Convolutional Neural Networks

保存几张1D卷积的图和文章，方便以后查找，摘自互联网。说不准以后搞深度学呢🫠🙃卷的结果就是以后不上人工智能都不要意思说自己是搞生信的。

主要是看到一篇文章，用了卷积神经网络，我一直就好奇组学数据怎么做卷积，所以就看了下文章，发现用的是Conv1D。（不知道理解的对不对）对于1D卷积Conv1D而言，如果卷积核kernel size是2的话，最终会生成一个 “行数-kernel size+1“的向量，如果数据分批给的话，就有batch，比如如果样本是21000，batch size是128的话，每个batch有165个样本，所以Nature Machine Intelligence的附图 Fig. 10这篇文章还进行了BatchNormalization。

Samples = 21000, batch_size=128 -> training_sample for each epoch = 21000/128 = 164.06 ~= 165

https://stackoverflow.com/questions/72529761/batch-size-in-input-shape-statement-for-keras-conv1d-layers

filters可以指定多次卷积（相同kernel size），这样可以生成二维的数据。

Args	https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D
filters	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size	An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.

Deep learning decodes the principles of differential gene expression

Convolutional Neural Networks for Sentence Classification

A Convolutional Neural Network for Modelling Sentences

A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification

来自：

知乎：https://www.zhihu.com/question/305080152/answer/2223288342

知乎：https://zhuanlan.zhihu.com/p/602769738

CSDN：https://blog.csdn.net/u012325865/article/details/109158275

Nature Machine Intelligence：https://www.nature.com/articles/s42256-020-0201-6