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For a neural network with predicted outputs yhat, I have the ground truth of y. The error function is defined as the square root of the sum of the squares of the differences between each element of yhat and y. Is this the p norm? Also, what is the derivative of this? Please show all the math step-by-step. | |
ChatGPT | |
The error function you described is the Euclidean distance between the predicted output yhat and the ground truth label y. This is also known as L2 loss or L2 distance. | |
The expression for L2 Loss is as follows: | |
L2 Loss = sqrt((yhat - y)^2) |
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(infosec) nobu@LAPTOP-DNCQ5AAC:/mnt/d/1_gatechspring2023/infosec/showcase/mlonclampnobukim/mycode/solara$ solara run sol.py | |
Solara server is starting at http://localhost:8765 | |
ERROR: Exception in ASGI application | |
Traceback (most recent call last): | |
File "/home/nobu/miniconda3/envs/infosec/lib/python3.8/site-packages/uvicorn/protocols/http/h11_impl.py", line 428, in run_asgi | |
result = await app( # type: ignore[func-returns-value] | |
File "/home/nobu/miniconda3/envs/infosec/lib/python3.8/site-packages/uvicorn/middleware/proxy_headers.py", line 78, in __call__ | |
return await self.app(scope, receive, send) | |
File "/home/nobu/miniconda3/envs/infosec/lib/python3.8/site-packages/starlette/applications.py", line 122, in __call__ | |
await self.middleware_stack(scope, receive, send) |
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