# Tag Info

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ReLU is piecewise linear function that outputs the received input directly if it's positive, or outputs a zero. i.e., $max(0, x)$ How significant is adding relu to full connected layers? ReLU, being an activation function, will determine what the output of the nodes in your FCs are. Since it's a non-linear function, one significance is it will allow the ...

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Batch size affects how many training updates (steps) will happen during each epoch. When the batch size is small, this means that the model sees fewer data in each weights update. Thus, your question really depends on the data you have, along with the corresponding task (classification / RL etc.) If your data is highly imbalanced, then I would not suggest a ...

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Mean Absolute Error is nothing but the mean of absolute errors. If your model gave $n$ predictions $\{\hat{y}_i\}_{i = 1}^{n}$ against $n$ ground truths $\{y_i\}_{i = 1}^{n}$, then MAE is defines as follows $$MAE_{model} = \dfrac{\sum\limits_{i = 1}^{n} |y_i - \hat{y}_i|}{n}$$. Thus, MAE gives the average amount of error. So, the machine learning model with ...

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There are some works that do this comparison. Briefly, it's been observed that the performance of models trained via FL drops as data distributions between participating agents differ. When data is IID-like though, performance is comparable to centralized training. Some works that I'm aware of are as follows: Overcoming Forgetting in Federated Learning on ...

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Here is what I discovered empirically, trial and error. Since tuning the parameters are going to be environment specific, I'll lay out mine to give a better understanding of what I found to work for my case. Hopefully someone with better understanding of the algorithm will weigh in: Environment: A 2D map where an agent controls a simulated PC mouse pad and ...

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