RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.
Your framework is very great but there are something I think you should change a little to get your code more easy to use. Can you change your code in the way that it can receive directly a matrix, a jagged array as input, have a train and predict method like here ? (The example)
Thanks for your suggestion. I'm thinking about refactoring each neural network layer to abstract interface. If you have any idea about it, please let me know. Thanks.
Hello zhongkaifu,
Your framework is very great but there are something I think you should change a little to get your code more easy to use. Can you change your code in the way that it can receive directly a matrix, a jagged array as input, have a train and predict method like here ? (The example)
http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_MulticlassSupportVectorMachine.htm
With this change, I think you will make a big milestone.
Regards