Merge branch 'develop' of github.com:baidu/Paddle into add_comments_to_v2_module

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

@@ -98,7 +98,7 @@ 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),
@@ -115,7 +115,7 @@ 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/v2/data.rst

@@ -22,6 +22,13 @@ Reader
 ..  automodule:: paddle.v2.reader.creator
     :members:
 
+#########
+minibatch
+#########
+
+..  automodule:: paddle.v2.minibatch
+    :members:
+
 #######
 Dataset
 #######

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/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/dataset/__init__.py

@@ -23,8 +23,9 @@ import movielens
 import conll05
 import uci_housing
 import sentiment
+import wmt14
 
 __all__ = [
     'mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05', 'sentiment'
-    'uci_housing'
+    'uci_housing', 'wmt14'
 ]

python/paddle/v2/dataset/wmt14.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.
+"""
+wmt14 dataset
+"""
+import paddle.v2.dataset.common
+import tarfile
+import os.path
+import itertools
+
+__all__ = ['train', 'test', 'build_dict']
+
+URL_DEV_TEST = 'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
+MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
+URL_TRAIN = 'http://localhost:8000/train.tgz'
+MD5_TRAIN = '72de99da2830ea5a3a2c4eb36092bbc7'
+
+
+def word_count(f, word_freq=None):
+    add = paddle.v2.dataset.common.dict_add
+    if word_freq == None:
+        word_freq = {}
+
+    for l in f:
+        for w in l.strip().split():
+            add(word_freq, w)
+        add(word_freq, '<s>')
+        add(word_freq, '<e>')
+
+    return word_freq
+
+
+def get_word_dix(word_freq):
+    TYPO_FREQ = 50
+    word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
+    word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*word_freq_sorted))
+    word_idx = dict(zip(words, xrange(len(words))))
+    word_idx['<unk>'] = len(words)
+    return word_idx
+
+
+def get_word_freq(train, dev):
+    word_freq = word_count(train, word_count(dev))
+    if '<unk>' in word_freq:
+        # remove <unk> for now, since we will set it as last index
+        del word_freq['<unk>']
+    return word_freq
+
+
+def build_dict():
+    base_dir = './wmt14-data'
+    train_en_filename = base_dir + '/train/train.en'
+    train_fr_filename = base_dir + '/train/train.fr'
+    dev_en_filename = base_dir + '/dev/ntst1213.en'
+    dev_fr_filename = base_dir + '/dev/ntst1213.fr'
+
+    if not os.path.exists(train_en_filename) or not os.path.exists(
+            train_fr_filename):
+        with tarfile.open(
+                paddle.v2.dataset.common.download(URL_TRAIN, 'wmt14',
+                                                  MD5_TRAIN)) as tf:
+            tf.extractall(base_dir)
+
+    if not os.path.exists(dev_en_filename) or not os.path.exists(
+            dev_fr_filename):
+        with tarfile.open(
+                paddle.v2.dataset.common.download(URL_DEV_TEST, 'wmt14',
+                                                  MD5_DEV_TEST)) as tf:
+            tf.extractall(base_dir)
+
+    f_en = open(train_en_filename)
+    f_fr = open(train_fr_filename)
+    f_en_dev = open(dev_en_filename)
+    f_fr_dev = open(dev_fr_filename)
+
+    word_freq_en = get_word_freq(f_en, f_en_dev)
+    word_freq_fr = get_word_freq(f_fr, f_fr_dev)
+
+    f_en.close()
+    f_fr.close()
+    f_en_dev.close()
+    f_fr_dev.close()
+
+    return get_word_dix(word_freq_en), get_word_dix(word_freq_fr)
+
+
+def reader_creator(directory, path_en, path_fr, URL, MD5, dict_en, dict_fr):
+    def reader():
+        if not os.path.exists(path_en) or not os.path.exists(path_fr):
+            with tarfile.open(
+                    paddle.v2.dataset.common.download(URL, 'wmt14', MD5)) as tf:
+                tf.extractall(directory)
+
+        f_en = open(path_en)
+        f_fr = open(path_fr)
+        UNK_en = dict_en['<unk>']
+        UNK_fr = dict_fr['<unk>']
+
+        for en, fr in itertools.izip(f_en, f_fr):
+            src_ids = [dict_en.get(w, UNK_en) for w in en.strip().split()]
+            tar_ids = [
+                dict_fr.get(w, UNK_fr)
+                for w in ['<s>'] + fr.strip().split() + ['<e>']
+            ]
+
+            # remove sequence whose length > 80 in training mode
+            if len(src_ids) == 0 or len(tar_ids) <= 1 or len(
+                    src_ids) > 80 or len(tar_ids) > 80:
+                continue
+
+            yield src_ids, tar_ids[:-1], tar_ids[1:]
+
+        f_en.close()
+        f_fr.close()
+
+    return reader
+
+
+def train(dict_en, dict_fr):
+    directory = './wmt14-data'
+    return reader_creator(directory, directory + '/train/train.en',
+                          directory + '/train/train.fr', URL_TRAIN, MD5_TRAIN,
+                          dict_en, dict_fr)
+
+
+def test(dict_en, dict_fr):
+    directory = './wmt14-data'
+    return reader_creator(directory, directory + '/dev/ntst1213.en',
+                          directory + '/dev/ntst1213.fr', URL_DEV_TEST,
+                          MD5_DEV_TEST, dict_en, dict_fr)

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/reader/decorator.py

@@ -12,16 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+__all__ = [
+    'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
+    'ComposeNotAligned', 'firstn'
+]
+
 import itertools
 import random
 from Queue import Queue
 from threading import Thread
 
-__all__ = [
-    'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
-    'ComposeNotAligned', 'batched', 'firstn'
-]
-
 
 def map_readers(func, *readers):
     """
@@ -202,32 +202,6 @@ def buffered(reader, size):
     return data_reader
 
 
-def batched(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 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.

python/paddle/v2/trainer.py

@@ -109,7 +109,7 @@ class SGD(object):
                 for each_param in self.__gradient_machine__.getNonStaticParameters(
                 ):
                     updater.update(each_param)
-                cost_sum = out_args.sumCosts()
+                cost_sum = out_args.sum()
                 cost = cost_sum / len(data_batch)
                 updater.finishBatch(cost)
                 batch_evaluator.finish()
@@ -145,7 +145,7 @@ class SGD(object):
             num_samples += len(data_batch)
             self.__gradient_machine__.forward(
                 feeder(data_batch), out_args, api.PASS_TEST)
-            total_cost += out_args.sumCosts()
+            total_cost += out_args.sum()
             self.__gradient_machine__.eval(evaluator)
 
         evaluator.finish()