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

.gitignore

@@ -9,3 +9,6 @@ build/
 .pydevproject
 Makefile
 .test_env/
+
+*~
+bazel-*

.pre-commit-config.yaml

@@ -7,6 +7,7 @@
     sha: v0.13.2
     hooks:
     - id: yapf
+      files: (.*\.(py|bzl)|BUILD|.*\.BUILD|WORKSPACE)$  # Bazel BUILD files follow Python syntax.
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     sha: 7539d8bd1a00a3c1bfd34cdb606d3a6372e83469
     hooks:

.travis.yml

@@ -8,10 +8,13 @@ os:
 env:
   - JOB=DOCS
   - JOB=BUILD_AND_TEST
+  - JOB=PRE_COMMIT
 matrix:
   exclude:
     - os: osx
-      env: JOB=DOCS  # Only generate documentation in linux
+      env: JOB=DOCS  # Only generate documentation in linux.
+    - os: osx
+      env: JOB=PRE_COMMIT # Only check pre-commit hook in linux
 
 addons:
   apt:
@@ -39,18 +42,23 @@ addons:
       - lcov
       - graphviz
       - swig
+      - clang-format-3.8
 before_install:
   - |
     if [ ${JOB} == "BUILD_AND_TEST" ]; then
-      if ! git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)|(\.rst$)|(\.jpg$)|(\.png$)'
+      local change_list=`git diff --name-only $TRAVIS_COMMIT_RANGE`
+      if [ $? -eq 0 ]; then  # if git diff return no zero, then rerun unit test.
+        if ! echo ${change_list} | grep -qvE '(\.md$)|(\.rst$)|(\.jpg$)|(\.png$)'
         then
           echo "Only markdown docs were updated, stopping build process."
           exit
         fi
       fi
+    fi
   - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
   - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
-  - pip install wheel protobuf sphinx breathe recommonmark virtualenv numpy sphinx_rtd_theme
+  - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
+  - pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme pre-commit
 script:
   - paddle/scripts/travis/main.sh
 notifications:

CMakeLists.txt

@@ -25,8 +25,8 @@ find_package(ZLIB REQUIRED)
 find_package(NumPy REQUIRED)
 find_package(Threads REQUIRED)
 find_package(AVX QUIET)
-find_package(Glog)
-find_package(Gflags QUIET)
+find_package(Glog REQUIRED)
+find_package(Gflags REQUIRED)
 find_package(GTest)
 find_package(Sphinx)
 find_package(Doxygen)
@@ -40,8 +40,6 @@ option(WITH_AVX "Compile PaddlePaddle with avx intrinsics" ${AVX_FOUND})
 option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
 option(WITH_STYLE_CHECK "Style Check for PaddlePaddle" ${PYTHONINTERP_FOUND})
 option(WITH_RDMA "Compile PaddlePaddle with rdma support" OFF)
-option(WITH_GLOG "Compile PaddlePaddle use glog, otherwise use a log implement internally" ${LIBGLOG_FOUND})
-option(WITH_GFLAGS "Compile PaddlePaddle use gflags, otherwise use a flag implement internally" ${GFLAGS_FOUND})
 option(WITH_TIMER "Compile PaddlePaddle use timer" OFF)
 option(WITH_PROFILER "Compile PaddlePaddle use gpu profiler" OFF)
 option(WITH_TESTING "Compile and run unittest for PaddlePaddle" ${GTEST_FOUND})
@@ -136,16 +134,12 @@ else(WITH_RDMA)
   add_definitions(-DPADDLE_DISABLE_RDMA)
 endif(WITH_RDMA)
 
-if(WITH_GLOG)
-    add_definitions(-DPADDLE_USE_GLOG)
-    include_directories(${LIBGLOG_INCLUDE_DIR})
-endif()
+# glog
+include_directories(${LIBGLOG_INCLUDE_DIR})
 
-if(WITH_GFLAGS)
-    add_definitions(-DPADDLE_USE_GFLAGS)
-    add_definitions(-DGFLAGS_NS=${GFLAGS_NAMESPACE})
-    include_directories(${GFLAGS_INCLUDE_DIRS})
-endif()
+#gflags
+add_definitions(-DGFLAGS_NS=${GFLAGS_NAMESPACE})
+include_directories(${GFLAGS_INCLUDE_DIRS})
 
 if(WITH_TESTING)
     enable_testing()
@@ -169,5 +163,4 @@ add_subdirectory(paddle)
 add_subdirectory(python)
 if(WITH_DOC)
     add_subdirectory(doc)
-    add_subdirectory(doc_cn)
 endif()

CONTRIBUTING.md

+./doc/howto/contribute_to_paddle_en.md
\ No newline at end of file

WORKSPACE

+# External dependency to Google protobuf.
+http_archive(
+    name="protobuf",
+    url="http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
+    sha256="0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
+    strip_prefix="protobuf-3.1.0")
+
+# External dependency to gtest 1.7.0.  This method comes from
+# https://www.bazel.io/versions/master/docs/tutorial/cpp.html.
+new_http_archive(
+    name="gtest",
+    url="https://github.com/google/googletest/archive/release-1.7.0.zip",
+    sha256="b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
+    build_file="third_party/gtest.BUILD",
+    strip_prefix="googletest-release-1.7.0")
+
+# External dependency to gflags.  This method comes from
+# https://github.com/gflags/example/blob/master/WORKSPACE.
+new_git_repository(
+    name="gflags",
+    tag="v2.2.0",
+    remote="https://github.com/gflags/gflags.git",
+    build_file="third_party/gflags.BUILD")
+
+# External dependency to glog.  This method comes from
+# https://github.com/reyoung/bazel_playground/blob/master/WORKSPACE
+new_git_repository(
+    name="glog",
+    remote="https://github.com/google/glog.git",
+    commit="b6a5e0524c28178985f0d228e9eaa43808dbec3c",
+    build_file="third_party/glog.BUILD")

benchmark/tensorflow/rnn/run_multi.sh

@@ -25,4 +25,3 @@ test 4 2 256 512
 test 4 2 512 128 
 test 4 2 512 256 
 test 4 2 512 512 
-

cmake/FindSphinx.cmake

@@ -72,6 +72,7 @@ function( Sphinx_add_target target_name builder conf cache source destination )
     ${source}
     ${destination}
     COMMENT "Generating sphinx documentation: ${builder}"
+    COMMAND ln -s ${destination}/index_*.html ${destination}/index.html
     )
 
   set_property(

cmake/check_packages.cmake

@@ -14,13 +14,9 @@ if(WITH_STYLE_CHECK)
   find_package(PythonInterp REQUIRED)
 endif()
 
-if(WITH_GLOG)
-  find_package(Glog REQUIRED)
-endif()
+find_package(Glog REQUIRED)
 
-if(WITH_GFLAGS)
-  find_package(Gflags REQUIRED)
-endif()
+find_package(Gflags REQUIRED)
 
 if(WITH_TESTING)
   find_package(GTest REQUIRED)
@@ -30,7 +26,6 @@ if(WITH_DOC)
   find_package(Sphinx REQUIRED)
   find_package(Doxygen REQUIRED)
   find_python_module(recommonmark REQUIRED)
-  find_python_module(breathe REQUIRED)
 endif()
 
 if(WITH_SWIG_PY)

cmake/util.cmake

@@ -65,7 +65,7 @@ endmacro()
 # link_paddle_exe
 # add paddle library for a paddle executable, such as trainer, pserver.
 #
-# It will handle WITH_PYTHON/WITH_GLOG etc.
+# It will handle WITH_PYTHON etc.
 function(link_paddle_exe TARGET_NAME)
     if(WITH_RDMA)
         generate_rdma_links()
@@ -108,6 +108,8 @@ function(link_paddle_exe TARGET_NAME)
         paddle_cuda
         ${METRIC_LIBS}
         ${PROTOBUF_LIBRARY}
+        ${LIBGLOG_LIBRARY}
+        ${GFLAGS_LIBRARIES}
         ${CMAKE_THREAD_LIBS_INIT}
         ${CBLAS_LIBS}
         ${ZLIB_LIBRARIES}
@@ -125,16 +127,6 @@ function(link_paddle_exe TARGET_NAME)
             ${PYTHON_LIBRARIES})
     endif()
 
-    if(WITH_GLOG)
-        target_link_libraries(${TARGET_NAME}
-            ${LIBGLOG_LIBRARY})
-    endif()
-
-    if(WITH_GFLAGS)
-        target_link_libraries(${TARGET_NAME}
-            ${GFLAGS_LIBRARIES})
-    endif()
-
     if(WITH_GPU)
         if(NOT WITH_DSO OR WITH_METRIC)
             target_link_libraries(${TARGET_NAME}
@@ -206,5 +198,5 @@ function(create_resources res_file output)
     # Convert hex data for C compatibility
     string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," filedata ${filedata})
     # Append data to output file
-    file(APPEND ${output} "const unsigned char ${filename}[] = {${filedata}};\nconst unsigned ${filename}_size = sizeof(${filename});\n")
+    file(APPEND ${output} "const unsigned char ${filename}[] = {${filedata}0};\nconst unsigned ${filename}_size = sizeof(${filename});\n")
 endfunction()

demo/gan/data/download_cifar.sh

@@ -15,4 +15,3 @@ set -e
 wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
 tar zxf cifar-10-python.tar.gz
 rm cifar-10-python.tar.gz
-

demo/gan/data/get_mnist_data.sh

@@ -15,5 +15,3 @@ do
         gunzip ${fname}.gz
     fi
 done
-
-

demo/gan/gan_conf.py

@@ -14,10 +14,9 @@
 from paddle.trainer_config_helpers import *
 
 mode = get_config_arg("mode", str, "generator")
-assert mode in set(["generator",
-                    "discriminator",
-                    "generator_training",
-                    "discriminator_training"])
+assert mode in set([
+    "generator", "discriminator", "generator_training", "discriminator_training"
+])
 
 is_generator_training = mode == "generator_training"
 is_discriminator_training = mode == "discriminator_training"
@@ -38,8 +37,8 @@ sample_dim = 2
 settings(
     batch_size=128,
     learning_rate=1e-4,
-    learning_method=AdamOptimizer(beta1=0.5)
-)
+    learning_method=AdamOptimizer(beta1=0.5))
+
 
 def discriminator(sample):
     """
@@ -50,71 +49,88 @@ def discriminator(sample):
     of the sample is from real data.
     """
     param_attr = ParamAttr(is_static=is_generator_training)
-    bias_attr = ParamAttr(is_static=is_generator_training,
-                          initial_mean=1.0,
-                          initial_std=0)
+    bias_attr = ParamAttr(
+        is_static=is_generator_training, initial_mean=1.0, initial_std=0)
 
-    hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
+    hidden = fc_layer(
+        input=sample,
+        name="dis_hidden",
+        size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=ReluActivation())
 
-    hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
+    hidden2 = fc_layer(
+        input=hidden,
+        name="dis_hidden2",
+        size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
-    hidden_bn = batch_norm_layer(hidden2, 
+    hidden_bn = batch_norm_layer(
+        hidden2,
         act=ReluActivation(),
         name="dis_hidden_bn",
         bias_attr=bias_attr,
-                     param_attr=ParamAttr(is_static=is_generator_training,
-                           initial_mean=1.0,
+        param_attr=ParamAttr(
+            is_static=is_generator_training, initial_mean=1.0,
             initial_std=0.02),
         use_global_stats=False)
 
-    return fc_layer(input=hidden_bn, name="dis_prob", size=2,
+    return fc_layer(
+        input=hidden_bn,
+        name="dis_prob",
+        size=2,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=SoftmaxActivation())
 
+
 def generator(noise):
     """
     generator generates a sample given noise
     """
     param_attr = ParamAttr(is_static=is_discriminator_training)
-    bias_attr = ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=1.0,
-                           initial_std=0)
+    bias_attr = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
 
-    hidden = fc_layer(input=noise,
+    hidden = fc_layer(
+        input=noise,
         name="gen_layer_hidden",
         size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=ReluActivation())
 
-    hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
+    hidden2 = fc_layer(
+        input=hidden,
+        name="gen_hidden2",
+        size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
-    hidden_bn = batch_norm_layer(hidden2, 
+    hidden_bn = batch_norm_layer(
+        hidden2,
         act=ReluActivation(),
         name="gen_layer_hidden_bn",
         bias_attr=bias_attr,
-                     param_attr=ParamAttr(is_static=is_discriminator_training,
+        param_attr=ParamAttr(
+            is_static=is_discriminator_training,
             initial_mean=1.0,
             initial_std=0.02),
         use_global_stats=False)
 
