config.txt

##Training config

[dataset]
#Folder containing the training and testing datasets
dataset_folder = /root/neuralnetwork-IoT/dataset/
#Dataset used for training and validation
train_dataset_name = UNSW_2018_IoT_Botnet_Full5pc_Train_Small.csv
#Dataset used for testing
test_dataset_name = UNSW_2018_IoT_Botnet_Full5pc_Test_Small.csv

[hyperparameters]
#Learning rate values used during tuning
lr = [1e-3,5e-4,1e-4]
#Batch size values used during tuning
batch_size = [32,64,100,128,1000]
#Epoch values used during tuning
epochs = [5,10,100]
#Neural network architectures used during tuning; accepted values are: MLP1,MLP2,RNN1,RNN2,RNND,LSTM1,LSTMD,BLSTM1
architecture = [MLP1,MLP2,RNN1,RNN2,RNND,LSTM1,LSTMD,BLSTM1]

[pc-specs]
#Number of CPU cores used during training
num_cpu = 4
#Number of GPUs used during training
num_gpu = 0


##Tool config

[quantization]
#Obtain results using PTQ (Post-Training Quantization) on the selected model
quantize = False

[model]
#Folder containing the classification model
model_location = /root/neuralnetwork-IoT/models/
#Neural network architecture of the classification model; accepted values are: MLP1,MLP2,RNN1,RNN2,RNND,LSTM1,LSTMD,BLSTM1
architecture = RNN1