提交 22454ed4 编写于 作者: J jacquesqiao 提交者: GitHub

Merge pull request #971 from reyoung/feature/mnist_train_api

[Done] Feature/mnist train api
......@@ -4,3 +4,4 @@ mnist_vgg_model
plot.png
train.log
*pyc
.ipynb_checkpoints
"""
A very basic example for how to use current Raw SWIG API to train mnist network.
Current implementation uses Raw SWIG, which means the API call is directly \
passed to C++ side of Paddle.
The user api could be simpler and carefully designed.
"""
import py_paddle.swig_paddle as api
from py_paddle import DataProviderConverter
import paddle.trainer.PyDataProvider2 as dp
import numpy as np
import random
from mnist_util import read_from_mnist
from paddle.trainer_config_helpers import *
def optimizer_config():
settings(
learning_rate=1e-4,
learning_method=AdamOptimizer(),
batch_size=1000,
model_average=ModelAverage(average_window=0.5),
regularization=L2Regularization(rate=0.5))
def network_config():
imgs = data_layer(name='pixel', size=784)
hidden1 = fc_layer(input=imgs, size=200)
hidden2 = fc_layer(input=hidden1, size=200)
inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation())
cost = classification_cost(
input=inference, label=data_layer(
name='label', size=10))
outputs(cost)
def init_parameter(network):
assert isinstance(network, api.GradientMachine)
for each_param in network.getParameters():
assert isinstance(each_param, api.Parameter)
array_size = len(each_param)
array = np.random.uniform(-1.0, 1.0, array_size).astype('float32')
each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(array)
def generator_to_batch(generator, batch_size):
ret_val = list()
for each_item in generator:
ret_val.append(each_item)
if len(ret_val) == batch_size:
yield ret_val
ret_val = list()
if len(ret_val) != 0:
yield ret_val
class BatchPool(object):
def __init__(self, generator, batch_size):
self.data = list(generator)
self.batch_size = batch_size
def __call__(self):
random.shuffle(self.data)
for offset in xrange(0, len(self.data), self.batch_size):
limit = min(offset + self.batch_size, len(self.data))
yield self.data[offset:limit]
def input_order_converter(generator):
for each_item in generator:
yield each_item['pixel'], each_item['label']
def main():
api.initPaddle("-use_gpu=false", "-trainer_count=4") # use 4 cpu cores
# get enable_types for each optimizer.
# enable_types = [value, gradient, momentum, etc]
# For each optimizer(SGD, Adam), GradientMachine should enable different
# buffers.
opt_config_proto = parse_optimizer_config(optimizer_config)
opt_config = api.OptimizationConfig.createFromProto(opt_config_proto)
_temp_optimizer_ = api.ParameterOptimizer.create(opt_config)
enable_types = _temp_optimizer_.getParameterTypes()
# Create Simple Gradient Machine.
model_config = parse_network_config(network_config)
m = api.GradientMachine.createFromConfigProto(
model_config, api.CREATE_MODE_NORMAL, enable_types)
# This type check is not useful. Only enable type hint in IDE.
# Such as PyCharm
assert isinstance(m, api.GradientMachine)
# Initialize Parameter by numpy.
init_parameter(network=m)
# Create Local Updater. Local means not run in cluster.
# For a cluster training, here we can change to createRemoteUpdater
# in future.
updater = api.ParameterUpdater.createLocalUpdater(opt_config)
assert isinstance(updater, api.ParameterUpdater)
# Initialize ParameterUpdater.
updater.init(m)
# DataProvider Converter is a utility convert Python Object to Paddle C++
# Input. The input format is as same as Paddle's DataProvider.
converter = DataProviderConverter(
input_types=[dp.dense_vector(784), dp.integer_value(10)])
train_file = './data/raw_data/train'
test_file = './data/raw_data/t10k'
# start gradient machine.
# the gradient machine must be started before invoke forward/backward.
# not just for training, but also for inference.
m.start()
# evaluator can print error rate, etc. It is a C++ class.
batch_evaluator = m.makeEvaluator()
test_evaluator = m.makeEvaluator()
# Get Train Data.
# TrainData will stored in a data pool. Currently implementation is not care
# about memory, speed. Just a very naive implementation.
train_data_generator = input_order_converter(read_from_mnist(train_file))
train_data = BatchPool(train_data_generator, 512)
# outArgs is Neural Network forward result. Here is not useful, just passed
# to gradient_machine.forward
outArgs = api.Arguments.createArguments(0)
for pass_id in xrange(2): # we train 2 passes.
