提交 049df036 编写于 作者: 王益

Resovle conflict with upstream/develop

......@@ -16,7 +16,8 @@
set(CBLAS_FOUND OFF)
## Find MKL First.
set(MKL_ROOT $ENV{MKLROOT} CACHE PATH "Folder contains MKL")
set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
find_path(MKL_INCLUDE_DIR mkl.h PATHS
${MKL_ROOT}/include)
......
from paddle.trainer_config_helpers import *
from paddle.trainer.PyDataProvider2 import dense_vector, integer_value
import paddle.v2 as paddle
import numpy
import mnist_util
def train_reader():
train_file = './data/raw_data/train'
generator = mnist_util.read_from_mnist(train_file)
for item in generator:
yield item
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 main():
paddle.init(use_gpu=False, trainer_count=1)
topology = parse_network_config(network_config)
parameters = paddle.parameters.create(topology)
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.Optimizer(
learning_rate=0.01, learning_method=AdamOptimizer())
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
para = parameters.get('___fc_layer_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())
else:
pass
trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
trainer.train(train_data_reader=train_reader,
topology=topology,
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
# network topology
'pixel': dense_vector(784),
'label': integer_value(10)
})
if __name__ == '__main__':
main()
......@@ -11,9 +11,21 @@
# 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 optimizer
import layer
import activation
import parameters
import trainer
import event
import py_paddle.swig_paddle as api
__all__ = ['optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event']
def init(**kwargs):
args = []
for key in kwargs.keys():
args.append('--%s=%s' % (key, str(kwargs[key])))
__all__ = ['optimizer', 'layer', 'activation']
api.initPaddle(*args)
"""
All training events.
There are:
* BeginTraining
* EndTraining
* BeginIteration
* EndIteration
* BeginPass
* EndPass
TODO(yuyang18): Complete it!
"""
__all__ = ['EndIteration']
class EndIteration(object):
"""
Event On One Batch Training Complete.
"""
def __init__(self, pass_id, batch_id, cost):
self.pass_id = pass_id
self.batch_id = batch_id
self.cost = cost
import numpy as np
from paddle.proto.ModelConfig_pb2 import ModelConfig
from paddle.proto.ParameterConfig_pb2 import ParameterConfig
import py_paddle.swig_paddle as api
__all__ = ['Parameters', 'create']
def create(*topologies):
"""
Create parameter pool by topologies.
:param topologies:
:return:
"""
pool = Parameters()
for topo in topologies:
if not isinstance(topo, ModelConfig):
raise ValueError(
'create must pass a topologies which type is ModelConfig')
for param in topo.parameters:
pool.__append_config__(param)
return pool
class Parameters(object):
"""
Parameters is a dictionary contains Paddle's parameter. The key of
Parameters is the name of parameter. The value of Parameters is a plain
:code:`numpy.ndarry` .
Basically usage is
.. code-block:: python
data = paddle.layers.data(...)
...
out = paddle.layers.fc(...)
parameters = paddle.parameters.create(out)
parameter_names = parameters.names()
fc_mat = parameters.get('fc')
print fc_mat
"""
def __init__(self):
self.__param_conf__ = dict()
self.__gradient_machines__ = []
self.__tmp_params__ = []
def __append_config__(self, param_conf):
"""
Append a parameter configuration. It used to initialize Parameters and
should be invoked only in paddle.parameters.create
:param param_conf: The parameter configuration in protobuf
:type param_conf: ParameterConfig
:return: Nothing
"""
if not isinstance(param_conf, ParameterConfig):
raise ValueError("param_conf must be paddle.proto.ParameterConfig")
if param_conf.name in self.__param_conf__:
raise ValueError("duplicated parameter %s" % param_conf.name)
self.__param_conf__[param_conf.name] = param_conf
def keys(self):
"""
keys are the names of each parameter.
:return: list of parameter name
:rtype: list
"""
return self.__param_conf__.keys()
def names(self):
"""
names of each parameter.
:return: list of parameter name
:rtype: list
"""
return self.keys()
def has_key(self, key):
"""
has_key return true if there are such parameter name == key
:param key: Parameter name
:type key: basestring
:return: True if contains such key
"""
return key in self.__param_conf__.keys()
def __iter__(self):
"""
Return an iterator of parameter name. It is used by `for loop`
or `in` operator.
