # 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.
+"""
+Movielens 1-M dataset.
+
+TODO(yuyang18): Complete comments.
+"""
 
 import zipfile
 from common import download

python/paddle/v2/dataset/sentiment.py

@@ -15,18 +15,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-The script fetch and preprocess movie_reviews data set
+The script fetch and preprocess movie_reviews data set that provided by NLTK
 
-that provided by NLTK
+TODO(yuyang18): Complete dataset.
 """
 
-import common
 import collections
-import nltk
-import numpy as np
 from itertools import chain
+
+import nltk
 from nltk.corpus import movie_reviews
 
+import common
+
 __all__ = ['train', 'test', 'get_word_dict']
 NUM_TRAINING_INSTANCES = 1600
 NUM_TOTAL_INSTANCES = 2000

python/paddle/v2/dataset/uci_housing.py

@@ -11,6 +11,11 @@
 # 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.
+"""
+UCI Housing dataset.
+
+TODO(yuyang18): Complete comments.
+"""
 
 import numpy as np
 import os

python/paddle/v2/event.py

@@ -34,6 +34,10 @@ class WithMetric(object):
 
 
 class TestResult(WithMetric):
+    """
+    Result that trainer.test return.
+    """
+
     def __init__(self, evaluator, cost):
         super(TestResult, self).__init__(evaluator)
         self.cost = cost

python/paddle/v2/layer.py

@@ -28,7 +28,7 @@ The primary usage shows below.
                                  act=paddle.activation.Softmax())
 
     # use prediction instance where needed.
-    parameters = paddle.v2.parameters.create(cost)
+    parameters = paddle.parameters.create(cost)
 """
 
 import collections
@@ -47,26 +47,32 @@ from paddle.trainer.config_parser import \
     RecurrentLayerGroupEnd, model_type
 
 import activation
+import re
 import data_type
 
 __all__ = ['parse_network', 'data']
 
-__projection_names__ = filter(lambda x: x.endswith('_projection'),
-                              dir(conf_helps))
-__all__ += __projection_names__
-
-__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps))
-__all__ += __operator_names__
-
 
 def parse_network(*outputs):
     """
-    parse all output layers and then generate a model config proto.
-    :param outputs:
-    :return:
+    Parse all output layers and then generate a ModelConfig object.
+
+    ..  note::
+
+        This function is used internally in paddle.v2 module. User should never
+        invoke this method.
+
+    :param outputs: Output layers.
+    :type outputs: Layer
+    :return: A ModelConfig object instance.
+    :rtype: ModelConfig
     """
 
     def __real_func__():
+        """
+        __real_func__ is the function that config_parser.parse invoked. It is
+        the plain old paddle configuration function.
+        """
         context = dict()
         real_output = [each.to_proto(context=context) for each in outputs]
         conf_helps.outputs(real_output)
@@ -81,6 +87,8 @@ So we also need to implement some special LayerV2.
 
 
 class DataLayerV2(Layer):
+    METHOD_NAME = 'data_layer'
+
     def __init__(self, name, type, **kwargs):
         assert isinstance(type, data_type.InputType)
 
@@ -99,6 +107,17 @@ class DataLayerV2(Layer):
             args[each] = self.__kwargs__[each]
         return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
 
+    def __map_docstr__(doc):
+        doc = re.sub(r'(data = [^\)]+)\).*',
+                     "data = paddle.layer.data(name=\"input\", "
+                     "type=paddle.data_type.dense_vector(1000))", doc)
+
+        doc = re.sub(r':param size:.*',
+                     ':param type: Data type of this data layer', doc)
+        doc = re.sub(r':type size:.*',
+                     ":type size: paddle.v2.data_type.InputType", doc)
+        return doc
+
 
 class WithExtraParent(Layer):
     def extra_parent(self):
@@ -347,6 +366,7 @@ class RecurrentLayerOutput(Layer):
 
 LayerV2 = Layer
 data = DataLayerV2
+data.__name__ = 'data'
 AggregateLevel = conf_helps.layers.AggregateLevel
 ExpandLevel = conf_helps.layers.ExpandLevel
 memory = MemoryV2
@@ -386,6 +406,7 @@ def __convert_layer__(_new_name_, _old_name_, _parent_names_):
     global __all__
     __all__.append(_new_name_)
     globals()[new_name] = __convert_to_v2__(_old_name_, _parent_names_)
+    globals()[new_name].__name__ = new_name
 
 
 for each_layer_name in dir(conf_helps):
@@ -399,21 +420,6 @@ del parent_names
 del new_name
 del each_layer_name
 
