Configuration File

The configuration file CNNTestApp_conf.cnn is a text file included in \CNN_APP\Release\ that allows the user to select the CDF files used for training (or testing) and to specify the CNN training and configuration parameters.

CDF file selection:

  • item_no is the ID of the CDF training and testing files.

  • dir is the directory of the CDF files. Please make sure that it includes no spaces.

  • title is the title of the CDF training and testing files. Note that the application requires naming the CDF files as title_ID_train.CDF and title_ID_test.CDF.

  • id is the ID of the CDF training and testing files.

  • btrain determines whether the application will be used for CNN training or testing. Use btrain t to tell the application to use the data in title_ID_train.CDF to train a CNN. If any value other than t is used for this parameter, the application will run CNN testing using the data in title_ID_test.CDF.

CNN Training Parameters:

  • no_iter is the no of iterations.

  • no_runs is the total number of runs.

  • update 0: batch, 1: online, N>1: update sensitivities every N iteration.

  • tr_rate is the train rate.

  • lp is the BP eps.

  • decay_lp is the lp decay factor.

  • stop_ce is the target train CE to check stopping criteria.

  • delta_mse is the train stop criteria over delta MSE.

  • flags: 4 output flags for MATLAB, CNN, AS and LOG files. Use flags 1 1 1 1 to activate the 4 outputs.

CNN Configuration Parameters:

  • ch_bias: please keep this as 0.

  • in_size: please keep this as 1.

  • fs is the filter size.

  • ssx is the subsampling factor.

  • ssy: use any number here. It has no effect on the results.

  • fn: specifies the activation functions: _tanh = 0, _sigm = 1, _linear = 2, _binary = 3, _relu = 4, _lincut = 5.

  • ctype: please keep this as 0.

  • ptype is the subsampling mode: max. pool = 0, avg. pool=1

  • cnn_nol is the number of CNN layers (including the input layer).

  • mlp_nol is the number of MLP layers (including the output layer).

  • width is the number of samples in the input frame (i.e. array).

  • heigth use any number here. It has no effect on the results.

  • min_non indicates the number of neuron in each hidden CNN and MLP layer. For example, if your CNN has 2 hidden CNN layers each with 60 neurons and 3 hidden MLP layers each with 10 neurons, you need to write: min_non 0 60 60 10 10 10 0.

  • max_non: please use the same value specified for min_non.

For more information regarding the 1D CNN parameters and structure, please refer to the following papers:

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks.