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Add cpp trainer lib and demo (#10681)
add cpp trainer lib and demo
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‎paddle/fluid/train/demo/CMakeLists.txt

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cmake_minimum_required(VERSION 3.0)
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project(cpp_train_demo CXX C)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
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if(NOT DEFINED PADDLE_LIB)
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message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/paddle/lib/dir")
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endif()
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option(WITH_MKLDNN "Compile PaddlePaddle with MKLDNN" OFF)
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option(WITH_MKL "Compile PaddlePaddle with MKL support, default use openblas." OFF)
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include_directories("${PADDLE_LIB}")
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include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
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include_directories("${PADDLE_LIB}/third_party/install/glog/include")
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include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
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include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
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include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
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include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
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include_directories("${PADDLE_LIB}/third_party/boost")
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include_directories("${PADDLE_LIB}/third_party/eigen3")
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link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
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link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
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link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
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link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
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link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
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link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
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add_executable(demo_trainer demo_trainer.cc)
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if(WITH_MKLDNN)
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include_directories("${PADDLE_LIB}/third_party/install/mkldnn/include")
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set(MKLDNN_LIB ${PADDLE_LIB}/third_party/install/mkldnn/lib/libmkldnn.so.0)
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endif()
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if(WITH_MKL)
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include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
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set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel.so)
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else()
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if(APPLE)
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set(MATH_LIB cblas)
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else(APPLE)
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set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.a)
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endif(APPLE)
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endif()
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if(APPLE)
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set(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load -framework CoreFoundation -framework Security")
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else(APPLE)
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set(ARCHIVE_START "-Wl,--whole-archive")
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set(ARCHIVE_END "-Wl,--no-whole-archive")
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set(EXTERNAL_LIB "-lrt -ldl -lpthread")
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endif(APPLE)
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target_link_libraries(demo_trainer
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${MACOS_LD_FLAGS}
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${ARCHIVE_START}
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${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.a
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${ARCHIVE_END}
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${MATH_LIB}
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${MKLDNN_LIB}
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glog gflags protobuf snappystream snappy z
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${EXTERNAL_LIB})

‎paddle/fluid/train/demo/README.md

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### step 1. build paddle lib
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```
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# WITH_MKL=ON|OFF
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# WITH_MKLDNN=ON|OFF
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PADDLE_LIB=/paddle/lib/dir
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cmake .. -DCMAKE_INSTALL_PREFIX=$PADDLE_LIB \
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-DCMAKE_BUILD_TYPE=Release \
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-DWITH_FLUID_ONLY=ON \
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-DWITH_GPU=OFF \
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-DWITH_STYLE_CHECK=OFF \
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-DWITH_MKL=OFF \
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-DWITH_MKLDNN=OFF
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make -j8
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make -j8 inference_lib_dist
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```
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### step 2. generate program desc
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```
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# please install paddle before run this scripe
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pip install --upgrade paddlepaddle-*.whl
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python demo_network.py
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```
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This will generate two program desc files:
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- startup_program: used to init all parameters
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- main_program: main logic of the network
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### step 3. build demo_trainer and run it.
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```
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# Make a build dir at the same dir of this README.md document.
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# The demo dir can be put anywhere.
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mkdir build
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cd build
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# WITH_MKL=ON|OFF
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# WITH_MKLDNN=ON|OFF
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PADDLE_LIB=/paddle/lib/dir
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# PADDLE_LIB is the same with CMAKE_INSTALL_PREFIX when building the lib
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cmake .. -DPADDLE_LIB=$PADDLE_LIB \
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-DWITH_MKLDNN=OFF \
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-DWITH_MKL=OFF
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make
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# copy startup_program and main_program to this dir
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cp ../startup_program .
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cp ../main_program .
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# run demo cpp trainer
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./demo_trainer
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```
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The output will be:
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```
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step: 0 loss: 1069.02
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step: 1 loss: 1069.02
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step: 2 loss: 1069.02
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....
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```

‎paddle/fluid/train/demo/demo_network.py

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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle.fluid as fluid
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import paddle.fluid.framework as framework
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def train_network(with_optimize):
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(cost)
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if with_optimize:
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.00001)
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sgd_optimizer.minimize(avg_cost)
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else:
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fluid.backward.append_backward(avg_cost)
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def save_program_desc(network_func):
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startup_program = framework.Program()
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train_program = framework.Program()
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with framework.program_guard(train_program, startup_program):
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network_func(with_optimize=False)
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with open("startup_program", "w") as f:
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f.write(startup_program.desc.serialize_to_string())
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with open("main_program", "w") as f:
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f.write(train_program.desc.serialize_to_string())
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save_program_desc(train_network)

‎paddle/fluid/train/demo/demo_trainer.cc

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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <fstream>
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#include "paddle/fluid/framework/executor.h"
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#include "paddle/fluid/framework/init.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/program_desc.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/platform/device_context.h"
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#include "paddle/fluid/platform/place.h"
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namespace paddle {
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namespace train {
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void ReadBinaryFile(const std::string& filename, std::string* contents) {
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std::ifstream fin(filename, std::ios::in | std::ios::binary);
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PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
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fin.seekg(0, std::ios::end);
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contents->clear();
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contents->resize(fin.tellg());
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fin.seekg(0, std::ios::beg);
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fin.read(&(contents->at(0)), contents->size());
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fin.close();
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}
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std::unique_ptr<paddle::framework::ProgramDesc> Load(
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paddle::framework::Executor* executor, const std::string& model_filename) {
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VLOG(3) << "loading model from " << model_filename;
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std::string program_desc_str;
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ReadBinaryFile(model_filename, &program_desc_str);
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std::unique_ptr<paddle::framework::ProgramDesc> main_program(
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new paddle::framework::ProgramDesc(program_desc_str));
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return main_program;
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}
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} // namespace train
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} // namespace paddle
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int main() {
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paddle::framework::InitDevices(false);
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const auto cpu_place = paddle::platform::CPUPlace();
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paddle::framework::Executor executor(cpu_place);
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paddle::framework::Scope scope;
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auto startup_program = paddle::train::Load(&executor, "startup_program");
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auto train_program = paddle::train::Load(&executor, "main_program");
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std::string loss_name = "";
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for (auto op_desc : train_program->Block(0).AllOps()) {
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if (op_desc->Type() == "mean") {
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loss_name = op_desc->Output("Out")[0];
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break;
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}
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}
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PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
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// init all parameters
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executor.Run(*startup_program.get(), &scope, 0);
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// prepare data
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auto x_var = scope.Var("x");
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auto x_tensor = x_var->GetMutable<paddle::framework::LoDTensor>();
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x_tensor->Resize({2, 13});
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auto x_data = x_tensor->mutable_data<float>(cpu_place);
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for (int i = 0; i < 2 * 13; ++i) {
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x_data[i] = static_cast<float>(i);
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}
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auto y_var = scope.Var("y");
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auto y_tensor = y_var->GetMutable<paddle::framework::LoDTensor>();
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y_tensor->Resize({2, 1});
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auto y_data = y_tensor->mutable_data<float>(cpu_place);
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for (int i = 0; i < 2 * 1; ++i) {
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y_data[i] = static_cast<float>(i);
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}
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auto loss_var = scope.Var(loss_name);
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for (int i = 0; i < 10; ++i) {
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executor.Run(*train_program.get(), &scope, 0, false, true);
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std::cout << "step: " << i << " loss: "
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<< loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]
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<< std::endl;
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}
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return 0;
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}

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