diff --git a/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py b/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py index 5fba561e024b0690f10939267146f2622c567fa5..de3906fc6a005181b0ab04a846eb2e7ce14004c2 100644 --- a/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py +++ b/python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py @@ -48,7 +48,7 @@ def linear(): return avg_loss -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() trainer = fluid.Trainer( @@ -68,8 +68,8 @@ def train(use_cuda, train_program, save_dirname): ['15.343549569447836'] ... ''' - if save_dirname is not None: - trainer.save_params(save_dirname) + if params_dirname is not None: + trainer.save_params(params_dirname) trainer.stop() trainer.train( @@ -80,13 +80,13 @@ def train(use_cuda, train_program, save_dirname): # infer -def infer(use_cuda, inference_program, save_dirname=None): - if save_dirname is None: +def infer(use_cuda, inference_program, params_dirname=None): + if params_dirname is None: return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - infer_func=inference_program, param_path=save_dirname, place=place) + infer_func=inference_program, param_path=params_dirname, place=place) batch_size = 10 tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32") @@ -100,10 +100,10 @@ def main(use_cuda): return # Directory for saving the trained model - save_dirname = "fit_a_line.inference.model" + params_dirname = "fit_a_line.inference.model" - train(use_cuda, linear, save_dirname) - infer(use_cuda, inference_program, save_dirname) + train(use_cuda, linear, params_dirname) + infer(use_cuda, inference_program, params_dirname) class TestFitALine(unittest.TestCase): diff --git a/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py b/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py index 1160e500dbd6db784eeb81b72968386347fec59a..63dc1b6ce30974ede22a3f7772b76bf207bbae39 100644 --- a/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py +++ b/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py @@ -85,7 +85,7 @@ def train_network(): return [avg_cost, accuracy] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): BATCH_SIZE = 128 EPOCH_NUM = 1 @@ -105,8 +105,8 @@ def train(use_cuda, train_program, save_dirname): print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy)) if accuracy > 0.01: # Low threshold for speeding up CI - if save_dirname is not None: - trainer.save_params(save_dirname) + if params_dirname is not None: + trainer.save_params(params_dirname) return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() @@ -122,10 +122,10 @@ def train(use_cuda, train_program, save_dirname): feed_order=['pixel', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - infer_func=inference_program, param_path=save_dirname, place=place) + infer_func=inference_program, param_path=params_dirname, place=place) # The input's dimension of conv should be 4-D or 5-D. # Use normilized image pixels as input data, which should be in the range @@ -142,12 +142,14 @@ def main(use_cuda): save_path = "image_classification_resnet.inference.model" train( - use_cuda=use_cuda, train_program=train_network, save_dirname=save_path) + use_cuda=use_cuda, + train_program=train_network, + params_dirname=save_path) infer( use_cuda=use_cuda, inference_program=inference_network, - save_dirname=save_path) + params_dirname=save_path) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py b/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py index 1e3e955ba0299f2cc0fcc02d79ae6fd8ff4c1171..0bf8f265a1c1b11364ecfa11061af183ce20d51e 100644 --- a/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py +++ b/python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py @@ -64,7 +64,7 @@ def train_network(): return [avg_cost, accuracy] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): BATCH_SIZE = 128 train_reader = paddle.batch( paddle.reader.shuffle( @@ -82,8 +82,8 @@ def train(use_cuda, train_program, save_dirname): print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy)) if accuracy > 0.01: # Low threshold for speeding up CI - if save_dirname is not None: - trainer.save_params(save_dirname) + if params_dirname is not None: + trainer.save_params(params_dirname) return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() @@ -99,10 +99,10 @@ def train(use_cuda, train_program, save_dirname): feed_order=['pixel', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - infer_func=inference_program, param_path=save_dirname, place=place) + infer_func=inference_program, param_path=params_dirname, place=place) # The input's dimension of conv should be 4-D or 5-D. # Use normilized image pixels as input data, which should be in the range @@ -119,12 +119,14 @@ def main(use_cuda): save_path = "image_classification_vgg.inference.model" train( - use_cuda=use_cuda, train_program=train_network, save_dirname=save_path) + use_cuda=use_cuda, + train_program=train_network, + params_dirname=save_path) infer( use_cuda=use_cuda, inference_program=inference_network, - save_dirname=save_path) + params_dirname=save_path) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py b/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py index f4344988141af44af83fda24d73da25f597796ef..9464df59797c0b8c35611ee56de6bf362ac7a4a5 100755 --- a/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py +++ b/python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py @@ -141,7 +141,7 @@ def train_program(): return [avg_cost] -def train(use_cuda, train_program, save_path): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.SGD(learning_rate=0.01) @@ -172,7 +172,7 @@ def train(use_cuda, train_program, save_path): print("avg_cost: %s" % avg_cost) if float(avg_cost) < 100.0: # Large value to increase CI speed - trainer.save_params(save_path) + trainer.save_params(params_dirname) else: print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, float(avg_cost))) @@ -183,7 +183,7 @@ def train(use_cuda, train_program, save_path): print("Step {0}, Epoch {1} Metrics {2}".format( event.step, event.epoch, map(np.array, event.metrics))) if event.step == 1: # Run 2 iterations to speed CI - trainer.save_params(save_path) + trainer.save_params(params_dirname) trainer.stop() train_reader = paddle.batch( @@ -197,10 +197,10 @@ def train(use_cuda, train_program, save_path): feed_order=feed_order) -def infer(use_cuda, inference_program, save_path): +def infer(use_cuda, inference_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - inference_program, param_path=save_path, place=place) + inference_program, param_path=params_dirname, place=place) # Setup inputs by creating LoDTensors to represent sequences of words. # Here each word is the basic element of these LoDTensors and the shape of @@ -251,9 +251,9 @@ def infer(use_cuda, inference_program, save_path): def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "label_semantic_roles.inference.model" - train(use_cuda, train_program, save_path) - infer(use_cuda, inference_program, save_path) + params_dirname = "label_semantic_roles.inference.model" + train(use_cuda, train_program, params_dirname) + infer(use_cuda, inference_program, params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py index 2aac70463c64019ec97b0c3893b4b52f77967797..03439cbd37671b4727879bf3d0793f016f55247a 100644 --- a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py +++ b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py @@ -57,7 +57,7 @@ def train_program(): return [avg_cost, acc] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adam(learning_rate=0.001) @@ -78,7 +78,7 @@ def train(use_cuda, train_program, save_dirname): print("acc : %s" % acc) if acc > 0.2: # Smaller value to increase CI speed - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) else: print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( event.epoch + 1, avg_cost, acc)) @@ -100,11 +100,11 @@ def train(use_cuda, train_program, save_dirname): feed_order=['img', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - infer_func=inference_program, param_path=save_dirname, place=place) + infer_func=inference_program, param_path=params_dirname, place=place) batch_size = 1 tensor_img = numpy.random.uniform(-1.0, 1.0, @@ -116,17 +116,17 @@ def infer(use_cuda, inference_program, save_dirname=None): def main(use_cuda): - save_dirname = "recognize_digits_conv.inference.model" + params_dirname = "recognize_digits_conv.inference.model" # call train() with is_local argument to run distributed train train( use_cuda=use_cuda, train_program=train_program, - save_dirname=save_dirname) + params_dirname=params_dirname) infer( use_cuda=use_cuda, inference_program=inference_program, - save_dirname=save_dirname) + params_dirname=params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py index 32653157994f81c46f420c1b55ceddbbbf06f2fe..89bbd21bea7d64a8dd6fc32829b6addb680da62e 100644 --- a/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py +++ b/python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py @@ -44,7 +44,7 @@ def train_program(): return [avg_cost, acc] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adam(learning_rate=0.001) @@ -62,7 +62,7 @@ def train(use_cuda, train_program, save_dirname): print("acc : %s" % acc) if acc > 0.2: # Smaller value to increase CI speed - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) else: print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( event.epoch + 1, avg_cost, acc)) @@ -81,11 +81,11 @@ def train(use_cuda, train_program, save_dirname): feed_order=['img', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - infer_func=inference_program, param_path=save_dirname, place=place) + infer_func=inference_program, param_path=params_dirname, place=place) batch_size = 1 tensor_img = numpy.random.uniform(-1.0, 1.0, @@ -97,17 +97,17 @@ def