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Autoencoders
An impressive reconstruction of images from noisy images, I'm sure you'll agree.
You can access the code in the notebook DenoisingAutoencoder.ipynb if you want
to play around with it.
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, we know that for images, a convolutional al network can give better
results, so in this section we will use a convolution network for both the encoder and
decoder. To get better results we will use multiple convolution layers in both the
encoder and decoder networks; that is, we will make stacks of convolutional layers
(along with maxpooling or upsample layers). We will also be training the entire
autoencoder as a single entity.
1. We import all the required modules; also for convenience import specific
layers from tensorflow.keras.layers:
import numpy as np
import tensorflow as tf
import tensorflow.keras as K
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