The api made simple as possible.
Also added Optuna wrapper - Use the relevant switches in order to ask Optuna to find the best parameters for your model
git clone https://github.com/tomerkeren42/MetabolicRate.git
pip install -r requirements.txt
usage: RMR Predictor -p PATH [-h] [-o] [--study-name STUDY_NAME] [--trials TRIALS] [--log LOG] [--epochs EPOCHS] [--learning-rate LEARNING_RATE] [--hidden-units HIDDEN_UNITS] [--optimizer-name {Adam,RMSprop,SGD}] [--dropout DROPOUT] [--weights_file WEIGHTS_FILE]
Starting RMR predictor tool - trainable deep learning net, which is compared to other ML algorithms
positional arguments:
-p PATH, --path Enter path to dataset
optional arguments:
-h, --help show this help message and exit
-o, --optuna Run Optuna optimization for detecting best DL model parameters
--study-name Run Optuna optimization for detecting best DL model parameters
--trials Number of epoch for Deep Learning Model
--log Write output to new log file at logs/ directory
--epochs Number of epoch for Deep Learning Model
--learning-rate Step size for the optimizer which trains the DL model
--hidden-units Number of hidden units in the hidden layer of the DL model
--optimizer-name {Adam,RMSprop,SGD}
Optimizer for training the DL model
--dropout Probability of dropout layer for turning off neurons in the DL model
--weights_file Path to weight file, if exist and do not want to train new net
