From 8119706b5baf9fd7fe4ea102e82ad0b54da1553a Mon Sep 17 00:00:00 2001 From: Yibing Liu Date: Thu, 9 Aug 2018 06:39:08 +0000 Subject: [PATCH] Decrease batch_size to run demo of sequence_tagging_for_ner_ce --- fluid/sequence_tagging_for_ner/train.py | 35 +++++++++++++++---------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/fluid/sequence_tagging_for_ner/train.py b/fluid/sequence_tagging_for_ner/train.py index 6ed77cd5..0e05f655 100644 --- a/fluid/sequence_tagging_for_ner/train.py +++ b/fluid/sequence_tagging_for_ner/train.py @@ -2,7 +2,7 @@ import os import math import numpy as np -import paddle.v2 as paddle +import paddle import paddle.fluid as fluid import reader @@ -24,12 +24,19 @@ def test(exe, chunk_evaluator, inference_program, test_data, place): return chunk_evaluator.eval(exe) -def main(train_data_file, test_data_file, vocab_file, target_file, emb_file, - model_save_dir, num_passes, use_gpu, parallel): +def main(train_data_file, + test_data_file, + vocab_file, + target_file, + emb_file, + model_save_dir, + num_passes, + use_gpu, + parallel, + batch_size=200): if not os.path.exists(model_save_dir): os.mkdir(model_save_dir) - BATCH_SIZE = 200 word_dict = load_dict(vocab_file) label_dict = load_dict(target_file) @@ -62,12 +69,12 @@ def main(train_data_file, test_data_file, vocab_file, target_file, emb_file, paddle.reader.shuffle( reader.data_reader(train_data_file, word_dict, label_dict), buf_size=20000), - batch_size=BATCH_SIZE) + batch_size=batch_size) test_reader = paddle.batch( paddle.reader.shuffle( reader.data_reader(test_data_file, word_dict, label_dict), buf_size=20000), - batch_size=BATCH_SIZE) + batch_size=batch_size) place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() feeder = fluid.DataFeeder(feed_list=[word, mark, target], place=place) @@ -79,34 +86,33 @@ def main(train_data_file, test_data_file, vocab_file, target_file, emb_file, embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor() embedding_param.set(word_vector_values, place) - batch_id = 0 for pass_id in xrange(num_passes): chunk_evaluator.reset(exe) - for data in train_reader(): + for batch_id, data in enumerate(train_reader()): cost, batch_precision, batch_recall, batch_f1_score = exe.run( fluid.default_main_program(), feed=feeder.feed(data), fetch_list=[avg_cost] + chunk_evaluator.metrics) if batch_id % 5 == 0: + print(cost) print("Pass " + str(pass_id) + ", Batch " + str( batch_id) + ", Cost " + str(cost[0]) + ", Precision " + str( batch_precision[0]) + ", Recall " + str(batch_recall[0]) + ", F1_score" + str(batch_f1_score[0])) - batch_id = batch_id + 1 pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(exe) print("[TrainSet] pass_id:" + str(pass_id) + " pass_precision:" + str( pass_precision) + " pass_recall:" + str(pass_recall) + " pass_f1_score:" + str(pass_f1_score)) - pass_precision, pass_recall, pass_f1_score = test( + test_pass_precision, test_pass_recall, test_pass_f1_score = test( exe, chunk_evaluator, inference_program, test_reader, place) print("[TestSet] pass_id:" + str(pass_id) + " pass_precision:" + str( - pass_precision) + " pass_recall:" + str(pass_recall) + - " pass_f1_score:" + str(pass_f1_score)) + test_pass_precision) + " pass_recall:" + str(test_pass_recall) + + " pass_f1_score:" + str(test_pass_f1_score)) save_dirname = os.path.join(model_save_dir, "params_pass_%d" % pass_id) fluid.io.save_inference_model(save_dirname, ['word', 'mark', 'target'], - [crf_decode], exe) + crf_decode, exe) if __name__ == "__main__": @@ -117,6 +123,7 @@ if __name__ == "__main__": target_file="data/target.txt", emb_file="data/wordVectors.txt", model_save_dir="models", - num_passes=1000, + num_passes=100, + batch_size=1, use_gpu=False, parallel=False) -- GitLab