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

demo/image_classification/prediction.py

@@ -126,7 +126,7 @@ class ImageClassifier():
         # For oversampling, average predictions across crops.
         # If not, the shape of output[name]: (1, class_number),
         # the mean is also applicable.
-        return output[output_layer].mean(0)
+        return output[output_layer]['value'].mean(0)
 
     def predict(self, image=None, output_layer=None):
         assert isinstance(image, basestring)

demo/mnist/api_train_v2.py

@@ -27,19 +27,14 @@ def main():
     cost = paddle.layer.classification_cost(input=inference, label=label)
 
     parameters = paddle.parameters.create(cost)
-    for param_name in parameters.keys():
-        array = parameters.get(param_name)
-        array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
-        parameters.set(parameter_name=param_name, value=array)
 
     adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
 
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            para = parameters.get('___fc_2__.w0')
-            print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % (
-                event.pass_id, event.batch_id, event.cost, para.mean())
-
+            if event.batch_id % 100 == 0:
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
         else:
             pass
 
@@ -50,11 +45,12 @@ def main():
                   parameters=parameters,
                   event_handler=event_handler,
                   batch_size=32,  # batch size should be refactor in Data reader
-                  data_types={  # data_types will be removed, It should be in
+                  data_types=[  # data_types will be removed, It should be in
                       # network topology
-                      'pixel': images.type,
-                      'label': label.type
-                  })
+                      ('pixel', images.type),
+                      ('label', label.type)],
+                  reader_dict={'pixel':0, 'label':1}
+                  )
 
 
 if __name__ == '__main__':

demo/model_zoo/resnet/classify.py

@@ -156,7 +156,7 @@ class ImageClassifier():
             # For oversampling, average predictions across crops.
             # If not, the shape of output[name]: (1, class_number),
             # the mean is also applicable.
-            res[name] = output[name].mean(0)
+            res[name] = output[name]['value'].mean(0)
 
         return res
 

paddle/api/Arguments.cpp

@@ -38,6 +38,13 @@ Arguments* Arguments::createByPaddleArgumentVector(void* ptr) {
   return args;
 }
 
+Arguments* Arguments::createByPaddleArgument(const void* ptr) {
+  auto p = (paddle::Argument*)(ptr);
+  auto args = new Arguments();
+  args->m->outputs.push_back(*p);
+  return args;
+}
+
 Matrix* Arguments::getSlotValue(size_t idx) const throw(RangeError) {
   auto& a = m->getArg(idx);
   return Matrix::createByPaddleMatrixPtr(&a.value);

paddle/api/Evaluator.cpp

@@ -27,3 +27,18 @@ std::string Evaluator::toString() {
   m->rawPtr->printStats(sout);
   return sout.str();
 }
+
+std::vector<std::string> Evaluator::getNames() const {
+  std::vector<std::string> retv;
+  m->rawPtr->getNames(&retv);
+  return retv;
+}
+
+double Evaluator::getValue(const std::string name) const {
+  paddle::Error err;
+  double v = m->rawPtr->getValue(name, &err);
+  if (err) {
+    throw std::runtime_error(err.msg());
+  }
+  return v;
+}

paddle/api/GradientMachine.cpp

@@ -144,12 +144,12 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
 
 void GradientMachine::randParameters() { m->machine->randParameters(); }
 
-Matrix* GradientMachine::getLayerOutput(const std::string& layerName) const
+Arguments* GradientMachine::getLayerOutput(const std::string& layerName) const
     throw(UnsupportError) {
-  auto nn = std::dynamic_pointer_cast<paddle::NeuralNetwork>(m->machine);
+  auto nn = m->machine;
   if (nn) {
-    auto mat = nn->getLayerOutput(layerName);
-    return Matrix::createByPaddleMatrixPtr(&mat);
+    auto arg = nn->getLayerOutput(layerName);
+    return Arguments::createByPaddleArgument(&arg);
   } else {
     throw UnsupportError();
   }

paddle/api/PaddleAPI.h

@@ -454,6 +454,7 @@ public:
 
 private:
   static Arguments* createByPaddleArgumentVector(void* ptr);
+  static Arguments* createByPaddleArgument(const void* ptr);
   void* getInternalArgumentsPtr() const;
 
 private:
@@ -769,7 +770,7 @@ public:
 
   void randParameters();
 
-  Matrix* getLayerOutput(const std::string& layerName) const
+  Arguments* getLayerOutput(const std::string& layerName) const
       throw(UnsupportError);
 
   /**
@@ -900,6 +901,10 @@ public:
    */
   std::string toString();
 
+  std::vector<std::string> getNames() const;
+
+  double getValue(const std::string name) const;
+
 private:
   EvaluatorPrivate* m;
 
@@ -952,7 +957,7 @@ public:
 
   Arguments* getForwardOutput();
 
-  Matrix* getLayerOutput(const std::string& layerName);
+  Arguments* getLayerOutput(const std::string& layerName) const;
 };
 
 /// the N-Best results generated from one input sequence.

paddle/api/Trainer.cpp

@@ -131,12 +131,11 @@ void Trainer::testOneDataBatch(size_t batchSize, const Arguments& args) {
 void TrainerPrivate::finishTestPeriod() { tester_->finishTestPeriod(); }
 void Trainer::finishTestPeriod() { m->finishTestPeriod(); }
 
-Matrix* Trainer::getLayerOutput(const std::string& layerName) {
-  auto nn = std::dynamic_pointer_cast<paddle::NeuralNetwork>(
-      this->m->getGradientMachine());
+Arguments* Trainer::getLayerOutput(const std::string& layerName) const {
+  auto nn = this->m->getGradientMachine();
   CHECK(nn) << "trainerInternal_.getGradientMachine() is not NeuralNetwork";
-  auto m = nn->getLayerOutput(layerName);
-  return Matrix::createByPaddleMatrixPtr(&m);
+  auto arg = nn->getLayerOutput(layerName);
+  return Arguments::createByPaddleArgument(&arg);
 }
 
 void Trainer::forwardOneBatch(size_t batchSize) {

paddle/api/test/testTrain.py

@@ -89,9 +89,14 @@ def main():
             except Exception as e:
                 print e
 
