#coding:utf-8 import argparse import ast import io import numpy as np from paddle.fluid.framework import switch_main_program import paddle.fluid as fluid import paddlehub as hub # yapf: disable parser = argparse.ArgumentParser(__doc__) parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False") parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint") parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.") parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate used to train with warmup.") parser.add_argument("--weight_decay", type=float, default=5, help="Weight decay rate for L2 regularizer.") parser.add_argument("--warmup_proportion", type=float, default=0.05, help="Warmup proportion params for warmup strategy") args = parser.parse_args() # yapf: enable. def bow_net(program, input_feature, hid_dim=128, hid_dim2=96): switch_main_program(program) bow = fluid.layers.sequence_pool(input=input_feature, pool_type='sum') bow_tanh = fluid.layers.tanh(bow) fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh") fc = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh") return fc def cnn_net(program, input_feature, win_size=3, hid_dim=128, hid_dim2=96): switch_main_program(program) conv_3 = fluid.nets.sequence_conv_pool( input=input_feature, num_filters=hid_dim, filter_size=win_size, act="relu", pool_type="max") fc = fluid.layers.fc(input=conv_3, size=hid_dim2) return fc def gru_net(program, input_feature, hid_dim=128, hid_dim2=96): switch_main_program(program) fc0 = fluid.layers.fc(input=input_feature, size=hid_dim * 3) gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False) gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max') gru_max_tanh = fluid.layers.tanh(gru_max) fc = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh') return fc def bilstm_net(program, input_feature, hid_dim=128, hid_dim2=96): switch_main_program(program) fc0 = fluid.layers.fc(input=input_feature, size=hid_dim * 4) rfc0 = fluid.layers.fc(input=input_feature, size=hid_dim * 4) lstm_h, c = fluid.layers.dynamic_lstm( input=fc0, size=hid_dim * 4, is_reverse=False) rlstm_h, c = fluid.layers.dynamic_lstm( input=rfc0, size=hid_dim * 4, is_reverse=True) # extract last step lstm_last = fluid.layers.sequence_last_step(input=lstm_h) rlstm_last = fluid.layers.sequence_last_step(input=rlstm_h) lstm_last_tanh = fluid.layers.tanh(lstm_last) rlstm_last_tanh = fluid.layers.tanh(rlstm_last) # concat layer lstm_concat = fluid.layers.concat(input=[lstm_last, rlstm_last], axis=1) # full connect layer fc = fluid.layers.fc(input=lstm_concat, size=hid_dim2, act='tanh') return fc def lstm_net(program, input_feature, hid_dim=128, hid_dim2=96): switch_main_program(program) fc0 = fluid.layers.fc(input=input_feature, size=hid_dim * 4) lstm_h, c = fluid.layers.dynamic_lstm( input=fc0, size=hid_dim * 4, is_reverse=False) lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max') lstm_max_tanh = fluid.layers.tanh(lstm_max) fc = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh') return fc if __name__ == '__main__': # Step1: load Paddlehub elmo pretrained model module = hub.Module(name="elmo") inputs, outputs, program = module.context(trainable=True) # Step2: Download dataset and use LACClassifyReade to read dataset dataset = hub.dataset.ChnSentiCorp() reader = hub.reader.LACClassifyReader( dataset=dataset, vocab_path=module.get_vocab_path()) word_dict_len = len(reader.vocab) word_ids = inputs["word_ids"] elmo_embedding = outputs["elmo_embed"] # Step3: switch program and build network # Choose the net which you would like: bow, cnn, gru, bilstm, lstm switch_main_program(program) # Embedding layer word_embed_dims = 128 word_embedding = fluid.layers.embedding( input=word_ids, size=[word_dict_len, word_embed_dims], param_attr=fluid.ParamAttr( learning_rate=30, initializer=fluid.initializer.Uniform(low=-0.1, high=0.1))) # Add elmo embedding input_feature = fluid.layers.concat( input=[elmo_embedding, word_embedding], axis=1) # Choose the net which you would like: bow, cnn, gru, bilstm, lstm # We recommend you to choose the gru_net fc = gru_net(program, input_feature) # Setup feed list for data feeder # Must feed all the tensor of senta's module need feed_list = [word_ids.name] # Step4: Select finetune strategy, setup config and finetune strategy = hub.AdamWeightDecayStrategy( weight_decay=args.weight_decay, learning_rate=args.learning_rate, lr_scheduler="linear_decay", warmup_proportion=args.warmup_proportion) # Step5: Setup runing config for PaddleHub Finetune API config = hub.RunConfig( use_cuda=args.use_gpu, use_data_parallel=True, batch_size=args.batch_size, checkpoint_dir=args.checkpoint_dir, strategy=strategy) # Step6: Define a classfication finetune task by PaddleHub's API elmo_task = hub.TextClassifierTask( data_reader=reader, feature=fc, feed_list=feed_list, num_classes=dataset.num_labels, config=config) # Data to be prdicted data = [ "这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般", "交通方便；环境很好；服务态度很好 房间较小", "还稍微重了点，可能是硬盘大的原故，还要再轻半斤就好了。其他要进一步验证。贴的几种膜气泡较多，用不了多久就要更换了，屏幕膜稍好点，但比没有要强多了。建议配赠几张膜让用用户自己贴。", "前台接待太差，酒店有A B楼之分，本人check－in后，前台未告诉B楼在何处，并且B楼无明显指示；房间太小，根本不像4星级设施，下次不会再选择入住此店啦", "19天硬盘就罢工了~~~算上运来的一周都没用上15天~~~可就是不能换了~~~唉~~~~你说这算什么事呀~~~" ] index = 0 results = elmo_task.predict(data=data) for batch_result in results: # get predict index batch_result = np.argmax(batch_result, axis=2)[0] for result in batch_result: print("%s\tpredict=%s" % (data[index], result)) index += 1