-    return fc_layer(input=hidden_bn,
+    return fc_layer(
+        input=hidden_bn,
         name="gen_layer1",
         size=sample_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
+
 if is_generator_training:
     noise = data_layer(name="noise", size=noise_dim)
     sample = generator(noise)
@@ -126,7 +142,8 @@ if is_generator_training or is_discriminator_training:
     label = data_layer(name="label", size=1)
     prob = discriminator(sample)
     cost = cross_entropy(input=prob, label=label)
-    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
+    classification_error_evaluator(
+        input=prob, label=label, name=mode + '_error')
     outputs(cost)
 
 if is_generator:

demo/gan/gan_conf_image.py

@@ -15,10 +15,9 @@ from paddle.trainer_config_helpers import *
 
 mode = get_config_arg("mode", str, "generator")
 dataSource = get_config_arg("data", str, "mnist")
-assert mode in set(["generator",
-                    "discriminator",
-                    "generator_training",
-                    "discriminator_training"])
+assert mode in set([
+    "generator", "discriminator", "generator_training", "discriminator_training"
+])
 
 is_generator_training = mode == "generator_training"
 is_discriminator_training = mode == "discriminator_training"
@@ -41,19 +40,28 @@ if dataSource == "mnist":
 else:
     sample_dim = 32
     c_dim = 3
-s2, s4 = int(sample_dim/2), int(sample_dim/4), 
-s8, s16 = int(sample_dim/8), int(sample_dim/16)
+s2, s4 = int(sample_dim / 2), int(sample_dim / 4),
+s8, s16 = int(sample_dim / 8), int(sample_dim / 16)
 
 settings(
     batch_size=128,
     learning_rate=2e-4,
-    learning_method=AdamOptimizer(beta1=0.5)
-)
-
-def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, 
-                 param_attr, bias_attr, param_attr_bn, bn, trans=False, 
+    learning_method=AdamOptimizer(beta1=0.5))
+
+
+def conv_bn(input,
+            channels,
+            imgSize,
+            num_filters,
+            output_x,
+            stride,
+            name,
+            param_attr,
+            bias_attr,
+            param_attr_bn,
+            bn,
+            trans=False,
             act=ReluActivation()):
-    
     """
     conv_bn is a utility function that constructs a convolution/deconv layer 
     with an optional batch_norm layer
@@ -76,7 +84,7 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
         filter_size = tmp
         padding = 0
 
-    print (imgSize, output_x, stride, filter_size, padding)
+    print(imgSize, output_x, stride, filter_size, padding)
 
     if trans:
         nameApx = "_conv"
@@ -84,16 +92,27 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
         nameApx = "_convt"
 
     if bn:
-        conv = img_conv_layer(input, filter_size=filter_size, 
+        conv = img_conv_layer(
+            input,
+            filter_size=filter_size,
             num_filters=num_filters,
-                   name=name + nameApx, num_channels=channels,
-                   act=LinearActivation(), groups=1, stride=stride, 
-                   padding=padding, bias_attr=bias_attr,
-                   param_attr=param_attr, shared_biases=True, layer_attr=None,
-                   filter_size_y=None, stride_y=None, padding_y=None, 
+            name=name + nameApx,
+            num_channels=channels,
+            act=LinearActivation(),
+            groups=1,
+            stride=stride,
+            padding=padding,
+            bias_attr=bias_attr,
+            param_attr=param_attr,
+            shared_biases=True,
+            layer_attr=None,
+            filter_size_y=None,
+            stride_y=None,
+            padding_y=None,
             trans=trans)
 
-        conv_bn = batch_norm_layer(conv, 
+        conv_bn = batch_norm_layer(
+            conv,
             act=act,
             name=name + nameApx + "_bn",
             bias_attr=bias_attr,
@@ -102,49 +121,60 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
 
         return conv_bn
     else:
-        conv = img_conv_layer(input, filter_size=filter_size, 
+        conv = img_conv_layer(
+            input,
+            filter_size=filter_size,
             num_filters=num_filters,
-                   name=name + nameApx, num_channels=channels,
-                   act=act, groups=1, stride=stride, 
-                   padding=padding, bias_attr=bias_attr,
-                   param_attr=param_attr, shared_biases=True, layer_attr=None,
-                   filter_size_y=None, stride_y=None, padding_y=None,
+            name=name + nameApx,
+            num_channels=channels,
+            act=act,
+            groups=1,
+            stride=stride,
+            padding=padding,
+            bias_attr=bias_attr,
+            param_attr=param_attr,
+            shared_biases=True,
+            layer_attr=None,
+            filter_size_y=None,
+            stride_y=None,
+            padding_y=None,
             trans=trans)
         return conv
 
+
 def generator(noise):
     """
     generator generates a sample given noise
     """
-    param_attr = ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=0.0,
-                           initial_std=0.02)
-    bias_attr = ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=0.0,
-                           initial_std=0.0)
-    
-    param_attr_bn=ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=1.0,
-                           initial_std=0.02)
-    
-    h1 = fc_layer(input=noise,
+    param_attr = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.02)
+    bias_attr = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.0)
+
+    param_attr_bn = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=1.0, initial_std=0.02)
+
+    h1 = fc_layer(
+        input=noise,
         name="gen_layer_h1",
         size=s8 * s8 * gf_dim * 4,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
-    h1_bn = batch_norm_layer(h1, 
+    h1_bn = batch_norm_layer(
+        h1,
         act=ReluActivation(),
         name="gen_layer_h1_bn",
         bias_attr=bias_attr,
         param_attr=param_attr_bn,
         use_global_stats=False)
 
-    h2_bn = conv_bn(h1_bn, 
-                    channels=gf_dim*4, 
+    h2_bn = conv_bn(
+        h1_bn,
+        channels=gf_dim * 4,
         output_x=s8,
-                    num_filters=gf_dim*2, 
+        num_filters=gf_dim * 2,
         imgSize=s4,
         stride=2,
         name="gen_layer_h2",
@@ -154,8 +184,9 @@ def generator(noise):
         bn=True,
         trans=True)
 
-    h3_bn = conv_bn(h2_bn, 
-                    channels=gf_dim*2, 
+    h3_bn = conv_bn(
+        h2_bn,
+        channels=gf_dim * 2,
         output_x=s4,
         num_filters=gf_dim,
         imgSize=s2,
@@ -167,8 +198,8 @@ def generator(noise):
         bn=True,
         trans=True)
 
-    
-    return conv_bn(h3_bn,
+    return conv_bn(
+        h3_bn,
         channels=gf_dim,
         output_x=s2,
         num_filters=c_dim,
@@ -191,18 +222,16 @@ def discriminator(sample):
     of the sample is from generator and dimension 1 is the probabblity
     of the sample is from real data.
     """
-    param_attr = ParamAttr(is_static=is_generator_training,
-                           initial_mean=0.0,
-                           initial_std=0.02)
-    bias_attr = ParamAttr(is_static=is_generator_training,
-                          initial_mean=0.0,
-                          initial_std=0.0)
-    
-    param_attr_bn=ParamAttr(is_static=is_generator_training,
-                           initial_mean=1.0,
-                           initial_std=0.02)
-    
-    h0 = conv_bn(sample, 
+    param_attr = ParamAttr(
+        is_static=is_generator_training, initial_mean=0.0, initial_std=0.02)
+    bias_attr = ParamAttr(
+        is_static=is_generator_training, initial_mean=0.0, initial_std=0.0)
+
+    param_attr_bn = ParamAttr(
+        is_static=is_generator_training, initial_mean=1.0, initial_std=0.02)
+
+    h0 = conv_bn(
+        sample,
         channels=c_dim,
         imgSize=sample_dim,
         num_filters=df_dim,
@@ -214,10 +243,11 @@ def discriminator(sample):
         param_attr_bn=param_attr_bn,
         bn=False)
 
-    h1_bn = conv_bn(h0, 
+    h1_bn = conv_bn(
+        h0,
         channels=df_dim,
         imgSize=s2,
-                 num_filters=df_dim*2, 
+        num_filters=df_dim * 2,
         output_x=s4,
         stride=2,
         name="dis_h1",
@@ -226,10 +256,11 @@ def discriminator(sample):
         param_attr_bn=param_attr_bn,
         bn=True)
 
-    h2_bn = conv_bn(h1_bn, 
-                 channels=df_dim*2,
+    h2_bn = conv_bn(
+        h1_bn,
+        channels=df_dim * 2,
         imgSize=s4,
-                 num_filters=df_dim*4, 
+        num_filters=df_dim * 4,
         output_x=s8,
         stride=2,
         name="dis_h2",
@@ -238,25 +269,28 @@ def discriminator(sample):
         param_attr_bn=param_attr_bn,
         bn=True)
 
-    return fc_layer(input=h2_bn, name="dis_prob", size=2,
+    return fc_layer(
+        input=h2_bn,
+        name="dis_prob",
+        size=2,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=SoftmaxActivation())
 
 
-
 if is_generator_training:
     noise = data_layer(name="noise", size=noise_dim)
     sample = generator(noise)
 
 if is_discriminator_training:
-    sample = data_layer(name="sample", size=sample_dim * sample_dim*c_dim)
+    sample = data_layer(name="sample", size=sample_dim * sample_dim * c_dim)
 
 if is_generator_training or is_discriminator_training:
     label = data_layer(name="label", size=1)
     prob = discriminator(sample)
     cost = cross_entropy(input=prob, label=label)
-    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
+    classification_error_evaluator(
+        input=prob, label=label, name=mode + '_error')
     outputs(cost)
 
 if is_generator:

demo/gan/gan_trainer.py

@@ -16,7 +16,7 @@ import argparse
 import random
 import numpy
 import cPickle
-import sys,os
+import sys, os
 from PIL import Image
 
 from paddle.trainer.config_parser import parse_config
@@ -24,6 +24,7 @@ from paddle.trainer.config_parser import logger
 import py_paddle.swig_paddle as api
 import matplotlib.pyplot as plt
 
+
 def plot2DScatter(data, outputfile):
     '''
     Plot the data as a 2D scatter plot and save to outputfile
@@ -41,9 +42,11 @@ def plot2DScatter(data, outputfile):
     plt.scatter(x, y)
     plt.savefig(outputfile, bbox_inches='tight')
 
+
 def CHECK_EQ(a, b):
     assert a == b, "a=%s, b=%s" % (a, b)
 
+
 def copy_shared_parameters(src, dst):
     '''
     copy the parameters from src to dst
@@ -52,11 +55,9 @@ def copy_shared_parameters(src, dst):
     :param dst: the destination of the parameters
     :type dst: GradientMachine
     '''
-    src_params = [src.getParameter(i)
-               for i in xrange(src.getParameterSize())]
+    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
     src_params = dict([(p.getName(), p) for p in src_params])
 
-
     for i in xrange(dst.getParameterSize()):
         dst_param = dst.getParameter(i)
         src_param = src_params.get(dst_param.getName(), None)
@@ -68,14 +69,16 @@ def copy_shared_parameters(src, dst):
         dst_value.copyFrom(src_value)
         dst_param.setValueUpdated()
 
+
 def print_parameters(src):
-    src_params = [src.getParameter(i)
-               for i in xrange(src.getParameterSize())]
+    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
 
     print "***************"
     for p in src_params:
         print "Name is %s" % p.getName()
-        print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray()
+        print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray(
+        )
+
 
 def load_mnist_data(imageFile):
     f = open(imageFile, "rb")
@@ -87,32 +90,35 @@ def load_mnist_data(imageFile):
     else:
         n = 10000
 
-    data = numpy.fromfile(f, 'ubyte', count=n*28*28).reshape((n, 28*28))
+    data = numpy.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
     data = data / 255.0 * 2.0 - 1.0
 
     f.close()
     return data.astype('float32')
 
+
 def load_cifar_data(cifar_path):
     batch_size = 10000
-    data = numpy.zeros((5*batch_size, 32*32*3), dtype = "float32")
+    data = numpy.zeros((5 * batch_size, 32 * 32 * 3), dtype="float32")
     for i in range(1, 6):
         file = cifar_path + "/data_batch_" + str(i)
         fo = open(file, 'rb')
         dict = cPickle.load(fo)
         fo.close()
-        data[(i - 1)*batch_size:(i*batch_size), :] = dict["data"]
+        data[(i - 1) * batch_size:(i * batch_size), :] = dict["data"]
 
     data = data / 255.0 * 2.0 - 1.0
     return data
 
+
 # synthesize 2-D uniform data
 def load_uniform_data():
     data = numpy.random.rand(1000000, 2).astype('float32')
     return data
 
+
 def merge(images, size):
-    if images.shape[1] == 28*28:
+    if images.shape[1] == 28 * 28:
         h, w, c = 28, 28, 1
     else:
         h, w, c = 32, 32, 3
@@ -124,6 +130,7 @@ def merge(images, size):
           ((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
     return img.astype('uint8')
 
+
 def save_images(images, path):
     merged_img = merge(images, [8, 8])
     if merged_img.shape[2] == 1:
@@ -132,13 +139,16 @@ def save_images(images, path):
         im = Image.fromarray(merged_img, mode="RGB")
     im.save(path)
 
+
 def get_real_samples(batch_size, data_np):
-    return data_np[numpy.random.choice(data_np.shape[0], batch_size, 
-                                       replace=False),:]
+    return data_np[numpy.random.choice(
+        data_np.shape[0], batch_size, replace=False), :]
+
 
 def get_noise(batch_size, noise_dim):
     return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
 
+
 def get_fake_samples(generator_machine, batch_size, noise):
     gen_inputs = api.Arguments.createArguments(1)
     gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
@@ -147,12 +157,14 @@ def get_fake_samples(generator_machine, batch_size, noise):
     fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
     return fake_samples
 
+
 def get_training_loss(training_machine, inputs):
     outputs = api.Arguments.createArguments(0)
     training_machine.forward(inputs, outputs, api.PASS_TEST)
     loss = outputs.getSlotValue(0).copyToNumpyMat()
     return numpy.mean(loss)
 