+        ev = m.makeEvaluator()
+        ev.start()
         m.forwardBackward(inArgs, outArgs, swig_paddle.PASS_TRAIN,
                           update_callback)
-
+        m.eval(ev)
+        ev.finish()
+        for name in ev.getNames():
+            print name, ev.getValue(name)
         for optimizer in optimizers:
             optimizer.finishBatch()
 

paddle/gserver/gradientmachines/GradientMachine.h

@@ -134,6 +134,10 @@ public:
     backward(callback);
   }
 
+  virtual Argument getLayerOutput(const std::string& layerName) {
+    return *((Argument*)nullptr);
+  }
+
   // see comment in Layer.h for the function with the same name
   virtual void resetState() {}
 

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -282,6 +282,18 @@ void MultiGradientMachine::forwardBackward(const std::vector<Argument>& inArgs,
   backwardImp(callback);
 }
 
+Argument MultiGradientMachine::getLayerOutput(const std::string& layerName) {
+  std::vector<Argument> args;
+  args.reserve(threads_.size());
+
+  for (auto& thread : threads_) {
+    args.push_back(thread->getGradientMachine()->getLayerOutput(layerName));
+  }
+  outLayerArgs_.concat(args, false /* use_gpu */, outArgStream_, passType_);
+
+  return outLayerArgs_;
+}
+
 void MultiGradientMachine::backwardImp(const UpdateCallback& callback) {
   for (size_t i = 0; i < parameters_.size(); i++) {
     if (!parameters_[i]->useGpu() || parameters_[i]->isStatic()) continue;

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -189,6 +189,8 @@ public:
                        PassType passType,
                        const UpdateCallback& callback);
 
+  virtual Argument getLayerOutput(const std::string& layerName);
+
   virtual void onPassEnd();
 
   virtual void finish();
@@ -314,6 +316,8 @@ protected:
   std::vector<Argument> outArgs_;
   hl_stream_t outArgStream_;
 
+  Argument outLayerArgs_;
+
   /// ParameterType which needs to be merged from each GPU
   std::vector<ParameterType> mergeTypes_;
   int numDevices_;         /* number of gpu devices */

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -293,11 +293,10 @@ void NeuralNetwork::backward(const UpdateCallback& callback) {
   }
 }
 
-MatrixPtr NeuralNetwork::getLayerOutput(const std::string& layerName) {
-  auto it = layerMap_.find(layerName);
-  CHECK(it != layerMap_.end()) << "Cannot find layer: " << layerName;
-  return it->second->getOutputValue();
+Argument NeuralNetwork::getLayerOutput(const std::string& layerName) {
+  return getLayer(layerName)->getOutput();
 }
+
 void NeuralNetwork::onPassEnd() {
   for (auto& layer : layers_) {
     layer->onPassEnd();

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -87,7 +87,8 @@ public:
 
   virtual void backward(const UpdateCallback& callback = nullptr);
 
-  MatrixPtr getLayerOutput(const std::string& layerName);
+  virtual Argument getLayerOutput(const std::string& layerName);
+
   const LayerPtr& getLayer(const std::string& layerName) const {
     auto it = layerMap_.find(layerName);
     CHECK(it != layerMap_.end()) << "Unknown layer " << layerName;

paddle/gserver/layers/CosSimLayer.cpp

@@ -42,7 +42,7 @@ void CosSimLayer::forward(PassType passType) {
   /* malloc memory for the output_ if necessary */
   int batchSize = getInputValue(0)->getHeight();
   int size = getSize();
-  CHECK_EQ(forward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed";
 
   {
     REGISTER_TIMER_INFO("CosFwResetTimer", getName().c_str());
@@ -68,7 +68,7 @@ void CosSimLayer::forward(PassType passType) {
 void CosSimLayer::backward(const UpdateCallback& callback) {
   /* activation */ {
     REGISTER_TIMER_INFO("CosBpAtvTimer", getName().c_str());
-    CHECK_EQ(backward_.size(), 1) << "Only one backward function needed";
+    CHECK_EQ(backward_.size(), 1UL) << "Only one backward function needed";
 
     const auto outG = this->getOutputGrad();
     const auto outV = this->getOutputValue();

paddle/gserver/layers/CosSimVecMatLayer.cpp

@@ -112,7 +112,7 @@ bool CosSimVecMatLayer::init(const LayerMap& layerMap,
 
 void CosSimVecMatLayer::forward(PassType passType) {
   Layer::forward(passType);
-  CHECK_EQ(forward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed";
 
   MatrixPtr inV0 = getInputValue(0);
   MatrixPtr inV1 = getInputValue(1);
@@ -145,7 +145,7 @@ void CosSimVecMatLayer::forward(PassType passType) {
 }
 
 void CosSimVecMatLayer::backward(const UpdateCallback& callback) {
-  CHECK_EQ(backward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(backward_.size(), 1UL) << "Only one forward function needed";
 
   MatrixPtr inV0 = getInputValue(0);
   MatrixPtr inV1 = getInputValue(1);

paddle/math/tests/test_RowBuffer.cpp

@@ -17,10 +17,10 @@ limitations under the License. */
 
 TEST(RowBuffer, testAutoGrow) {
   paddle::RowBuffer buf(128);
-  ASSERT_EQ(128, buf.getWidth());
+  ASSERT_EQ(128UL, buf.getWidth());
   ASSERT_TRUE(buf.isAutoGrowth());
   buf.resize(2);
-  ASSERT_EQ(2, buf.getRowCount());
+  ASSERT_EQ(2UL, buf.getRowCount());
   for (size_t i = 0; i < buf.getWidth() * 2; ++i) {
     buf.data()[i] = i;
   }
@@ -35,7 +35,7 @@ TEST(RowBuffer, testAutoGrow) {
     data[i] = i;
   }
 
