September 2020
Intermediate to advanced
434 pages
9h 6m
Chinese
Content preview from TinyML：基于TensorFlow Lite 在Arduino 和超低功耗微控制器上部署机器学习
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360
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第
15
章
诸如 AutoML 之类的服务已经可以让用户避免许多烦琐的训练细节，希望这
种趋势会继续下去，这样你将能够更轻松地权衡精确度和运行效率，选择最
适合你的模型。
15.4 量化
运行神经网络需要为每个预测进行数十万甚至数百万次计算。大多数执行这种复杂计算
的程序对数值精度非常敏感。否则，错误会累积，导致结果误差过大而无法使用。深度
学习模型在这一点上非常不同
—
它能够应付中间计算期间数值精度的巨大损失，并且
仍然可以产生总体上准确的最终结果。这个属性似乎是其训练过程的副产品
—
在深度
网络的训练过程中，通常会输入大量噪声，因此模型学习了应对微小变化的鲁棒性，并
专注于学习数据中的重要模式。
这意味着，在实践中，使用 32 位浮点表示进行的操作几乎比推断所需的精度更高。32
位浮点的训练要求更高，因为它需要对权重进行许多小的更改才能学习，但即使这样，
16 位表示形式也被广泛使用。大多数推断应用程序仅使用 8 位存储权重和激活值，就可
以产生与浮点等效项几乎无差别的结果。这对于嵌入式应用程序来说是个好消息，许多
平台都对这些模型所依赖的 8 位乘法和累加指令提供了强大的支持，因为这些指令在信
号处理算法中非常常见。
不过，将模型从浮点数转换为 8 位数值并不容易。为了有效执行计算，8 位数值需要被
线性转换为实数。这对于权重来说很容易，因为我们可以根据训练后的值知道每一层的
范围，因此可以得出正确的缩放系数来执行转换。不过，这对于激活来说比较棘手，因
为通过检查模型参数和架构并不能清楚地得到每层输出的实际范围。如果选择的范围太 ...
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