Model Configuration

The Model Configuration file contains configuration for training the model, conducting data study, KMC Generation, Benchmarking and Transfer Learning

Model Training Parameters

param model_parameters[‘model_type’]

The type of model to be used for training, currently defaults to 3D CNN

type model_parameters[‘model_type’]

str (required)

param model_parameters[‘output_type’]

(regression, classification) The output type of the model used to initialize the output layer, currently defaults to regression

type model_parameters[‘output_type’]

str (required)

param model_parameters[‘batch_size’]

The batch size while training, can be tuned based on the hardware specifications, currently defaults to 32

type model_parameters[‘batch_size’]

int (required)

param model_parameters[‘epocs’]

The number of epocs the model is to be trained for, currently defaults to 150

type model_parameters[‘epocs’]

int (required)

param model_parameters[‘split_ratio’]

Split Ratio for train and validation

type model_parameters[‘split_ratio’]

float (required)

param model_parameters[‘optimizer’]

The optimizer to be used for model training, refer https://keras.io/optimizers/ for more information, currently defaults to adam

type model_parameters[‘optimizer’]

keras.optimizer (required)

param model_parameters[‘loss_func’]

The loss function to be optimized while model training, refer https://keras.io/losses/ for more information, currently defaults to Mean Squared Error (MSE)

type model_parameters[‘loss_func’]

keras.losses (required)

param model_parameters[‘regularizer_coeff’]

The regularizing coefficient to be used for L2 norm regularization of the fully connected layer, refer https://keras.io/regularizers/ for more information currently defaults to 0.1

type model_parameters[‘regularizer_coeff’]

float (required)

param model_parameters[‘activate_tensorboard’]

Tensorboard activation flag https://www.tensorflow.org/tensorboard, currently set to 0, changes to 1 for activating tensorbiard, Warning: There can be some compatibility issues with different Tensorflow and Cuda Toolkit Versions

type model_parameters[‘loss_func’]

int (required)

Data Study Parameters

param data_study_params[‘batch_size’]

The batch size while conducting data study, can be tuned based on the hardware specifications, currently defaults to 32

type data_study_params[‘batch_size’]

int (required)

param data_study_params[‘epocs’]

The number of epocs the model is to be trained for, currently defaults to 50

type data_study_params[‘epocs’]

int (required)

param data_study_params[‘split_ratio’]

Split Ratio for train and validation during data study

type data_study_params[‘split_ratio’]

float (required)

param data_study_params[‘min_train_samples’]

Minimum train Samples for data study, currently defaults to 100

type data_study_params[‘min_train_samples’]

int (required)

param data_study_params[‘max_train_samples’]

Maximum train samples for data study, dataset size is the maximum value

type data_study_params[‘max_train_samples’]

int (required)

param data_study_params[‘train_increment’]

Increment in the train size with each iteration, currently defaults to 100

type data_study_params[‘train_increment’]

int (required)

Key Measurment Characteristics Generation Parameters

param kmc_params[‘tree_based_model’]

The model to be used while generating feature importance, refer: https://xgboost.readthedocs.io/en/latest/R-package/discoverYourData.html#measure-feature-importance for more details, currently defaults to xgb, random forests can also be used

type kmc_params[‘tree_based_model’]

str (required)

param kmc_params[‘tree_based_model’]

The importance criteria to be used, currently defaults to gini index

type kmc_params[‘tree_based_model’]

str (required)

param kmc_params[‘split_ratio’]

Split Ratio for train and validation during data study

type kmc_params[‘split_ratio’]

float (required)

param kmc_params[‘save_model’]

Flag to save the model, Currently defaults to 0, change to 1 if model needs to be saved

type kmc_params[‘save_model’]

int (required)

param kmc_params[‘plot_kmc’]

Flag to plot the KMC, Currently defaults to 1, change to 0 if no plotting is required

type kmc_params[‘plot_kmc’]

int (required)

Benchmarking Parameters

param bm_params[‘max_models’]

The maximum number of models to be used for benchmarking, currently defaults to 10

type bm_params[‘max_models’]

int (required)

param bm_params[‘runs’]

Number of benchmarking runs to be conducted

type bm_params[‘runs’]

int (required)

Transfer Learning Parameters

param transfer_learning[‘tl_type’]

The type of transfer learning to be used (full_fine_tune, variable_lr, feature_extractor) currently defaults to full_fine_tune

type transfer_learning[‘tl_type’]

str (required)

param transfer_learning[‘tl_base’]

The type of base model (3D CNN Architecture) to be used (pointdevnet, voxnet, 3d-UNet), currently defaults to PointdevNet

type transfer_learning[‘tl_base’]

str (required)

param transfer_learning[‘tl_app’]

The application of the transfer learning model (classification, regression), currently defaults to regression

type transfer_learning[‘tl_app’]

str (required)

param transfer_learning[‘conv_layer_m’]

The learning rate multiplier for convolution layers (only needed when tl_type is variable_lr), defaults to 0.1 (10% of the network Learning Rate)

type transfer_learning[‘conv_layer_m’]

float (required)

param transfer_learning[‘dense_layer_m’]

The learning rate multiplier for dense layers (only needed when tl_type is variable_lr), defaults to 1 (100% of the network Learning Rate)

type transfer_learning[‘dense_layer_m’]

float (required)