config.py:

• Switch different deep learning models and backends
  (1) Current deep learning models available: CNN, LSTM
  (2) Current backends available: Keras, Pyotrch, TensorFlow

data_file.py:

• Data file location

data_load.py:

• Data load function

data_review.py:

• Explore the dataset

flask_webservice.py:

• Web service, flask server
  Input: dataset & model name
  Output: performance (accuracy, precision, recall, f1score)

main.py:

• Main function

main_kfold.py:

• K-fold cross-validation with the dataset

main_onnx.py:

• Translate saved model file to .onnx file

main_original_keras.py:

• Original file using Keras

main_original_pytorch.py:

• Original file using Pytorch

main_original_tensorflow.py:

• Original file using TensorFlow

main-split.py:

• Manually separate train and test dataset

model.py:

• Model architecture
  Current architectures available:
  CNN_Keras, LSTM_Keras, CNN_Pytorch, LSTM_Pytorch, CNN_TensorFlow, LSTM_TensorFlow

pytorch_minst.py:

• Minst dataset using Pytorch

util.py:

• Utilities