提交 0690a9fb 编写于 作者: Q qijun

add v2 run logic doc

上级 c36bf197
......@@ -6,22 +6,26 @@ Parameters
==========
.. automodule:: paddle.v2.parameters
:members: Parameters
:noindex:
Trainer
=======
.. automodule:: paddle.v2.trainer
:members: Trainer
:noindex:
Event
=====
.. automodule:: paddle.v2.event
:members: Event
:noindex:
Inference
=========
.. autofunction:: paddle.v2.infer
:members: Inference
:noindex:
......@@ -9,6 +9,16 @@ __all__ = ['infer']
class Inference(object):
"""
Inference combines neural network output and parameters together
to do inference.
:param outptut_layer: The neural network that should be inferenced.
:type output_layer: paddle.v2.config_base.Layer or the sequence
of paddle.v2.config_base.Layer
:param parameters: The parameters dictionary.
:type parameters: paddle.v2.parameters.Parameters
"""
def __init__(self, output_layer, parameters):
topo = topology.Topology(output_layer)
gm = api.GradientMachine.createFromConfigProto(
......
......@@ -29,7 +29,8 @@ def default_event_handler(event):
class SGD(object):
"""
Simple SGD Trainer.
TODO(yuyang18): Complete comments
SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.
:param update_equation: The optimizer object.
:type update_equation: paddle.v2.optimizer.Optimizer
......@@ -65,7 +66,9 @@ class SGD(object):
"""
Training method. Will train num_passes of input data.
:param reader:
:param reader: A reader that reads and yeilds data items. Usually we use a
batched reader to do mini-batch training.
:type reader: collections.Iterable
:param num_passes: The total train passes.
:param event_handler: Event handler. A method will be invoked when event
occurred.
......@@ -123,6 +126,16 @@ class SGD(object):
self.__gradient_machine__.finish()
def test(self, reader, feeding=None):
"""
Testing method. Will test input data.
:param reader: A reader that reads and yeilds data items.
:type reader: collections.Iterable
:param feeding: Feeding is a map of neural network input name and array
index that reader returns.
:type feeding: dict
:return:
"""
feeder = DataFeeder(self.__data_types__, feeding)
evaluator = self.__gradient_machine__.makeEvaluator()
out_args = api.Arguments.createArguments(0)
......
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