tf-keras-stacked-convolutional-autoenconder

• Implementation of Stacked Convolutional Autoencoder.
• Evaluated the denoising performance using MNIST.
• Used Python3.7 + TensorFlow 2.3 + tf.keras.

Settings

• I recommend to make virtual environment by venv.
  1. python -m venv .venv

0. Enter to virtual environment .venv/Scripts/activate.ps1 (Windows) or .venv/Scripts/activate (Linux)
1. pip install -r requirements.txt

Run

1. python train.py --epochs 20 --batch_size 100 --stacked 1 --snr -40 (defalut options)

   • --epochs or -e: The number of epochs
   • --batch_size or -b: The number of batch size
   • --stacked or -s: 1 or 0 (stacked or not)
   • --snr or -n: The value of SNR for denoising autoencoder. The smaller value is noisy.

2. python eval.py --dir_model path_to_hdf5 --snr -40

   • --dir_model or -d: path to model.
     • hdf5 file will generate in ../dist/train_conditions/models
   • --snr or n: same to train.py
   • SNR value will printed.

Results

• Denoising performance improved slightly.
  • SNR comparison:
    • stacked: 9.307
    • no_stacked: 9.184

Others

• If you have any problems, please let me know.
• Please send me the pull request if you find wrong.