updater.startPass()
for batch_id, data_batch in enumerate(train_data()):
# data_batch is input images.
# here, for online learning, we could get data_batch from network.
# Start update one batch.
pass_type = updater.startBatch(len(data_batch))
# Start BatchEvaluator.
# batch_evaluator can be used between start/finish.
batch_evaluator.start()
# forwardBackward is a shortcut for forward and backward.
# It is sometimes faster than invoke forward/backward separately,
# because in GradientMachine, it may be async.
m.forwardBackward(converter(data_batch), outArgs, pass_type)
for each_param in m.getParameters():
updater.update(each_param)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec = outArgs.getSlotValue(0)
cost_vec = cost_vec.copyToNumpyMat()
cost = cost_vec.sum() / len(data_batch)
# Make evaluator works.
m.eval(batch_evaluator)
# Print logs.
print 'Pass id', pass_id, 'Batch id', batch_id, 'with cost=', \
cost, batch_evaluator
batch_evaluator.finish()
# Finish batch.
# * will clear gradient.
# * ensure all values should be updated.
updater.finishBatch(cost)
# testing stage. use test data set to test current network.
updater.apply()
test_evaluator.start()
test_data_generator = input_order_converter(read_from_mnist(test_file))
for data_batch in generator_to_batch(test_data_generator, 512):
# in testing stage, only forward is needed.
m.forward(converter(data_batch), outArgs, api.PASS_TEST)
m.eval(test_evaluator)
# print error rate for test data set
print 'Pass', pass_id, ' test evaluator: ', test_evaluator
test_evaluator.finish()
updater.restore()
updater.catchUpWith()
params = m.getParameters()
for each_param in params:
assert isinstance(each_param, api.Parameter)
value = each_param.getBuf(api.PARAMETER_VALUE)
value = value.copyToNumpyArray()
# Here, we could save parameter to every where you want
print each_param.getName(), value
updater.finishPass()
m.finish()
if __name__ == '__main__':
main()
from paddle.trainer.PyDataProvider2 import *
import numpy
from mnist_util import read_from_mnist
# Define a py data provider
......@@ -8,27 +8,5 @@ import numpy
'label': integer_value(10)},
cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, filename): # settings is not used currently.
imgf = filename + "-images-idx3-ubyte"
labelf = filename + "-labels-idx1-ubyte"
f = open(imgf, "rb")
l = open(labelf, "rb")
f.read(16)
l.read(8)
# Define number of samples for train/test
if "train" in filename:
n = 60000
else:
n = 10000
images = numpy.fromfile(
f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
images = images / 255.0 * 2.0 - 1.0
labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
for i in xrange(n):
yield {"pixel": images[i, :], 'label': labels[i]}
f.close()
l.close()
for each in read_from_mnist(filename):
yield each
import numpy
__all__ = ['read_from_mnist']
def read_from_mnist(filename):
imgf = filename + "-images-idx3-ubyte"
labelf = filename + "-labels-idx1-ubyte"
f = open(imgf, "rb")
l = open(labelf, "rb")
f.read(16)
l.read(8)
# Define number of samples for train/test
if "train" in filename:
n = 60000
else:
n = 10000
images = numpy.fromfile(
f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
images = images / 255.0 * 2.0 - 1.0
labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
for i in xrange(n):
yield {"pixel": images[i, :], 'label': labels[i]}
f.close()
l.close()
set(API_SOURCES
Arguments.cpp
ConfigParser.cpp
Evaluator.cpp
GradientMachine.cpp
Matrix.cpp
Parameter.cpp
ParameterOptimizer.cpp
ParameterUpdater.cpp
SequenceGenerator.cpp
Trainer.cpp
