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all data points will be classified as normal given enough training epochs #13
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Training epochs is also hyperparameyer to tune if you can share the code I
will try to debug at my end
…On Thu, Jul 29, 2021, 05:55 YingxiaoKong ***@***.***> wrote:
Hello,
I tried to implement the LSTM-OCNN on my own dataset, and it seems that
the results depends heavily on the number of training epochs: if the
training epochs are long enough, all the samples will have same scores and
no data point is classified as anomalous. Do you know how to solve this?
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Thank you for your quick reply! I have included a LSTM layer as I have time series data, do you also need the data? |
if you can run it on colab and share the notebook which I can execute it
will be great!!
…On Thu, Jul 29, 2021 at 8:37 AM YingxiaoKong ***@***.***> wrote:
Thank you for your quick reply! I have included a LSTM layer as I have
time series data, do you also need the data?
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https://colab.research.google.com/drive/1c2uhmeZU40L0Pv0VSeTtHRVjvGaM5gVk?usp=sharing Hello, here is the link. Unfortunately it's not working on colab. It's working perfectly on my PC. I tried to debug it whole day and still no result. Maybe you could find this out if you're familiar with tensorflow? The tensorflow on my PC is 2.1.0 and keras is 2.3.1 However these two combinations won't work on colab. |
Hi I just debugged it and it's working now!!!! |
I was trying to run the code but it shows the error while installing the Fuel library. |
Hello,
I tried to implement the LSTM-OCNN on my own dataset, and it seems that the results depends heavily on the number of training epochs: if the training epochs are long enough, all the samples will have same scores and no data point is classified as anomalous. Do you know how to solve this?
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