For example, the first layer maybe looks for edges or corners. We know that after training, each layer progressively extracts higher and higher-level features of the image, until the final layer essentially makes a decision on what the image shows. One of the challenges of neural networks is understanding what exactly goes on at each layer. The network’s “answer” comes from this final output layer. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network typically consists of 10-30 stacked layers of artificial neurons. We train an artificial neural network by showing it millions of training examples and gradually adjusting the network parameters until it gives the classifications we want.
So let’s take a look at some simple techniques for peeking inside these networks. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. However, images based on places by MIT Computer Science and AI Laboratory require additional permissions from MIT for use.Īrtificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition.
under a Creative Commons Attribution 4.0 International License. Images in this blog post are licensed by Google Inc. Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer
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