-  ASSERT_EQ(3, buf.getRowCount());
+  ASSERT_EQ(3UL, buf.getRowCount());
   for (size_t i = 0; i < buf.getRowCount() - 1; ++i) {
     for (size_t j = 0; j < buf.getWidth(); ++j) {
       ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5);
@@ -51,7 +51,7 @@ TEST(RowBuffer, testWithMemBuf) {
       std::make_shared<paddle::CpuMemoryHandle>(128 * 2 * sizeof(real));
   paddle::RowBuffer buf(mem, 128);
   ASSERT_TRUE(!buf.isAutoGrowth());
-  ASSERT_EQ(2, buf.getRowCount());
+  ASSERT_EQ(2UL, buf.getRowCount());
   for (size_t i = 0; i < buf.getWidth() * 2; ++i) {
     buf.data()[i] = i;
   }

paddle/py_paddle/dataprovider_converter.py

@@ -23,7 +23,8 @@ __all__ = ['DataProviderConverter']
 class IScanner(object):
     def __init__(self, input_type, pos):
         self.input_type = input_type
-        assert isinstance(self.input_type, dp2.InputType)
+        if not isinstance(self.input_type, dp2.InputType):
+            raise ValueError("input type should be dataprovider2.InputType")
         self.pos = pos
 
     def scan(self, dat):
@@ -50,7 +51,6 @@ class DenseScanner(IScanner):
 
     def finish_scan(self, argument):
         assert isinstance(argument, swig_paddle.Arguments)
-        assert isinstance(self.input_type, dp2.InputType)
         if self.__mat__.dtype != numpy.float32:
             self.__mat__ = self.__mat__.astype(numpy.float32)
         m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False)
@@ -63,7 +63,6 @@ class SparseBinaryScanner(IScanner):
         self.__rows__ = [0]
         self.__cols__ = []
         self.__height__ = 0
-        self.__nnz__ = 0
         self.__value__ = []
 
     def scan(self, dat):
@@ -76,7 +75,6 @@ class SparseBinaryScanner(IScanner):
 
     def finish_scan(self, argument):
         assert isinstance(argument, swig_paddle.Arguments)
-        assert isinstance(self.input_type, dp2.InputType)
         m = swig_paddle.Matrix.createSparse(self.__height__,
                                             self.input_type.dim,
                                             len(self.__cols__),

paddle/py_paddle/util.py

@@ -208,7 +208,7 @@ def __monkeypatch_gradient_machine__():
 
         output = dict()
         for name in layerNames:
-            output[name] = __matrix_to_numpy__(self.getLayerOutput(name))
+            output[name] = __arguments_to_numpy__(0, self.getLayerOutput(name))
         return output
 
     swig_paddle.GradientMachine.getLayerOutputs = getLayerOutputs

paddle/scripts/docker/Dockerfile

@@ -10,28 +10,30 @@ RUN apt-get update && \
     apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \
     apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \
     apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \
-    apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \
+    apt-get install -y automake && \
     apt-get clean -y
 
 RUN pip install --upgrade pip && \
-    pip install -U protobuf && \
+    pip install -U "protobuf==3.1.0" && \
     pip install -U wheel pillow BeautifulSoup && \
     pip install -U docopt PyYAML sphinx && \
     pip install -U sphinx_rtd_theme recommonmark jupyter
 
 RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \
-    cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \
+    cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \
     cd .. && rm -rf cmake-3.4.1
 
+ARG BUILD_WOBOQ
 ARG BUILD_AND_INSTALL
 ARG WITH_AVX
 ARG WITH_DOC
 ARG WITH_STYLE_CHECK
 
+ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF}
 ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF}
 ENV WITH_GPU=OFF
 ENV WITH_AVX=${WITH_AVX:-ON}
-ENV WITH_DOC=${WITH_DOC:-ON}
+ENV WITH_DOC=${WITH_DOC:-OFF}
 ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF}
 
 RUN mkdir /paddle

paddle/scripts/docker/Dockerfile.gpu

@@ -10,28 +10,30 @@ RUN apt-get update && \
     apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \
     apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \
     apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \
-    apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \
+    apt-get install -y automake && \
     apt-get clean -y
 
 RUN pip install --upgrade pip && \
-    pip install -U protobuf && \
+    pip install -U "protobuf==3.1.0" && \
     pip install -U wheel pillow BeautifulSoup && \
     pip install -U docopt PyYAML sphinx && \
     pip install -U sphinx_rtd_theme recommonmark jupyter
 
 RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \
-    cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \
+    cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \
     cd .. && rm -rf cmake-3.4.1
 
+ARG BUILD_WOBOQ
 ARG BUILD_AND_INSTALL
 ARG WITH_AVX
 ARG WITH_DOC
 ARG WITH_STYLE_CHECK
 
+ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF}
 ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF}
 ENV WITH_GPU=ON
 ENV WITH_AVX=${WITH_AVX:-ON}
-ENV WITH_DOC=${WITH_DOC:-ON}
+ENV WITH_DOC=${WITH_DOC:-OFF}
 ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF}
 
