9/7/2023 0 Comments Photo privacy tensorflowWhy does TensorFlow matter? AI has become crucial to the evolution of how users interact with services and devices.TensorFlow is an open source library of tools that enable software developers to apply deep learning to their products. What is TensorFlow? Google has the single greatest machine learning infrastructure in the world and with TensorFlow, Google now has the ability to share that.SEE: How to build a successful developer career (free PDF) (TechRepublic) Executive summary We’ll update this guide periodically when news and updates about TensorFlow are released. This cheat sheet is an easy way to get up to speed on TensorFlow. ( Note: This article about TensorFlow is also available as a free PDF download.) With TensorFlow in place, Google is able to apply deep learning across numerous areas using perceptual and language-understanding tasks. Most of the bugs in my code were related to the build-graph-then-execute model of Tensorflow which can be a little surprising when you are used to imperative code.TensorFlow was originally a deep learning research project of the Google Brain Team that has since become–by way of collaboration with 50 teams at Google–an open source library deployed across the Google ecosystem, including Google Assistant, Google Photos, Gmail, search and more. Ideally I would have been able to export the pix2pix trained network weights into Tensorflow to verify the graph construction, but that was annoying enough, or I am bad enough at Torch, that I did not do it. I looked in the Torch framework source for the different layer types and found what settings and operations were present and implemented those in Tensorflow. The generator and discriminator graph were printed at runtime using the Torch pix2pix code. The implementation started with the creation of the generator graph, then the discriminator graph, then the training system. ![]() Debugging a broken implementation can be time consuming, so I attempted to be careful about the conversion to avoid having to do extensive debugging. The porting process was mostly a matter of looking at the existing Torch implementation as well as the Torch source code to figure out what sorts of layers and settings were being used in order to make the Tensorflow version as true to the original as possible. The implementation is a single file, pix2pix.py, that does as much as possible inside the Tensorflow graph. Here are some examples of what this thing does, from the original paper: The single-file implementation is available as pix2pix-tensorflow on github. looked pretty cool and wanted to implement an adversarial net, so I ported the Torch code to Tensorflow. I thought that the results from pix2pix by Isola et al. ![]() As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either. ![]() Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
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