word2veckeras.py 文件源码

def build_keras_model_sg(index_size,vector_size,
                         context_size,
                         #code_dim,
                         sub_batch_size=256,
                         learn_vectors=True,learn_hidden=True,
                         model=None):

    kerasmodel = Graph()
    kerasmodel.add_input(name='point' , input_shape=(1,), dtype=int)
    kerasmodel.add_input(name='index' , input_shape=(1,), dtype=int)
    kerasmodel.add_node(Embedding(index_size, vector_size, input_length=sub_batch_size,weights=[model.syn0]),name='embedding', input='index')
    kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size,weights=[model.keras_syn1]),name='embedpoint', input='point')
    kerasmodel.add_node(Lambda(lambda x:x.sum(2))   , name='merge',inputs=['embedding','embedpoint'], merge_mode='mul')
    kerasmodel.add_node(Activation('sigmoid'), name='sigmoid', input='merge')
    kerasmodel.add_output(name='code',input='sigmoid')
    kerasmodel.compile('rmsprop', {'code':'mse'})
    return kerasmodel