+
 def prepare_discriminator_data_batch_pos(batch_size, data_np):
     real_samples = get_real_samples(batch_size, data_np)
     labels = numpy.ones(batch_size, dtype='int32')
@@ -161,6 +173,7 @@ def prepare_discriminator_data_batch_pos(batch_size, data_np):
     inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
     return inputs
 
+
 def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
     fake_samples = get_fake_samples(generator_machine, batch_size, noise)
     labels = numpy.zeros(batch_size, dtype='int32')
@@ -169,6 +182,7 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
     inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
     return inputs
 
+
 def prepare_generator_data_batch(batch_size, noise):
     label = numpy.ones(batch_size, dtype='int32')
     inputs = api.Arguments.createArguments(2)
@@ -193,10 +207,9 @@ def get_layer_size(model_conf, layer_name):
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
-    parser.add_argument("--use_gpu", default="1", 
-                        help="1 means use gpu for training")
-    parser.add_argument("--gpu_id", default="0", 
-                        help="the gpu_id parameter")
+    parser.add_argument(
+        "--use_gpu", default="1", help="1 means use gpu for training")
+    parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
     args = parser.parse_args()
     data_source = args.data_source
     use_gpu = args.use_gpu
@@ -209,8 +222,9 @@ def main():
     if not os.path.exists("./%s_params/" % data_source):
         os.makedirs("./%s_params/" % data_source)
 
-    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10', '--log_period=100', 
-                   '--gpu_id=' + args.gpu_id, '--save_dir=' + "./%s_params/" % data_source)
+    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
+                   '--log_period=100', '--gpu_id=' + args.gpu_id,
+                   '--save_dir=' + "./%s_params/" % data_source)
 
     if data_source == "uniform":
         conf = "gan_conf.py"
@@ -220,7 +234,8 @@ def main():
         num_iter = 1000
 
     gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source)
-    dis_conf = parse_config(conf, "mode=discriminator_training,data=" + data_source)
+    dis_conf = parse_config(conf,
+                            "mode=discriminator_training,data=" + data_source)
     generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
     batch_size = dis_conf.opt_config.batch_size
     noise_dim = get_layer_size(gen_conf.model_config, "noise")
@@ -245,11 +260,9 @@ def main():
     generator_machine = api.GradientMachine.createFromConfigProto(
         generator_conf.model_config)
 
-    dis_trainer = api.Trainer.create(
-        dis_conf, dis_training_machine)
+    dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
 
-    gen_trainer = api.Trainer.create(
-        gen_conf, gen_training_machine)
+    gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
 
     dis_trainer.startTrain()
     gen_trainer.startTrain()
@@ -272,21 +285,23 @@ def main():
             noise = get_noise(batch_size, noise_dim)
             data_batch_dis_pos = prepare_discriminator_data_batch_pos(
                 batch_size, data_np)
-            dis_loss_pos = get_training_loss(dis_training_machine, data_batch_dis_pos)
+            dis_loss_pos = get_training_loss(dis_training_machine,
+                                             data_batch_dis_pos)
 
             data_batch_dis_neg = prepare_discriminator_data_batch_neg(
                 generator_machine, batch_size, noise)
-            dis_loss_neg = get_training_loss(dis_training_machine, data_batch_dis_neg)            
+            dis_loss_neg = get_training_loss(dis_training_machine,
+                                             data_batch_dis_neg)
 
             dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
 
             # Do forward pass in generator to get the gen_loss
-            data_batch_gen = prepare_generator_data_batch(
-                    batch_size, noise)
+            data_batch_gen = prepare_generator_data_batch(batch_size, noise)
             gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
 
             if i % 100 == 0:
-                print "d_pos_loss is %s     d_neg_loss is %s" % (dis_loss_pos, dis_loss_neg) 
+                print "d_pos_loss is %s     d_neg_loss is %s" % (dis_loss_pos,
+                                                                 dis_loss_neg)
                 print "d_loss is %s    g_loss is %s" % (dis_loss, gen_loss)
 
             # Decide which network to train based on the training history
@@ -300,7 +315,8 @@ def main():
                     curr_strike = 1
                 dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
                 dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
-                copy_shared_parameters(dis_training_machine, gen_training_machine)
+                copy_shared_parameters(dis_training_machine,
+                                       gen_training_machine)
 
             else:
                 if curr_train == "gen":
@@ -311,7 +327,8 @@ def main():
                 gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
                 # TODO: add API for paddle to allow true parameter sharing between different GradientMachines 
                 # so that we do not need to copy shared parameters. 
-                copy_shared_parameters(gen_training_machine, dis_training_machine)
+                copy_shared_parameters(gen_training_machine,
+                                       dis_training_machine)
                 copy_shared_parameters(gen_training_machine, generator_machine)
 
         dis_trainer.finishTrainPass()
@@ -319,11 +336,14 @@ def main():
         # At the end of each pass, save the generated samples/images
         fake_samples = get_fake_samples(generator_machine, batch_size, noise)
         if data_source == "uniform":
-            plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
+            plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
+                          (data_source, train_pass))
         else:
-            save_images(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
+            save_images(fake_samples, "./%s_samples/train_pass%s.png" %
+                        (data_source, train_pass))
     dis_trainer.finishTrain()
     gen_trainer.finishTrain()
 
+
 if __name__ == '__main__':
     main()

demo/quick_start/trainer_config.resnet-lstm.py

@@ -13,7 +13,6 @@
 # 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.
-
 """
 This configuration is a demonstration of how to implement the stacked LSTM
 with residual connections, i.e. an LSTM layer takes the sum of the hidden states
@@ -46,7 +45,8 @@ is_predict = get_config_arg('is_predict', bool, False)
 trn = 'data/train.list' if not is_predict else None
 tst = 'data/test.list' if not is_predict else 'data/pred.list'
 process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(train_list=trn,
+define_py_data_sources2(
+    train_list=trn,
     test_list=tst,
     module="dataprovider_emb",
     obj=process,
@@ -58,10 +58,9 @@ settings(
     learning_rate=2e-3,
     learning_method=AdamOptimizer(),
     regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25
-)
+    gradient_clipping_threshold=25)
 
-bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
+bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
 
 data = data_layer(name="word", size=len(word_dict))
 emb = embedding_layer(input=data, size=128)
@@ -73,17 +72,15 @@ for i in range(3):
     # The input to the current layer is the sum of the hidden state
     # and input of the previous layer.
     current_input = addto_layer(input=[previous_input, previous_hidden_state])
-    hidden_state = simple_lstm(input=current_input, size=128,
-                               lstm_cell_attr=ExtraAttr(drop_rate=0.1))
+    hidden_state = simple_lstm(
+        input=current_input, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
     previous_input, previous_hidden_state = current_input, hidden_state
 
 lstm = previous_hidden_state
 
 lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
-output = fc_layer(input=lstm_last, size=2,
-                  bias_attr=bias_attr,
-                  act=SoftmaxActivation())
-
+output = fc_layer(
+    input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
 
 if is_predict:
     maxid = maxid_layer(output)

demo/semantic_role_labeling/data/extract_dict_feature.py

@@ -43,13 +43,13 @@ def extract_dict_features(pair_file, feature_file):
             mark[verb_index] = 1
             ctx_0 = sentence_list[verb_index]
 
-            if verb_index < len(labels_list) - 2:
+            if verb_index < len(labels_list) - 1:
                 mark[verb_index + 1] = 1
                 ctx_p1 = sentence_list[verb_index + 1]
             else:
                 ctx_p1 = 'eos'
 
-            if verb_index < len(labels_list) - 3:
+            if verb_index < len(labels_list) - 2:
                 mark[verb_index + 2] = 1
                 ctx_p2 = sentence_list[verb_index + 2]
             else:
@@ -69,7 +69,6 @@ def extract_dict_features(pair_file, feature_file):
             feature_out.write(feature_str + '\n')
 
 
-
 if __name__ == '__main__':
 
     usage = '-p pair_file -f feature_file'

demo/semantic_role_labeling/data/extract_pairs.py

@@ -66,7 +66,7 @@ def transform_labels(sentences, labels):
         else:
             verb_list = []
             for x in labels[i][0]:
-                if x !='-':
+                if x != '-':
                     verb_list.append(x)
 
             for j in xrange(1, len(labels[i])):
@@ -93,7 +93,7 @@ def transform_labels(sentences, labels):
                         is_in_bracket = True
                     else:
                         print 'error:', ll
-                sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq))
+                sen_lab_pair.append((sentences[i], verb_list[j - 1], label_seq))
     return sen_lab_pair
 
 
@@ -103,7 +103,7 @@ def write_file(sen_lab_pair, output_file):
             sentence = x[0]
             label_seq = ' '.join(x[2])
             assert len(sentence.split()) == len(x[2])
-            fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n')
+            fout.write(sentence + '\t' + x[1] + '\t' + label_seq + '\n')
 
 
 if __name__ == '__main__':

demo/semantic_role_labeling/dataprovider.py

@@ -30,8 +30,7 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(predicate_dict)),
-        integer_value_sequence(2),
+        integer_value_sequence(len(predicate_dict)), integer_value_sequence(2),
         integer_value_sequence(len(label_dict))
     ]
 
@@ -40,8 +39,12 @@ def get_batch_size(yeild_data):
     return len(yeild_data[0])
 
 
-@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, 
-          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
+@provider(
+    init_hook=hook,
+    should_shuffle=True,
+    calc_batch_size=get_batch_size,
+    can_over_batch_size=False,
+    cache=CacheType.CACHE_PASS_IN_MEM)
 def process(settings, file_name):
     with open(file_name, 'r') as fdata:
         for line in fdata:

demo/semantic_role_labeling/db_lstm.py

@@ -20,7 +20,7 @@ from paddle.trainer_config_helpers import *
 #file paths
 word_dict_file = './data/wordDict.txt'
 label_dict_file = './data/targetDict.txt'
-predicate_file= './data/verbDict.txt'
+predicate_file = './data/verbDict.txt'
 train_list_file = './data/train.list'
 test_list_file = './data/test.list'
 
@@ -47,7 +47,6 @@ if not is_predict:
             w = line.strip()
             predicate_dict[w] = i
 
-
     if is_test:
         train_list_file = None
 
@@ -57,9 +56,11 @@ if not is_predict:
         test_list=test_list_file,
         module='dataprovider',
         obj='process',
-        args={'word_dict': word_dict,
+        args={
+            'word_dict': word_dict,
             'label_dict': label_dict,
-              'predicate_dict': predicate_dict })
+            'predicate_dict': predicate_dict
+        })
 
     word_dict_len = len(word_dict)
     label_dict_len = len(label_dict)
@@ -77,24 +78,16 @@ mark_dim = 5
 hidden_dim = 512
 depth = 8
 
-
-
 ########################### Optimizer #######################################
 
-
 settings(
     batch_size=150,
     learning_method=MomentumOptimizer(momentum=0),
     learning_rate=2e-2,
     regularization=L2Regularization(8e-4),
     is_async=False,
-    model_average=ModelAverage(average_window=0.5,
-                               max_average_window=10000),
-                               
-)
-
-
-
+    model_average=ModelAverage(
+        average_window=0.5, max_average_window=10000), )
 
 ####################################### network ##############################
 #8 features and 1 target
@@ -108,22 +101,28 @@ ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
 ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
 mark = data_layer(name='mark_data', size=mark_dict_len)
 
-
 if not is_predict:
     target = data_layer(name='target', size=label_dict_len)
 
-
-default_std=1/math.sqrt(hidden_dim)/3.0
+default_std = 1 / math.sqrt(hidden_dim) / 3.0
 
 emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
 std_0 = ParameterAttribute(initial_std=0.)
 std_default = ParameterAttribute(initial_std=default_std)
 
-predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std))
-mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
-
-word_input=[word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
-emb_layers = [embedding_layer(size=word_dim, input=x, param_attr=emb_para) for x in word_input]
+predicate_embedding = embedding_layer(
+    size=word_dim,
+    input=predicate,
+    param_attr=ParameterAttribute(
+        name='vemb', initial_std=default_std))
+mark_embedding = embedding_layer(
+    name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
+
+word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
+emb_layers = [
+    embedding_layer(
+        size=word_dim, input=x, param_attr=emb_para) for x in word_input
+]
 emb_layers.append(predicate_embedding)
 emb_layers.append(mark_embedding)
 
@@ -131,14 +130,18 @@ hidden_0 = mixed_layer(
     name='hidden0',
     size=hidden_dim,
     bias_attr=std_default,
-    input=[ full_matrix_projection(input=emb, param_attr=std_default ) for emb in emb_layers ])
-
+    input=[
+        full_matrix_projection(
+            input=emb, param_attr=std_default) for emb in emb_layers
+    ])
 
 mix_hidden_lr = 1e-3
 lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
-hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr)
+hidden_para_attr = ParameterAttribute(
+    initial_std=default_std, learning_rate=mix_hidden_lr)
 
-lstm_0 = lstmemory(name='lstm0',
+lstm_0 = lstmemory(
+    name='lstm0',
     input=hidden_0,
     act=ReluActivation(),
     gate_act=SigmoidActivation(),
@@ -149,66 +152,67 @@ lstm_0 = lstmemory(name='lstm0',
 #stack L-LSTM and R-LSTM with direct edges
 input_tmp = [hidden_0, lstm_0]
 
