keras 解决加载lstm+crf模型出错的问题

错误展示

new_model = load_model(“model.h5”)

报错：

1、keras load_model valueError: Unknown Layer :CRF

2、keras load_model valueError: Unknown loss function:crf_loss

错误修改

1、load_model修改源码：custom_objects = None 改为 def load_model(filepath, custom_objects, compile=True)：

2、new_model = load_model(“model.h5”,custom_objects={‘CRF': CRF,‘crf_loss': crf_loss,‘crf_viterbi_accuracy': crf_viterbi_accuracy}

以上修改后，即可运行。

补充知识：用keras搭建bilstm crf

使用 https://github.com/keras-team/keras-contrib实现的crf layer，

安装 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():
 """
 带embedding的双向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：

如果执行build模型报错，则很可能是keras版本的问题。在keras-contrib==2.0.8且keras==2.0.8时，上面代码不会报错。

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