def weight_mapping(weights): #调换 gamma和beta参数位置
return_weights = []
gamma = None
for w in weights:
if 'gamma' in w.name:
gamma = w
elif 'beta' in w.name:
return_weights.append(w)
assert gamma is not None
return_weights.append(gamma)
gamma = None
else:
return_weights.append(w)
return return_weights
在使用keras_bert加载预训练权重，已进行过训练或微调之后，如果要在bert4keras模块下复用keras_bert模型权重时，发现结果不一致的问题，经过反复测试后发现，是两边LayerNorm中gamma和beta 参数的顺序不一致。你可以通过调换gamma和beta的位置来加载weights， 比如
def weight_mapping(weights): #调换 gamma和beta参数位置 return_weights = [] gamma = None for w in weights: if 'gamma' in w.name: gamma = w elif 'beta' in w.name: return_weights.append(w) assert gamma is not None return_weights.append(gamma) gamma = None else: return_weights.append(w) return return_weights
bert4keras_model.set_weights(K.batch_get_value(weight_mapping(keras_bert_model.weights)))