-
 for i in range(1, depth):
 
-    mix_hidden = mixed_layer(name='hidden'+str(i),
+    mix_hidden = mixed_layer(
+        name='hidden' + str(i),
         size=hidden_dim,
         bias_attr=std_default,
-                             input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
-                                    full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
-                                   ]
-                             )
-
-    lstm = lstmemory(name='lstm'+str(i),
+        input=[
+            full_matrix_projection(
+                input=input_tmp[0], param_attr=hidden_para_attr),
+            full_matrix_projection(
+                input=input_tmp[1], param_attr=lstm_para_attr)
+        ])
+
+    lstm = lstmemory(
+        name='lstm' + str(i),
         input=mix_hidden,
         act=ReluActivation(),
         gate_act=SigmoidActivation(),
         state_act=SigmoidActivation(),
-                     reverse=((i % 2)==1),
+        reverse=((i % 2) == 1),
         bias_attr=std_0,
         param_attr=lstm_para_attr)
 
     input_tmp = [mix_hidden, lstm]
 
-feature_out = mixed_layer(name='output',
+feature_out = mixed_layer(
+    name='output',
     size=label_dict_len,
     bias_attr=std_default,
-                          input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
-                                 full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
-                                ],
-                          )
-
-
+    input=[
+        full_matrix_projection(
+            input=input_tmp[0], param_attr=hidden_para_attr),
+        full_matrix_projection(
+            input=input_tmp[1], param_attr=lstm_para_attr)
+    ], )
 
 if not is_predict:
-    crf_l = crf_layer( name = 'crf',
-                       size = label_dict_len,
-                       input = feature_out, 
-                       label = target,
-                       param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr)
-
-                      )
-
-    
-    crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
-                                   size = label_dict_len,
-                                   input = feature_out,
-                                   label = target,
-                                   param_attr=ParameterAttribute(name='crfw')
-                                       )
+    crf_l = crf_layer(
+        name='crf',
+        size=label_dict_len,
+        input=feature_out,
+        label=target,
+        param_attr=ParameterAttribute(
+            name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
 
+    crf_dec_l = crf_decoding_layer(
+        name='crf_dec_l',
+        size=label_dict_len,
+        input=feature_out,
+        label=target,
+        param_attr=ParameterAttribute(name='crfw'))
 
     eval = sum_evaluator(input=crf_dec_l)
 
     outputs(crf_l)
 
 else:
-    crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
-                                   size = label_dict_len,
-                                   input = feature_out,
-                                   param_attr=ParameterAttribute(name='crfw')
-                                       )
+    crf_dec_l = crf_decoding_layer(
+        name='crf_dec_l',
+        size=label_dict_len,
+        input=feature_out,
+        param_attr=ParameterAttribute(name='crfw'))
 
     outputs(crf_dec_l)
-

demo/semantic_role_labeling/predict.py

@@ -26,7 +26,8 @@ UNK_IDX = 0
 
 
 class Prediction():
-    def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file):
+    def __init__(self, train_conf, dict_file, model_dir, label_file,
+                 predicate_dict_file):
         """
         train_conf: trainer configure.
         dict_file: word dictionary file name.
@@ -35,7 +36,7 @@ class Prediction():
 
         self.dict = {}
         self.labels = {}
-        self.predicate_dict={}
+        self.predicate_dict = {}
         self.labels_reverse = {}
         self.load_dict_label(dict_file, label_file, predicate_dict_file)
 
@@ -44,24 +45,17 @@ class Prediction():
         len_pred = len(self.predicate_dict)
 
         conf = parse_config(
-            train_conf,
-            'dict_len=' + str(len_dict) + 
-            ',label_len=' + str(len_label) +
-            ',pred_len=' + str(len_pred) +
-            ',is_predict=True')
+            train_conf, 'dict_len=' + str(len_dict) + ',label_len=' +
+            str(len_label) + ',pred_len=' + str(len_pred) + ',is_predict=True')
         self.network = swig_paddle.GradientMachine.createFromConfigProto(
             conf.model_config)
         self.network.loadParameters(model_dir)
 
         slots = [
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict), 
-            integer_value_sequence(len_pred),
-            integer_value_sequence(2)
+            integer_value_sequence(len_dict), integer_value_sequence(len_dict),
+            integer_value_sequence(len_dict), integer_value_sequence(len_dict),
+            integer_value_sequence(len_dict), integer_value_sequence(len_dict),
+            integer_value_sequence(len_pred), integer_value_sequence(2)
         ]
         self.converter = DataProviderConverter(slots)
 
@@ -78,6 +72,7 @@ class Prediction():
 
         for line_count, line in enumerate(open(predicate_dict_file, 'r')):
             self.predicate_dict[line.strip()] = line_count
+
     def get_data(self, data_file):
         """
         Get input data of paddle format.
@@ -90,7 +85,8 @@ class Prediction():
                 sen_len = len(words)
 
                 word_slot = [self.dict.get(w, UNK_IDX) for w in words]
-                predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len
+                predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)
+                                  ] * sen_len
                 ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len
                 ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len
                 ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len
@@ -123,7 +119,8 @@ class Prediction():
 
 
 def option_parser():
-    usage = ("python predict.py -c config -w model_dir " 
+    usage = (
+        "python predict.py -c config -w model_dir "
         "-d word dictionary -l label_file -i input_file  -p pred_dict_file")
     parser = OptionParser(usage="usage: %s [options]" % usage)
     parser.add_option(
@@ -187,8 +184,9 @@ def main():
     output_file = options.output_file
 
     swig_paddle.initPaddle("--use_gpu=0")
-    predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file)
-    predict.predict(data_file,output_file)
+    predict = Prediction(train_conf, dict_file, model_path, label_file,
+                         predict_dict_file)
+    predict.predict(data_file, output_file)
 
 
 if __name__ == '__main__':

demo/sentiment/predict.py

@@ -71,9 +71,7 @@ class SentimentPrediction():
         transform word into integer index according to the dictionary.
         """
         words = data.strip().split()
-        word_slot = [
-            self.word_dict[w] for w in words if w in self.word_dict
-        ]
+        word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
         return word_slot
 
     def batch_predict(self, data_batch):
@@ -85,8 +83,8 @@ class SentimentPrediction():
             if self.label is None:
                 print("predicting label is %d" % (lab[0]))
             else:
-                print("predicting label is %s" %
-                      (self.label[lab[0]]))
+                print("predicting label is %s" % (self.label[lab[0]]))
+
 
 def option_parser():
     usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
@@ -143,9 +141,10 @@ def main():
         batch.append([predict.get_index(line)])
         if len(batch) == batch_size:
             predict.batch_predict(batch)
-            batch=[]
+            batch = []
     if len(batch) > 0:
         predict.batch_predict(batch)
 
+
 if __name__ == '__main__':
     main()

doc/CMakeLists.txt

@@ -7,25 +7,50 @@ if(NOT DEFINED SPHINX_THEME_DIR)
 endif()
 
 # configured documentation tools and intermediate build results
-set(BINARY_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/_build")
+set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
 
 # Sphinx cache with pickled ReST documents
-set(SPHINX_CACHE_DIR "${CMAKE_CURRENT_BINARY_DIR}/_doctrees")
+set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
 
-# HTML output directory
-set(SPHINX_HTML_DIR "${CMAKE_CURRENT_BINARY_DIR}/html")
+# HTML output director
+set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
 
 configure_file(
-    "${CMAKE_CURRENT_SOURCE_DIR}/conf.py.in"
-    "${BINARY_BUILD_DIR}/conf.py"
+    "${CMAKE_CURRENT_SOURCE_DIR}/conf.py.en.in"
+    "${BINARY_BUILD_DIR_EN}/conf.py"
     @ONLY)
 
 sphinx_add_target(paddle_docs
                   html
-                  ${BINARY_BUILD_DIR}
-                  ${SPHINX_CACHE_DIR}
+                  ${BINARY_BUILD_DIR_EN}
+                  ${SPHINX_CACHE_DIR_EN}
                   ${CMAKE_CURRENT_SOURCE_DIR}
-                  ${SPHINX_HTML_DIR})
+                  ${SPHINX_HTML_DIR_EN})
 
 add_dependencies(paddle_docs
   gen_proto_py)
+
+
+# configured documentation tools and intermediate build results
+set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
+
+# Sphinx cache with pickled ReST documents
+set(SPHINX_CACHE_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_doctrees")
+
+# HTML output directory
+set(SPHINX_HTML_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/html")
+
+configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/conf.py.cn.in"
+    "${BINARY_BUILD_DIR_CN}/conf.py"
+    @ONLY)
+
+sphinx_add_target(paddle_docs_cn
+                  html
+                  ${BINARY_BUILD_DIR_CN}
+                  ${SPHINX_CACHE_DIR_CN}
+                  ${CMAKE_CURRENT_SOURCE_DIR}
+                  ${SPHINX_HTML_DIR_CN})
+
+add_dependencies(paddle_docs_cn
+  gen_proto_py)

doc_cn/ui/data_provider/pydataprovider2.rst → doc/api/data_provider/pydataprovider2_cn.rst

@@ -15,23 +15,23 @@ MNIST的使用场景
 
 MNIST是一个包含有70,000张灰度图片的数字分类数据集。样例数据 ``mnist_train.txt`` 如下：
 
-..  literalinclude:: mnist_train.txt
+..  literalinclude:: src/mnist_train.txt
 
 其中每行数据代表一张图片，行内使用 ``;`` 分成两部分。第一部分是图片的标签，为0-9中的一个数字；第二部分是28*28的图片像素灰度值。 对应的 ``train.list`` 即为这个数据文件的名字：
 
-..  literalinclude:: train.list
+..  literalinclude:: src/train.list
 
 dataprovider的使用
 ++++++++++++++++++
 
-..  literalinclude:: mnist_provider.dict.py
+..  literalinclude:: src/mnist_provider.dict.py
 
 - 首先，引入PaddlePaddle的PyDataProvider2包。
 - 其次，定义一个Python的 `Decorator <http://www.learnpython.org/en/Decorators>`_ `@provider`_ 。用于将下一行的数据输入函数标记成一个PyDataProvider2，同时设置它的input_types属性。
   
   - `input_types`_：设置这个PyDataProvider2返回什么样的数据。本例根据网络配置中 ``data_layer`` 的名字，显式指定返回的是一个28*28维的稠密浮点数向量和一个[0-9]的10维整数标签。
 
-    ..  literalinclude:: mnist_config.py
+    ..  literalinclude:: src/mnist_config.py
          :lines: 9-10
 
   - 注意：如果用户不显示指定返回数据的对应关系，那么PaddlePaddle会根据layer的声明顺序，来确定对应关系。但这个关系可能不正确，所以推荐使用显式指定的方式来设置input_types。
@@ -53,7 +53,7 @@ dataprovider的使用
 
 在网络配置里，只需要一行代码就可以调用这个PyDataProvider2，如，
 
-..  literalinclude:: mnist_config.py
+..  literalinclude:: src/mnist_config.py
      :lines: 1-7
 
 训练数据是 ``train.list`` ，没有测试数据，调用的PyDataProvider2是 ``mnist_provider`` 模块中的 ``process`` 函数。
@@ -80,7 +80,7 @@ dataprovider的使用
 
 本例采用英文情感分类的数据，即将一段英文文本数据，分类成正面情绪和负面情绪两类(用0和1表示)。样例数据 ``sentimental_train.txt`` 如下：
 
-..  literalinclude:: sentimental_train.txt
+..  literalinclude:: src/sentimental_train.txt
 
 dataprovider的使用
 ++++++++++++++++++
@@ -90,7 +90,7 @@ dataprovider的使用
 - 其中 ``input_types`` 和在 `@provider`_ 中配置的效果一致。本例中的输入特征是词ID的序列，因此使用 ``integer_value_sequence`` 类型来设置。
 - 将 ``dictionary`` 存入settings对象，在 ``process`` 函数中使用。 dictionary是从网络配置中传入的dict对象，即一个将单词字符串映射到单词ID的字典。
 
-..  literalinclude:: sentimental_provider.py
+..  literalinclude:: src/sentimental_provider.py
 
 网络配置中的调用
 ++++++++++++++++
@@ -100,7 +100,7 @@ dataprovider的使用
 * 在配置中需要读取外部字典。
 * 在声明DataProvider的时候传入dictionary作为参数。
 
-..  literalinclude:: sentimental_config.py
+..  literalinclude:: src/sentimental_config.py
      :emphasize-lines: 12-14
 
 参考(Reference)

doc/api/data_provider/pydataprovider2_en.rst

-..  _api_pydataprovider:
+..  _api_pydataprovider2:
 
 PyDataProvider2
 ===============
@@ -24,18 +24,18 @@ of 28 x 28 pixels.
 
 A small part of the original data as an example is shown as below:
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/mnist_train.txt
+.. literalinclude:: src/mnist_train.txt
 
 Each line of the data contains two parts, separated by :code:`;`. The first part is
 label of an image. The second part contains 28x28 pixel float values.
 