Util.cpp
......@@ -63,6 +65,15 @@ install(DIRECTORY ${PROJ_ROOT}/paddle/dist/
add_custom_target(python_api_wheel ALL DEPENDS
${PROJ_ROOT}/paddle/dist/.timestamp)
add_dependencies(python_api_wheel python_swig_sources
paddle_parameter
paddle_math
paddle_utils
paddle_gserver
paddle_pserver
paddle_trainer
paddle_api
paddle_cuda)
if(WITH_TESTING)
add_subdirectory(test)
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include <sstream>
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
Evaluator::Evaluator() : m(new EvaluatorPrivate()) {}
Evaluator::~Evaluator() { delete m; }
void Evaluator::start() { m->rawPtr->start(); }
void Evaluator::finish() { m->rawPtr->finish(); }
std::string Evaluator::toString() {
std::ostringstream sout;
m->rawPtr->printStats(sout);
return sout.str();
}
......@@ -64,6 +64,10 @@ GradientMachine* GradientMachine::createByModelConfig(
return GradientMachine::createFromPaddleModelPtr(confPtr, mode, types);
}
void GradientMachine::start() { m->machine->start(); }
void GradientMachine::finish() { m->machine->finish(); }
void GradientMachine::onPassEnd() { m->machine->onPassEnd(); }
void GradientMachine::prefetch(const Arguments& inArgs) {
......@@ -166,3 +170,13 @@ SequenceGenerator* GradientMachine::asSequenceGenerator(
r->setBeamSize(beam_size);
return r;
}
Evaluator* GradientMachine::makeEvaluator() {
auto ev = new Evaluator();
ev->m->rawPtr = m->machine->makeEvaluator();
return ev;
}
void GradientMachine::eval(Evaluator* evaluator) {
m->machine->eval(evaluator->m->rawPtr);
}
......@@ -96,7 +96,9 @@ namespace std {
%rename(__getitem__) Vector::get;
%rename(__setitem__) Vector::set;
%rename(__len__) Vector::getSize;
%rename(__len__) Parameter::getSize;
%rename(__call__) ParameterTraverseCallback::apply;
%rename(__repr__) Evaluator::toString;
%apply (float* INPLACE_ARRAY2, int DIM1, int DIM2) {
(float* data, int dim1, int dim2)
......@@ -167,6 +169,7 @@ namespace std {
%newobject GradientMachine::asSequenceGenerator;
%newobject GradientMachine::getParameter;
%newobject GradientMachine::getLayerOutput;
%newobject GradientMachine::makeEvaluator;
%newobject TrainerConfig::createFromTrainerConfigFile;
%newobject TrainerConfig::getModelConfig;
%newobject TrainerConfig::getOptimizationConfig;
......@@ -174,6 +177,7 @@ namespace std {
%newobject Parameter::getConfig;
%newobject ParameterOptimizer::create;
%newobject ParameterOptimizer::needSpecialTraversal;
%newobject ParameterUpdater::createLocalUpdater;
%feature("director") UpdateCallback;
%feature("autodoc", 1); // To generate method stub, for code hint in ide
......
......@@ -515,6 +515,7 @@ private:
friend class TrainerConfig;
friend class ParameterOptimizer;
friend class ParameterUpdater;
friend class Trainer;
};
......@@ -545,6 +546,8 @@ public:
ParameterConfig* getConfig();
void setValueUpdated();
size_t getSize() const;
private:
static Parameter* createFromRawPtr(void* ptr);
static Parameter* createFromSharedPtr(void* ptr);
......@@ -553,6 +556,7 @@ private:
ParameterPrivate* m;
friend class UpdateCallbackWrapper;
friend class GradientMachine;
friend class ParameterUpdater;
};
struct ModelConfigPrivate;
......@@ -679,7 +683,7 @@ private:
};
class SequenceGenerator;
class Evaluator;
struct GradientMachinePrivate;
class GradientMachine {
private:
......@@ -710,6 +714,13 @@ public:
GradientMatchineCreateMode mode = CREATE_MODE_NORMAL,
const std::vector<int>& parameterTypes = defaultParamTypes);
/**
* @brief finish
*/
void finish();
void start();
/**
* Prefetch row ids of sparse parameter.
