提交 7cfe34da 编写于 作者: Q qiaolongfei

modify api_train_v2

上级 361dc27a
...@@ -26,7 +26,9 @@ def main(): ...@@ -26,7 +26,9 @@ def main():
act=paddle.activation.Softmax()) act=paddle.activation.Softmax())
cost = paddle.layer.classification_cost(input=inference, label=label) cost = paddle.layer.classification_cost(input=inference, label=label)
parameters = paddle.parameters.create(cost) topology = paddle.topology.Topology(cost)
parameters = paddle.parameters.create(topology)
for param_name in parameters.keys(): for param_name in parameters.keys():
array = parameters.get(param_name) array = parameters.get(param_name)
array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
...@@ -45,16 +47,12 @@ def main(): ...@@ -45,16 +47,12 @@ def main():
trainer = paddle.trainer.SGD(update_equation=adam_optimizer) trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
trainer.train(train_data_reader=train_reader, trainer.train(
topology=cost, train_data_reader=train_reader,
topology=topology,
parameters=parameters, parameters=parameters,
event_handler=event_handler, event_handler=event_handler,
batch_size=32, # batch size should be refactor in Data reader 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': images.type,
'label': label.type
})
if __name__ == '__main__': if __name__ == '__main__':
......
...@@ -66,12 +66,14 @@ Also, the creation of a protobuf message is hidden in the invocation of ...@@ -66,12 +66,14 @@ Also, the creation of a protobuf message is hidden in the invocation of
paddle.v2.parameters.create, no longer exposed to users. paddle.v2.parameters.create, no longer exposed to users.
""" """
import collections
import paddle.trainer_config_helpers as conf_helps import paddle.trainer_config_helpers as conf_helps
from . import data_type as v2_data
from paddle.trainer_config_helpers.config_parser_utils import \ from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as __parse__ parse_network_config as __parse__
from paddle.trainer_config_helpers.default_decorators import wrap_name_default from paddle.trainer_config_helpers.default_decorators import wrap_name_default
import collections
import data_type as v2_data
__all__ = [ __all__ = [
'parse_network', 'data', 'fc', 'max_id', 'classification_cost', 'parse_network', 'data', 'fc', 'max_id', 'classification_cost',
...@@ -184,6 +186,8 @@ class DataLayerV2(Layer): ...@@ -184,6 +186,8 @@ class DataLayerV2(Layer):
return getattr(conf_helps, self.__method_name__)(name=self.name, **args) return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
LayerV2 = Layer
data = DataLayerV2 data = DataLayerV2
fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input'])
max_id = __convert_to_v2__( max_id = __convert_to_v2__(
......
import numpy as np import numpy as np
from . import layer as v2_layer
import py_paddle.swig_paddle as api import py_paddle.swig_paddle as api
from paddle.proto.ParameterConfig_pb2 import ParameterConfig from paddle.proto.ParameterConfig_pb2 import ParameterConfig
import topology as v2_topology
__all__ = ['Parameters', 'create'] __all__ = ['Parameters', 'create']
def create(*layers): def create(topology):
""" """
Create parameter pool by layers. In paddle, layer can be represent a Create parameter pool by topology.
model config. :param topology:
:param layers:
:return: :return:
""" """
for layer in layers: if not isinstance(topology, v2_topology.Topology):
if not isinstance(layer, v2_layer.Layer):
raise ValueError( raise ValueError(
'create must pass a topologies which type is paddle.layer.Layer') 'create must pass a topology which type is topology.Topology')
model_config = v2_layer.parse_network(*layers)
pool = Parameters() pool = Parameters()
for param in model_config.parameters: for param in topology.proto().parameters:
pool.__append_config__(param) pool.__append_config__(param)
return pool return pool
......
...@@ -12,7 +12,10 @@ ...@@ -12,7 +12,10 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from . import layer from paddle.proto.ModelConfig_pb2 import ModelConfig
import paddle.trainer_config_helpers as conf_helps
import layer as v2_layer
import data_type
__all__ = ['Topology'] __all__ = ['Topology']
...@@ -23,22 +26,101 @@ class Topology(object): ...@@ -23,22 +26,101 @@ class Topology(object):
and network configs. and network configs.
