Building and training an image denoising autoencoder using Keras with Tensorflow 2.0 as a backend.
- Import Key libraries, dataset and visualize images
- Perform image normalization, pre-processing, and add random noise to images
- Build an Autoencoder using Keras with Tensorflow 2.0 as a backend
- Compile and fit Autoencoder model to training data
- Assess the performance of trained Autoencoder using various KPIs
- Basic programming experience in python
- Basic understanding of Tensorflow
- Working knowledge of Machine Learning and Deep Learning
Autoencoders are a type of Artificial Neural Networks that are used to perform a task of data encoding (representation learning). Autoencoders use same input data for input as well as output, crazy right?
Autoencoders work by adding a bottleneck in the network. This bottleneck forces the network to create a compressed (encoded) version of the original input. Autoencoders work well if correlations exist between input data and (performs poorly if all the input data is independent).
Great reference: “Intro to Autoencoders by Jeremy Jordan”
Encoder: h(x) = sigmoid (W * x + b)
Decoder: x̂ = sigmoid (W* * h(x) + c)
Autoencoders objective is to minimize the reconstruction error which is the difference between the input X and the network output X̂.
Autoencoders dimensionality reduction (latent space) is quite similar to PCA (Principal Component Analysis) if linear activation functions are used.
A step by step series of examples that tell you how to get a development env running
Download the prerequisites from the requirements.txt file.
pip install -r requirements.txt
Open jupyter notebook on your local host or you can use Google Colab too.
Follow the steps in the notebook if you want to train your own or you can simply run the notebook in this repo.
This is a side-by-side comparison of the input images with added noise and the reconstructed output from the network.
