By Annie Goetzinger, Pierre Christin
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Extra resources for Agence Hardy, Tome 5 : Berlin, zone française
Perhaps this might provide insight into how our own brains take simple electrical signals and combine them to perform complex reactions. We will also see in this book how you can “trick” a neural network after training it! You may think it has learned to recognize all the images in your dataset, but add some intelligently designed noise, and the neural network will think it’s seeing something else, even when the picture looks exactly the same to you! So if the machines ever end up taking over the world, you’ll at least have some tools to combat them.
One small note about the proper terminology here - “deep belief network” refers to the unsupervised model, whereas if you train it in a supervised way it is called a “deep neural network”. Sometimes “deep belief network” can also refer to the stacked autoencoder configuration from the last chapter. Contrastive Divergence In this section we are going to talk about the sampling algorithm, called contrastive divergence, that we use to estimate the gradient we derived in the last section. If you found the material in the last lecture hard, don’t worry too much about it, because following the steps of the CD algorithm is actually much easier.
Recall that W is a DxM matrix, so taking 1 column will give us a D-dimensional vector. imshow() from matplotlib. imshow(X, cmap=‘gray’) To ensure the image is grayscale rather than a heatmap. For the layers beyond the input-to-hidden, we can’t solve in closed-form, so we return to our old friend gradient descent. This should be simple since Theano can calculate gradients for us. The only caveat is that you now need to take the derivative with respect to the input, not the weights, like we usually do during training.
Agence Hardy, Tome 5 : Berlin, zone française by Annie Goetzinger, Pierre Christin