本项目是从 https://github.com/nicodjimenez/lstm fork过来的，稍作修改。原来的模型已经非常好，层次结构很清晰，非常有利于学习，但是不支持batch训练，我想这是非常起码的，所以修改为支持batch训练，有了lstm_batch.py，每个数据格式为[batchs_size,n_length,x_dim] ,另外，我认为代码的接口与loss结合得太过于紧密，我做了一定的分离。
新增加了几个玩具任务（toy_task）:
（1）sin_train.py,这是对函数y=sin(x)的拟合；
（2）mnist_train.py，数据集mnist的文字识别;
(3)序列的copy、reverse、sort（计划中），参照项目： AttentionNeuralNetworkToyTasks
（4）序列文字识别（计划中），参照项目：attention-ocr-toy-example
另外，从 https://github.com/qixianbiao/RNN fork过来的项目也放到一起了，便于学习和阅读。
A basic lstm network can be written from scratch in a few hundred lines of python, yet most of us have a hard time figuring out how lstm's actually work. The original Neural Computation paper is too technical for non experts. Most blogs online on the topic seem to be written by people who have never implemented lstm's for people who will not implement them either. Other blogs are written by experts (like this blog post) and lack simplified illustrative source code that actually does something. The Apollo library built on top of caffe is terrific and features a fast lstm implementation. However, the downside of efficient implementations is that the source code is hard to follow.
This repo features a minimal lstm implementation for people that are curious about lstms to the point of wanting to know how lstm's might be implemented. The code here follows notational conventions set forth in this
well written tutorial introduction. This article should be read before trying to understand this code (at least the part about lstm's). By running python test.py you will have a minimal example of an lstm network learning to predict an output sequence of numbers in [-1,1] by using a Euclidean loss on the first element of each node's hidden layer.
Play with code, add functionality, and try it on different datasets. Pull requests welcome.
Please read my blog article if you want details on the backprop part of the code.
This sample code has been ported to the D programming language by Mathias Baumann: https://github.com/Marenz/lstm as well as Julia by @hyperdo https://github.com/hyperdo/julia-lstm, Alfiuman in C++ (with CUDA) https://github.com/Alfiuman/WhydahGally-LSTM-MLP and Ascari in JavaScript (for nodejs) https://github.com/carlosascari/lstm.