-# convert projection
-for prj in __projection_names__:
-    globals()[prj] = __convert_to_v2__(
-        prj, parent_names=['input'], is_default_name=False)
-
-# convert operator
-operator_list = [
-    # [V1_method_name, parent_names],
-    ['dotmul_operator', ['a', 'b']],
-    ['conv_operator', ['img', 'filter']]
-]
-for op in operator_list:
-    globals()[op[0]] = __convert_to_v2__(
-        op[0], parent_names=op[1], is_default_name=False)
-
 
 @wrap_name_default()
 def recurrent_group(step, input, name=None):
@@ -464,3 +470,29 @@ def recurrent_group(step, input, name=None):
         return retv[0]
     else:
         return retv
+
+
+__projection_names__ = filter(lambda x: x.endswith('_projection'),
+                              dir(conf_helps))
+
+__all__ += __projection_names__
+
+__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps))
+__all__ += __operator_names__
+
+# convert projection
+for prj in __projection_names__:
+    globals()[prj] = __convert_to_v2__(
+        prj, parent_names=['input'], is_default_name=False)
+    globals()[prj].__name__ = prj
+
+# convert operator
+operator_list = [
+    # [V1_method_name, parent_names],
+    ['dotmul_operator', ['a', 'b']],
+    ['conv_operator', ['img', 'filter']]
+]
+for op in operator_list:
+    globals()[op[0]] = __convert_to_v2__(
+        op[0], parent_names=op[1], is_default_name=False)
+    globals()[op[0]].__name__ = op[0]

python/paddle/v2/minibatch.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+__all__ = ['batch']
+
+
+def batch(reader, batch_size):
+    """
+    Create a batched reader.
+
+    :param reader: the data reader to read from.
+    :type reader: callable
+    :param batch_size: size of each mini-batch
+    :type batch_size: int
+    :return: the batched reader.
+    :rtype: callable
+    """
+
+    def batch_reader():
+        r = reader()
+        b = []
+        for instance in r:
+            b.append(instance)
+            if len(b) == batch_size:
+                yield b
+                b = []
+        if b:
+            yield b
+
+    return batch_reader

python/paddle/v2/networks.py

@@ -38,6 +38,7 @@ def __initialize__():
             parent_names=parents,
             is_default_name='name' in argspec.args)
         globals()[each_subnetwork] = v2_subnet
+        globals()[each_subnetwork].__name__ = each_subnetwork
         global __all__
         __all__.append(each_subnetwork)
 

python/paddle/v2/optimizer.py

 import py_paddle.swig_paddle as swig_api
-import paddle.trainer_config_helpers.optimizers as v1_optimizers
+
 import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
-import paddle.v2
+import paddle.trainer_config_helpers.optimizers as v1_optimizers
+"""
+Optimizers(update equation) for SGD method.
+
+TODO(yuyang18): Complete comments.
+"""
 
 __all__ = [
     'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
@@ -44,7 +49,7 @@ class Optimizer(object):
 class Momentum(Optimizer):
     def __init__(self, momentum=None, sparse=False, **kwargs):
         learning_method = v1_optimizers.MomentumOptimizer(
-            momentum=None, sparse=False)
+            momentum=momentum, sparse=sparse)
         super(Momentum, self).__init__(
             learning_method=learning_method, **kwargs)
 

python/paddle/v2/parameters.py

@@ -12,6 +12,7 @@ __all__ = ['Parameters', 'create']
 def create(layers):
     """
     Create parameter pool by topology.
+
     :param layers:
     :return:
     """
@@ -69,6 +70,7 @@ class Parameters(object):
     def keys(self):
         """
         keys are the names of each parameter.
+
         :return: list of parameter name
         :rtype: list
         """
@@ -77,6 +79,7 @@ class Parameters(object):
     def names(self):
         """
         names of each parameter.
+
         :return: list of parameter name
         :rtype: list
         """
@@ -85,6 +88,7 @@ class Parameters(object):
     def has_key(self, key):
         """
         has_key return true if there are such parameter name == key
+
         :param key: Parameter name
         :type key: basestring
         :return: True if contains such key
@@ -138,6 +142,7 @@ class Parameters(object):
     def get_shape(self, key):
         """
         get shape of the parameter.
+
         :param key: parameter name
         :type key: basestring
         :return: parameter's shape
@@ -192,6 +197,7 @@ class Parameters(object):
     def set(self, parameter_name, value):
         """
         Set parameter by parameter name & matrix.
+
         :param parameter_name: parameter name
         :type parameter_name: basestring
         :param value: parameter matrix

python/paddle/v2/pooling.py

@@ -12,13 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.trainer_config_helpers.poolings import *
+import paddle.trainer_config_helpers.poolings
+import copy
 
-__all__ = ["Max", "CudnnMax", "Avg", "CudnnAvg", "Sum", "SquareRootN"]
+__all__ = []
+suffix = 'Pooling'
 
