In this paper, we propose a new definition of data dependent tensor rank named tensor Q-rank by a learnable orthogonal matrix \\(\\mathbf {Q}\\), and further introduce a unified data dependent low rank tensor recovery model. According to the low rank hypothesis, we introduce two ...
However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of data dependent tensor rank named tensor Q-rank by a learnable orthogonal matrix Q, and further introduce a unified data ...
We introduce an explainable selection method of Q \mathbf{Q} , under which the data tensor may have a more significant low tensor Q-rank structure than that of low tubal-rank structure. We also provide a corresponding envelope of our rank function and apply it to the low rank tensor ...
张量积可以在有更多指标的量上进行。举个例子,你可以把量 Q^{ijk} 和矩阵分量 M^i_j 相乘得到一个巨大的5指标量 B^{ijkl}_m 的分量: B^{ijkl}_m:=Q^{ijk}M^l_m 指标操作的一个有用的工具是改写哑指标。哑指标是求和过的指标,比如表达式 g_{ik}Q^{ijk} 中的指标 i 和k 。它们之所以被称...
Tensor Q-Rank: A New Data Dependent Tensor Rank In this paper, we propose a new definition of tensor rank named extit{tensor Q-rank} by a column orthonormal matrix Q \\mathbf{Q} , and further make Q \\mathbf{Q} data-dependent. We introduce an explainable selection method ... H Kong...
通过这种分解,每个 Q_t, K_t, V_t 的存储需求从完整矩阵变为存储因子 a_r 和b_r,从而降低内存消耗,同时保留模型性能。 用户问题 详细解释需要的内存级为什么可以节约内存 chatGPT 在Tensor Product Attention (TPA) 中,节约内存的关键原因在于通过 张量分解 将传统注意力机制中的全矩阵存储形式替换为低秩...
关于tensor的rank、shape属性对应的api: import tensorflow as tf a = tf.constant(5) # Tensor.shape返回类型为TensorShape # a.shape等效于a.get_shape() shape = a.shape.as_list() # 用TensorShape.as_list()将shape值转化为list print(shape) # 输出: [] ...
Tensor对象由原始数据组成的多维的数组,Tensor的rank(秩)其实是表示数组的维数,如下所示的tensor例子: Rank 数学实例 Python 例子 0 标量(点) 666 1 向量(直线) [6,6] 2 矩阵(平面) [[6,6,6],[6,6,6]] 3 立体(图片) [[[6,6],[6,6]] , [[6,6],[6,6]]] n n阶 (立体+时间轴,可参...
tensorflow中不同类型的数据可以用不同维度(rank)的张量来表示。 标量为0维张量,向量为1维张量,矩阵为2维张量。 彩色图像有rgb三个通道,可以表示为3维张量。 视频还有时间维,可以表示为4维张量。 可以简单地总结为:有几层中括号,就是多少维的张量。 pytorch scalar = torch.tensor(True) print(scalar) print...
qscheme, float, int], Tuple[torch.qscheme, Tensor, Tensor, int]] if self.qscheme() == torch.per_tensor_affine: quantizer_params = self.qscheme(), self.q_scale(), self.q_zero_point() elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams): ...