.. code-block:: python
parameters = paddle.parameters.create(...)
if "fc_param" in parameters:
print 'OK'
:return: an iterator of parameter name
:rtype: iterator
"""
return iter(self.__param_conf__)
def __getitem__(self, key):
"""
Get parameter by parameter name. It uses Python dict syntax.
:note: It will always copy the parameter from C++ side.
:param key: Parameter name
:type key: basestring
:return: parameter value
:rtype: np.ndarray
"""
shape = self.get_shape(key)
if len(self.__gradient_machines__) == 0:
# create new parameter in python numpy.
return np.ndarray(shape=shape, dtype=np.float32)
else:
for each_gradient_machine in self.__gradient_machines__:
param = __get_parameter_in_gradient_machine__(
each_gradient_machine, key)
# for simplify implementation now, we always copy from C++
assert isinstance(param, api.Parameter)
val = param.getBuf(api.PARAMETER_VALUE)
assert isinstance(val, api.Vector)
val = val.copyToNumpyArray()
return val
# else continue
raise RuntimeError("Unexpected branch")
def get_shape(self, key):
"""
get shape of the parameter.
:param key: parameter name
:type key: basestring
:return: parameter's shape
:rtype: tuple
"""
if not isinstance(key, basestring):
raise ValueError("parameter name should be string")
if not self.has_key(key):
raise ValueError("No such parameter %s" % key)
conf = self.__param_conf__[key]
return tuple(map(int, conf.dims))
def __setitem__(self, key, value):
"""
Set parameter by parameter name & value. It use Python dict syntax.
:note: It will always copy the parameter to C++ side.
:param key: Parameter name
:type key: basestring
:param value: Parameter matrix.
:type value: np.ndarray
:return: Nothing
"""
if not isinstance(value, np.ndarray):
raise ValueError("Must return ndarray")
value = value.astype(dtype=np.float32)
shape = self.get_shape(key)
if value.shape != shape:
raise ValueError("Value shape mismatch, expect %s, should %s" %
(shape, value.shape))
if len(self.__gradient_machines__) == 0:
self.__tmp_params__.append((key, value))
else:
for each_gradient_machine in self.__gradient_machines__:
__copy_parameter_to_gradient_machine__(each_gradient_machine,
key, value)
def get(self, parameter_name):
"""
Get parameter by parameter name.
:note: It will always copy the parameter from C++ side.
:param parameter_name: parameter name
:type parameter_name: basestring
:return: The parameter matrix.
:rtype: np.ndarray
"""
return self.__getitem__(key=parameter_name)
def set(self, parameter_name, value):
"""
Set parameter by parameter name & matrix.
:param parameter_name: parameter name
:type parameter_name: basestring
:param value: parameter matrix
:type value: np.ndarray
:return: Nothing.
"""
self.__setitem__(key=parameter_name, value=value)
def append_gradient_machine(self, gradient_machine):
"""
append gradient machine to parameters. This method is used internally in
Trainer.train.
:param gradient_machine: Paddle C++ GradientMachine object.
:type gradient_machine: api.GradientMachine
:return:
"""
if not isinstance(gradient_machine, api.GradientMachine):
raise ValueError("gradient_machine should be api.GradientMachine")
if len(self.__tmp_params__) != 0:
for name, val in self.__tmp_params__:
try:
__copy_parameter_to_gradient_machine__(gradient_machine,
name, val)
except ValueError:
# If no such parameter in gradient machine, then don't copy
pass
self.__gradient_machines__.append(gradient_machine)
def __get_parameter_in_gradient_machine__(gradient_machine, name):
"""
:param gradient_machine:
:type gradient_machine: api.GradientMachine
:param name:
:return:
:rtype: api.Parameter
"""
params = filter(lambda p: p.getName() == name,
gradient_machine.getParameters())
if len(params) == 0:
raise ValueError("No such parameter")
elif len(params) > 1:
raise ValueError("Unexpected branch")
else:
return params[0]
def __copy_parameter_to_gradient_machine__(gradient_machine, name, arr):
"""
Copy a python ndarray into the gradient machine.
