An input image x, with 65 values between 0 and 1 is fed to the autoencoder. A neural layer transforms the 65-values tensor down to 32 values. The next layer produces a core tensor with 8 values. The ...

The main disadvantage of using a neural autoencoder is that you must fine-tune the training parameters (max epochs, learning rate, batch size) and the number of nodes in the hidden layer. [Click on ...