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residuals是什么意思

时间: 2023-08-23 18:16:03 浏览: 592

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Abstract In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art perfor- mance of mobile models on multiple tasks and bench- marks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottle- neck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demon- strate that this improves performance and provide an in- tuition that led to this design. Finally, our approach allows decoupling of the in- put/output domains from the expressiveness of the trans- formation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters.

在线性最小二乘问题中，残差（residuals）是指最小二乘解与目标值之间的差异或误差。更具体地说，对于方程Ax = b，其中A是一个矩阵，x是未知向量，b是目标向量，残差表示为e = Ax - b。 np.linalg.lstsq函数返回的residuals是一个包含了每个观测值的残差平方和的数组。这些残差是实际目标值与通过最小二乘解计算得到的预测目标值之间的差异。较小的残差值表示最小二乘解更好地拟合了目标数据。通过分析残差可以评估最小二乘解的准确性和拟合程度。如果残差较小，则意味着最小二乘解能够较好地解释目标数据，而较大的残差可能表示模型在拟合数据时存在较大的误差或不适合该数据集。

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