blocks|key|1750169|text|不存在纯反向传播或纯前馈神经网络。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1750170|反向传播是神经网络训练(调整权值)的算法。反向传播的输入是output_vector，target_output_vector，输出是adjusted_weight_vector。|1750171|前馈算法是从输入向量中求出输出向量的算法.前馈输入为input_vector，输出输入为output_vector.|1750172|在训练神经网络时，需要同时使用这两种算法。|1750173|当你使用神经网络(已经训练过)时，你只使用前馈。|1750174|神经网络的基本类型是多层感知器，即前馈反向传播神经网络.|1750175|还有更先进的类型的神经网络，使用改进的算法。|1750176|还有很好的学习来源：ftp://ftp.sas.com/pub/neural/FAQ.html最需要理解的原理是编程它(本视频教程)+https://www.youtube.com/watch?v=KkwX7FkLfug|offset|length|1750177|entityMap|0|LINK|mutability|MUTABLE|url|ftp://ftp.sas.com/pub/neural/FAQ.html|1|https://www.youtube.com/watch?v=KkwX7FkLfug^0|0|0|0|0|0|0|0|A|11|0|1V|17|1|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|12|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|13|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|14|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|15|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|16|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|17|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|18|8|@]|9|@$P|19|Q|1A|1|1B]|$P|1C|Q|1D|1|1E]]|A|$]]|$1|R|3|-4|5|6|7|1F|8|@]|9|@]|A|$]]]|S|$T|$5|U|V|W|A|$X|Y]]|Z|$5|U|V|W|A|$X|10]]]]

There is no pure backpropagation or pure feed-forward neural network.

Backpropagation is algorithm to train (adjust weight) of neural network.
Input for backpropagation is output_vector, target_output_vector,
output is adjusted_weight_vector.

Feed-forward is algorithm to calculate output vector from input vector.
Input for feed-forward is input_vector,
output is output_vector.

When you are training neural network, you need to use both algorithms.

When you are using neural network (which have been trained), you are using only feed-forward.

Basic type of neural network is multi-layer perceptron, which is Feed-forward backpropagation neural network.

There are also more advanced types of neural networks, using modified algorithms.

Also good source to study : <a href="ftp://ftp.sas.com/pub/neural/FAQ.html" rel="noreferrer">ftp://ftp.sas.com/pub/neural/FAQ.html</a>
Best to understand principle is to program it (tutorial in this video) <a href="https://www.youtube.com/watch?v=KkwX7FkLfug" rel="noreferrer">https://www.youtube.com/watch?v=KkwX7FkLfug</a>

blocks|key|453584|text|前馈神经网络是一种神经网络体系结构，其中的连接是“前馈”的，即不形成循环(如在递归网中)。|type|unordered-list-item|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|453585|术语“前馈”也是在输入层输入某物时使用的，它从输入层传递到隐藏层，从隐藏层传递到输出层。
值是“前馈”。|453586|这两种短语“前馈”的使用都与培训本身无关。|unstyled|453587|反向传播是一种训练算法，由两个步骤组成:+1)+前馈值，2)计算误差，将其传回到早期层。因此，准确地说，前向传播是反向传播算法的一部分，但在反向传播之前。|453588|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Feedforward_neural_network^0|D|4|0|6|0|0|19|6|0|0|7|4|O|2|Z|4|1P|2|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|13|8|@]|D|@]|E|$]]|$1|K|3|L|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|J|7|1D|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

<ul>
<li>A <a href="http://en.wikipedia.org/wiki/Feedforward_neural_network" rel="noreferrer">Feed-Forward Neural Network</a> is a type of Neural Network architecture where the connections are "fed forward", i.e. do not form cycles (like in recurrent nets).</li>
<li>The term "Feed forward" is also used when you input something at the input layer and it travels from input to hidden and from hidden to output layer. The values are "fed forward".</li>
</ul>

Both of these uses of the phrase "feed forward" are in a context that has nothing to do with training per se.

