models.py 文件源码

def r2r(dic_len,input_length,output_length,emb_dim=128,hidden=512,deepth=(1,1)):
    model = Sequential()
    model.add(Embedding(input_dim=dic_len, mask_zero=True, output_dim=emb_dim, input_length=input_length))
    for l in range(deepth[0]):
        model.add(LSTM(output_dim=hidden, return_sequences=(False if l==deepth[0]-1 else True)))
    model.add(RepeatVector(output_length))
    model.add(Dropout(0.5))
    for l in range(deepth[0]):
        model.add(LSTM(hidden, return_sequences=True))
    model.add(TimeDistributed(Dense(units=dic_len, activation='softmax')))
    model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['acc'])
    return model