:param gradient_machine:
:type gradient_machine: api.GradientMachine
:param name:
:param arr:
:type arr: np.ndarray
:return:
:rtype: api.Parameter
"""
param = __get_parameter_in_gradient_machine__(gradient_machine, name)
vec = param.getBuf(api.PARAMETER_VALUE)
assert isinstance(vec, api.Vector)
vec.copyFromNumpyArray(arr.flatten())
import collections
import py_paddle.swig_paddle as api
from py_paddle import DataProviderConverter
from paddle.proto.ModelConfig_pb2 import ModelConfig
from . import optimizer as v2_optimizer
from . import parameters as v2_parameters
from . import event as v2_event
__all__ = ['ITrainer', 'SGD']
def default_event_handler(event):
"""
Default event handler. It will print some log and save mode.
TODO(yuyang18): Complete it!
:param event:
:return:
"""
pass
class ITrainer(object):
"""
The interface of Trainer. The only exposed method is `train`.
"""
def train(self,
train_data_reader,
topology,
parameters,
test_data_reader=None,
event_handler=None):
"""
train method.
:param train_data_reader:
:param topology:
:param parameters:
:param test_data_reader:
:param event_handler:
:return:
"""
raise NotImplementedError()
class SGD(ITrainer):
def __init__(self, update_equation):
"""
Simple SGD Trainer.
:param update_equation: The optimizer object.
:type update_equation: v2_optimizer.Optimizer
"""
if not isinstance(update_equation, v2_optimizer.Optimizer):
raise ValueError("update equation parameter must be "
"paddle.v2.optimizer.Optimizer")
self.__optimizer__ = update_equation
def train(self,
train_data_reader,
topology,
parameters,
num_passes=1,
test_data_reader=None,
event_handler=None,
batch_size=32,
data_types=None):
"""
Training method. Will train num_passes of input data.
:param train_data_reader:
:param topology: Network Topology, a protobuf ModelConfig message.
:param parameters: The parameter pools.
:param num_passes: The total train passes.
:param test_data_reader:
:param event_handler: Event handler. A method will be invoked when event
occurred.
:type event_handler: (BaseEvent) => None
:param batch_size: Not important, will be removed after data refactor.
:param data_types: Not important, will be removed after data refactor.
:return:
"""
if event_handler is None:
event_handler = default_event_handler
__check_train_args__(**locals())
gm = api.GradientMachine.createFromConfigProto(
topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types())
assert isinstance(gm, api.GradientMachine)
parameters.append_gradient_machine(gm)
updater = self.__optimizer__.create_local_updater()
updater.init(gm)
gm.start()
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)
for pass_id in xrange(num_passes):
updater.startPass()
for batch_id, data_batch in enumerate(
__data_reader_to_batch__(train_data_reader, batch_size,
topology)):
pass_type = updater.startBatch(len(data_batch))
gm.forwardBackward(converter(data_batch), out_args, pass_type)
for each_param in gm.getParameters():
updater.update(each_param)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec = out_args.getSlotValue(0)
cost_vec = cost_vec.copyToNumpyMat()
cost = cost_vec.sum() / len(data_batch)
updater.finishBatch(cost)
event_handler(
v2_event.EndIteration(
pass_id=pass_id, batch_id=batch_id, cost=cost))
updater.finishPass()
gm.finish()
def __data_reader_to_batch__(reader, batch_size, topology):
"""
This function is not important, and will be removed when data refactored.
"""
def input_reorder(func):
for item in func():
retv = []
for __layer_name__ in topology.input_layer_names:
retv.append(item[__layer_name__])
yield retv
return __generator_to_batch__(input_reorder(reader), batch_size=batch_size)
def __generator_to_batch__(generator, batch_size):
"""
This function is not important, and will be removed when data refactored.
"""
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
def __check_train_args__(train_data_reader, topology, parameters,
test_data_reader, event_handler, **kwargs):
"""
Check train function's argument types
"""
if not callable(train_data_reader) or not isinstance(train_data_reader(),
collections.Iterator):
raise ValueError('train_data_reader should be a function, '
'which can return a iterator')
if test_data_reader is not None:
if not callable(test_data_reader) or not isinstance(
test_data_reader(), collections.Iterator):
raise ValueError('test_data_reader should be a function, which can '
'return a iterator')
if not isinstance(topology, ModelConfig):
raise ValueError('topology should be a model config')
if not isinstance(parameters, v2_parameters.Parameters):
raise ValueError('parameters should be a parameter pool')
if not callable(event_handler):
raise ValueError('event handler should be a function')
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