gradient_machine.startPass() updater.startPass() for each_batch in data: gradient_machine.startBatch() updater.startBatch() gradient_machine.train() updater.finishBatch() gradient_machine.finishBatch() updater.finishPass() gradient_machine.finishPass()

class Runner(object): def add_item(self, item): """  Add a runner item to runner.  """ def run_one_pass(self): """  Run one pass for runner. The parent argument will passed to context.  """

 runner = Runner() runner.add_item(ItemA()) runner.add_item(ItemB()) with runner: runner.run_one_pass()

class RunnerItem(object): """  RunnerItem is an item in Runner. Runner will composite the  RunnerItems together and invoke the first RunnerChainItem's methods.  And Runner will pass the next chain item's method as `next_callback`.  If current chain item is the last item. A default next_callback will be  passed.   Context is a global object shared by items.  """ def __init__(self): pass def initialize(self, context, next_callback): """  initialize method. It will be invoked when Runner start to run.   :param context: a global object shared by items.  :type context: RunnerContext  :param next_callback: next item's initialize method.  :type next_callback: callable  :return: None  :rtype: None  """ next_callback(context) def finalize(self, next_callback): """  Finalize method. It will be invoked when Runner complete run, and clean  some state in RunnerItem.   :param next_callback: next item's initialize method.  :type next_callback: callable  :return: None  :rtype: None  """ next_callback() def on_pass_begin(self, next_callback): """  Pass Begin Method. Invoked when a pass begins.   :param next_callback: next item's initialize method.  :type next_callback: callable  :return: None  :rtype: None  """ next_callback() def on_pass_end(self, next_callback): """  Pass End Method. Invoked when a pass ends.   :param next_callback: next item's initialize method.  :type next_callback: callable  :return: None  :rtype: None  """ next_callback() def on_batch_begin(self, next_callback): """  Batch Begin Method. Invoked when a batch begins. Return true if there is  no more batch could be processed.   :param next_callback: next item's initialize method.  :type next_callback: callable  :return: True if no more batch could be processed.  :rtype: bool  """ return next_callback() def on_batch_end(self, next_callback): """  Batch End Method. Invoked when a batch ends. Return true if there is  no more batch could be processed.   :param next_callback: next item's initialize method.  :type next_callback: callable  :return: True if no more batch could be processed.  :rtype: bool  """ return next_callback()

import paddle.trainer.PyDataProvider2 as dp from paddle.trainer_config_helpers import * import mnist_provider from py_paddle.trainer import * @network(  inputs={  'pixel': dp.dense_vector(784),  'label': dp.integer_value(10),  },  learning_rate=1e-4,  learning_method=AdamOptimizer(),  batch_size=1000,  model_average=ModelAverage(average_window=0.5),  regularization=L2Regularization(rate=0.5)) def mnist_network(pixel, label): hidden1 = fc_layer(input=pixel, size=200) hidden2 = fc_layer(input=hidden1, size=200) inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation()) cost = classification_cost(input=inference, label=label) return cost def main(): mnist = mnist_network() runner = RunnerBuilder( network=mnist, device_count=2).with_std_local_trainer( method=mnist_provider.process, file_list=['./data/raw_data/train']).with_std_tester( method=mnist_provider.process, file_list=['./data/raw_data/t10k']).build() with runner: for _ in xrange(2): runner.run_one_pass() if __name__ == '__main__': main()