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There are two weight-initializing methods for neural networks: 1-Zero initializing 2-Random initializing https://towardsdatascience.com/weight-initialization-techniques-in-neural-networks-26c649eb3b78 If you choose zero initalizing method in every train loop, you may get same results OR you can use transfer learning according to your problem, it allows ...


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I don't think you can. Say a NN with 3 layers gives an accuracy of 95.3% and another NN with 4 layers gives an accuracy of 95.4%. Then there is no guarantee that the 4 layer NN is better than the 3 layered NN. Since with different initial values the 3 layer NN might perform better. You could run multiple times and probabilistically say that this is better, ...


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Simply said, predicting pseudo random number is just not possible for now. Pseudo random numbers generated now have a high enough "randomness" so that it cannot be predicted. Pseudo random numbers is the basis of modern cryptography which is widely used in the world wide web and more. It may be possible in the future through faster computers and stronger AI, ...


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I think if you got the dataset, then a standard 1d convolutional neural network would work to some extent. It's not that there is some property of nearby sounds that it would pick up on. It would just memorize all the sounds that tend to come from your desk. I think the coding part would be pretty standard stuff. But collecting the data will be hard. You ...


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It's difficult to say without knowing what your data looks like but from the numbers it seems too less and the images might be too similar to one another or very different. In any case, I'd have checked using other networks like Inception and decreasing learning rate even further (say 0.0001) to not mess with the Imagenet weights if your data is not very ...


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I found that the peak was caused by the data I am using. Specifically, the MinMaxScaler changed the data shape and I resolved the issue by simply dividing to the max value.


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