Command to run CLI

python run.py --infile <input filename> --outfile <output filename> --learnrate <(0.0,1.0]>

Other command-line options are given below.

Configuration parameters as command-line options

Specify the learning rate (default value is 0.01) -- *required.
```
 --learnrate
```
Determines the step size of the weight update and is critical to the convergence of the algorithm to the global minima of the objective function. A very high learning rate might end up missing certain local minima. A very low learning will end up slowing down the learning process.
Specify the number of layers in the neural network.
```
 --nlayers
```
Number of layers depends on the number of features that need to be extracted from the data.
Specify the dimensions of each layer in the neural network.
```
 --layerdim
```
Number of computation units in each layer.
Specify the fraction of RUnits that are dropped out (value is in range [0.0, 1.0).
```
 --dropout_fraction_ru
```
Drop a fraction of recurrent connections that remember the past results. Dropping RUnits would reduce exposure to data and hence, prevent overfitting.
Specify the fraction of input units that are dropped out (value is in range [0.0, 1.0).
```
 --dropout_fraction_rw
```
Drop fraction of inputs from passing to the next layer in the network. This helps in being more robust to noise.
Specify the optimizer.
```
 --optimizer
```
There are currently three optimizers (Adam, SGD and RMSprop).
Specify the momentum.
```
 --momentum
```
Weighted sum of past gradients that is used to accelerate learning and provide direction to the optimiser.
Specify the training percent (The value is in range (0.0, 1.0]).
```
 --trainpct
```
Percent of data to be used for training. The remaining would be used for testing and evaluation.
Specify the error metric.
```
 --errmetric
```
Specify the number of epochs.
```
 --epoch
```
Number of passes through the whole training data.

Command-line options

Specify input filename (.csv) -- *required.
```
 --infile
```
The input file currently needs to contain 1-dimensional data.
Specify output filename -- *required.
```
 --outfile
```

Specify a config JSON file as input.

 --config

Can use this to provide a file containing a JSON with appropriate parameters as the configuration to run the neural network. If config file provided, then all configuration parameters specified (those specified above) on the command-line would be ignored.

*Example config JSON is shown below*,
```
{  
  "n_layers": 4,  
  "dropout_fraction_ru": 0.1,  
  "dropout_fraction_rw": 0.1,  
  "layer_dimensions": [1, 60, 60, 1],  
  "optimizer": "adam",  
  "learning_rate": 0.001,  
  "momentum": 0.1,  
  "training_percent": 0.5,  
  "err_metric": "mean_squared_error",  
  "epoch": 10  
}
```

Specify log filename (default logfile is _log).
```
 --logfile
```
Append the run configuration to the logfile.
```
 --append
```

Command to run GUI

python gui.py

GUI Guide -- Configuration Input

tomfisher/Deep_Learning_time_series_data

Command to run CLI

Configuration parameters as command-line options

Command-line options

Command to run GUI

GUI Guide -- Configuration Input

GUI Guide -- Results