From 39d0b3de99112e6b90c22147b6c15917f5a3e1d5 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Sat, 10 Jun 2017 21:18:03 +0800 Subject: [PATCH] add test file mnist_test.py, free resource of newRemoteParameterUpdater properly --- go/pserver/cclient/test/mnist_test.py | 134 +++++++++++++++++++++ paddle/trainer/NewRemoteParameterUpdater.h | 14 +-- 2 files changed, 141 insertions(+), 7 deletions(-) create mode 100644 go/pserver/cclient/test/mnist_test.py diff --git a/go/pserver/cclient/test/mnist_test.py b/go/pserver/cclient/test/mnist_test.py new file mode 100644 index 0000000000..c77af49130 --- /dev/null +++ b/go/pserver/cclient/test/mnist_test.py @@ -0,0 +1,134 @@ +import paddle.v2 as paddle +import gzip + + +def softmax_regression(img): + predict = paddle.layer.fc(input=img, + size=10, + act=paddle.activation.Softmax()) + return predict + + +def multilayer_perceptron(img): + # The first fully-connected layer + hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu()) + # The second fully-connected layer and the according activation function + hidden2 = paddle.layer.fc(input=hidden1, + size=64, + act=paddle.activation.Relu()) + # The thrid fully-connected layer, note that the hidden size should be 10, + # which is the number of unique digits + predict = paddle.layer.fc(input=hidden2, + size=10, + act=paddle.activation.Softmax()) + return predict + + +def convolutional_neural_network(img): + # first conv layer + conv_pool_1 = paddle.networks.simple_img_conv_pool( + input=img, + filter_size=5, + num_filters=20, + num_channel=1, + pool_size=2, + pool_stride=2, + act=paddle.activation.Tanh()) + # second conv layer + conv_pool_2 = paddle.networks.simple_img_conv_pool( + input=conv_pool_1, + filter_size=5, + num_filters=50, + num_channel=20, + pool_size=2, + pool_stride=2, + act=paddle.activation.Tanh()) + # The first fully-connected layer + fc1 = paddle.layer.fc(input=conv_pool_2, + size=128, + act=paddle.activation.Tanh()) + # The softmax layer, note that the hidden size should be 10, + # which is the number of unique digits + predict = paddle.layer.fc(input=fc1, + size=10, + act=paddle.activation.Softmax()) + return predict + + +def main(): + paddle.init(use_gpu=False, trainer_count=1, trainer_id=1) + + # define network topology + images = paddle.layer.data( + name='pixel', type=paddle.data_type.dense_vector(784)) + label = paddle.layer.data( + name='label', type=paddle.data_type.integer_value(10)) + + # Here we can build the prediction network in different ways. Please + # choose one by uncomment corresponding line. + predict = softmax_regression(images) + #predict = multilayer_perceptron(images) + #predict = convolutional_neural_network(images) + + cost = paddle.layer.classification_cost(input=predict, label=label) + parameters = paddle.parameters.create(cost) + + optimizer = paddle.optimizer.Momentum( + learning_rate=0.1 / 128.0, + momentum=0.9, + regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128)) + + trainer = paddle.trainer.SGD(cost=cost, + parameters=parameters, + update_equation=optimizer, + is_local=False, + pserver_spec="localhost:3000") + + lists = [] + + def event_handler(event): + if isinstance(event, paddle.event.EndIteration): + if event.batch_id % 1000 == 0: + print "Pass %d, Batch %d, Cost %f, %s" % ( + event.pass_id, event.batch_id, event.cost, event.metrics) + + with gzip.open('params.tar.gz', 'w') as f: + parameters.to_tar(f) + + elif isinstance(event, paddle.event.EndPass): + result = trainer.test(reader=paddle.batch( + paddle.dataset.mnist.test(), batch_size=128)) + print "Test with Pass %d, Cost %f, %s\n" % ( + event.pass_id, result.cost, result.metrics) + lists.append((event.pass_id, result.cost, + result.metrics['classification_error_evaluator'])) + + trainer.train( + reader=paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=8192), + batch_size=128), + event_handler=event_handler, + num_passes=100) + + # find the best pass + best = sorted(lists, key=lambda list: float(list[1]))[0] + print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1]) + print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100) + + test_creator = paddle.dataset.mnist.test() + test_data = [] + for item in test_creator(): + test_data.append((item[0], )) + if len(test_data) == 100: + break + + # output is a softmax layer. It returns probabilities. + # Shape should be (100, 10) + probs = paddle.infer( + output_layer=predict, parameters=parameters, input=test_data) + print probs.shape + + +if __name__ == '__main__': + main() diff --git a/paddle/trainer/NewRemoteParameterUpdater.h b/paddle/trainer/NewRemoteParameterUpdater.h index d3a032badc..db9b44af19 100644 --- a/paddle/trainer/NewRemoteParameterUpdater.h +++ b/paddle/trainer/NewRemoteParameterUpdater.h @@ -32,9 +32,11 @@ public: NewRemoteParameterUpdater(const OptimizationConfig& config, const std::string pserverSpec); ~NewRemoteParameterUpdater() { - LOG(INFO) << "~NewRemoteParameterUpdater in"; -// releaseNewParameter(newParameters_); -// releaseNewParameter(newGradients_); + if (names_ != nullptr) { + free(names_); + } + releaseNewParameter(newParameters_); + releaseNewParameter(newGradients_); if (parameterClient_ >= 0) paddle_pserver_client_release(parameterClient_); } @@ -95,11 +97,9 @@ private: void releaseNewParameter(paddle_parameter** newParams) { if (newParams != nullptr) { for (int i = 0; i < parameterSize(); ++i) { - auto param = newParams[i]; - if (param != nullptr) { - paddle_release_param(param); - } + free(newParams[i]); } + free(newParams); } } -- GitLab