-Max = MaxPooling
-CudnnMax = CudnnMaxPooling
-Avg = AvgPooling
-CudnnAvg = CudnnAvgPooling
-Sum = SumPooling
-SquareRootN = SquareRootNPooling
+for name in paddle.trainer_config_helpers.poolings.__all__:
+    new_name = name[:-len(suffix)]
+    globals()[new_name] = copy.copy(
+        getattr(paddle.trainer_config_helpers.poolings, name))
+    globals()[new_name].__name__ = new_name
+    __all__.append(new_name)

python/paddle/v2/reader/__init__.py

@@ -11,15 +11,64 @@
 # 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.
+"""
+At training and testing time, PaddlePaddle programs need to read data. To ease
+the users' work to write data reading code, we define that
 
-# It would be too lengthy to require our users to prefix decorators with `decorator`.
-# For example, we want the following line
-#
-#     r = paddle.reader.decorator.bufferd(paddle.reader.creator.text("hello.txt"))
-#
-# to be a shorter version:
-#
-#     r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt"))
+- A *reader* is a function that reads data (from file, network, random number
+  generator, etc) and yields data items.
+- A *reader creator* is a function that returns a reader function.
+- A *reader decorator* is a function, which accepts one or more readers, and
+  returns a reader.
+- A *batch reader* is a function that reads data (from *reader*, file, network,
+  random number generator, etc) and yields a batch of data items.
+
+#####################
+Data Reader Interface
+#####################
+
+Indeed, *data reader* doesn't have to be a function that reads and yields data
+items. It can be any function with no parameter that creates a iterable
+(anything can be used in :code:`for x in iterable`)\:
+
+..  code-block:: python
+
+    iterable = data_reader()
+
+Element produced from the iterable should be a **single** entry of data,
+**not** a mini batch. That entry of data could be a single item, or a tuple of
+items.
+Item should be of `supported type <http://www.paddlepaddle.org/doc/ui/data_provider
+/pydataprovider2.html?highlight=dense_vector#input-types>`_ (e.g., numpy 1d
+array of float32, int, list of int)
+
+An example implementation for single item data reader creator:
+
+..  code-block:: python
+
+    def reader_creator_random_image(width, height):
+        def reader():
+            while True:
+                yield numpy.random.uniform(-1, 1, size=width*height)
+    return reader
+
+An example implementation for multiple item data reader creator:
+
+..  code-block:: python
+
+    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
+
+
+TODO(yuyang18): Should we add whole design doc here?
+"""
+
+import decorator
 from decorator import *
 
 import creator
+
+__all__ = decorator.__all__ + ['creator']

python/paddle/v2/reader/creator.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+Creator package contains some simple reader creator, which could be used in user
+program.
+"""
 
 __all__ = ['np_array', 'text_file']
 
@@ -38,7 +42,7 @@ def np_array(x):
 def text_file(path):
     """
     Creates a data reader that outputs text line by line from given text file.
-    Trailing new line ('\n') of each line will be removed.
+    Trailing new line ('\\\\n') of each line will be removed.
 
     :path: path of the text file.
     :returns: data reader of text file

python/paddle/v2/reader/decorator.py

@@ -14,7 +14,7 @@
 
 __all__ = [
     'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
-    'ComposeNotAligned', 'batched', 'firstn'
+    'ComposeNotAligned', 'firstn'
 ]
 
 import itertools
@@ -28,9 +28,11 @@ def map_readers(func, *readers):
     Creates a data reader that outputs return value of function using
     output of each data readers as arguments.
 
-    :param func: function to use.
-    :param *readers: readers whose outputs will be used as arguments of func.
-    :returns: the created data reader.
+    :param func: function to use. The type of func should be (Sample) => Sample
+    :type: callable
+    :param readers: readers whose outputs will be used as arguments of func.
+    :return: the created data reader.
+    :rtype: callable
     """
 
     def reader():
@@ -45,16 +47,19 @@ def map_readers(func, *readers):
 
 def shuffle(reader, buf_size):
     """
-    Creates a data reader whose data output is suffled.
+    Creates a data reader whose data output is shuffled.
 
     Output from the iterator that created by original reader will be
     buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
     is determined by argument buf_size.
 
     :param reader: the original reader whose output will be shuffled.
+    :type reader: callable
     :param buf_size: shuffle buffer size.
+    :type buf_size: int
 
-    :returns:the new reader whose output is shuffled.
+    :return: the new reader whose output is shuffled.
+    :rtype: callable
     """
 
     def data_reader():
@@ -88,7 +93,8 @@ def chain(*readers):
     [0, 0, 0, 1, 1, 1, 2, 2, 2]
 
     :param readers: input readers.
-    :returns: the new data reader.
+    :return: the new data reader.
+    :rtype: callable
     """
 
     def reader():
@@ -115,12 +121,13 @@ def compose(*readers, **kwargs):
     The composed reader will output:
     (1, 2, 3, 4, 5)
 
-    :*readers: readers that will be composed together.
-    :check_alignment: if True, will check if input readers are aligned
+    :param readers: readers that will be composed together.
+    :param check_alignment: if True, will check if input readers are aligned
         correctly. If False, will not check alignment and trailing outputs
         will be discarded. Defaults to True.
+    :type check_alignment: bool
 
-    :returns: the new data reader.
+    :return: the new data reader.
 
