This is an implementation of an autoencoder with an attention mechanism in keras using the SeqSelfAttention layer from the keras-self-attention library.
keraskeras-self-attention
To use the autoencoder, simply run the autoencoder.py file using Python. The autoencoder can be trained on your own dataset by loading the data into the x_train and x_test variables and modifying the input_dim variable to match the shape of your input data.
The results of the autoencoder with attention can be evaluated using the reconstruction loss and other metrics such as accuracy. The reconstructed outputs can also be visualized to see how well the autoencoder is able to reconstruct the original inputs.
The addition of an attention mechanism to the autoencoder can help to improve the reconstruction of the input data by allowing the model to focus on the most important features in the encoding.