From be89b4ab22f8d48b8bd0d952005ef446e5efab5a Mon Sep 17 00:00:00 2001 From: yongzhuo <2714618994@qq.com> Date: Sat, 24 Aug 2019 23:40:02 +0800 Subject: [PATCH] bert layers --- .../chatbot_sentence_vec_by_bert.py | 7 +------ ClassificationText/bert/args.py | 4 ++-- .../bert/extract_keras_bert_feature.py | 16 ++++++++-------- Ner/bert/args.py | 2 +- Ner/bert/keras_bert_embedding.py | 4 ++-- conf/feature_config.py | 2 +- 6 files changed, 15 insertions(+), 20 deletions(-) diff --git a/ChatBot/chatbot_search/chatbot_sentence_vec_by_bert.py b/ChatBot/chatbot_search/chatbot_sentence_vec_by_bert.py index 10f0ab8..db6ae84 100644 --- a/ChatBot/chatbot_search/chatbot_sentence_vec_by_bert.py +++ b/ChatBot/chatbot_search/chatbot_sentence_vec_by_bert.py @@ -84,9 +84,4 @@ if __name__=="__main__": # 小姜机器人是什么 # Tokens: ['[CLS]', '小', '姜', '机', '器', '人', '是', '什', '么', '[SEP]'] # (1, 32, 768) -# [CLS] [768] [1.0393640995025635, -0.31394684314727783, -0.08567211031913757, -0.12281288206577301, 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0.40125012397766113, 0.8932784795761108, 0.5221062898635864, 0.3605510890483856, 0.1881365031003952, 0.433641254901886, 1.5821797847747803, -0.2167215347290039, 0.6520259380340576, 0.2035215049982071, 0.48246681690216064, -0.31714242696762085, 0.24047553539276123, -0.3073540925979614, -1.332682490348816, -0.02192789316177368, 0.2924443185329437, -0.3183808922767639, 0.4149169325828552, 0.2792930603027344, -0.07717074453830719, 1.6913739442825317, 0.40769097208976746, -0.21851569414138794, -1.0361289978027344, 0.25582218170166016, 0.07622536271810532, -0.7013002038002014, -1.1194427013397217, -0.32200413942337036, 0.3157781660556793, -0.2570304572582245, 0.3225705325603485, 0.027198493480682373, -0.045928969979286194, 0.11507914215326309, -0. +# [CLS] [768] [1.0393640995025635, -0.31394684314727783, -0.08567211031913757, -0.12281288206577301, diff --git a/ClassificationText/bert/args.py b/ClassificationText/bert/args.py index d98ac71..d87e540 100644 --- a/ClassificationText/bert/args.py +++ b/ClassificationText/bert/args.py @@ -21,7 +21,7 @@ path_save_model = 'model_webank_tdt/bert_avt_cnn.h5' # 'bert_bi_lstm_pair.h5' # text-cnn filters = [3, 4, 5] -num_filters = 512 +num_filters = 300 @@ -29,7 +29,7 @@ num_filters = 512 gpu_memory_fraction = 0.3 # 默认取倒数第二层的输出值作为句向量 -layer_indexes = [-2] +layer_indexes = [-1] # 序列的最大程度，单文本建议把该值调小 max_seq_len = 98 diff --git a/FeatureProject/bert/extract_keras_bert_feature.py b/FeatureProject/bert/extract_keras_bert_feature.py index 190eb64..ac709c3 100644 --- a/FeatureProject/bert/extract_keras_bert_feature.py +++ b/FeatureProject/bert/extract_keras_bert_feature.py @@ -42,28 +42,28 @@ class KerasBertVector(): print(len(model.layers)) # lay = model.layers #一共104个layer，其中前八层包括token,pos,embed等， - # 每4层（MultiHeadAttention,Dropout,Add,LayerNormalization） - # 一共24层 + # 每8层（MultiHeadAttention,Dropout,Add,LayerNormalization） resnet + # 一共12层 layer_dict = [7] layer_0 = 7 for i in range(12): - layer_0 = layer_0 + 4 + layer_0 = layer_0 + 8 layer_dict.append(layer_0) # 输出它本身 if len(layer_indexes) == 0: encoder_layer = model.output # 分类如果只有一层，就只取最后那一层的weight，取得不正确 elif len(layer_indexes) == 1: - if layer_indexes[0] in [i+1 for i in range(12)]: + if layer_indexes[0] in [i+1 for i in range(13)]: encoder_layer = model.get_layer(index=layer_dict[layer_indexes[0]]).output else: - encoder_layer = model.get_layer(index=layer_dict[-2]).output + encoder_layer = model.get_layer(index=layer_dict[-1]).output # 否则遍历需要取的层，把所有层的weight取出来并拼接起来shape:768*层数 else: - # layer_indexes must be [1,2,3,......12...24] + # layer_indexes must be [1,2,3,......13] # all_layers = [model.get_layer(index=lay).output if lay is not 1 else model.get_layer(index=lay).output[0] for lay in layer_indexes] - all_layers = [model.get_layer(index=layer_dict[lay-1]).output if lay in [i+1 for i in range(12)] - else model.get_layer(index=layer_dict[-1]).output #如果给出不正确，就默认输出最后一层 + all_layers = [model.get_layer(index=layer_dict[lay-1]).output if lay in [i+1 for i in range(13)] + else model.get_layer(index=layer_dict[-1]).output # 如果给出不正确，就默认输出最后一层 for lay in layer_indexes] print(layer_indexes) print(all_layers) diff --git a/Ner/bert/args.py b/Ner/bert/args.py index cb42163..867f25e 100644 --- a/Ner/bert/args.py +++ b/Ner/bert/args.py @@ -27,7 +27,7 @@ path_tag_li = 'models/bilstm/tag_l_i.pkl' gpu_memory_fraction = 0.32 # ner当然是所有层都会提取啦，句向量默认取倒数第二层的输出值作为句向量 -layer_indexes = [i for i in range(13)] # [-2] +layer_indexes = [i+1 for i in range(13)] # [-2] # 序列的最大程度 max_seq_len = 50 diff --git a/Ner/bert/keras_bert_embedding.py b/Ner/bert/keras_bert_embedding.py index 307100f..3781d77 100644 --- a/Ner/bert/keras_bert_embedding.py +++ b/Ner/bert/keras_bert_embedding.py @@ -49,7 +49,7 @@ class KerasBertEmbedding(): # 一共12层+最开始未处理那层(可以理解为input) layer_dict = [7] layer_0 = 7 - for i in range(13): + for i in range(12): layer_0 = layer_0 + 8 layer_dict.append(layer_0) @@ -58,7 +58,7 @@ class KerasBertEmbedding(): encoder_layer = model.output # 分类如果只有一层，就只取最后那一层的weight，取得不正确 elif len(layer_indexes) == 1: - if layer_indexes[0] in [i+1 for i in range(12)]: + if layer_indexes[0] in [i+1 for i in range(13)]: encoder_layer = model.get_layer(index=layer_dict[layer_indexes[0]]).output else: encoder_layer = model.get_layer(index=layer_dict[-1]).output diff --git a/conf/feature_config.py b/conf/feature_config.py index 8a045d9..3186820 100644 --- a/conf/feature_config.py +++ b/conf/feature_config.py @@ -21,6 +21,6 @@ vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 -layer_indexes = [-2] +layer_indexes = [-2] # 可填 1, 2, 3, 4, 5, 6, 7..., 13,其中1为embedding层 # 序列的最大程度 max_seq_len = 32