     :raises ComposeNotAligned: outputs of readers are not aligned.
         Will not raise when check_alignment is set to False.
@@ -161,7 +168,9 @@ def buffered(reader, size):
     as the buffer is not empty.
     
     :param reader: the data reader to read from.
+    :type reader: callable
     :param size: max buffer size.
+    :type size: int
     
     :returns: the buffered data reader.
     """
@@ -193,31 +202,16 @@ def buffered(reader, size):
     return data_reader
 
 
-def batched(reader, batch_size):
-    """
-    Create a batched reader.
-    :param reader: the data reader to read from.
-    :param batch_size: batch_size
-    :return: the batched reader.
-    """
-
-    def batched_reader():
-        r = reader()
-        batch = []
-        for instance in r:
-            batch.append(instance)
-            if len(batch) == batch_size:
-                yield batch
-                batch = []
-        if batch:
-            yield batch
-
-    return batched_reader
-
-
 def firstn(reader, n):
     """
     Limit the max number of samples that reader could return.
+
+    :param reader: the data reader to read from.
+    :type reader: callable
+    :param n: the max number of samples that return.
+    :type n: int
+    :return: the decorated reader.
+    :rtype: callable
     """
 
     # TODO(yuyang18): Check if just drop the reader, could clean the opened

python/paddle/v2/tests/test_topology.py

@@ -16,6 +16,7 @@ import paddle.v2.layer as layer
 import paddle.v2.topology as topology
 import paddle.v2.data_type as data_type
 import paddle.trainer_config_helpers as conf_helps
+import paddle.trainer.PyDataProvider2 as pydp2
 
 
 class TestTopology(unittest.TestCase):
@@ -35,13 +36,13 @@ class TestTopology(unittest.TestCase):
         pixel_data_type = filter(lambda type: type[0] == "pixel", data_types)
         self.assertEqual(len(pixel_data_type), 1)
         pixel_data_type = pixel_data_type[0]
-        self.assertEqual(pixel_data_type[1].type, data_type.DataType.Dense)
+        self.assertEqual(pixel_data_type[1].type, pydp2.DataType.Dense)
         self.assertEqual(pixel_data_type[1].dim, 784)
 
         label_data_type = filter(lambda type: type[0] == "label", data_types)
         self.assertEqual(len(label_data_type), 1)
         label_data_type = label_data_type[0]
-        self.assertEqual(label_data_type[1].type, data_type.DataType.Index)
+        self.assertEqual(label_data_type[1].type, pydp2.DataType.Index)
         self.assertEqual(label_data_type[1].dim, 10)
 
     def test_get_layer(self):

python/paddle/v2/trainer.py

@@ -9,6 +9,10 @@ from . import optimizer as v2_optimizer
 from . import parameters as v2_parameters
 
 __all__ = ['SGD']
+"""
+Trainer package
+TODO(yuyang18): Complete comments.
+"""
 
 
 def default_event_handler(event):
@@ -22,14 +26,20 @@ def default_event_handler(event):
     pass
 
 
-class SGD():
-    def __init__(self, cost, parameters, update_equation):
-        """
-        Simple SGD Trainer.
+class SGD(object):
+    """
+    Simple SGD Trainer.
+    TODO(yuyang18): Complete comments
+
+    :param update_equation: The optimizer object.
+    :type update_equation: paddle.v2.optimizer.Optimizer
+    :param cost: Target cost that neural network should be optimized.
+    :type cost: paddle.v2.config_base.Layer
+    :param parameters: The parameters dictionary.
+    :type parameters: paddle.v2.parameters.Parameters
+    """
 
-        :param update_equation: The optimizer object.
-        :type update_equation: v2_optimizer.Optimizer
-        """
+    def __init__(self, cost, parameters, update_equation):
 
         if not isinstance(parameters, v2_parameters.Parameters):
             raise TypeError('parameters should be parameters')
@@ -56,8 +66,6 @@ class SGD():
         Training method. Will train num_passes of input data.
 
         :param reader:
-        :param topology: Network Topology, use one or more Layers to represent it.
-        :param parameters: The parameter pools.
         :param num_passes: The total train passes.
         :param event_handler: Event handler. A method will be invoked when event
                               occurred.