 Just write path of the above data into train.list. It looks like this:
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/train.list
+.. literalinclude:: src/train.list
 
 The corresponding dataprovider is shown as below:
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/mnist_provider.py
+.. literalinclude:: src/mnist_provider.dict.py
 
 The first line imports PyDataProvider2 package.
 The main function is the process function, that has two parameters.
@@ -74,7 +74,7 @@ sample by using keywords :code:`yield`.
 Only a few lines of codes need to be added into the training configuration file,
 you can take this as an example.
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/mnist_config.py
+.. literalinclude:: src/mnist_config.py
 
 Here we specify training data by :code:`train.list`, and no testing data is specified.
 The method which actually provide data is :code:`process`.
@@ -83,7 +83,7 @@ User also can use another style to provide data, which defines the
 :code:`data_layer`'s name explicitly when `yield`. For example,
 the :code:`dataprovider` is shown as below.
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/mnist_provider.dict.py
+.. literalinclude:: src/mnist_provider.dict.py
    :linenos:
 
 If user did't give the :code:`data_layer`'s name, PaddlePaddle will use
@@ -104,6 +104,8 @@ And PaddlePadle will do all of the rest things\:
 
 Is this cool?
 
+..  _api_pydataprovider2_sequential_model:
+
 DataProvider for the sequential model
 -------------------------------------
 A sequence model takes sequences as its input. A sequence is made up of several
@@ -119,11 +121,11 @@ negative sentiment (marked by 0 and 1 respectively).
 
 A small part of the original data as an example can be found in the path below:
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/sentimental_train.txt
+.. literalinclude:: src/sentimental_train.txt
 
 The corresponding data provider can be found in the path below:
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/sentimental_provider.py
+.. literalinclude:: src/sentimental_provider.py
 
 This data provider for sequential model is a little more complex than that
 for MINST dataset.
@@ -141,7 +143,7 @@ initialized. The :code:`on_init` function has the following parameters:
 To pass these parameters into DataProvider, the following lines should be added
 into trainer configuration file.
 
-.. literalinclude:: ../../../doc_cn/ui/data_provider/sentimental_config.py
+.. literalinclude:: src/sentimental_config.py
 
 The definition is basically same as MNIST example, except:
 * Load dictionary in this configuration

doc/api/index_cn.rst

+API
+===
+
+DataProvider API
+----------------
+
+..  toctree::
+    :maxdepth: 1
+
+    data_provider/dataprovider_cn.rst
+    data_provider/pydataprovider2_cn.rst
+
+..  _api_trainer_config:
+
+Model Config API
+----------------
+
+..  toctree::
+    :maxdepth: 1
+
+    trainer_config_helpers/optimizers.rst
+    trainer_config_helpers/data_sources.rst
+    trainer_config_helpers/layers.rst
+    trainer_config_helpers/activations.rst 
+    trainer_config_helpers/poolings.rst
+    trainer_config_helpers/networks.rst
+    trainer_config_helpers/evaluators.rst
+    trainer_config_helpers/attrs.rst
+
+
+Applications API
+----------------
+
+..  toctree::
+    :maxdepth: 1
+
+    predict/swig_py_paddle_cn.rst

doc/api/index_en.rst

@@ -7,7 +7,7 @@ DataProvider API
 ..  toctree::
     :maxdepth: 1
 
-    data_provider/index_en.rst
+    data_provider/dataprovider_en.rst
     data_provider/pydataprovider2_en.rst
 
 ..  _api_trainer_config:

doc_cn/ui/predict/swig_py_paddle.rst → doc/api/predict/swig_py_paddle_cn.rst

@@ -34,7 +34,7 @@ PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会
 
 如下是一段使用mnist model来实现手写识别的预测代码。完整的代码见 ``src_root/doc/ui/predict/predict_sample.py`` 。mnist model可以通过 ``src_root\demo\mnist`` 目录下的demo训练出来。
 
-..  literalinclude:: ../../../doc/ui/predict/predict_sample.py
+..  literalinclude:: src/predict_sample.py
     :language: python
     :lines: 15-18,121-136
 

doc/api/predict/swig_py_paddle_en.rst

@@ -13,7 +13,7 @@ Here is a sample python script that shows the typical prediction process for the
 MNIST classification problem. A complete sample code could be found at
 :code:`src_root/doc/ui/predict/predict_sample.py`.
 
-..  literalinclude:: ./predict_sample.py
+..  literalinclude:: src/predict_sample.py
     :language: python
     :lines: 15-18,90-100,101-104
 
@@ -23,7 +23,7 @@ python's :code:`help()` function. Let's walk through the above python script:
 
 * At the beginning, use :code:`swig_paddle.initPaddle()` to initialize
   PaddlePaddle with command line arguments, for more about command line arguments
-  see `Command Line Arguments <../cmd_argument/detail_introduction.html>`_.
+  see :ref:`cmd_detail_introduction` .
 * Parse the configuration file that is used in training with :code:`parse_config()`.
   Because data to predict with always have no label, and output of prediction work
   normally is the output layer rather than the cost layer, so you should modify
@@ -36,7 +36,7 @@ python's :code:`help()` function. Let's walk through the above python script:
     - Note: As swig_paddle can only accept C++ matrices, we offer a utility
       class DataProviderConverter that can accept the same input data with
       PyDataProvider2, for more information please refer to document
-      of `PyDataProvider2 <../data_provider/pydataprovider2.html>`_.
+      of :ref:`api_pydataprovider2` .
 * Do the prediction with :code:`forwardTest()`, which takes the converted
   input data and outputs the activations of the output layer.
 

doc/api/trainer_config_helpers/layers.rst

+..  _api_trainer_config_helpers_layers:
+
 ======
 Layers
 ======

doc_cn/conf.py.in → doc/conf.py.cn.in

@@ -62,7 +62,7 @@ source_suffix = ['.rst', '.md', '.Rmd']
 source_encoding = 'utf-8'
 
 # The master toctree document.
-master_doc = 'index'
+master_doc = 'index_cn'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -79,7 +79,7 @@ language = 'zh_CN'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ['_build']
+exclude_patterns = ['_build', '**/*_en*', '*_en*']
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

doc/conf.py.in → doc/conf.py.en.in

@@ -63,7 +63,7 @@ source_suffix = ['.rst', '.md', '.Rmd']
 source_encoding = 'utf-8'
 
 # The master toctree document.
-master_doc = 'index'
+master_doc = 'index_en'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -80,7 +80,7 @@ language = None
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ['_build']
+exclude_patterns = ['_build', '**/*_cn*', '*_cn*']
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

doc_cn/faq/index.rst → doc/faq/index_cn.rst

 ####################
-PaddlePaddle常见问题
+FAQ
 ####################
 
 ..  contents::
@@ -33,10 +33,9 @@ PyDataProvider使用的是异步加载，同时在内存里直接随即选取数
 个内存池实际上决定了shuffle的粒度。所以，如果将这个内存池减小，又要保证数据是随机的，
 那么最好将数据文件在每次读取之前做一次shuffle。可能的代码为
 
-..  literalinclude:: reduce_min_pool_size.py
+..  literalinclude:: src/reduce_min_pool_size.py
 
-这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 `这里
-<../ui/data_provider/pydataprovider2.html#provider>`_ 。
+这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 `这里 <../ui/data_provider/pydataprovider2.html#provider>`_ 。
 
 神经元激活内存
 ++++++++++++++
@@ -76,7 +75,7 @@ PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需
 使用 :code:`pydataprovider`时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。
 :code:`DataProvider` 缓存池的减小，和之前减小通过减小缓存池来减小内存占用的原理一致。
 
-..  literalinclude:: reduce_min_pool_size.py
+..  literalinclude:: src/reduce_min_pool_size.py
 
 同时 :code:`@provider` 接口有一个 :code:`cache` 参数来控制缓存方法，将其设置成 :code:`CacheType.CACHE_PASS_IN_MEM` 的话，会将第一个 :code:`pass` (过完所有训练数据即为一个pass)生成的数据缓存在内存里，在之后的 :code:`pass` 中，不会再从 :code:`python` 端读取数据，而是直接从内存的缓存里读取数据。这也会极大减少数据读入的耗时。
 
@@ -90,11 +89,11 @@ PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 :code:`spa
 
 使用一个词前两个词和后两个词，来预测这个中间的词。这个任务的DataProvider为\:
 
-..  literalinclude:: word2vec_dataprovider.py
+..  literalinclude:: src/word2vec_dataprovider.py
 
 这个任务的配置为\:
 
-..  literalinclude:: word2vec_config.py
+..  literalinclude:: src/word2vec_config.py
 
 更多关于sparse训练的内容请参考 `sparse训练的文档 <TBD>`_
 
@@ -158,7 +157,7 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字
 这里 :code:`hidden_a` 和 :code:`hidden_b` 使用了同样的parameter和bias。并且softmax层的两个输入也使用了同样的参数 :code:`softmax_param`。
 
 7. *-cp27mu-linux_x86_64.whl is not a supported wheel on this platform.
------------------------------------------------------------------------
+---------------------------------------------------------------------------
 
 出现这个问题的主要原因是，系统编译wheel包的时候，使用的 :code:`wheel` 包是最新的，
 而系统中的 :code:`pip` 包比较老。具体的解决方法是，更新 :code:`pip` 包并重新编译PaddlePaddle。
@@ -220,7 +219,7 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字
 
 
 10. CMake源码编译, 找到的PythonLibs和PythonInterp版本不一致
-----------------------------------------------------------
+----------------------------------------------------------------
 
 这是目前CMake寻找Python的逻辑存在缺陷，如果系统安装了多个Python版本，CMake找到的Python库和Python解释器版本可能有不一致现象，导致编译PaddlePaddle失败。正确的解决方法是，
 用户强制指定特定的Python版本，具体操作如下：

doc_cn/introduction/index.rst → doc/getstarted/basic_usage/index_cn.rst

@@ -58,6 +58,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
     cost = regression_cost(input= ȳ, label=y)
     outputs(cost)
 
+
 这段简短的配置展示了PaddlePaddle的基本用法：
 
 - 第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的 `process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。

doc/getstarted/basic_usage/index_en.rst

@@ -99,11 +99,3 @@ In PaddlePaddle, training is just to get a collection of model parameters, which
 Although starts from a random guess, you can see that value of ``w`` changes quickly towards 2 and ``b`` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer.
 
 There, you have recovered the underlying pattern between ``X`` and ``Y`` only from observed data.
-
-
-5. Where to Go from Here
--------------------------
-
-- `Install and Build <../build_and_install/index.html>`_
-- `Tutorials <../demo/quick_start/index_en.html>`_
-- `Example and Demo <../demo/index.html>`_