 RUN mkdir /paddle

paddle/scripts/docker/build.sh

@@ -11,7 +11,7 @@ set -e
 # If Dockerfile.* sets BUILD_AND_INSTALL to 'ON', it would have copied
 # source tree to /paddle, and this scripts should build it into
 # /paddle/build.
-if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then
+if [[ ${BUILD_AND_INSTALL:-OFF} == 'ON' ]]; then
     if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
 	ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so
     fi
@@ -19,7 +19,7 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then
     mkdir -p /paddle/build # -p means no error if exists
     cd /paddle/build
     cmake .. \
-	  -DWITH_DOC=ON \
+	  -DWITH_DOC=${WITH_DOC:-OFF} \
 	  -DWITH_GPU=${WITH_GPU:-OFF} \
 	  -DWITH_AVX=${WITH_AVX:-OFF} \
 	  -DWITH_SWIG_PY=ON \
@@ -29,28 +29,32 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then
     make -j `nproc`
     make install
 
-    # Install woboq_codebrowser.
-    git clone https://github.com/woboq/woboq_codebrowser /woboq
-    cd /woboq
-    cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \
-	  -DCMAKE_BUILD_TYPE=Release \
-	  .
-    make
-
-    export WOBOQ_OUT=/usr/share/nginx/html/paddle
-    export BUILD_DIR=/paddle/build
-    mkdir -p $WOBOQ_OUT
-    cp -rv /woboq/data $WOBOQ_OUT/../data
-    /woboq/generator/codebrowser_generator \
-	-b /paddle/build \
-	-a \
-	-o $WOBOQ_OUT \
-	-p paddle:/paddle
-    /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT
-    cd /woboq
-    make clean
-
-    pip install /usr/local/opt/paddle/share/wheels/*.whl
+    if [[ ${BUILD_WOBOQ:-OFF} == 'ON' ]]; then
+        apt-get install -y clang-3.8 llvm-3.8 libclang-3.8-dev 
+        # Install woboq_codebrowser.
+        git clone https://github.com/woboq/woboq_codebrowser /woboq
+        cd /woboq
+        cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \
+        -DCMAKE_BUILD_TYPE=Release \
+        .
+        make
+
+        export WOBOQ_OUT=/usr/share/nginx/html/paddle
+        export BUILD_DIR=/paddle/build
+        mkdir -p $WOBOQ_OUT
+        cp -rv /woboq/data $WOBOQ_OUT/../data
+        /woboq/generator/codebrowser_generator \
+        -b /paddle/build \
+        -a \
+        -o $WOBOQ_OUT \
+        -p paddle:/paddle
+        /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT
+        cd /woboq
+        make clean
+    fi
+
+    pip install /usr/local/opt/paddle/share/wheels/py_paddle*linux*.whl
+    pip install /usr/local/opt/paddle/share/wheels/paddle*.whl
     paddle version
 fi
 

python/CMakeLists.txt

@@ -25,6 +25,7 @@ add_custom_target(paddle_python ALL DEPENDS
 
 add_subdirectory(paddle/trainer_config_helpers/tests)
 add_subdirectory(paddle/reader/tests)
+add_subdirectory(paddle/v2/tests)
 
 install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/
     DESTINATION opt/paddle/share/wheels

python/paddle/reader/__init__.py

@@ -21,3 +21,5 @@
 #
 #     r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt"))
 from decorator import *
+
+import creator

python/paddle/reader/creator.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__all__ = ['np_array', 'text_file']
+
+
+def np_array(x):
+    """
+    Creates a reader that yields elements of x, if it is a
+    numpy vector. Or rows of x, if it is a numpy matrix.
+    Or any sub-hyperplane indexed by the highest dimension.
+
+    :param x: the numpy array to create reader from.
+    :returns: data reader created from x.
+    """
+
+    def reader():
+        if x.ndim < 1:
+            yield x
+
+        for e in x:
+            yield e
+
+    return reader
+
+
+def text_file(path):
+    """
+    Creates a data reader that outputs text line by line from given text file.
+    Trailing new line ('\n') of each line will be removed.
+
+    :path: path of the text file.
+    :returns: data reader of text file
+    """
+
+    def reader():
+        f = open(path, "r")
+        for l in f:
+            yield l.rstrip('\n')
+        f.close()
+
+    return reader

python/paddle/reader/tests/CMakeLists.txt

@@ -2,3 +2,8 @@ add_test(NAME reader_decorator_test
   COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
         ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py
     WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
+
+add_test(NAME reader_creator_test
+  COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/creator_test.py
+    WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)

python/paddle/reader/tests/creator_test.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+import paddle.reader.creator
+import numpy as np
+import os
+
+
+class TestNumpyArray(unittest.TestCase):
+    def test_numpy_array(self):
+        l = [[1, 2, 3], [4, 5, 6]]
+        x = np.array(l, np.int32)
+        reader = paddle.reader.creator.np_array(x)
+        for idx, e in enumerate(reader()):
+            self.assertItemsEqual(e, l[idx])
+
+
+class TestTextFile(unittest.TestCase):
+    def test_text_file(self):
+        path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt")
+        reader = paddle.reader.creator.text_file(path)
+        for idx, e in enumerate(reader()):
+            self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/reader/tests/test_data_creator.txt

+0 1
+2 3
+4 5

python/paddle/v2/__init__.py

@@ -18,12 +18,13 @@ import parameters
 import trainer
 import event
 import data_type
+import data_feeder
 import attr
 import py_paddle.swig_paddle as api
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
-    'event', 'data_type', 'attr'
+    'event', 'data_type', 'attr', 'data_feeder'
 ]
 
