Stacked autoencoder

Until now we have restricted ourselves to autoencoders with only one hidden layer. We can build Deep autoencoders by stacking many layers of both encoder and decoder; such an autoencoder is called a Stacked autoencoder. The features extracted by one encoder are passed on to the next encoder as input. The stacked autoencoder can be trained as a whole network with an aim to minimize the reconstruction error. Or each individual encoder/decoder network can first be pretrained using the unsupervised method you learned earlier, and then the complete network can be fine-tuned. When the deep autoencoder network is a convolutional network, we call it a Convolutional Autoencoder. Let us implement a convolutional autoencoder in TensorFlow 2.0 next.

Convolutional autoencoder for removing noise from images

In the previous section we reconstructed handwritten digits from noisy input images. We used a fully connected network as the encoder and decoder for the work. However...