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First of all, you mentioned that you installed the correct version of Tensorflow on Jetson. You can list the available Tensorflow devices with: from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) And make sure that you see the GPU available. More importantly, Jetson Nano has a 128-core NVIDIA Maxwell™ architecture-based ...


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I think you're looking for the minimization of false positives, that is, the instances that are classified as belonging to the desired class (the positive part of false positives) but that do not actually belong to that class (the false part of false positives). In practice, given your constraints, you may want to maximize the precision, while maintaining a ...


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There are a few things you can play with: Try reducing the learning rate, or increasing decay. Try using regularization(L1/L2 or dropout) Try using momentum(your model may be stuck in a local minima) Adjust other hyperparams(nodes, layers, batch size, etc.) Unless you have some knowledge about the specific cause of high loss variance, the above steps in ...


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I would recommend learning about Reinforcement learning first. You don't need a dataset as you train your network by letting it play the game over and over again. but knowing how to do so doea mean finding out about markov decision process and how you can use the neural network to solve this.


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The best way is probably to Google it with "[org name] tensorflow github" and look what you get. For instance I found: Microsoft Nvidia Intel


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The first place I would have directed you would be Sklearn and pydiffmap. I found this paper specifically about the problem you are doing using python the reference a package called megaman It seems like an active Github . I suggest not just looking at manifold learning papers but leaning towards a search toward non linear embedding or non linear ...


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First of all, you should add the argument workers = n in the fit generator call. n should be bigger than 1 to prefetch data. As your data processing requires the data be taken from a server or port, you should do pre fetching data as that would fetch the next data while GPU is processing. If you call fit_generator with workers > 1 , use_multiprocessing=...


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