 

python/paddle/v2/data_feeder.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from py_paddle import swig_paddle
+from py_paddle import DataProviderConverter
+import data_type
+
+__all__ = ['DataFeeder']
+
+
+class DataFeeder(DataProviderConverter):
+    """
+    DataFeeder converts the data returned by paddle.reader into a data structure
+    of Arguments which is defined in the API. The paddle.reader usually returns
+    a list of mini-batch data entries. Each data entry in the list is one sampe.
+    Each sample is a list or a tuple with one feature or multiple features.
+    DataFeeder converts this mini-batch data entries into Arguments in order
+    to feed it to C++ interface.
+    
+    The example usage:
+    
+        data_types = [('image', paddle.data_type.dense_vector(784)),
+                      ('label', paddle.data_type.integer_value(10))]
+        reader_dict = {'image':0, 'label':1}
+        feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict)
+        minibatch_data = [
+                           ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ),  # first sample
+                           ( [1.0,2.0,3.0,4.0], 5, [6,7,8] )   # second sample
+                         ]
+        # or minibatch_data = [
+        #                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ],  # first sample
+        #                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample
+        #                     ]
+        arg = feeder(minibatch_data)
+    """
+
+    def __init__(self, data_types, reader_dict):
+        """
+        :param data_types: A list to specify data name and type. Each item is
+                           a tuple of (data_name, data_type). For example:
+                           [('image', paddle.data_type.dense_vector(784)),
+                            ('label', paddle.data_type.integer_value(10))]
+
+        :type data_types: A list of tuple
+        :param reader_dict: A dictionary to specify the position of each data
+                            in the input data.
+        :type reader_dict: dict()
+        """
+        self.input_names = []
+        input_types = []
+        self.reader_dict = reader_dict
+        for each in data_types:
+            self.input_names.append(each[0])
+            assert isinstance(each[1], data_type.InputType)
+            input_types.append(each[1])
+        DataProviderConverter.__init__(self, input_types)
+
+    def convert(self, dat, argument=None):
+        """
+        :param dat: A list of mini-batch data. Each sample is a list or tuple
+                    one feature or multiple features.
+                    for example:
+                    [ 
+                      ([0.2, 0.2], ), # first sample
+                      ([0.8, 0.3], ), # second sample
+                    ]
+                    or,
+                    [ 
+                      [[0.2, 0.2], ], # first sample
+                      [[0.8, 0.3], ], # second sample
+                    ]
+
+        :type dat: List
+        :param argument: An Arguments object contains this mini-batch data with
+                         one or multiple features. The Arguments definition is
+                         in the API.
+        :type argument: swig_paddle.Arguments
+        """
+
+        def reorder_data(data):
+            retv = []
+            for each in data:
+                reorder = []
+                for name in self.input_names:
+                    reorder.append(each[self.reader_dict[name]])
+                retv.append(reorder)
+            return retv
+
+        return DataProviderConverter.convert(self, reorder_data(dat), argument)

python/paddle/v2/data_set/mnist.py

-import sklearn.datasets.mldata
-import sklearn.model_selection
-import numpy
-from config import DATA_HOME
-
-__all__ = ['MNIST', 'train_creator', 'test_creator']
-
-
-def __mnist_reader_creator__(data, target):
-    def reader():
-        n_samples = data.shape[0]
-        for i in xrange(n_samples):
-            yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
-
-    return reader
-
-
-class MNIST(object):
-    """
-    mnist dataset reader. The `train_reader` and `test_reader` method returns
-    a iterator of each sample. Each sample is combined by 784-dim float and a
-    one-dim label
-    """
-
-    def __init__(self, random_state=0, test_size=10000, **options):
-        data = sklearn.datasets.mldata.fetch_mldata(
-            "MNIST original", data_home=DATA_HOME)
-        self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split(
-            data.data,
-            data.target,
-            test_size=test_size,
-            random_state=random_state,
-            **options)
-
-    def train_creator(self):
-        return __mnist_reader_creator__(self.X_train, self.y_train)
-
-    def test_creator(self):
-        return __mnist_reader_creator__(self.X_test, self.y_test)
-
-
-__default_instance__ = MNIST()
-train_creator = __default_instance__.train_creator
-test_creator = __default_instance__.test_creator
-
-
-def unittest():
-    size = 12045
-    mnist = MNIST(test_size=size)
-    assert len(list(mnist.test_creator()())) == size
-
-
-if __name__ == '__main__':
-    unittest()

python/paddle/v2/data_type.py

@@ -14,9 +14,9 @@
 
 from paddle.trainer.PyDataProvider2 import \
     InputType, dense_vector, sparse_binary_vector,\
-    sparse_vector, integer_value
+    sparse_vector, integer_value, integer_value_sequence
 
 __all__ = [
     'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector',
-    'integer_value'
+    'integer_value', 'integer_value_sequence'
 ]

python/paddle/v2/dataset/mnist.py

+import sklearn.datasets.mldata
+import sklearn.model_selection
+import numpy
+from config import DATA_HOME
+
+__all__ = ['train_creator', 'test_creator']
+
+
+def __mnist_reader_creator__(data, target):
+    def reader():
+        n_samples = data.shape[0]
+        for i in xrange(n_samples):
+            yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
+
+    return reader
+
+
+TEST_SIZE = 10000
+
+data = sklearn.datasets.mldata.fetch_mldata(
+    "MNIST original", data_home=DATA_HOME)
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    data.data, data.target, test_size=TEST_SIZE, random_state=0)
+
+
+def train_creator():
+    return __mnist_reader_creator__(X_train, y_train)
+
+
+def test_creator():
+    return __mnist_reader_creator__(X_test, y_test)
+
+
+def unittest():
+    assert len(list(test_creator()())) == TEST_SIZE
+
+
+if __name__ == '__main__':
+    unittest()

python/paddle/v2/event.py

@@ -3,8 +3,6 @@ All training events.
 
