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 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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