用keras搭建bilstm crf

使用 https://github.com/keras-team/keras-contrib實現的crf layer，

安裝 keras-contrib

pip install git+https://www.github.com/keras-team/keras-contrib.git

Code Example：

# coding: utf-8
from keras.models import Sequential
from keras.layers import Embedding
from keras.layers import LSTM
from keras.layers import Bidirectional
from keras.layers import Dense
from keras.layers import TimeDistributed
from keras.layers import Dropout
from keras_contrib.layers.crf import CRF
from keras_contrib.utils import save_load_utils


VOCAB_SIZE = 2500
EMBEDDING_OUT_DIM = 128
TIME_STAMPS = 100
HIDDEN_UNITS = 200
DROPOUT_RATE = 0.3
NUM_CLASS = 5


def build_embedding_bilstm2_crf_model():
    """
    帶embedding的雙向LSTM + crf
    """
    model = Sequential()
    model.add(Embedding(VOCAB_SIZE, output_dim=EMBEDDING_OUT_DIM, input_length=TIME_STAMPS))
    model.add(Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True)))
    model.add(Dropout(DROPOUT_RATE))
    model.add(Bidirectional(LSTM(HIDDEN_UNITS, return_sequences=True)))
    model.add(Dropout(DROPOUT_RATE))
    model.add(TimeDistributed(Dense(NUM_CLASS)))
    crf_layer = CRF(NUM_CLASS)
    model.add(crf_layer)
    model.compile('rmsprop', loss=crf_layer.loss_function, metrics=[crf_layer.accuracy])
    return model

def save_embedding_bilstm2_crf_model(model, filename):
    save_load_utils.save_all_weights(model,filename)

def load_embedding_bilstm2_crf_model(filename):
    model = build_embedding_bilstm2_crf_model()
    save_load_utils.load_all_weights(model, filename)
    return model


if __name__ == '__main__':
    model = build_embedding_bilstm2_crf_model()

注意：

1. 如果執行build模型報錯，則很可能是keras版本的問題。在keras-contrib==2.0.8且keras==2.0.8時，上面代碼不會報錯。

Ref
http://blog.csdn.net/Treasure_Z/article/details/78853265
https://www.depends-on-the-definition.com/sequence-tagging-lstm-crf/