 There are:
 
-* BeginTraining
-* EndTraining
 * BeginIteration
 * EndIteration
 * BeginPass
@@ -12,15 +10,62 @@ There are:
 
 TODO(yuyang18): Complete it!
 """
-__all__ = ['EndIteration']
+import py_paddle.swig_paddle as api
+__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass']
 
 
-class EndIteration(object):
+class WithMetric(object):
+    def __init__(self, evaluator):
+        if not isinstance(evaluator, api.Evaluator):
+            raise TypeError("Evaluator should be api.Evaluator type")
+        self.__evaluator__ = evaluator
+
+    @property
+    def metrics(self):
+        names = self.__evaluator__.getNames()
+        retv = dict()
+        for each_name in names:
+            val = self.__evaluator__.getValue(each_name)
+            retv[each_name] = val
+        return retv
+
+
+class BeginPass(object):
+    """
+    Event On One Pass Training Start.
+    """
+
+    def __init__(self, pass_id):
+        self.pass_id = pass_id
+
+
+class EndPass(WithMetric):
+    """
+    Event On One Pass Training Complete.
+    """
+
+    def __init__(self, pass_id, evaluator):
+        self.pass_id = pass_id
+        WithMetric.__init__(self, evaluator)
+
+
+class BeginIteration(object):
+    """
+    Event On One Batch Training Start.
+    """
+
+    def __init__(self, pass_id, batch_id):
+        self.pass_id = pass_id
+        self.batch_id = batch_id
+
+
+class EndIteration(WithMetric):
     """
     Event On One Batch Training Complete.
     """
 
-    def __init__(self, pass_id, batch_id, cost):
+    def __init__(self, pass_id, batch_id, cost, evaluator):
         self.pass_id = pass_id
         self.batch_id = batch_id
         self.cost = cost
+        WithMetric.__init__(self, evaluator)

python/paddle/v2/optimizer.py

@@ -3,7 +3,10 @@ import paddle.trainer_config_helpers.optimizers as v1_optimizers
 import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
 import paddle.v2
 
-__all__ = ['Adam', 'Adamax']
+__all__ = [
+    'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
+    'RMSProp', 'ModelAverage', 'L2Regularization'
+]
 
 
 class Optimizer(object):
@@ -38,6 +41,14 @@ class Optimizer(object):
                                                              pass_num)
 
 
+class Momentum(Optimizer):
+    def __init__(self, momentum=None, sparse=False, **kwargs):
+        learning_method = v1_optimizers.MomentumOptimizer(
+            momentum=None, sparse=False)
+        super(Momentum, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
 class Adam(Optimizer):
     def __init__(self, beta1=0.9, beta2=0.999, epsilon=1e-8, **kwargs):
         learning_method = v1_optimizers.AdamOptimizer(
@@ -52,7 +63,45 @@ class Adamax(Optimizer):
         super(Adamax, self).__init__(learning_method=learning_method, **kwargs)
 
 
+class AdaGrad(Optimizer):
+    def __init__(self, **kwargs):
+        learning_method = v1_optimizers.AdaGradOptimizer()
+        super(AdaGrad, self).__init__(learning_method=learning_method, **kwargs)
+
+
+class DecayedAdaGrad(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-06, **kwargs):
+        learning_method = v1_optimizers.DecayedAdaGradOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(DecayedAdaGrad, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
+class AdaDelta(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-06, **kwargs):
+        learning_method = v1_optimizers.AdaDeltaOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(AdaDelta, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
+class RMSProp(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-6, **kwargs):
+        learning_method = v1_optimizers.RMSPropOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(RMSProp, self).__init__(learning_method=learning_method, **kwargs)
+
+
+ModelAverage = v1_optimizers.ModelAverage
+L2Regularization = v1_optimizers.L2Regularization
+
 if __name__ == '__main__':
     swig_api.initPaddle('--use_gpu=false')
-    opt = paddle.v2.optimizer.Adam()
-    print opt.enable_types()
+    for opt in [
+            Momentum(), Adam(), Adamax(), AdaGrad(), DecayedAdaGrad(),
+            AdaDelta(), RMSProp(), Adam(
+                model_average=ModelAverage(average_window=0.5),
+                regularization=L2Regularization(rate=0.5),
+                gradient_clipping_threshold=25)
+    ]:
+        print opt, opt.enable_types()

python/paddle/v2/tests/CMakeLists.txt

+add_test(NAME test_v2_layer
+        COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/test_layer.py
+        WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
+add_test(NAME test_v2_api
+    COMMAND bash ${PROJ_ROOT}/python/paddle/v2/tests/run_tests.sh ${PYTHON_EXECUTABLE})

python/paddle/v2/tests/run_tests.sh

+#!/bin/bash
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+pushd `dirname $0` > /dev/null
+SCRIPTPATH=$PWD
+popd > /dev/null
+
+cd $SCRIPTPATH
+
+$1 -m pip install ../../../../paddle/dist/*.whl
+
+test_list="test_data_feeder.py"
+
+export PYTHONPATH=$PWD/../../../../python/
+
+for fn in $test_list
+do
+  echo "test $fn"
+  $1 $fn
+  if [ $? -ne 0 ]; then
+    exit 1
+  fi
+done