doc/getstarted/build_and_install/build_from_source_en.md

@@ -14,6 +14,13 @@ cd paddle
 git submodule update --init --recursive
 ```
 
+If you already have a local PaddlePaddle repo and have not initialized the submodule, your local submodule folder will be empty. You can simply run the last line of the above codes in your PaddlePaddle home directory to initialize your submodule folder.
+
+If you have already initialized your submodule and you would like to sync with the upstream submodule repo, you can run the following command
+```
+git submodule update --remote
+```
+
 ## <span id="requirements">Requirements</span>
 
 To compile the source code, your computer must be equipped with the following dependencies.
@@ -42,8 +49,6 @@ PaddlePaddle supports some build options. To enable it, first you need to instal
 <tbody>
 <tr><td class="left">WITH_GPU</td><td class="left">Compile with GPU mode.</td></tr>
 <tr><td class="left">WITH_DOUBLE</td><td class="left">Compile with double precision floating-point, default: single precision.</td></tr>
-<tr><td class="left">WITH_GLOG</td><td class="left">Compile with glog. If not found, default: an internal log implementation.</td></tr>
-<tr><td class="left">WITH_GFLAGS</td><td class="left">Compile with gflags. If not found, default: an internal flag implementation.</td></tr>
 <tr><td class="left">WITH_TESTING</td><td class="left">Compile with gtest for PaddlePaddle's unit testing.</td></tr>
 <tr><td class="left">WITH_DOC</td><td class="left">    Compile to generate PaddlePaddle's docs, default: disabled (OFF).</td></tr>
 <tr><td class="left">WITH_SWIG_PY</td><td class="left">Compile with python predict API, default: disabled (OFF).</td></tr>
@@ -79,7 +84,7 @@ As a simple example, consider the following:
 
     ```bash
     pip install 'sphinx>=1.4.0'
-    pip install sphinx_rtd_theme breathe recommonmark
+    pip install sphinx_rtd_theme recommonmark
 
     # install doxygen on Ubuntu
     sudo apt-get install doxygen 

doc_cn/build_and_install/cmake/compile_options.csv → doc/getstarted/build_and_install/cmake/compile_options.csv

@@ -6,8 +6,6 @@ WITH_AVX,是否编译含有AVX指令集的PaddlePaddle二进制文件,是
 WITH_PYTHON,是否内嵌PYTHON解释器。方便今后的嵌入式移植工作。,是
 WITH_STYLE_CHECK,是否编译时进行代码风格检查,是
 WITH_RDMA,是否开启RDMA,否
-WITH_GLOG,是否开启GLOG。如果不开启，则会使用一个简化版的日志，同时方便今后的嵌入式移植工作。,取决于是否寻找到GLOG
-WITH_GFLAGS,是否使用GFLAGS。如果不开启，则会使用一个简化版的命令行参数解析器，同时方便今后的嵌入式移植工作。,取决于是否寻找到GFLAGS
 WITH_TIMER,是否开启计时功能。如果开启会导致运行略慢，打印的日志变多，但是方便调试和测Benchmark,否
 WITH_TESTING,是否开启单元测试,取决于是否寻找到GTEST
 WITH_DOC,是否编译中英文文档,否

doc_cn/build_and_install/install/docker_install.rst → doc/getstarted/build_and_install/docker_install_cn.rst

@@ -111,7 +111,24 @@ cuda相关的Driver和设备映射进container中，脚本类似于
 
 简单的含有ssh的Dockerfile如下：
 
-..  literalinclude:: paddle_ssh.Dockerfile
+..  code-block:: bash
+
+    FROM paddledev/paddle:cpu-latest
+
+    MAINTAINER PaddlePaddle dev team <paddle-dev@baidu.com>
+
+    RUN apt-get update
+    RUN apt-get install -y openssh-server
+    RUN mkdir /var/run/sshd
+    RUN echo 'root:root' | chpasswd
+
+    RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
+    RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
+
+    EXPOSE 22
+
+    CMD    ["/usr/sbin/sshd", "-D"]
+
 
 使用该Dockerfile构建出镜像，然后运行这个container即可。相关命令为\:
 

doc/getstarted/build_and_install/docker_install_en.rst

@@ -17,7 +17,7 @@ CPU-only one and a CUDA GPU one.  We do so by configuring
 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
 automatically runs the following commands:
 
-.. code-block:: base
+.. code-block:: bash
 
    docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile .
    docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu .
@@ -104,3 +104,78 @@ container:
 
 Then we can direct our Web browser to the HTML version of source code
 at http://localhost:8088/paddle/
+
+
+Development Using Docker
+------------------------
+
+Develpers can work on PaddlePaddle using Docker.  This allows
+developers to work on different platforms -- Linux, Mac OS X, and
+Windows -- in a consistent way.
+
+The general development workflow with Docker and Bazel is as follows:
+
+1. Get the source code of Paddle:
+
+   .. code-block:: bash
+
+      git clone --recursive https://github.com/paddlepaddle/paddle
+
+
+2. Build a development Docker image :code:`paddle:dev` from the source
+   code.  This image contains all the development tools and
+   dependencies of PaddlePaddle.
+
+
+   .. code-block:: bash
+
+      cd paddle
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
+
+
+3. Run the image as a container and mounting local source code
+   directory into the container.  This allows us to change the code on
+   the host and build it within the container.
+
+   .. code-block:: bash
+
+      docker run       \
+       -d              \
+       --name paddle   \
+       -p 2022:22      \
+       -v $PWD:/paddle \
+       -v $HOME/.cache/bazel:/root/.cache/bazel \
+       paddle:dev
+
+   where :code:`-d` makes the container running in background,
+   :code:`--name paddle` allows us to run a nginx container to serve
+   documents in this container, :code:`-p 2022:22` allows us to SSH
+   into this container, :code:`-v $PWD:/paddle` shares the source code
+   on the host with the container, :code:`-v
+   $HOME/.cache/bazel:/root/.cache/bazel` shares Bazel cache on the
+   host with the container.
+
+4. SSH into the container:
+
+   .. code-block:: bash
+
+      ssh root@localhost -p 2022
+
+5. We can edit the source code in the container or on this host.  Then
+   we can build using cmake
+
+   .. code-block:: bash
+
+      cd /paddle # where paddle source code has been mounted into the container
+      mkdir -p build
+      cd build
+      cmake -DWITH_TESTING=ON ..
+      make -j `nproc`
+      CTEST_OUTPUT_ON_FAILURE=1 ctest
+
+   or Bazel in the container:
+
+   .. code-block:: bash
+
+      cd /paddle
+      bazel test ...

doc_cn/build_and_install/index.rst → doc/getstarted/build_and_install/index_cn.rst

@@ -9,8 +9,8 @@ PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜
 .. toctree::
    :maxdepth: 1
    
-   install/docker_install.rst 
-   install/ubuntu_install.rst
+   docker_install_cn.rst 
+   ubuntu_install_cn.rst
 
 
 
@@ -24,4 +24,4 @@ PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜
 ..  toctree::
     :maxdepth: 1
 
-   cmake/index.rst
+    cmake/build_from_source_cn.rst
\ No newline at end of file

doc_cn/build_and_install/install/ubuntu_install.rst → doc/getstarted/build_and_install/ubuntu_install_cn.rst

@@ -38,7 +38,18 @@ PaddlePaddle提供了ubuntu 14.04 deb安装包。
 
 安装完成后，可以使用命令 :code:`paddle version` 查看安装后的paddle 版本:
 
-..  literalinclude:: paddle_version.txt
+..  code-block:: shell
+
+    PaddlePaddle 0.8.0b1, compiled with
+        with_avx: ON
+        with_gpu: OFF
+        with_double: OFF
+        with_python: ON
+        with_rdma: OFF
+        with_metric_learning:
+        with_timer: OFF
+        with_predict_sdk:
+
 
 可能遇到的问题
 --------------

doc/getstarted/index_cn.rst

+GET STARTED
+============
+
+..  toctree::
+  :maxdepth: 2
+
+  build_and_install/index_cn.rst
+  basic_usage/index_cn.rst

doc_cn/cluster/k8s/start_paddle.py → doc/howto/cluster/k8s/start_paddle.py

@@ -19,7 +19,6 @@ import socket
 import os
 import argparse
 
-
 # configuration for cluster
 API = "/api/v1/namespaces/"
 JOBSELECTOR = "labelSelector=job-name="
@@ -145,8 +144,8 @@ def startPaddle(idMap={}, train_args_dict=None):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(prog="start_paddle.py",
-                                     description='simple tool for k8s')
+    parser = argparse.ArgumentParser(
+        prog="start_paddle.py", description='simple tool for k8s')
     args, train_args_list = parser.parse_known_args()
     train_args = refine_unknown_args(train_args_list)
     train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2]))

doc/howto/cmd_parameter/detail_introduction_en.md

+```eval_rst
+..  _cmd_detail_introduction:
+```
+
 # Detail Description
 
 ## Common

doc/howto/cmd_parameter/index_en.md

 ```eval_rst
-..  _cmd_line_index_en:
+..  _cmd_line_index:
 ```
 # How to Set Command-line Parameters
 

doc_cn/concepts/use_concepts.rst → doc/howto/concepts/use_concepts_cn.rst

@@ -47,7 +47,7 @@ DataProvider是PaddlePaddle系统的数据提供器，将用户的原始数据
 
 一个简单的训练配置文件为：
 
-..  literalinclude:: trainer_config.py
+..  literalinclude:: src/trainer_config.py
     :linenos:
 
 文件开头 ``from paddle.trainer_config_helpers import *`` ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf，为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是`trainer_config_helpers`_的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。
@@ -114,7 +114,7 @@ PaddlePaddle 可以使用 ``mixed layer`` 配置出非常复杂的网络，甚
 
 PaddlePaddle多机采用经典的 Parameter Server 架构对多个节点的 trainer 进行同步。多机训练的经典拓扑结构如下\:
 
-..	graphviz:: pserver_topology.dot
+..    graphviz:: src/pserver_topology.dot
 
 图中每个灰色方块是一台机器，在每个机器中，先使用命令 ``paddle pserver`` 启动一个pserver进程，并指定端口号，可能的参数是\:
 

doc/howto/contribute_to_paddle_en.md

@@ -47,6 +47,22 @@ Then you can start to develop by making a local developement branch
 git checkout -b MY_COOL_STUFF_BRANCH
 ```
 
+## Using `pre-commit` hook
+
+Paddle developers use [pre-commit](http://pre-commit.com/) tool to manage git
+pre-commit hooks. It can help us format source codes (cpp, python), check some
+basic thing before commit (only one EOL for each file, do not add a huge file
+in git). `pre-commit` tests is a part of unit tests in Travis-CI now, every
+PR doesn't fit hook can not be merged into Paddle.
+
+To use [pre-commit](http://pre-commit.com/), you should install it by
+`pip install pre-commit`, and currently, Paddle uses `clang-format` to format
+c/cpp sources. Please make sure clang-format 3.8+ installed.
+
+Then just run `pre-commit install` in your Paddle clone directory. When you
+commit your code, the pre-commit hook will check the local code if there is
+anything not suitable to commit, and so on.
+
 ## Commit
 
 Commit your changes by following command lines:

doc/howto/deep_model/index_cn.rst

+How to Configure Deep Models
+============================
+
+..  toctree::
+  :maxdepth: 1
+
+  rnn/recurrent_group_cn.md
+  rnn/hierarchical_layer_cn.rst
+  rnn/hrnn_rnn_api_compare_cn.rst
+  rnn/hrnn_demo_cn.rst

doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst → doc/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.rst

@@ -24,18 +24,18 @@
 
 - 本例中的原始数据一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。这个数据也被单层RNN网络直接使用。
 
-..  literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg
+..  literalinclude:: ../../../../paddle/gserver/tests/Sequence/tour_train_wdseg
     :language: text
 
 
 - 双层序列数据一共有4个样本。 每个样本间用空行分开，整体数据和原始数据完全一样。但于双层序列的LSTM来说，第一个样本同时encode两条数据成两个向量。这四条数据同时处理的句子数量为\ :code:`[2, 3, 2, 3]`\ 。
 
-..  literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest
+..  literalinclude:: ../../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest
     :language: text
 
 其次，对于两种不同的输入数据类型，不同DataProvider对比如下(`sequenceGen.py <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/gserver/tests/sequenceGen.py>`_)\：
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py
+..  literalinclude:: ../../../../paddle/gserver/tests/sequenceGen.py
     :language: python
     :lines: 21-39
     :linenos:
@@ -43,10 +43,11 @@
 - 这是普通的单层时间序列的DataProvider代码，其说明如下：
   
   * DataProvider共返回两个数据，分别是words和label。即上述代码中的第19行。
+
     - words是原始数据中的每一句话，所对应的词表index数组。它是integer_value_sequence类型的，即整数数组。words即为这个数据中的单层时间序列。
     - label是原始数据中对于每一句话的分类标签，它是integer_value类型的。
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py
+..  literalinclude:: ../../../../paddle/gserver/tests/sequenceGen.py
     :language: python
     :lines: 42-71
     :linenos:
@@ -63,7 +64,7 @@
 
 首先，我们看一下单层RNN的配置。代码中9-15行(高亮部分)即为单层RNN序列的使用代码。这里使用了PaddlePaddle预定义好的RNN处理函数。在这个函数中，RNN对于每一个时间步通过了一个LSTM网络。
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequence_layer_group.conf
+..  literalinclude:: ../../../../paddle/gserver/tests/sequence_layer_group.conf
     :language: python
     :lines: 38-63
     :linenos:
@@ -84,7 +85,7 @@
 
 * 至此，\ :code:`lstm_last`\ 便和单层RNN配置中的\ :code:`lstm_last`\ 具有相同的结果了。
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequence_nest_layer_group.conf
+..  literalinclude:: ../../../../paddle/gserver/tests/sequence_nest_layer_group.conf
     :language: python
     :lines: 38-64
     :linenos:
@@ -106,7 +107,7 @@
 
 - 单层RNN：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequence_rnn.conf
+..  literalinclude:: ../../../../paddle/gserver/tests/sequence_rnn.conf
     :language: python
     :lines: 36-48
 
@@ -115,7 +116,7 @@
   - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。
   - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每个时间步都用了上一个时间步的输出结果”一致。
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf
+..  literalinclude:: ../../../../paddle/gserver/tests/sequence_nest_rnn.conf
     :language: python
     :lines: 39-66
 
@@ -151,14 +152,14 @@
 
 * 单层RNN\:
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py
+..  literalinclude:: ../../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py
     :language: python
     :lines: 42-59
     :linenos:
 
 * 双层RNN\ \:
 
-..  literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py
+..  literalinclude:: ../../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py
     :language: python
     :lines: 41-80
     :linenos:
@@ -181,11 +182,11 @@ Memory
 
 Memory是PaddlePaddle实现RNN时候使用的一个概念。RNN即时间递归神经网络，通常要求时间步之间具有一些依赖性，即当前时间步下的神经网络依赖前一个时间步神经网络中某一个神经元输出。如下图所示。
 
-..  graphviz:: glossary_rnn.dot
+..  graphviz:: src/glossary_rnn.dot
 
 上图中虚线的连接，即是跨越时间步的网络连接。PaddlePaddle在实现RNN的时候，将这种跨越时间步的连接用一个特殊的神经网络单元实现。这个神经网络单元就叫Memory。Memory可以缓存上一个时刻某一个神经元的输出，然后在下一个时间步输入给另一个神经元。使用Memory的RNN实现便如下图所示。
 
-..  graphviz:: glossary_rnn_with_memory.dot
+..  graphviz:: src/glossary_rnn_with_memory.dot
 
 使用这种方式，PaddlePaddle可以比较简单的判断哪些输出是应该跨越时间步的，哪些不是。
 

doc/howto/deep_model/rnn/rnn_en.rst

@@ -30,7 +30,7 @@ Then at the :code:`process` function, each :code:`yield` function will return th
     yield src_ids, trg_ids, trg_ids_next
 
 
-For more details description of how to write a data provider, please refer to `PyDataProvider2 <../../ui/data_provider/index.html>`_. The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
+For more details description of how to write a data provider, please refer to :ref:`api_pydataprovider2` . The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
 
 ===============================================
 Configure Recurrent Neural Network Architecture
@@ -42,7 +42,7 @@ Simple Gated Recurrent Neural Network
 
 Recurrent neural network process a sequence at each time step sequentially. An example of the architecture of LSTM is listed below.
 
