The output image can then be fed through the network again, repeatedly, until features are magnified and related to objects the network already recognizes from its training process. However, this feature selection is then used to alter the input image by enhancing the features it notices. When fed an image, like a normal neural network, it performs careful feature selection to try and classify the image (noticing features like lines and shapes, eyes and noses). The Deep Dreamer is different than a typical neural network. I’ll be referencing the Google Deep Dreamer neural network a bit here, used to visualize neural network image interpretation. After this training process, the network is then able to operate normally. Then, for each picture, the network will then be given the correct answer, and through a method called “back propagation,” it will adjust the biases of the neurons in all three layers so that it would correctly classify the image if presented again. For each picture, the neural network will guess whether it’s a chair or not. For example, the network will be presented with 10 pictures of a chair, and 10 pictures of anything but a chair. The higher the weight, the more intensity the neuron’s excitatory/suppressive function has on its neighbors.Īrtificial Neural Networks learn by being “trained” on an initial dataset. ![]() The hidden layer consists of the remaining layers of interconnected neurons.Įach of these neurons has a “weight” that’s positive or negative, which excites or suppresses neighboring neurons. The input layer receives sensory data, which is then fed into the hidden layer, which interprets the data and signals a response that is reported by the output layer. These layers are classified into three main groups- an input layer, a hidden layer, and the output layer. Usage: deepdreamer.Google Deep Dreamer: An Introduction to Artificial Neural NetsĪn Artificial Neural Network consists of anywhere from a few dozen to millions of artificial neurons arranged in interconnected layers. So the effort was now dubbed Deep Dream, a portmanteau that evoked both deep learning of neural nets and the dreamy surrealism of the systems outputs.
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