then the array is slowly perturbed so that it increases the excitation of the target neuron. a test stimulus starts out as a random noisy array. The stimulus is a 2d matrix of pixels, usually grayscale, but possibly in color. one can choose different neurons that were assigned during training. the ANN will have neurons that classify based on image types. the weights are 'frozen' and one can analyze what types of 'stimulus' 'most excite' certain 'neurons' in this ANN. Now you have weights and a network for this ANN. this is also usually done on a cluster supercomputer. sometimes this is collected from youtube videos, and there are other large standard 'corpuses' (collections) of data used in machine learning. Train a 'deep' artificial neural network using large amounts of image data. There are several basic concepts going on here that are not all explained in a straightforward/ brief way in most references, of which there are now many, which tend to range from scientific articles to handwaving/ 'glossing over' popular science accounts. For other images, they start with one image that is not related to the training set, send it through the network, then send the output through the network repeatedly to accentuate features that the network found in the image. For some of the images, they observe the state of the image in layers between the input and output, resulting in artistic looking sketches of the original. The images published for the Deep Dream project are different ways of visualizing the data from trained networks. After some training, they begin to recognize basic features that are common between members of the training set, such as edges or colors. All of the images are generally of some type, like animals. Deep Belief Networks are trained to reconstruct their original image. That works, essentially, by transforming an input by several matrices (layers) through the network to get an output. In short, it's a way for machines to 'learn' about data (images, in this case). Google's Deep Dream project is research being done to visualize neural networks' learning to understand more about them.įirst, you have to understand a Deep Belief Network on a high level.

Deep dreamer 21