-.. image:: ../../../tutorials/sentiment_analysis/bi_lstm.jpg
+.. image:: ../../../tutorials/sentiment_analysis/src/bi_lstm.jpg
      :align: center
 
 Generally speaking, a recurrent network perform the following operations from :math:`t=1` to :math:`t=T`, or reversely from :math:`t=T` to :math:`t=1`.
@@ -106,7 +106,7 @@ We will use the sequence to sequence model with attention as an example to demon
 
 In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`.
 
-The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to `Layers <../../ui/api/trainer_config_helpers/layers_index.html>`_  for more details.
+The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to :ref:`api_trainer_config_helpers_layers` for more details.
 
 We also project the encoder vector to :code:`decoder_size` dimensional space, get the first instance of the backward recurrent network, and project it to :code:`decoder_size` dimensional space:
 
@@ -246,6 +246,6 @@ The code is listed below:
     outputs(beam_gen)
 
 
-Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to `Semantic Role Labeling Demo <../../demo/semantic_role_labeling/index.html>`_ for more details.
+Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`semantic_role_labeling` for more details.
 
 The full configuration file is located at :code:`demo/seqToseq/seqToseq_net.py`.

doc/howto/index_cn.rst

+HOW TO
+=======
+
+Usage
+-------
+
+..  toctree::
+  :maxdepth: 1
+
+  concepts/use_concepts_cn.rst
+  cluster/k8s/paddle_on_k8s_cn.md
+  cluster/k8s/distributed_training_on_k8s_cn.md
+
+Development
+------------
+
+..  toctree::
+  :maxdepth: 1
+
+  write_docs/index_cn.rst
+  deep_model/index_cn.rst
+
+Optimization
+-------------
+
+..  toctree::
+  :maxdepth: 1

doc/howto/optimization/gpu_profiling_en.rst

@@ -51,7 +51,7 @@ In this tutorial, we will focus on nvprof and nvvp.
 :code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate
 above profilers. 
 
-.. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+.. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
    :language: c++
    :lines: 111-124
    :linenos:
@@ -77,7 +77,7 @@ As a simple example, consider the following:
 
 1. Add :code:`REGISTER_TIMER_INFO` and :code:`printAllStatus` functions (see the emphasize-lines).
 
-    .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 111-124
         :emphasize-lines: 8-10,13
@@ -124,7 +124,7 @@ To use this command line profiler **nvprof**, you can simply issue the following
 
 1. Add :code:`REGISTER_GPU_PROFILER` function (see the emphasize-lines).
 
-    .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 111-124
         :emphasize-lines: 6-7

doc/howto/source/api.rst

-API
-===
-
-.. doxygenfile:: paddle/api/PaddleAPI.h
-.. doxygenfile:: paddle/api/Internal.h

doc/howto/source/cuda/index.rst

-CUDA
-====
-
-.. toctree::
-  :maxdepth: 2
-
-  matrix.rst
-  nn.rst
-  utils.rst

doc/howto/source/cuda/matrix.rst

-Matrix
-======
-
-Base
-----
-
-hl_matrix.h
-```````````
-.. doxygenfile:: paddle/cuda/include/hl_matrix.h
-
-hl_matrix_base.h
-````````````````
-.. doxygenfile:: paddle/cuda/include/hl_matrix_base.cuh
-
-hl_matrix_apply.cuh
-```````````````````
-.. doxygenfile:: paddle/cuda/include/hl_matrix_apply.cuh
-
-hl_matrix_ops.cuh
-`````````````````
-.. doxygenfile:: paddle/cuda/include/hl_matrix_ops.cuh
-
-hl_matrix_type.cuh
-``````````````````
-.. doxygenfile:: paddle/cuda/include/hl_matrix_type.cuh
-
-hl_sse_matrix_kernel.cuh
-````````````````````````
-.. doxygenfile:: paddle/cuda/include/hl_sse_matrix_kernel.cuh
-
-Matrix Function 
----------------
-
-hl_batch_transpose.h
-````````````````````
-.. doxygenfile:: paddle/cuda/include/hl_batch_transpose.h
-
-hl_aggregate.h
-``````````````
-.. doxygenfile:: paddle/cuda/include/hl_aggregate.h
-
-hl_top_k.h
-``````````
-.. doxygenfile:: paddle/cuda/include/hl_top_k.h
-
-hl_table_apply.h
-````````````````
-.. doxygenfile:: paddle/cuda/include/hl_table_apply.h
-
-Sparse Matrix
--------------
-
-hl_sparse.h
-```````````
-.. doxygenfile:: paddle/cuda/include/hl_sparse.h
-
-hl_sparse.ph
-````````````
-.. doxygenfile:: paddle/cuda/include/hl_sparse.ph

doc/howto/source/cuda/nn.rst

-Neural Network
-==============
-
-Base
-----
-
-.. doxygenfile:: paddle/cuda/include/hl_gpu.h
-.. doxygenfile:: paddle/cuda/include/hl_functions.h
-.. doxygenfile:: paddle/cuda/include/hl_avx_functions.h
-.. doxygenfile:: paddle/cuda/include/hl_gpu_functions.cuh
-.. doxygenfile:: paddle/cuda/include/hl_activation_functions.h
-
-
-CNN Related APIs
-----------------
-.. doxygenfile:: paddle/cuda/include/hl_cnn.h
-.. doxygenfile:: paddle/cuda/include/hl_cuda_cudnn.h
-.. doxygenfile:: paddle/cuda/include/hl_cuda_cudnn.ph
-
-RNN Related APIs
-----------------
-
-.. doxygenfile:: paddle/cuda/include/hl_recurrent_apply.cuh
-.. doxygenfile:: paddle/cuda/include/hl_sequence.h
-
-LSTM Model
-``````````
-
-.. doxygenfile:: paddle/cuda/include/hl_lstm.h
-.. dpxygenfile:: paddle/cuda/include/hl_cpu_lstm.cuh
-.. doxygenfile:: paddle/cuda/include/hl_gpu_lstm.cuh
-.. doxygenfile:: paddle/cuda/include/hl_lstm_ops.cuh
-
-GRU Model
-`````````
-
-.. doxygenfile:: paddle/cuda/include/hl_gru_ops.cuh
-.. doxygenfile:: paddle/cuda/include/hl_cpu_gru.cuh
-.. doxygenfile:: paddle/cuda/include/hl_gpu_gru.cuh

doc/howto/source/cuda/utils.rst

-Utils
-=====
-
-Dynamic Link Libs
------------------
-.. doxygenfile:: paddle/cuda/include/hl_dso_loader.h
-
-GPU Resources
--------------
-
-hl_cuda.ph
-``````````
-.. doxygenfile:: paddle/cuda/include/hl_cuda.ph
-
-hl_cuda.h
-`````````
-.. doxygenfile:: paddle/cuda/include/hl_cuda.h
-
-HPPL Base
----------
-.. doxygenfile:: paddle/cuda/include/hl_base.h
-
-CUBLAS Wrapper
---------------
-.. doxygenfile:: paddle/cuda/include/hl_cuda_cublas.h
-
-Timer
------
-.. doxygenfile:: paddle/cuda/include/hl_time.h
-
-Thread Resource
----------------
-.. doxygenfile:: paddle/cuda/include/hl_thread.ph
-
-Device Function
----------------
-.. doxygenfile:: paddle/cuda/include/hl_device_functions.cuh

doc/howto/source/gserver/activations.rst

-Activations
-===========
-
-..  doxygenclass:: paddle::ActivationFunction
-    :members:

doc/howto/source/gserver/dataproviders.rst

-==============
-Data Providers
-==============
-
-DataProviders
-=============
-
-Base
-----
-..  doxygenclass:: paddle::DataProvider
-    :members:
-
-DataProviderGroup
------------------
-..  doxygenclass:: paddle::DataProviderGroup
-    :members:
-
-MultiDataProvider
------------------
-..  doxygenclass:: paddle::MultiDataProvider
-    :members:
-
-PyDataProvider
-==============
-
-IFieldScanner
--------------
-..  doxygenclass:: paddle::IFieldScanner
-    :members:
-
-DenseScanner
--------------
-..  doxygenclass:: paddle::DenseScanner
-    :members:
-
-IndexScanner
--------------
-..  doxygenclass:: paddle::IndexScanner
-    :members:
-
-SparseNonValueScanner
----------------------
-..  doxygenclass:: paddle::SparseNonValueScanner
-    :members:
-
-SparseValueScanner
-------------------
-..  doxygenclass:: paddle::SparseValueScanner
-    :members:
-
-SequenceScanner
----------------
-..  doxygenclass:: paddle::SparseValueScanner
-    :members:
-
-IPyDataProviderCache
---------------------
-..  doxygenclass:: paddle::IPyDataProviderCache
-    :members:
-
-NoCacheStrategy
----------------
-..  doxygenclass:: paddle::NoCacheStrategy
-    :members:
-
-CacheOnePassInMemory
---------------------
-..  doxygenclass:: paddle::CacheOnePassInMemory
-    :members:
-
-IPyDataProvider
----------------
-..  doxygenclass:: paddle::PyDataProvider2
-    :members:
-
-ProtoDataProvider
-=================
-
-ProtoDataProvider
-----------------
-..  doxygenclass:: paddle::ProtoDataProvider
-    :members:
-
-ProtoSequenceDataProvider
--------------------------
-..  doxygenclass:: paddle::ProtoSequenceDataProvider
-    :members:

doc/howto/source/gserver/evaluators.rst

-==========
-Evaluators
-==========
-
-Base
-====
-
-..  doxygenclass:: paddle::Evaluator
-    :members:
-
-Sum
-===
-
-SumEvaluator
-------------
-..  doxygenclass:: paddle::SumEvaluator
-    :members:
-
-ColumnSumEvaluator
-------------------
-..  doxygenclass:: paddle::ColumnSumEvaluator
-    :members:
-
-Classification
-==============
-
-ClassificationErrorEvaluator
----------------------------
-..  doxygenclass:: paddle::ClassificationErrorEvaluator
-    :members:
-
-SequenceClassificationErrorEvaluator
-------------------------------------
-..  doxygenclass:: paddle::SequenceClassificationErrorEvaluator
-    :members:
-
-AucEvaluator
--------------
-..  doxygenclass:: paddle::AucEvaluator
-    :members:
-
-PrecisionRecallEvaluator
-------------------------
-..  doxygenclass:: paddle::PrecisionRecallEvaluator
-    :members:
-
-ChunkEvaluator
---------------
-..  doxygenclass:: paddle::ChunkEvaluator
-    :members:
-
-CTCEvaluator
-------------
-..  doxygenclass:: paddle::CTCErrorEvaluator
-    :members:
-
-
-Rank
-====
-
-PnpairEvaluator
--------------
-..  doxygenclass:: paddle::PnpairEvaluator
-    :members:
-
-AucEvaluator
--------------
-..  doxygenclass:: paddle::RankAucEvaluator
-    :members:
-
-
-Printer
-=======
-
-ValuePrinter
--------------
-..  doxygenclass:: paddle::ValuePrinter
-    :members:
-
-GradientPrinter
----------------
-..  doxygenclass:: paddle::GradientPrinter
-    :members:
-
-MaxIdPrinter
-------------
-..  doxygenclass:: paddle::MaxIdPrinter
-    :members:
-
-MaxFramePrinter
----------------
-..  doxygenclass:: paddle::MaxFramePrinter
-    :members:
-
-SequenceTextPrinter
-------------------
-..  doxygenclass:: paddle::SequenceTextPrinter
-    :members:
-
-ClassificationErrorPrinter
---------------------------
-..  doxygenclass:: paddle::ClassificationErrorPrinter
-    :members:

doc/howto/source/gserver/gradientmachines.rst

-Gradient Machines
-=================
-
-GradientMachine
----------------
-..  doxygenclass:: paddle::GradientMachine
-    :members:
-
-GradientMachineMode
--------------------
-..  doxygenclass:: paddle::IGradientMachineMode
-    :members:
-
-MultiGradientMachine
---------------------
-..  doxygenclass:: paddle::MultiGradientMachine
-    :members:
-
-TrainerThread
-`````````````
-..  doxygenclass:: paddle::TrainerThread
-    :members:
-
-RecurrentGradientMachine
-------------------------
-..  doxygenclass:: paddle::RecurrentGradientMachine
-    :members:

doc/howto/source/gserver/index.rst

-GServer
-=======
-
-.. toctree::
-  :maxdepth: 2
-
-  activations.rst
-  dataproviders.rst
-  evaluators.rst
-  gradientmachines.rst
-  layers.rst
-  neworks.rst