*/
......@@ -767,6 +778,10 @@ public:
size_t max_length = 100UL,
size_t beam_size = -1UL);
Evaluator* makeEvaluator();
void eval(Evaluator* evaluator);
private:
GradientMachinePrivate* m;
......@@ -778,6 +793,109 @@ private:
// Not to use c++ 11 init-list, so we use static var as function default arg.
static std::vector<int> defaultParamTypes;
friend class Trainer;
friend class ParameterUpdater;
};
struct ParameterUpdaterPrivate;
class ParameterUpdater {
private:
ParameterUpdater();
public:
static ParameterUpdater* createLocalUpdater(OptimizationConfig* config);
~ParameterUpdater();
/**
* @brief initialize Parameter Updater by GradientMachine.
* @param gm
*/
void init(const GradientMachine& gm);
/**
* @brief begin of a training/testing of one pass.
*/
void startPass();
/**
* @brief end of a traning/testing of one pass.
*/
void finishPass();
/**
* @brief begin of a training/testing of one batch.
* @param data batch's size
* @return PassType, mostly will be training.
*/
PassType startBatch(size_t batchSize);
/**
* @brief end of a traning/testing of one batch
* @param cost current batch cost.
*/
void finishBatch(float cost);
/**
* @brief update a parameter (by local optimizer or by cluster pserver)
* @param param
*/
void update(Parameter* param);
/**
* @brief restore the average parameter.
* @note It is only used in AverageOptimizer. Restore will get the current
* PARAMETER_VALUE back.
*/
void restore();
/**
* @brief apply. Store the average parameter.
* @note It is only used in AverageOptimizer. Apply will store the current
* PARAMETER_VALUE to buffer, calcaualte current Average Parameter, and save
* it to PARAMETER_VALUE.
*/
void apply();
/**
* @brief catchUpWith The Regularization will be delayed in many situations(
* pserver, local sparse). Catch Up means catch the regularization up, apply
* regularization to all params.
*/
void catchUpWith();
private:
ParameterUpdaterPrivate* m;
};
struct EvaluatorPrivate;
class Evaluator {
private:
Evaluator();
DISABLE_COPY(Evaluator);
public:
~Evaluator();
/**
* @brief begin an evaluate stage.
*/
void start();
/**
* @brief end an evaluate stage.
*/
void finish();
/**
* @brief toString will get a evaluate result.
*
* __repr__ method in python
*/
std::string toString();
private:
EvaluatorPrivate* m;
friend class GradientMachine;
};
struct TrainerPrivate;
......
......@@ -11,12 +11,14 @@ 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. */
#pragma once
#include <memory>
#include "PaddleAPI.h"
#include "paddle/gserver/evaluators/Evaluator.h"
#include "paddle/gserver/gradientmachines/GradientMachine.h"
#include "paddle/parameter/ParameterUpdaterBase.h"
#include "paddle/trainer/TrainerConfigHelper.h"
#pragma once
struct GradientMachinePrivate {
std::shared_ptr<paddle::GradientMachine> machine;
......@@ -65,3 +67,31 @@ struct ArgumentsPrivate {
return *(std::shared_ptr<T>*)(rawPtr);
}
};
struct ParameterUpdaterPrivate {
std::unique_ptr<paddle::ParameterUpdater> updater;
};
struct ParameterPrivate {
std::shared_ptr<paddle::Parameter> sharedPtr;
paddle::Parameter* rawPtr; // rawPtr only used in ParameterUpdater,
// in other situation sharedPtr should
// contains value.
