blocks|key|679550|text|那this+one呢？只是在谷歌上搜索一下就行了。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|679551|entityMap|0|LINK|mutability|MUTABLE|url|http://snarli.sourceforge.net/docs/edu/wlu/cs/levy/SNARLI/BPLayer.html^0|1|8|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

What about <a href="http://snarli.sourceforge.net/docs/edu/wlu/cs/levy/SNARLI/BPLayer.html" rel="nofollow">this one</a> ? Just a Google search to help...

blocks|key|2581935|text|假设您已经为BP使用了一些库，那么使用BP作为流程中的一个步骤来实现BPTT应该是相当简单的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2581936|BPTT+1的维基百科条目包括相关的伪代码。|2581937|我自己的出发点，大约是18年前，是“卡车杀手-上部:神经网络中自我学习的一个例子”2。|2581938|1|offset|length|2581939|2|2581940|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Backpropagation_through_time|http://www-isl.stanford.edu/~widrow/papers/c1989thetruck.pdf^0|0|0|0|0|1|0|0|0|1|1|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|X|8|@]|9|@$H|Y|I|Z|1|10]]|A|$]]|$1|J|3|K|5|6|7|11|8|@]|9|@$H|12|I|13|1|14]]|A|$]]|$1|L|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|M|$N|$5|O|P|Q|A|$R|S]]|G|$5|O|P|Q|A|$R|T]]]]

Assuming you're already using some library for BP, it should be (TM) rather straightforward to implement BPTT using BP as a step in the process.

The Wikipedia entry for BPTT [1] includes relevant pseudo code. 

My own starting point, about 18 years ago, was "The Truck Backer-Upper: An Example of Self-Learning in Neural Networks" [2].

[1] <a href="http://en.wikipedia.org/wiki/Backpropagation_through_time" rel="nofollow">http://en.wikipedia.org/wiki/Backpropagation_through_time</a>

[2] <a href="http://www-isl.stanford.edu/~widrow/papers/c1989thetruck.pdf" rel="nofollow">http://www-isl.stanford.edu/~widrow/papers/c1989thetruck.pdf</a>

blocks|key|2298632|text|不过，我使用NeuronDotNet的时间有限。它允许您创建前馈BackPropagation神经网络。我特别喜欢他们使用直观命名的类。祝好运!|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2298633|这是一个.net库。|2298634|entityMap|0|LINK|mutability|MUTABLE|url|http://neurondotnet.freehostia.com/manual/index.html^0|6|C|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

I've used <a href="http://neurondotnet.freehostia.com/manual/index.html" rel="nofollow noreferrer">NeuronDotNet</a> only for a limited time though. It allows you to create a feed-forward BackPropagation NN. I especially liked their use of intuitively named classes. Good luck!
This is a .net library.

blocks|key|2582598|text|我有Java背景，但Encog也有一个.net实现(这是一个非常好的NNets框架，有很好的时间序列支持)|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2582599|我对F#框架无能为力，但是你在为哪个领域编码呢？如果是金融，我会重申“看一看Encog”|2582600|entityMap|0|LINK|mutability|MUTABLE|url|http://www.heatonresearch.com/encog^0|A|5|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

I'm from a Java background but <a href="http://www.heatonresearch.com/encog" rel="nofollow">Encog</a> has a .net implementation as well (and is a seriously good framework for NNets, with good time series support)

Can't help with an F# framework, but what domain are you coding for? If it's finance I'll reassert the "take a look at Encog"

blocks|key|4373522|text|也许pybrain会怎么样呢？它的BackpropTrainer类的文档字符串表明它在时间上进行反向传播：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4373523|class+BackpropTrainer(Trainer):
++++"""Trainer+that+trains+the+parameters+of+a+module+according+to+a
++++supervised+dataset+(potentially+sequential)+by+backpropagating+the+errors
++++(through+time)."""|code-block|syntax|javascript|4373524|entityMap|0|LINK|mutability|MUTABLE|url|http://pybrain.org/^0|H|F|2|7|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

Perhaps <a href="http://pybrain.org/" rel="nofollow">pybrain</a> would do? The docstring for its <code>BackpropTrainer</code> class suggests that it does backpropagation through time:

