import paddle.v2 as paddle import paddle.v2.dataset.uci_housing as uci_housing def main(): # init paddle.init(use_gpu=False, trainer_count=1, trainer_id=1) # network config x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13)) y_predict = paddle.layer.fc(input=x, param_attr=paddle.attr.Param(name='w'), size=1, act=paddle.activation.Linear(), bias_attr=paddle.attr.Param(name='b')) y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1)) cost = paddle.layer.mse_cost(input=y_predict, label=y) # create parameters parameters = paddle.parameters.create(cost) # create optimizer optimizer = paddle.optimizer.Momentum(momentum=0) trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=optimizer, is_local=False, pserver_spec="localhost:3000") # event_handler to print training and testing info def event_handler(event): if isinstance(event, paddle.event.EndIteration): if event.batch_id % 100 == 0: print "Pass %d, Batch %d, Cost %f" % ( event.pass_id, event.batch_id, event.cost) if isinstance(event, paddle.event.EndPass): if (event.pass_id + 1) % 10 == 0: result = trainer.test( reader=paddle.batch( uci_housing.test(), batch_size=2), feeding={'x': 0, 'y': 1}) print "Test %d, %.2f" % (event.pass_id, result.cost) # training trainer.train( reader=paddle.batch( paddle.reader.shuffle( uci_housing.train(), buf_size=500), batch_size=2), feeding={'x': 0, 'y': 1}, event_handler=event_handler, num_passes=30) if __name__ == '__main__': main()