使用keras实现BiLSTM+CNN+CRF文字标记NER
xinfeng2005 人气:1本文着重讲解了使用keras实现BiLSTM+CNN+CRF文字标记NER,值得借鉴,相信能够帮助到您。我们一起来阅读吧
我就废话不多说了,大家还是直接看代码吧~
import keras from sklearn.model_selection import train_test_split import tensorflow as tf from keras.callbacks import ModelCheckpoint,Callback # import keras.backend as K from keras.layers import * from keras.models import Model from keras.optimizers import SGD, RMSprop, Adagrad,Adam from keras.models import * from keras.metrics import * from keras import backend as K from keras.regularizers import * from keras.metrics import categorical_accuracy # from keras.regularizers import activity_l1 #通过L1正则项,使得输出更加稀疏 from keras_contrib.layers import CRF from visual_callbacks import AccLossPlotter plotter = AccLossPlotter(graphs=['acc', 'loss'], save_graph=True, save_graph_path=sys.path[0]) # from crf import CRFLayer,create_custom_objects class LossHistory(Callback): def on_train_begin(self, logs={}): self.losses = [] def on_batch_end(self, batch, logs={}): self.losses.append(logs.get('loss')) # def on_epoch_end(self, epoch, logs=None): word_input = Input(shape=(max_len,), dtype='int32', name='word_input') word_emb = Embedding(len(char_value_dict)+2, output_dim=64, input_length=max_len, dropout=0.2, name='word_emb')(word_input) bilstm = Bidirectional(LSTM(32, dropout_W=0.1, dropout_U=0.1, return_sequences=True))(word_emb) bilstm_d = Dropout(0.1)(bilstm) half_window_size = 2 paddinglayer = ZeroPadding1D(padding=half_window_size)(word_emb) conv = Conv1D(nb_filter=50, filter_length=(2 * half_window_size + 1), border_mode='valid')(paddinglayer) conv_d = Dropout(0.1)(conv) dense_conv = TimeDistributed(Dense(50))(conv_d) rnn_cnn_merge = merge([bilstm_d, dense_conv], mode='concat', concat_axis=2) dense = TimeDistributed(Dense(class_label_count))(rnn_cnn_merge) crf = CRF(class_label_count, sparse_target=False) crf_output = crf(dense) model = Model(input=[word_input], output=[crf_output]) model.compile(loss=crf.loss_function, optimizer='adam', metrics=[crf.accuracy]) model.summary() # serialize model to JSON model_json = model.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) #编译模型 # model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['acc',]) # 用于保存验证集误差最小的参数,当验证集误差减少时,立马保存下来 checkpointer = ModelCheckpoint(filepath="bilstm_1102_k205_tf130.w", verbose=0, save_best_only=True, save_weights_only=True) #save_weights_only=True history = LossHistory() history = model.fit(x_train, y_train, batch_size=32, epochs=500,#validation_data = ([x_test, seq_lens_test], y_test), callbacks=[checkpointer, history, plotter], verbose=1, validation_split=0.1, )
补充知识:keras训练模型使用自定义CTC损失函数,重载模型时报错解决办法
使用keras训练模型,用到了ctc损失函数,需要自定义损失函数如下:
self.ctc_model.compile(loss={'ctc': lambda y_true, output: output}, optimizer=opt)
其中loss为自定义函数,使用字典{‘ctc': lambda y_true, output: output}
训练完模型后需要重载模型,如下:
from keras.models import load_model
model=load_model('final_ctc_model.h5')
报错:
Unknown loss function : <lambda>
由于是自定义的损失函数需要加参数custom_objects,这里需要定义字典{'': lambda y_true, output: output},正确代码如下:
model=load_model('final_ctc_model.h5',custom_objects={'<lambda>': lambda y_true, output: output})
可能是因为要将自己定义的loss函数加入到keras函数里
在这之前试了很多次,如果用lambda y_true, output: output定义loss
函数字典名只能是'<lambda>',不能是别的字符
如果自定义一个函数如loss_func作为loss函数如:
self.ctc_model.compile(loss=loss_func, optimizer=opt)
可以在重载时使用
am=load_model('final_ctc_model.h5',custom_objects={'loss_func': loss_func})
此时注意字典名和函数名要相同
以上这篇使用keras实现BiLSTM+CNN+CRF文字标记NER就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
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