recurrent.py 文件源码

def get_padded_shuffled_mask(self, train, X, pad=0):
        mask = self.get_input_mask(train)
        if mask is None:
            mask = T.ones_like(X.sum(axis=-1))  # is there a better way to do this without a sum?

        # mask is (nb_samples, time)
        mask = T.shape_padright(mask)  # (nb_samples, time, 1)
        mask = T.addbroadcast(mask, -1)  # the new dimension (the '1') is made broadcastable
        # see http://deeplearning.net/software/theano/library/tensor/basic.html#broadcasting-in-theano-vs-numpy
        mask = mask.dimshuffle(1, 0, 2)  # (time, nb_samples, 1)

        if pad > 0:
            # left-pad in time with 0
            padding = alloc_zeros_matrix(pad, mask.shape[1], 1)
            mask = T.concatenate([padding, mask], axis=0)
        return mask.astype('int8')