<ul>
<li>Backpropagation is a training algorithm consisting of 2 steps: 1) Feed forward the values 2) calculate the error and propagate it back to the earlier layers. So to be precise, forward-propagation is part of the backpropagation algorithm but comes before back-propagating.</li>
</ul>

blocks|key|1750232|text|神经网络可以有不同的结构。它们的神经元之间的联系决定了信息流的方向。根据网络连接的不同，它们被分为前馈和递归(反向传播).|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1750233|前馈神经网络|offset|length|style|BOLD|1750234|在这些类型的神经网络中，信息流只有一个方向，即从输入层到输出层。一旦确定了权重，它们通常不会改变。要么显式地确定权重，要么使用像径向基函数这样的函数来确定权重。这里的节点在不知道所产生的结果是否准确的情况下执行它们的工作(也就是说，它们不会根据所产生的结果重新调整)。没有从前面的层返回的通信。|1750235|递归神经网络(反向传播)|1750236|信息从输入层传递到输出层，从而产生结果。结果中的错误现在被传递回以前的层。节点可以知道他们对错误答案的贡献有多大。重量被重新调整。对神经网络进行了改进。它学会了。有双向的信息流。这基本上有两个算法实现，前馈和反向传播。|1750237|entityMap^0|0|0|6|0|0|0|C|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@$D|R|E|S|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|T|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|U|8|@$D|V|E|W|F|G]]|9|@]|A|$]]|$1|L|3|M|5|6|7|X|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|O|$]]

Neural Networks can have different architectures. The connections between their neurons decide direction of flow of information. Depending on network connections, they are categorised as - Feed-Forward and Recurrent (back-propagating).

Feed Forward Neural Networks

In these types of neural networks information flows in only one direction i.e. from input layer to output layer. When the weights are once decided, they are not usually changed. One either explicitly decides weights or uses functions like Radial Basis Function to decide weights. The nodes here do their job without being aware whether results produced are accurate or not(i.e. they don't re-adjust according to result produced). There is no communication back from the layers ahead.

Recurrent Neural Networks (Back-Propagating)

Information passes from input layer to output layer to produce result. Error in result is then communicated back to previous layers now. Nodes get to know how much they contributed in the answer being wrong. Weights are re-adjusted. Neural network is improved. It learns. There is bi-directional flow of information. This basically has both algorithms implemented, feed-forward and back-propagation.

blocks|key|1750260|text|简单地说：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1750261|饲料是一种建筑。相反的是递归神经网络。|1750262|反向传播(BP)是一种求解方法。BP神经网络可以同时解决饲料和递归神经网络问题.|1750263|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

To be simple: 

Feed-foward is an architecture. The contrary one is Recurrent Neural Networks. 

Back Propagation (BP) is a solving method. BP can solve both feed-foward and Recurrent Neural Networks.

What is the difference between back-propagation and feed-forward neural networks?

By googling and reading, I found that in feed-forward there is only forward direction, but in back-propagation once we need to do a forward-propagation and then back-propagation. I referred to <a href="http://www.nnwj.de/backpropagation.html" rel="noreferrer">this link</a>

<ol>
<li>Any other difference other than the direction of flow? What about the weight calculation? The outcome? </li>
<li>Say I am implementing back-propagation, i.e. it contains forward and backward flow. So is back-propagation enough for showing feed-forward?</li>
</ol>

What is the difference between back-propagation and feed-forward Neural Network?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋 

腾讯云代码助手

CODING DevOps

Cloud Studio

SDK中心

API中心

命令行工具

反向传播和前馈神经网络有什么区别？通过谷歌搜索和阅读，我发现在前馈中只有前进方向，但在反向传播中，一旦我们需要做一个前向传播，然后反向传播。我指的是除了流向之外，还有其他的区别吗？那重量计算呢？结果呢？假设我正在实现反向传播，即它包含正向和反向流。那么，反向传播是否足以显示前馈？

问反向传播和前馈神经网络有什么区别？
EN

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问反向传播和前馈神经网络有什么区别？EN