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CNN擅长处理图像数据,具有强大的特征提取能力;Transformer通过自注意力机制实现了高效的并行计算,适用于处理序列数据;而MLP则以其强大的表达能力和泛化能力,在多种类型的机器学习任务中都有应用。 1. CNN,Transformer,MLP 三大架构的特点是什么? 2. mlp之所以经久不衰,就是因为他简单,快速,能scale-up。 KAN让人想起来之前的Neural ODE,催生出来比如LTC(liquid time constant)网络这种宣称19个神经元做自动驾驶。 全连接(前馈)网络:是指每一层之间没有连接,只是前一层和后一层连接的网络都属于全连接 前馈神经网络。 多层感知器 MLP:是相对于最简单的单个感知器而言,多个感知器串联构成了MLP(Multilayer Perceptron)。 单个感知机:
MLP是 多层感知机,是多层的全连接的前馈网络,是而且仅仅是算法结构。输入样本后,样本在MLP在网络中逐层前馈(从输入层到隐藏层到输出层,逐层计算结果,即所谓前馈),得到最终输出值。 但,MLP的各层各神经元的连接系数和偏移量,并非MLP与生俱来的,需要训练和优化才能得到,BP派上. 都说1x1卷积能够替代fc层,更省参数,且效果差不多。那为什么现在还要使用mlp而不是堆叠1x1卷积层呢? 3.FFN(前馈神经网络)和 MLP(多层感知机): "FFN" 和 "MLP" 表示前馈神经网络和多层感知机,它们在概念上是相同的。 前馈神经网络是一种最常见的神经网络结构,由多个全连接层组成,层与层之间是前向传播的。
Transformer(这里指self-attention) 和 MLP 都是全局感知的方法,那么他们之间的差异在哪里呢?
KAN号称会取代传统MLP,只要理解了MLP,再看明白KAN和MLP的区别,就能拿理解KAN。 怎么理解MLP呢? MLP就是Mulit-Layer Perceptron,就是这么一个多层的神经元网络,其中每一个圆圈代表一个神经元,本质上MLP就是一个函数,根据输入产生输出。 MLP-Mixer 而MLP-Mixer这篇文章面对MLP计算量太大,参数量太大两大问题,换了一个解决思路。 这个解决思路跟depthwise separable conv是一致的,depthwise separable conv把经典的conv分解为两步,depthwise conv和pointwise conv,这样就降低了经典conv的计算量和参数量。 多层感知机(MLP)神经网络可以用于多分类预测。以下是一个基本的示例,用于使用TensorFlow Keras实现MLP多分类预测:
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