自动编码器：在Tensorflow 2.0中实现降噪，稀疏，压缩，可变自动编码器（VAE）和Beta-VAE

# Autoencoders in TensorFlow 2.0 Implementations of following Autoencoders; - Vanilla Autoencoder (AE) - Denoise Autoencoder - Sparese Autoencoder (in progress) - Contractive Autoencoder (in progress) - Variational Autoencoder (VAE) - Conditional Variational Autoencoder (CVAE) - Beat Variational Autoencoder (beta-VAE) (in progress) ## How to run: run ```python3 main.py --ae_type AE``` ### Parameters that we can pass: - ae_type: Type of autoencoder - AE, DAE, VAE, CVAE, BetaVAE - latent_dim: Degree of latent dimension - 2, 3, etc. - num_epochs: The number of training epochs - 100 etc. - learn_rate: Learning rate during training - 1e-4 - batch_size: Batch size - 1000