""" """
def __init__(self, cost): def __init__(self, *layers):
self.cost = cost for layer in layers:
self.__model_config__ = layer.parse_network(cost) if not isinstance(layer, v2_layer.LayerV2):
raise ValueError('create must pass a topologies '
'which type is paddle.layer.Layer')
self.layers = layers
self.__model_config__ = v2_layer.parse_network(*layers)
assert isinstance(self.__model_config__, ModelConfig)
def __call__(self): def proto(self):
return self.__model_config__ return self.__model_config__
def get_layer(self, name): def get_layer(self, name):
"""
get v2.Layer Class instance by layer name
:param name:
:return:
"""
result_layer = []
def find_layer_by_name(layer, layer_name):
if layer.name == layer_name and len(result_layer) == 0:
result_layer.append(layer)
for parent_layer in layer.__parent_layers__.values():
find_layer_by_name(parent_layer, layer_name)
for layer in self.layers:
find_layer_by_name(layer, name)
return result_layer[0]
def get_data_layer(self):
"""
get all data layer
:return:
"""
data_layers = []
def find_data_layer(layer):
assert isinstance(layer, layer.LayerV2)
if isinstance(layer, v2_layer.DataLayerV2):
if len(
filter(lambda data_layer: data_layer.name == layer.name,
data_layers)) == 0:
data_layers.append(layer)
for parent_layer in layer.__parent_layers__.values():
find_data_layer(parent_layer)
for layer in self.layers:
find_data_layer(layer)
return data_layers
def get_layer_proto(self, name):
""" """
get layer by layer name get layer by layer name
:param name: :param name:
:return: :return:
""" """
pass layers = filter(lambda layer: layer.name == name,
self.__model_config__.layers)
if len(layers) is 1:
return layers[0]
else:
return None
def data_type(self): def data_type(self):
""" """
get data_type from proto, such as:
[('image', dense_vector(768)), ('label', integer_value(10))]
the order is the same with __model_config__.input_layer_names
""" """
pass data_types_lists = []
for layer_name in self.__model_config__.input_layer_names:
data_types_lists.append(
(layer_name, self.get_layer(layer_name).type))
return data_types_lists
if __name__ == '__main__':
pixel = v2_layer.data(name='pixel', type=data_type.dense_vector(784))
label = v2_layer.data(name='label', type=data_type.integer_value(10))
hidden = v2_layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = v2_layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
maxid = v2_layer.max_id(input=inference)
cost1 = v2_layer.classification_cost(input=inference, label=label)
cost2 = v2_layer.cross_entropy_cost(input=inference, label=label)
print Topology(cost1).proto()
print Topology(cost2).proto()
print Topology(cost1, cost2).proto()
print Topology(cost2).proto()
print Topology(inference, maxid).proto()
import collections import collections
import py_paddle.swig_paddle as api import py_paddle.swig_paddle as api
from paddle.proto.ModelConfig_pb2 import ModelConfig
from py_paddle import DataProviderConverter from py_paddle import DataProviderConverter
from . import event as v2_event from . import event as v2_event
from . import layer as v2_layer
from . import optimizer as v2_optimizer from . import optimizer as v2_optimizer
from . import parameters as v2_parameters from . import parameters as v2_parameters
from . import topology as v2_topology
__all__ = ['ITrainer', 'SGD'] __all__ = ['ITrainer', 'SGD']
...@@ -88,12 +87,11 @@ class SGD(ITrainer): ...@@ -88,12 +87,11 @@ class SGD(ITrainer):
if event_handler is None: if event_handler is None:
event_handler = default_event_handler event_handler = default_event_handler
topology = v2_layer.parse_network(topology)
__check_train_args__(**locals()) __check_train_args__(**locals())
gm = api.GradientMachine.createFromConfigProto( gm = api.GradientMachine.createFromConfigProto(
topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) topology.proto(), api.CREATE_MODE_NORMAL,
self.__optimizer__.enable_types())
assert isinstance(gm, api.GradientMachine) assert isinstance(gm, api.GradientMachine)
parameters.append_gradient_machine(gm) parameters.append_gradient_machine(gm)
...@@ -102,13 +100,7 @@ class SGD(ITrainer): ...@@ -102,13 +100,7 @@ class SGD(ITrainer):
gm.start() gm.start()
out_args = api.Arguments.createArguments(0) out_args = api.Arguments.createArguments(0)
data_types_lists = [data_type[1] for data_type in topology.data_type()]
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) converter = DataProviderConverter(input_types=data_types_lists)
for pass_id in xrange(num_passes): for pass_id in xrange(num_passes):
...@@ -141,7 +133,7 @@ def __data_reader_to_batch__(reader, batch_size, topology): ...@@ -141,7 +133,7 @@ def __data_reader_to_batch__(reader, batch_size, topology):
def input_reorder(func): def input_reorder(func):
for item in func(): for item in func():
retv = [] retv = []
for __layer_name__ in topology.input_layer_names: for __layer_name__ in topology.proto().input_layer_names:
retv.append(item[__layer_name__]) retv.append(item[__layer_name__])
yield retv yield retv
...@@ -178,7 +170,7 @@ def __check_train_args__(train_data_reader, topology, parameters, ...@@ -178,7 +170,7 @@ def __check_train_args__(train_data_reader, topology, parameters,
raise ValueError('test_data_reader should be a function, which can ' raise ValueError('test_data_reader should be a function, which can '
'return a iterator') 'return a iterator')
if not isinstance(topology, ModelConfig): if not isinstance(topology, v2_topology.Topology):
raise ValueError('topology should be a model config') raise ValueError('topology should be a model config')
if not isinstance(parameters, v2_parameters.Parameters): if not isinstance(parameters, v2_parameters.Parameters):
......
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