python/paddle/v2/tests/test_data_feeder.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import py_paddle.swig_paddle as api
+import numpy as np
+
+from paddle.v2 import data_type
+from paddle.v2.data_feeder import DataFeeder
+
+
+class DataFeederTest(unittest.TestCase):
+    def dense_reader(self, size):
+        data = np.random.random(size)
+        return data
+
+    def sparse_binary_reader(self, high, size_limit, non_empty=False):
+        num = np.random.randint(size_limit)  # num could be 0
+        while non_empty and num == 0:
+            num = np.random.randint(size_limit)
+        return np.random.randint(high, size=num).tolist()
+
+    def test_dense(self):
+        def compare(input):
+            feeder = DataFeeder([('image', data_type.dense_vector(784))],
+                                {'image': 0})
+            arg = feeder(input)
+            output = arg.getSlotValue(0).copyToNumpyMat()
+            input = np.array(input, dtype='float32')
+            self.assertAlmostEqual(input.all(), output.all())
+
+        # test numpy array
+        batch_size = 32
+        dim = 784
+        data = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(self.dense_reader(dim))
+            data.append(each_sample)
+        compare(data)
+
+        # each feature is a list
+        data = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(self.dense_reader(dim).tolist())
+            data.append(each_sample)
+        compare(data)
+
+        # test tuple
+        data = []
+        for i in xrange(batch_size):
+            each_sample = (self.dense_reader(dim).tolist(), )
+            data.append(each_sample)
+        compare(data)
+
+    def test_sparse_binary(self):
+        dim = 10000
+        batch_size = 32
+        data = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(self.sparse_binary_reader(dim, 50))
+            data.append(each_sample)
+        feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))],
+                            {'input': 0})
+        arg = feeder(data)
+        output = arg.getSlotValue(0)
+        assert isinstance(output, api.Matrix)
+        for i in xrange(batch_size):
+            self.assertEqual(output.getSparseRowCols(i), data[i][0])
+
+    def test_sparse(self):
+        dim = 10000
+        batch_size = 32
+        v = []
+        w = []
+        data = []
+        for dat in xrange(batch_size):
+            each_sample = []
+            a = self.sparse_binary_reader(dim, 40, non_empty=True)
+            b = self.dense_reader(len(a)).tolist()
+            v.append(a)
+            w.append(np.array(b, dtype="float32"))
+            each_sample.append(zip(a, b))
+            data.append(each_sample)
+
+        feeder = DataFeeder([('input', data_type.sparse_vector(dim))],
+                            {'input': 0})
+        arg = feeder(data)
+        output = arg.getSlotValue(0)
+        assert isinstance(output, api.Matrix)
+        for i in xrange(batch_size):
+            self.assertEqual(output.getSparseRowCols(i), v[i])
+            cols_value = output.getSparseRowColsVal(i)
+            value = [val[1] for val in cols_value]
+            value = np.array(value, dtype="float32")
+            self.assertAlmostEqual(value.all(), w[i].all())
+
+    def test_integer(self):
+        dim = 100
+        batch_size = 32
+        index = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(np.random.randint(dim))
+            index.append(each_sample)
+        feeder = DataFeeder([('input', data_type.integer_value(dim))],
+                            {'input': 0})
+        arg = feeder(index)
+        output = arg.getSlotIds(0).copyToNumpyArray()
+        index = np.array(index, dtype='int')
+        self.assertEqual(output.all(), index.flatten().all())
+
+    def test_integer_sequence(self):
+        dim = 10000
+        batch_size = 32
+        start = [0]
+        data = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(
+                self.sparse_binary_reader(
+                    dim, 30, non_empty=True))
+            data.append(each_sample)
+            start.append(len(each_sample[0]) + start[-1])
+        feeder = DataFeeder([('input', data_type.integer_value_sequence(dim))],
+                            {'input': 0})
+        arg = feeder(data)
+        output_data = arg.getSlotIds(0).copyToNumpyArray()
+        output_start = arg.getSlotSequenceStartPositions(0).copyToNumpyArray()
+
+        index = []
+        for dat in data:
+            index.extend(x for x in dat[0])  # only one feature, so dat[0]
+        index = np.array(index, dtype='int')
+        start = np.array(start, dtype='int')
+        self.assertEqual(output_data.all(), index.all())
+        self.assertEqual(output_start.all(), start.all())
+
+    def test_multiple_features(self):
+        batch_size = 2
+        data = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(np.random.randint(10))
+            each_sample.append(
+                self.sparse_binary_reader(
+                    20000, 40, non_empty=True))
+            each_sample.append(self.dense_reader(100))
+            data.append(each_sample)
+
+        # test multiple features
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea1', data_type.sparse_binary_vector(20000)),
+                      ('fea2', data_type.integer_value(10))]
+        feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
+        arg = feeder(data)
+        output_dense = arg.getSlotValue(0).copyToNumpyMat()
+        output_sparse = arg.getSlotValue(1)
+        output_index = arg.getSlotIds(2).copyToNumpyArray()
+        for i in xrange(batch_size):
+            self.assertEqual(output_dense[i].all(), data[i][2].all())
+            self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1])
+            self.assertEqual(output_index[i], data[i][0])
+
+        # reader returns 3 features, but only use 2 features
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea2', data_type.integer_value(10))]
+        feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0})
+        arg = feeder(data)
+        output_dense = arg.getSlotValue(0).copyToNumpyMat()
+        output_index = arg.getSlotIds(1).copyToNumpyArray()
+        for i in xrange(batch_size):
+            self.assertEqual(output_dense[i].all(), data[i][2].all())
+            self.assertEqual(output_index[i], data[i][0])
+
+        # reader returns 3 featreus, one is duplicate data
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea1', data_type.sparse_binary_vector(20000)),
+                      ('fea2', data_type.integer_value(10)),
+                      ('fea3', data_type.dense_vector(100))]
+        feeder = DataFeeder(data_types,
+                            {'fea0': 2,
+                             'fea1': 1,
+                             'fea2': 0,
+                             'fea3': 2})
+        arg = feeder(data)
+        fea0 = arg.getSlotValue(0).copyToNumpyMat()
+        fea1 = arg.getSlotValue(1)
+        fea2 = arg.getSlotIds(2).copyToNumpyArray()
+        fea3 = arg.getSlotValue(3).copyToNumpyMat()
+        for i in xrange(batch_size):
+            self.assertEqual(fea0[i].all(), data[i][2].all())
+            self.assertEqual(fea1.getSparseRowCols(i), data[i][1])
+            self.assertEqual(fea2[i], data[i][0])
+            self.assertEqual(fea3[i].all(), data[i][2].all())
+
+    def test_multiple_features_tuple(self):
+        batch_size = 2
+        data = []
+        for i in xrange(batch_size):
+            a = np.random.randint(10)
+            b = self.sparse_binary_reader(20000, 40, non_empty=True)
+            c = self.dense_reader(100)
+            each_sample = (a, b, c)
+            data.append(each_sample)
+
+        # test multiple features
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea1', data_type.sparse_binary_vector(20000)),
+                      ('fea2', data_type.integer_value(10))]
+        feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
+        arg = feeder(data)
+        out_dense = arg.getSlotValue(0).copyToNumpyMat()
+        out_sparse = arg.getSlotValue(1)
+        out_index = arg.getSlotIds(2).copyToNumpyArray()
+        for i in xrange(batch_size):
+            self.assertEqual(out_dense[i].all(), data[i][2].all())
+            self.assertEqual(out_sparse.getSparseRowCols(i), data[i][1])
+            self.assertEqual(out_index[i], data[i][0])
+
+
+if __name__ == '__main__':
+    api.initPaddle("--use_gpu=0")
+    unittest.main()

