Detailed instructions for setting up DeepDream in Ubuntu can be found here. Detailed instructions for setting up DeepDream on a Windows machine can be found here. We're also open to suggestions, opinions, corrections and all kinds of comments, so don't hesitate to leave a message! What Is DeepDream? By applying some extra constraints manually, the results looks pretty good, if you ask me: By tuning the parameters in the neural network it can produce what we asked it to produce a banana So, the main result here is that the network stores features from the images and can reproduce them.
You can find the whole "official" Inceptionism gallery here , and if you want to see results from several other users just search for "DeepDream images" or use the deepdream hastag in Twitter : Others took it further and created animated GIFs using the program: Credit: Ross Goodwin And others proceeded even more and created videos too, like the one below.
You can also make requests here. Method 2 Medium : Do It Semi-Manually This guy right here has done more than half of the work for you, packaged the code with all required dependencies and stuff, so all you have to do is set up his package in your computer. I used sudo for each Get Google Protobuf. Follow the final steps here to run the code. Unix Detailed instructions for setting up DeepDream in Ubuntu can be found here.
Windows Detailed instructions for setting up DeepDream on a Windows machine can be found here. Stay Connected Follow us to get the latest tech tutorials, news, and giveaways as soon as we post them.
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Home Tools Gallery. Contact Log In Sign Up. Deep Dream Generator. Sign Up. Human Collaboration. Deep Style The technique is a much more advanced version of the original Deep Dream approach. There are 11 of these layers in InceptionV3, named 'mixed0' though 'mixed10'. Using different layers will result in different dream-like images. Deeper layers respond to higher-level features such as eyes and faces , while earlier layers respond to simpler features such as edges, shapes, and textures.
Feel free to experiment with the layers selected below, but keep in mind that deeper layers those with a higher index will take longer to train on since the gradient computation is deeper. The loss is the sum of the activations in the chosen layers. The loss is normalized at each layer so the contribution from larger layers does not outweigh smaller layers. Normally, loss is a quantity you wish to minimize via gradient descent. In DeepDream, you will maximize this loss via gradient ascent.
Once you have calculated the loss for the chosen layers, all that is left is to calculate the gradients with respect to the image, and add them to the original image. Adding the gradients to the image enhances the patterns seen by the network. At each step, you will have created an image that increasingly excites the activations of certain layers in the network.
The method that does this, below, is wrapped in a tf. See the Concrete functions guide for details. One approach that addresses all these problems is applying gradient ascent at different scales. This will allow patterns generated at smaller scales to be incorporated into patterns at higher scales and filled in with additional detail. To do this you can perform the previous gradient ascent approach, then increase the size of the image which is referred to as an octave , and repeat this process for multiple octaves.
One thing to consider is that as the image increases in size, so will the time and memory necessary to perform the gradient calculation.
The above octave implementation will not work on very large images, or many octaves. Applying random shifts to the image before each tiled computation prevents tile seams from appearing.
Here is a tiled equivalent of the deepdream function defined earlier:. Much better! Play around with the number of octaves, octave scale, and activated layers to change how your DeepDream-ed image looks.
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