doc/howto/source/gserver/layers.rst

-======
-Layers
-======
-
-Base
-====
-
-Layer 
------
-..  doxygenclass:: paddle::Layer
-    :members:
-
-Projection
-----------
-..  doxygenclass:: paddle::Projection
-    :members:
-
-Operator
---------
-..  doxygenclass:: paddle::Operator
-    :members:
-    
-Data Layer
-==========
-
-..  doxygenclass:: paddle::DataLayer
-    :members:
-
-Fully Connected Layers
-======================
-
-FullyConnectedLayer
--------------------
-..  doxygenclass:: paddle::FullyConnectedLayer
-    :members:
-
-SelectiveFullyConnectedLayer
-----------------------------
-..  doxygenclass:: paddle::SelectiveFullyConnectedLayer
-    :members:
-
-Conv Layers
-===========
-
-ConvBaseLayer
--------------
-..  doxygenclass:: paddle::ConvBaseLayer
-    :members:
-
-ConvOperator
-------------
-..  doxygenclass:: paddle::ConvOperator
-    :members:
-
-ConvShiftLayer
---------------
-..  doxygenclass:: paddle::ConvShiftLayer
-    :members:
-
-CudnnConvLayer
---------------
-..  doxygenclass:: paddle::CudnnConvLayer
-    :members:
-
-ExpandConvBaseLayer
--------------------
-..  doxygenclass:: paddle::ExpandConvBaseLayer
-    :members:
-
-ExpandConvLayer
----------------
-..  doxygenclass:: paddle::ExpandConvLayer
-    :members:
-
-ContextProjection
------------------
-..  doxygenclass:: paddle::ContextProjection
-    :members:
-
-Pooling Layers
-==============
-
-PoolLayer
----------
-..  doxygenclass:: paddle::PoolLayer
-    :members:
-
-PoolProjectionLayer
--------------------
-..  doxygenclass:: paddle::PoolProjectionLayer
-    :members:
-
-CudnnPoolLayer
---------------
-..  doxygenclass:: paddle::CudnnPoolLayer
-    :members:
-
-SpatialPyramidPoolLayer
------------------------
-..  doxygenclass:: paddle::SpatialPyramidPoolLayer
-    :members:
-
-MaxOutLayer
------------
-..  doxygenclass:: paddle::MaxOutLayer
-    :members:
-
-Norm Layers
-===========
-
-NormLayer
----------
-..  doxygenclass:: paddle::NormLayer
-    :members:
-
-CMRProjectionNormLayer
-----------------------
-..  doxygenclass:: paddle::CMRProjectionNormLayer
-    :members:
-
-DataNormLayer
--------------
-..  doxygenclass:: paddle::DataNormLayer
-    :members:
-
-ResponseNormLayer
------------------
-..  doxygenclass:: paddle::ResponseNormLayer
-    :members:
-
-BatchNormBaseLayer
-------------------
-..  doxygenclass:: paddle::BatchNormBaseLayer
-    :members:
-
-BatchNormalizationLayer
------------------------
-..  doxygenclass:: paddle::BatchNormalizationLayer
-    :members:
-
-CudnnBatchNormLayer
------------------------
-..  doxygenclass:: paddle::CudnnBatchNormLayer
-    :members:
-
-SumToOneNormLayer
------------------
-..  doxygenclass:: paddle::SumToOneNormLayer
-    :members:
-
-Activation Layer
-================
-
-ParameterReluLayer
-------------------
-..  doxygenclass:: paddle::ParameterReluLayer
-    :members:
-
-Recurrent Layers
-================
-
-RecurrentLayer
---------------
-..  doxygenclass:: paddle::RecurrentLayer
-    :members:
-
-SequenceToBatch
----------------
-..  doxygenclass:: paddle::SequenceToBatch
-    :members:
-
-LSTM
-----
-LstmLayer
-`````````
-..  doxygenclass:: paddle::LstmLayer
-    :members:
-
-LstmStepLayer
-`````````````
-..  doxygenclass:: paddle::LstmStepLayer
-    :members:
-
-LstmCompute
-```````````
-..  doxygenclass:: paddle::LstmCompute
-    :members:
-
-MDLSTM
-------
-MDLstmLayer
-```````````
-..  doxygenclass:: paddle::MDLstmLayer
-    :members:
-
-CoordIterator
-`````````````
-..  doxygenclass:: paddle::CoordIterator
-    :members:
-
-GRU
----
-GatedRecurrentLayer
-```````````````````
-..  doxygenclass:: paddle::GatedRecurrentLayer
-    :members:
-
-GruStepLayer
-````````````
-..  doxygenclass:: paddle::GruStepLayer
-    :members:
-
-GruCompute
-``````````
-..  doxygenclass:: paddle::GruCompute
-    :members:
-
-Recurrent Layer Group
-=====================
-
-AgentLayer
-----------
-..  doxygenclass:: paddle::AgentLayer
-    :members:
-
-SequenceAgentLayer
-------------------
-..  doxygenclass:: paddle::SequenceAgentLayer
-    :members:
-
-GatherAgentLayer
-----------------
-..  doxygenclass:: paddle::GatherAgentLayer
-    :members:
-
-SequenceGatherAgentLayer
-------------------------
-..  doxygenclass:: paddle::SequenceGatherAgentLayer
-    :members:
-
-ScatterAgentLayer
------------------
-..  doxygenclass:: paddle::ScatterAgentLayer
-    :members:
-
-SequenceScatterAgentLayer
--------------------------
-..  doxygenclass:: paddle::SequenceScatterAgentLayer
-    :members:
-
-GetOutputLayer
---------------
-..  doxygenclass:: paddle::GetOutputLayer
-    :members:
-
-Mixed Layer
-===========
-..  doxygenclass:: paddle::MixedLayer
-    :members:
-
-DotMulProjection
-----------------
-..  doxygenclass:: paddle::DotMulProjection
-    :members:
-
-DotMulOperator
---------------
-..  doxygenclass:: paddle::DotMulOperator
-    :members:
-
-FullMatrixProjection
---------------------
-..  doxygenclass:: paddle::FullMatrixProjection
-    :members:
-
-IdentityProjection
-------------------
-..  doxygenclass:: paddle::IdentityProjection
-    :members:
-
-IdentityOffsetProjection
-------------------------
-..  doxygenclass:: paddle::IdentityOffsetProjection
-    :members:
-
-TableProjection
----------------
-..  doxygenclass:: paddle::TableProjection
-    :members:
-
-TransposedFullMatrixProjection
-------------------------------
-..  doxygenclass:: paddle::TransposedFullMatrixProjection
-    :members:
-
-Aggregate Layers
-================
-
-Aggregate
----------
-AverageLayer
-````````````
-..  doxygenclass:: paddle::AverageLayer
-    :members:
-
-MaxLayer
-````````
-..  doxygenclass:: paddle::MaxLayer
-    :members:
-
-SequenceLastInstanceLayer
-`````````````````````````
-..  doxygenclass:: paddle::SequenceLastInstanceLayer
-    :members:
-
-Concat
-------
-ConcatenateLayer
-````````````````
-..  doxygenclass:: paddle::ConcatenateLayer
-    :members:
-
-ConcatenateLayer2
-`````````````````
-..  doxygenclass:: paddle::ConcatenateLayer2
-    :members:
-
-SequenceConcatLayer
-```````````````````
-..  doxygenclass:: paddle::SequenceConcatLayer
-    :members:
-
-Subset
-------
-SubSequenceLayer
-````````````````
-..  doxygenclass:: paddle::SubSequenceLayer
-    :members:
-
-Reshaping Layers
-================
-
-BlockExpandLayer
-----------------
-..  doxygenclass:: paddle::BlockExpandLayer
-    :members:
-
-ExpandLayer
------------
-..  doxygenclass:: paddle::ExpandLayer
-    :members:
-
-FeatureMapExpandLayer
----------------------
-..  doxygenclass:: paddle::FeatureMapExpandLayer
-    :members:
-
-ResizeLayer
------------
-..  doxygenclass:: paddle::ResizeLayer
-    :members:
-
-SequenceReshapeLayer
---------------------
-..  doxygenclass:: paddle::SequenceReshapeLayer
-    :members:
-
-Math Layers
-===========
-
-AddtoLayer
-----------
-..  doxygenclass:: paddle::AddtoLayer
-    :members:
-
-ConvexCombinationLayer
-----------------------
-..  doxygenclass:: paddle::ConvexCombinationLayer
-    :members:
-
-InterpolationLayer
-------------------
-..  doxygenclass:: paddle::InterpolationLayer
-    :members:
-
-MultiplexLayer
---------------
-..  doxygenclass:: paddle::MultiplexLayer
-    :members:
-
-OuterProdLayer
---------------
-..  doxygenclass:: paddle::OuterProdLayer
-    :members:
-
-PowerLayer
-----------
-..  doxygenclass:: paddle::PowerLayer
-    :members:
-
-ScalingLayer
-------------
-..  doxygenclass:: paddle::ScalingLayer
-    :members:
-
-SlopeInterceptLayer
--------------------
-..  doxygenclass:: paddle::SlopeInterceptLayer
-    :members:
-
-TensorLayer
-------------
-..  doxygenclass:: paddle::TensorLayer
-    :members:
-
-TransLayer
-----------
-..  doxygenclass:: paddle::TransLayer
-    :members:
-
-Sampling Layers
-===============
-
-BilinearInterpLayer
--------------------
-..  doxygenclass:: paddle::BilinearInterpLayer
-    :members:
-
-MultinomialSampler
-------------------
-..  doxygenclass:: paddle::MultinomialSampler
-    :members:
-
-MaxIdLayer
-----------
-..  doxygenclass:: paddle::MaxIdLayer
-    :members:
-
-SamplingIdLayer
----------------
-..  doxygenclass:: paddle::SamplingIdLayer
-    :members:
-
-Cost Layers
-===========
-
-CostLayer
------------
-..  doxygenclass:: paddle::CostLayer
-    :members:
-
-HuberTwoClass
-`````````````
-..  doxygenclass:: paddle::HuberTwoClass
-    :members:
-
-LambdaCost
-```````````
-..  doxygenclass:: paddle::LambdaCost
-    :members:
-
-MultiBinaryLabelCrossEntropy
-````````````````````````````
-..  doxygenclass:: paddle::MultiBinaryLabelCrossEntropy
-    :members:
-
-MultiClassCrossEntropy
-```````````````````````
-..  doxygenclass:: paddle::MultiClassCrossEntropy
-    :members:
-
-MultiClassCrossEntropyWithSelfNorm
-``````````````````````````````````
-..  doxygenclass:: paddle::MultiClassCrossEntropyWithSelfNorm
-    :members:
-
-RankingCost
-```````````
-..  doxygenclass:: paddle::RankingCost
-    :members:
-
-SoftBinaryClassCrossEntropy
-```````````````````````````
-..  doxygenclass:: paddle::SoftBinaryClassCrossEntropy
-    :members:
-
-SumOfSquaresCostLayer
-`````````````````````
-..  doxygenclass:: paddle::SumOfSquaresCostLayer
-    :members:
-
-SumCostLayer
-`````````````````````
-..  doxygenclass:: paddle::SumCostLayer
-    :members:
-
-CosSimLayer
------------
-..  doxygenclass:: paddle::CosSimLayer
-    :members:
-
-CosSimVecMatLayer
------------------
-..  doxygenclass:: paddle::CosSimVecMatLayer
-    :members:
-
-CRFDecodingLayer
-----------------
-..  doxygenclass:: paddle::CRFDecodingLayer
-    :members:
-
-CRFLayer
---------
-..  doxygenclass:: paddle::CRFLayer
-    :members:
-
-CTCLayer
---------
-..  doxygenclass:: paddle::CTCLayer
-    :members:
-
-HierarchicalSigmoidLayer
-------------------------
-..  doxygenclass:: paddle::HierarchicalSigmoidLayer
-    :members:
-
-LinearChainCRF
---------------
-..  doxygenclass:: paddle::LinearChainCRF
-    :members:
-
-LinearChainCTC
---------------
-..  doxygenclass:: paddle::LinearChainCTC
-    :members:
-
-NCELayer
---------
-..  doxygenclass:: paddle::NCELayer
-    :members:
-
-Validation Layers
------------------
-
-ValidationLayer
-```````````````
-..  doxygenclass:: paddle::ValidationLayer
-    :members:
-
-AucValidation
-`````````````
-..  doxygenclass:: paddle::AucValidation
-    :members:
-
-PnpairValidation
-````````````````
-..  doxygenclass:: paddle::PnpairValidation
-    :members:
-
-Check Layers
-============
-
-EosIdCheckLayer
----------------
-..  doxygenclass:: paddle::EosIdCheckLayer
-    :members:

doc/howto/source/gserver/neworks.rst

-Networks
-========
-
-NeuralNetwork
--------------
-..  doxygenclass:: paddle::NeuralNetwork
-    :members:
-
-ParallelNeuralNetwork
----------------------
-..  doxygenclass:: paddle::ParallelNeuralNetwork
-    :members:

doc/howto/source/index.rst

-Source Code Documents
-=====================
-
-.. toctree::
-  :maxdepth: 1
-
-  gserver/index.rst
-  trainer.rst
-  parameter/index.rst
-  pserver/index.rst
-  api.rst
-  cuda/index.rst
-  math/index.rst
-  utils/index.rst

doc/howto/source/math/functions.rst

-Functions
-=========
-
-MathFunctions
--------------
-.. doxygenfile:: paddle/math/MathFunctions.h
-
-SIMDFunctions
--------------
-.. doxygenfile:: paddle/math/SIMDFunctions.h

doc/howto/source/math/index.rst

-Math
-====
-
-.. toctree::
-  :maxdepth: 2
-
-  vector.rst
-  matrix.rst
-  functions.rst
-  utils.rst