<pre><code>class BackpropTrainer(Trainer):
 """Trainer that trains the parameters of a module according to a
 supervised dataset (potentially sequential) by backpropagating the errors
 (through time)."""
</code></pre>

blocks|key|4373687|text|我对Weka有很好的体验-在我看来，它是最好的，也是几乎可以肯定是最全面的通用机器学习库之一。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4373688|你当然可以用Weka做BPTT+-你可能会找到一个现成的分类器来做你需要的事情，但即使不是这样，你也可以按照非常好的wikipedia+article+on+BPTT将几个普通的反向传播单元链接在一起。|4373689|entityMap|0|LINK|mutability|MUTABLE|url|http://www.cs.waikato.ac.nz/ml/weka/|1|http://en.wikipedia.org/wiki/Backpropagation_through_time^0|2|4|0|0|1M|P|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

I've had good experiences with <a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow">Weka</a> - In my view one of the best and almost certainly the most comprehensive general purpose machine learning libraries around. 

You could certainly do BPTT with Weka - you may find a ready made classifier that does what you need but even if not you can just chain a few normal backpropagation units together as per the very good <a href="http://en.wikipedia.org/wiki/Backpropagation_through_time" rel="nofollow">wikipedia article on BPTT</a>

blocks|key|2582733|text|我很久以前就用Java做了反向传播算法。我已经把它上传到GitHub了，也许你会发现它很有用：https://github.com/bernii/NeuralNetwokPerceptronKohonen|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2582734|现在让我看看它是否对您有帮助:)|2582735|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/bernii/NeuralNetwokPerceptronKohonen^0|1B|1J|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

I made backpropagation algorithm in Java quite time ago. I uploaded it into GitHub, maybe you can find it useful: <a href="https://github.com/bernii/NeuralNetwokPerceptronKohonen" rel="nofollow">https://github.com/bernii/NeuralNetwokPerceptronKohonen</a>

Let me now if it was helpful :)

blocks|key|2582778|text|你可以使用TensorFlow的dynamic_rnn()函数(API+doc)，TensorFlow的tutorial+on+Recurrent+Neural+Networks将会有所帮助。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2582779|此外，这个很棒的blog+post为使用TensorFlow预测序列提供了一个很好的介绍。这是另一个使用some+code预测时间序列的blog+post。|2582780|entityMap|0|LINK|mutability|MUTABLE|url|http://tensorflow.org/|1|https://www.tensorflow.org/versions/r0.10/api_docs/python/nn.html#dynamic_rnn|2|https://www.tensorflow.org/versions/r0.10/tutorials/recurrent/index.html|3|https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/|4|https://github.com/mouradmourafiq/tensorflow-lstm-regression/blob/master/lstm.py|5|http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html^0|G|D|5|A|0|W|7|1|1G|11|2|0|8|9|3|1G|9|4|1W|9|5|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]]|D|@$9|12|A|13|1|14]|$9|15|A|16|1|17]|$9|18|A|19|1|1A]]|E|$]]|$1|F|3|G|5|6|7|1B|8|@]|D|@$9|1C|A|1D|1|1E]|$9|1F|A|1G|1|1H]|$9|1I|A|1J|1|1K]]|E|$]]|$1|H|3|-4|5|6|7|1L|8|@]|D|@]|E|$]]]|I|$J|$5|K|L|M|E|$N|O]]|P|$5|K|L|M|E|$N|Q]]|R|$5|K|L|M|E|$N|S]]|T|$5|K|L|M|E|$N|U]]|V|$5|K|L|M|E|$N|W]]|X|$5|K|L|M|E|$N|Y]]]]

You can use <a href="http://tensorflow.org" rel="nofollow">TensorFlow</a>'s <code>dynamic_rnn()</code> function (<a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/nn.html#dynamic_rnn" rel="nofollow">API doc</a>). TensorFlow's <a href="https://www.tensorflow.org/versions/r0.10/tutorials/recurrent/index.html" rel="nofollow">tutorial on Recurrent Neural Networks</a> will help.

Also, this great <a href="https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/" rel="nofollow">blog post</a> provides a nice introduction to predicting sequences using TensorFlow. Here's another <a href="http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html" rel="nofollow">blog post</a> with <a href="https://github.com/mouradmourafiq/tensorflow-lstm-regression/blob/master/lstm.py" rel="nofollow">some code</a> to predict a time series.

Does anyone know of a library with a working implementation of backpropagation through time?
Any of Java/Python/C#/VB.NET/F# (preferably the last one) will do!

Backpropagation through time

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。

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有没有人知道有一个可以通过时间反向传播的库？Java/Python/C#/VB.NET/F#中的任何一个(最好是最后一个)都可以！

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