The Keras-CRF-Layer module implements a linear-chain CRF layer for learning to predict tag sequences.This variant of the CRF is factored into unary potentials for every element in the sequence and binary potentials for every transition between output tags.
Below is an example of the API, which learns a CRF for some random data.The linear layer in the example can be replaced by any neural network.
importnumpyasnpfromkeras.layersimportEmbedding,Inputfromkeras.modelsimportModelfromcrfimportCRFLayer# Hyperparameter settings.vocab_size=20n_classes=11batch_size=2maxlen=2# Random features.x=np.random.randint(1,vocab_size,size=(batch_size,maxlen))# Random tag indices representing the gold sequence.y=np.random.randint(n_classes,size=(batch_size,maxlen))y=np.eye(n_classes)[y]# All sequences in this example have the same length, but they can be variable in a real model.s=np.asarray([maxlen]*batch_size,dtype='int32')# Build an example model.word_ids=Input(batch_shape=(batch_size,maxlen),dtype='int32')sequence_lengths=Input(batch_shape=[batch_size,1],dtype='int32')word_embeddings=Embedding(vocab_size,n_classes)(word_ids)crf=CRFLayer()pred=crf(inputs=[word_embeddings,sequence_lengths])model=Model(inputs=[word_ids,sequence_lengths],outputs=[pred])model.compile(loss=crf.loss,optimizer='sgd')# Train first 1 batch.model.train_on_batch([x,s],y)# Save the modelmodel.save('model.h5')When you want to load a saved model that has a crf output, then loadingthe model with 'keras.models.load_model' won't work properly becausethe reference of the loss function to the transition parameters is lost. Tofix this, you need to use the parameter 'custom_objects' as follows:
fromkeras.modelsimportload_modelfromcrfimportcreate_custom_objectsmodel=load_model('model.h5',custom_objects=create_custom_objects())