ParameterPrivate() : sharedPtr(nullptr), rawPtr(nullptr) {}
paddle::Parameter* getPtr() {
if (sharedPtr) {
return sharedPtr.get();
} else {
return rawPtr;
}
}
};
struct EvaluatorPrivate {
paddle::Evaluator* rawPtr;
EvaluatorPrivate() : rawPtr(nullptr) {}
~EvaluatorPrivate() { delete rawPtr; }
};
......@@ -14,21 +14,7 @@ limitations under the License. */
#include "paddle/parameter/Parameter.h"
#include "PaddleAPI.h"
struct ParameterPrivate {
std::shared_ptr<paddle::Parameter> sharedPtr;
paddle::Parameter* rawPtr;
ParameterPrivate() : sharedPtr(nullptr), rawPtr(nullptr) {}
paddle::Parameter* getPtr() {
if (sharedPtr) {
return sharedPtr.get();
} else {
return rawPtr;
}
}
};
#include "PaddleAPIPrivate.h"
Parameter::Parameter() : m(new ParameterPrivate()) {}
......@@ -70,3 +56,5 @@ ParameterConfig* Parameter::getConfig() {
size_t Parameter::getID() const { return m->getPtr()->getID(); }
void Parameter::setValueUpdated() { m->getPtr()->setValueUpdated(); }
size_t Parameter::getSize() const { return m->getPtr()->getSize(); }
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
#include "paddle/trainer/ThreadParameterUpdater.h"
ParameterUpdater::ParameterUpdater() : m(new ParameterUpdaterPrivate()) {}
ParameterUpdater *ParameterUpdater::createLocalUpdater(
OptimizationConfig *config) {
auto param = new ParameterUpdater();
param->m->updater.reset(new paddle::SgdThreadUpdater(config->m->getConfig()));
return param;
}
ParameterUpdater::~ParameterUpdater() { delete m; }
void ParameterUpdater::init(const GradientMachine &gm) {
m->updater->init(gm.m->machine->getNonStaticParameters());
}
void ParameterUpdater::startPass() { m->updater->startPass(); }
void ParameterUpdater::finishPass() { m->updater->finishPass(); }
PassType ParameterUpdater::startBatch(size_t batchSize) {
return m->updater->startBatch((int64_t)batchSize);
}
void ParameterUpdater::finishBatch(float cost) {
m->updater->finishBatch(cost);
}
void ParameterUpdater::update(Parameter *param) {
auto paddleParam = param->m->getPtr();
m->updater->update(paddleParam);
}
void ParameterUpdater::restore() { m->updater->restore(); }
void ParameterUpdater::apply() { m->updater->apply(); }
void ParameterUpdater::catchUpWith() { m->updater->catchUpWith(); }
......@@ -253,7 +253,7 @@ void Vector::copyToNumpyArray(float** view_m_data, int* dim1) {
*view_m_data = new float[*dim1];
if (auto cpuVec = dynamic_cast<paddle::CpuVector*>(m->vec.get())) {
std::memcpy(*view_m_data, cpuVec->getData(), sizeof(float) * (*dim1));
} else if (auto gpuVec = dynamic_cast<paddle::CpuVector*>(m->vec.get())) {
} else if (auto gpuVec = dynamic_cast<paddle::GpuVector*>(m->vec.get())) {
hl_memcpy_device2host(
*view_m_data, gpuVec->getData(), sizeof(float) * (*dim1));
} else {
......
......@@ -15,6 +15,7 @@
import paddle.trainer.PyDataProvider2 as dp2
import collections
import swig_paddle
import numpy
__all__ = ['DataProviderConverter']
......@@ -35,18 +36,18 @@ class IScanner(object):
class DenseScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__mat__ = []
self.__height__ = 0
self.__mat__ = None
def scan(self, dat):
self.__mat__.extend(dat)
self.__height__ += 1
if self.__mat__ is None:
self.__mat__ = numpy.array([dat], dtype='float32')
else:
self.__mat__ = numpy.append(self.__mat__, [dat], axis=0)
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
assert isinstance(self.input_type, dp2.InputType)
m = swig_paddle.Matrix.createDense(self.__mat__, self.__height__,
self.input_type.dim, False)
m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False)
argument.setSlotValue(self.pos, m)
......
......@@ -14,8 +14,6 @@ limitations under the License. */
#pragma once
namespace paddle {
/**
* Disable copy macro.
*/
......@@ -24,6 +22,8 @@ namespace paddle {
class_name(const class_name &other) = delete; \
class_name &operator=(const class_name &other) = delete
namespace paddle {
#ifdef PADDLE_TYPE_DOUBLE
using real = double;
#else
......
......@@ -3416,8 +3416,35 @@ def register_parse_config_hook(f):
_parse_config_hooks.add(f)
def parse_config(config_file, config_arg_str):
def update_g_config():
'''
Update g_config after execute config_file or config_functions.
