卷积神经网络（CNN）可以用数学公式表示吗？ [英] Can Convolutional Neural Networks (CNN) be represented by a Mathematical formula?

问题描述

请让我知道是否应将此问题发布在不同的堆栈中，例如 https://datascience.stackexchange.com/。

假设我已经训练了CNN。无论如何，我的输出是否像感知器罐子一样可以表示为公式（x1w1 + x2w2 + ... =预测）。

公式是否比感知器复杂，这并不重要，但通常可以用Python或Matlab训练CNN，获得权重并创建一个算术的，指数的，无论用那些获得的权重组成的公式是什么？认为自己没有能力执行CNN。

或者，例如，我应该追求某种可以进行CNN的 C库的想法吗？

解决方案

如果可以牺牲一些内存，则可以肯定将卷积转换为矩阵乘法。

事实证明自己的优点（比任何琐碎的
实施都要快，除非您认真认真地进行优化）和缺点（
的内存消耗很大）。

Please, let me know if this question should be posted in a differnt stack such as the https://datascience.stackexchange.com/.

Let's say that I already trained my CNN. Is there anyway of my ouput to be represented as a formula just like a perceptron can (x1w1 + x2w2 + ... = PREDICTION).

It does not matter if the formula is more complicated than the perceptron one, but in general would it be possible to train a CNN in Python or Matlab, get the weights and create an arithmetic, exponential, whatever formula made with those acquired weights?

I want to do this because I am trying classify in a PIC32 (a low cost microchip) which I think that does not have the capacity to perform a CNN within itself.

Or, for example, should I pursue the idea of some sort of "C library" that can do CNNs?

解决方案

If you can sacrifice some memory, you can certainly convert a convolution into a matrix multiplication.

It turns out to have its own pros (faster than any trivial implementation unless you optimize really seriously) and cons (large memory consumption).

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