Building a Stacked Autoencoder with TensorFlow and Keras
In this tutorial, we implement a Stacked Autoencoder using TensorFlow and Keras to compress and reconstruct the MNIST dataset. We explore how to take a 784-pixel image, compress it into a tiny 30-unit bottleneck, and successfully reconstruct the original digit.
Key Technical Concepts Covered:
Encoder vs. Decoder: How to design a symmetric architecture for feature extraction.
SELU Activation: Why Scaled Exponential Linear Units are used for self-normalizing networks.
Loss Functions: The reasoning behind using Binary Cross-Entropy over MSE for faster convergence in image reconstruction.
Optimization: Fine-tuning the model using SGD with a 1.5 learning rate.
The Results: Watch as the model learns to compress data by over 96% while maintaining the recognizable structure of handwritten digits. We analyze the learning curves as the validation loss drops to 0.098 and visualize the final reconstructions.
Tools Used: TensorFlow, Keras, NumPy, and Matplotlib.