__init__.py 文件源码

def __init__(self,optimizer=None,vocab=None,n_input_units=1000,
                    n_units=650,grad_clip=5,bproplen=35):

        if vocab is None:
            vocab=BatchTrainer.vocab
        self.vocab=vocab
        n_vocab = len(vocab)
        super(LSTM,self).__init__('LSTM')

        self.func = model.lstm.RNNLM(n_input_units=n_input_units,n_vocab=n_vocab,n_units=n_units)
        self.func.compute_accuracy = False 
        for param in self.func.params():
            data = param.data
            data[:] = np.random.uniform(-0.1, 0.1, data.shape)


        if Deel.gpu>=0:
            self.func.to_gpu()


        if optimizer is None:
            self.optimizer = optimizers.SGD(lr=1.)
        self.optimizer.setup(self.func)
        self.clip = chainer.optimizer.GradientClipping(grad_clip)
        self.optimizer.add_hook(self.clip)

        self.accum_loss = 0
        self.cur_log_perp =  Deel.xp.zeros(())