使用keras实现BiLSTM+CNN+CRF文字标记NER_Python

使用keras实现BiLSTM+CNN+CRF文字标记NER

2020-06-29 12:15xinfeng2005 Python

这篇文章主要介绍了使用keras实现BiLSTM+CNN+CRF文字标记NER，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

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,

									          )