python/paddle/v2/tests/test_layer.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import difflib
+import unittest
+
+import paddle.trainer_config_helpers as conf_helps
+import paddle.v2.activation as activation
+import paddle.v2.attr as attr
+import paddle.v2.data_type as data_type
+import paddle.v2.layer as layer
+from paddle.trainer_config_helpers.config_parser_utils import \
+    parse_network_config as parse_network
+
+pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
+label = layer.data(name='label', type=data_type.integer_value(10))
+weight = layer.data(name='weight', type=data_type.dense_vector(10))
+score = layer.data(name='score', type=data_type.dense_vector(1))
+hidden = layer.fc(input=pixel,
+                  size=100,
+                  act=activation.Sigmoid(),
+                  param_attr=attr.Param(name='hidden'))
+inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
+
+
+class CostLayerTest(unittest.TestCase):
+    def test_cost_layer(self):
+        cost1 = layer.classification_cost(input=inference, label=label)
+        cost2 = layer.classification_cost(
+            input=inference, label=label, weight=weight)
+        cost3 = layer.cross_entropy_cost(input=inference, label=label)
+        cost4 = layer.cross_entropy_with_selfnorm_cost(
+            input=inference, label=label)
+        cost5 = layer.regression_cost(input=inference, label=label)
+        cost6 = layer.regression_cost(
+            input=inference, label=label, weight=weight)
+        cost7 = layer.multi_binary_label_cross_entropy_cost(
+            input=inference, label=label)
+        cost8 = layer.rank_cost(left=score, right=score, label=score)
+        cost9 = layer.lambda_cost(input=inference, score=score)
+        cost10 = layer.sum_cost(input=inference)
+        cost11 = layer.huber_cost(input=score, label=label)
+
+        print dir(layer)
+        layer.parse_network(cost1, cost2)
+        print dir(layer)
+        #print layer.parse_network(cost3, cost4)
+        #print layer.parse_network(cost5, cost6)
+        #print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/trainer.py

@@ -2,7 +2,7 @@ import collections
 
 import py_paddle.swig_paddle as api
 from paddle.proto.ModelConfig_pb2 import ModelConfig
-from py_paddle import DataProviderConverter
+from data_feeder import DataFeeder
 
 from . import event as v2_event
 from . import layer as v2_layer
@@ -69,7 +69,8 @@ class SGD(ITrainer):
               test_data_reader=None,
               event_handler=None,
               batch_size=32,
-              data_types=None):
+              data_types=None,
+              reader_dict=None):
         """
         Training method. Will train num_passes of input data.
 
@@ -96,28 +97,34 @@ class SGD(ITrainer):
             topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types())
         assert isinstance(gm, api.GradientMachine)
         parameters.append_gradient_machine(gm)
-
+        gm.randParameters()
         updater = self.__optimizer__.create_local_updater()
         updater.init(gm)
 
         gm.start()
+        batch_evaluator = gm.makeEvaluator()
+        assert isinstance(batch_evaluator, api.Evaluator)
+        pass_evaluator = gm.makeEvaluator()
+        assert isinstance(pass_evaluator, api.Evaluator)
         out_args = api.Arguments.createArguments(0)
 
-        data_types_lists = []
-        for each in topology.input_layer_names:
-            if each not in data_types:
-                raise ValueError()
-            data_types_lists.append(data_types[each])
-
-        converter = DataProviderConverter(input_types=data_types_lists)
+        feeder = DataFeeder(data_types, reader_dict)
 
         for pass_id in xrange(num_passes):
+            event_handler(v2_event.BeginPass(pass_id))
+            pass_evaluator.start()
             updater.startPass()
             for batch_id, data_batch in enumerate(
                     __data_reader_to_batch__(train_data_reader, batch_size,
                                              topology)):
+                batch_evaluator.start()
+                event_handler(
+                    v2_event.BeginIteration(
+                        pass_id=pass_id, batch_id=batch_id))
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(converter(data_batch), out_args, pass_type)
+                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
+                gm.eval(pass_evaluator)
+                gm.eval(batch_evaluator)
                 for each_param in gm.getParameters():
                     updater.update(each_param)
                 # Get cost. We use numpy to calculate total cost for this batch.
@@ -125,11 +132,17 @@ class SGD(ITrainer):
                 cost_vec = cost_vec.copyToNumpyMat()
                 cost = cost_vec.sum() / len(data_batch)
                 updater.finishBatch(cost)
+                batch_evaluator.finish()
                 event_handler(
                     v2_event.EndIteration(
-                        pass_id=pass_id, batch_id=batch_id, cost=cost))
+                        pass_id=pass_id,
+                        batch_id=batch_id,
+                        cost=cost,
+                        evaluator=batch_evaluator))
 
             updater.finishPass()
+            pass_evaluator.finish()
+            event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
         gm.finish()