Python Image Processing Cookbook

How it works...

We used nn.Sequential() from PyTorch to build the autoencoder by sequentially specifying the building blocks (nn.Module). Each pixel from a noisy image forms an input node, hence the input has 50*37 nodes. The output also has the same number of nodes.

A fully connected layer of size 1,850 x 512 was instantiated with nn.Linear(50*37, 512). nn.ReLU(True) was used to apply in-place ReLU non-linearity in between the input/hidden layers and nn.Sigmoid() before the final layer to apply the sigmoid function at the output layer. Binary cross-entropy loss was used with nn.BCEloss(). The backward() function was used to backpropagate and recompute the weights in the neural net.

We can run the code on a GPU by setting cuda = True