Keras load_model raise ValueError: Unknown layer: TokenEmbedding问题_unknown layer: tcn. please ensure this object is p

问题复现: 

训练的模型存储方式: 

model.save_weights(model_path)

模型加载方式

from keras.models import load_model
model = load_model(model_path)

异常:

ValueError: Unknown layer: TokenEmbedding

出现该错误是因为要保存的model中包含了自定义的层（Custom Layer），导致加载模型的时候无法解析该Layer
解决该问题的方法是在load_model函数中添加custom_objects参数，该参数接受一个字典，键值为自定义的层，当然也可以偷懒，直接使用custom_objects=get_custom_objects()

解决方案:

from keras.models import load_model
from keras_bert import get_custom_objects
model = load_model(model_path, custom_objects=get_custom_objects())

keras_bert中提供的get_custom_objects() 中包含如下元素: 

{'LayerNormalization': <class 'keras_layer_normalization.layer_normalization.LayerNormalization'>, 'MultiHeadAttention': <class 'keras_multi_head.multi_head_attention.MultiHeadAttention'>, 'FeedForward': <class 'keras_position_wise_feed_forward.feed_forward.FeedForward'>, 'TrigPosEmbedding': <class 'keras_pos_embd.trig_pos_embd.TrigPosEmbedding'>, 'EmbeddingRet': <class 'keras_embed_sim.embeddings.EmbeddingRet'>, 'EmbeddingSim': <class 'keras_embed_sim.embeddings.EmbeddingSim'>, 'PositionEmbedding': <class 'keras_pos_embd.pos_embd.PositionEmbedding'>, 'TokenEmbedding': <class 'keras_bert.layers.embedding.TokenEmbedding'>, 'EmbeddingSimilarity': <class 'keras_bert.layers.embedding.EmbeddingSimilarity'>, 'Masked': <class 'keras_bert.layers.masked.Masked'>, 'Extract': <class 'keras_bert.layers.extract.Extract'>, 'gelu': <function gelu_tensorflow at 0x7fc1a0aed620>, 'gelu_tensorflow': <function gelu_tensorflow at 0x7fc1a0aed620>, 'gelu_fallback': <function gelu_fallback at 0x7fc1a0aed730>, 'AdamWarmup': <class 'keras_bert.optimizers.warmup.AdamWarmup'>}

当然，如果有些自定义的object不在上面这里面的话，那么需要将自定义的object放到里面,如下所示: 

from keras.models import load_model
custom_objects = get_custom_objects()
my_objects = {'RAdam': RAdam}
custom_objects.update(my_objects)
model = load_model(model_path, custom_objects=custom_objects)