'''
for k, v in settings.iteritems():
if v is None:
continue
g_config.opt_config.__setattr__(k, v)
for k, v in trainer_settings.iteritems():
if v is None:
continue
g_config.__setattr__(k, v)
for name in g_config.model_config.input_layer_names:
assert name in g_layer_map, \
'input name "%s" does not correspond to a layer name' % name
assert (g_layer_map[name].type == "data" or g_layer_map[name].type == "data_trim"), \
'The type of input layer "%s" is not "data"' % name
for name in g_config.model_config.output_layer_names:
assert name in g_layer_map, \
'input name "%s" does not correspond to a layer name' % name
return g_config
def parse_config(trainer_config, config_arg_str):
'''
@param trainer_config: can be a string of config file name or a function name
with config logic
@param config_arg_str: a string of the form var1=val1,var2=val2. It will be
passed to config script as a dictionary CONFIG_ARGS
'''
......@@ -3451,45 +3478,20 @@ def parse_config(config_file, config_arg_str):
g_root_submodel.is_recurrent_layer_group = False
g_current_submodel = g_root_submodel
# for paddle on spark, need support non-file config.
# you can use parse_config like below:
#
# from paddle.trainer.config_parser import parse_config
# def configs():
# #your paddle config code, which is same as config file.
#
# config = parse_config(configs, "is_predict=1")
# # then you get config proto object.
if hasattr(config_file, '__call__'):
config_file.func_globals.update(
if hasattr(trainer_config, '__call__'):
trainer_config.func_globals.update(
make_config_environment("", config_args))
config_file()
trainer_config()
else:
execfile(config_file, make_config_environment(config_file, config_args))
for k, v in settings.iteritems():
if v is None:
continue
g_config.opt_config.__setattr__(k, v)
for k, v in trainer_settings.iteritems():
if v is None:
continue
g_config.__setattr__(k, v)
execfile(trainer_config,
make_config_environment(trainer_config, config_args))
for name in g_config.model_config.input_layer_names:
assert name in g_layer_map, \
'input name "%s" does not correspond to a layer name' % name
assert (g_layer_map[name].type == "data" or g_layer_map[name].type == "data_trim"), \
'The type of input layer "%s" is not "data"' % name
for name in g_config.model_config.output_layer_names:
assert name in g_layer_map, \
'input name "%s" does not correspond to a layer name' % name
return g_config
return update_g_config()
def parse_config_and_serialize(config_file, config_arg_str):
def parse_config_and_serialize(trainer_config, config_arg_str):
try:
config = parse_config(config_file, config_arg_str)
config = parse_config(trainer_config, config_arg_str)
#logger.info(config)
return config.SerializeToString()
except:
......
......@@ -20,4 +20,6 @@ from layers import *
from networks import *
from optimizers import *
from attrs import *
from config_parser_utils import *
# This will enable operator overload for LayerOutput
import layer_math
# 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 paddle.trainer.config_parser as config_parser
'''
This file is a wrapper of formal config_parser. The main idea of this file is to
separete different config logic into different function, such as network configuration
and optimizer configuration.
'''
__all__ = [
"parse_trainer_config", "parse_network_config", "parse_optimizer_config"
]
def parse_trainer_config(trainer_conf, config_arg_str):
return config_parser.parse_config(trainer_conf, config_arg_str)
def parse_network_config(network_conf):
config = config_parser.parse_config(network_conf, '')
return config.model_config
def parse_optimizer_config(optimizer_conf):
config = config_parser.parse_config(optimizer_conf, '')
return config.opt_config
# 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 paddle.trainer.config_parser as config_parser
'''
This file is a wrapper of formal config_parser. The main idea of this file is to
separete different config logic into different function, such as network configuration
and optimizer configuration.
'''
__all__ = [
"parse_trainer_config", "parse_network_config", "parse_optimizer_config"
]
def parse_trainer_config(trainer_conf, config_arg_str):
return config_parser.parse_config(trainer_conf, config_arg_str)
def parse_network_config(network_conf, config_arg_str=''):
config = config_parser.parse_config(network_conf, config_arg_str)
return config.model_config
def parse_optimizer_config(optimizer_conf, config_arg_str=''):
config = config_parser.parse_config(optimizer_conf, config_arg_str)
return config.opt_config
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