README_LSTM

### Readme for the LSTM classifier in the gesture recognition application

The LSTM module uses a neuronal network (NN) with LSTM blocks as hidden layer. For NN training is necessary. 
The module provides both the classification with a trained network and the training itself. Additionally some
functions for better handling are provided. 

### Features:
- create a neuronal network with different input dimension size, a hidden Layer of n LSTM Blocks and an output 
Layer with different activation functions
- network can be trained with different training algorithms and different iterations and a trainig set. 
- network can be validated against a test dataset
- network and datasets can be saved and loaded. The network is saved in xml format and can be modified manually. 
Autosave and autoload is available
- almost all options are customizable through a personal config file. 
- network can classify with different variants live events
- recognized gestures can be bound to a simulated key press of a keyboard (through config file)
- and other

### Usage:
Config parameters are explained in config/default.cfg. On first start a config/personal.cfg file is created. Changes can be made here. 
An already trained network is provided and specified in default.cfg. 

some commands for console:
u lstm 				- loads LSTM module
v					- validates network against the testset and prints the error and a confmat
t					- trains the network with the number of configured epochs
t <num>				- rains the network with specifed epochs
p					- prints the modules and weight of the network to console
c					- start classification

# Use these commands with care as some states checks and parsing functions are not compatible to customnames
l <networkpath> 	- load network from this file (.xml will be appended automatically)
s <networkpath>		- saves the current network to this path (.xml will be appended automatically)
l ds <datasetpath> 	- load dataset from this file (.data will be appended automatically)
s ds <datasetpath>	- saves the current dataset to this path (.data will be appended automatically)