infer(use_cuda, inference_program, save_dirname=None): def main(use_cuda): - save_dirname = "recognize_digits_mlp.inference.model" + params_dirname = "recognize_digits_mlp.inference.model" # call train() with is_local argument to run distributed train train( use_cuda=use_cuda, train_program=train_program, - save_dirname=save_dirname) + params_dirname=params_dirname) infer( use_cuda=use_cuda, inference_program=inference_program, - save_dirname=save_dirname) + params_dirname=params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py b/python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py index 68457e475e7ebe27f22a9788ec419ef1e7951b11..dfc7325acf23176c05fe42761b9997b98d23372a 100644 --- a/python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py +++ b/python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py @@ -155,7 +155,7 @@ def train_program(): return [avg_cost, scale_infer] -def train(use_cuda, train_program, save_path): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.SGD(learning_rate=0.2) @@ -180,7 +180,7 @@ def train(use_cuda, train_program, save_path): print("avg_cost: %s" % avg_cost) if float(avg_cost) < 4: # Smaller value to increase CI speed - trainer.save_params(save_path) + trainer.save_params(params_dirname) trainer.stop() else: print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, @@ -200,10 +200,10 @@ def train(use_cuda, train_program, save_path): feed_order=feed_order) -def infer(use_cuda, inference_program, save_path): +def infer(use_cuda, inference_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - inference_program, param_path=save_path, place=place) + inference_program, param_path=params_dirname, place=place) # Use the first data from paddle.dataset.movielens.test() as input. # Use create_lod_tensor(data, lod, place) API to generate LoD Tensor, @@ -240,12 +240,15 @@ def infer(use_cuda, inference_program, save_path): def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "recommender_system.inference.model" - train(use_cuda=use_cuda, train_program=train_program, save_path=save_path) + params_dirname = "recommender_system.inference.model" + train( + use_cuda=use_cuda, + train_program=train_program, + params_dirname=params_dirname) infer( use_cuda=use_cuda, inference_program=inference_program, - save_path=save_path) + params_dirname=params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py b/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py index 7e32696f9909a0a440f6bdc401ac9f9594c4dec7..11e9fd1bec1450f6753dbe38c7014090d6e136b6 100644 --- a/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py +++ b/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py @@ -64,7 +64,7 @@ def train_program(word_dict): return [avg_cost, accuracy] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adagrad(learning_rate=0.002) @@ -85,7 +85,7 @@ def train(use_cuda, train_program, save_dirname): print("acc : %s" % acc) if acc > 0.2: # Smaller value to increase CI speed - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() else: @@ -97,7 +97,7 @@ def train(use_cuda, train_program, save_dirname): print("Step {0}, Epoch {1} Metrics {2}".format( event.step, event.epoch, map(np.array, event.metrics))) if event.step == 1: # Run 2 iterations to speed CI - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() train_reader = paddle.batch( @@ -112,13 +112,13 @@ def train(use_cuda, train_program, save_dirname): feed_order=['words', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() word_dict = paddle.dataset.imdb.word_dict() inferencer = fluid.Inferencer( infer_func=partial(inference_program, word_dict), - param_path=save_dirname, + param_path=params_dirname, place=place) # Setup input by creating LoDTensor to represent sequence of words. @@ -143,9 +143,9 @@ def infer(use_cuda, inference_program, save_dirname=None): def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "understand_sentiment_conv.inference.model" - train(use_cuda, train_program, save_path) - infer(use_cuda, inference_program, save_path) + params_dirname = "understand_sentiment_conv.inference.model" + train(use_cuda, train_program, params_dirname) + infer(use_cuda, inference_program, params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py b/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py index e50b7920b17f86eada3abc700c5403053fca8771..90757d54f99715163518ce5a094e6ba3a67efed3 100644 --- a/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py +++ b/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py @@ -79,7 +79,7 @@ def train_program(word_dict): return [avg_cost, accuracy] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adagrad(learning_rate=0.002) @@ -100,7 +100,7 @@ def train(use_cuda, train_program, save_dirname): print("acc : %s" % acc) if acc > 0.2: # Smaller value to increase CI speed - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() else: @@ -112,7 +112,7 @@ def train(use_cuda, train_program, save_dirname): print("Step {0}, Epoch {1} Metrics {2}".format( event.step, event.epoch, map(np.array, event.metrics))) if event.step == 1: # Run 2 iterations to speed CI - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() train_reader = paddle.batch( @@ -127,13 +127,13 @@ def train(use_cuda, train_program, save_dirname): feed_order=['words', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() word_dict = paddle.dataset.imdb.word_dict() inferencer = fluid.Inferencer( infer_func=partial(inference_program, word_dict), - param_path=save_dirname, + param_path=params_dirname, place=place) # Setup input by creating LoDTensor to represent sequence of words. @@ -158,9 +158,9 @@ def infer(use_cuda, inference_program, save_dirname=None): def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "understand_sentiment_conv.inference.model" - train(use_cuda, train_program, save_path) - infer(use_cuda, inference_program, save_path) + params_dirname = "understand_sentiment_conv.inference.model" + train(use_cuda, train_program, params_dirname) + infer(use_cuda, inference_program, params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py b/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py index d4fb80168814359827708ad921bd3f53b14bb2ee..52b7d4a83779d01936afb3d9d1e4864b05d55b5a 100644 --- a/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py +++ b/python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py @@ -71,7 +71,7 @@ def train_program(word_dict): return [avg_cost, accuracy] -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() optimizer = fluid.optimizer.Adagrad(learning_rate=0.002) @@ -92,7 +92,7 @@ def train(use_cuda, train_program, save_dirname): print("acc : %s" % acc) if acc > 0.2: # Smaller value to increase CI speed - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() else: @@ -104,7 +104,7 @@ def train(use_cuda, train_program, save_dirname): print("Step {0}, Epoch {1} Metrics {2}".format( event.step, event.epoch, map(np.array, event.metrics))) if event.step == 1: # Run 2 iterations to speed CI - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() train_reader = paddle.batch( @@ -119,13 +119,13 @@ def train(use_cuda, train_program, save_dirname): feed_order=['words', 'label']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() word_dict = paddle.dataset.imdb.word_dict() inferencer = fluid.Inferencer( infer_func=partial(inference_program, word_dict), - param_path=save_dirname, + param_path=params_dirname, place=place) # Setup input by creating LoDTensor to represent sequence of words. @@ -150,9 +150,9 @@ def infer(use_cuda, inference_program, save_dirname=None): def main(use_cuda): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "understand_sentiment_stacked_lstm.inference.model" - train(use_cuda, train_program, save_path) - infer(use_cuda, inference_program, save_path) + params_dirname = "understand_sentiment_stacked_lstm.inference.model" + train(use_cuda, train_program, params_dirname) + infer(use_cuda, inference_program, params_dirname) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py b/python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py index 16d73d4aff4ba31327e6d8f5ac04a36387f59daa..eeb8e67087334ea96aab9cdb6272e34e2eb99939 100644 --- a/python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py +++ b/python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py @@ -80,7 +80,7 @@ def train_program(is_sparse): return avg_cost -def train(use_cuda, train_program, save_dirname): +def train(use_cuda, train_program, params_dirname): train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) test_reader = paddle.batch( @@ -97,7 +97,7 @@ def train(use_cuda, train_program, save_dirname): print("loss= ", avg_cost) if avg_cost < 10.0: - trainer.save_params(save_dirname) + trainer.save_params(params_dirname) trainer.stop() if math.isnan(avg_cost): @@ -115,10 +115,10 @@ def train(use_cuda, train_program, save_dirname): feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw']) -def infer(use_cuda, inference_program, save_dirname=None): +def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( - infer_func=inference_program, param_path=save_dirname, place=place) + infer_func=inference_program, param_path=params_dirname, place=place) # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word # is simply an index to look up for the corresponding word vector and hence @@ -153,17 +153,17 @@ def main(use_cuda, is_sparse): if use_cuda and not fluid.core.is_compiled_with_cuda(): return - save_path = "word2vec.inference.model" + params_dirname = "word2vec.inference.model" train( use_cuda=use_cuda, train_program=partial(train_program, is_sparse), - save_dirname=save_path) + params_dirname=params_dirname) infer( use_cuda=use_cuda, inference_program=partial(inference_program, is_sparse), - save_dirname=save_path) + params_dirname=params_dirname) if __name__ == '__main__':