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Problem of different predicted results #20

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kasyful opened this issue Mar 11, 2024 · 5 comments
Open

Problem of different predicted results #20

kasyful opened this issue Mar 11, 2024 · 5 comments

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@kasyful
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kasyful commented Mar 11, 2024

Dear Dr @llmpass and Dr @xinwucwp,

I would say this work is great contribution to fault interpretation study.

I try to recreate and build a similar model (same training dataset and same architecture), however when i inference the model to my datasets, it seems to detect bright amplitude, rather than fault itself.

image

I am genuinely appreciate if you could provide any ideas or suggestion why the model work this way.

Thanks.

@llmpass
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llmpass commented Mar 11, 2024

Could you normalize your image, say minus mean and divide by standard deviation before you performing the inference?

@kasyful
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kasyful commented Mar 11, 2024

Thank you for your prompt response @llmpass ,

I did tried both z-score and minmax scaler normalization, it showing almost similar results.

image

My apologies, DataGenerator function already performed z-score for the training datasets. I still couldnt understand why the model predict differently.

Ive go thru each Unet architecture and cross entrophy balanced but still couldnt figure it out.

Thank you for your help.

@llmpass
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llmpass commented Mar 11, 2024

Yes, that's strange. I'm sorry that I cannot help you too much this time.

@suporange
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Thank you for your prompt response @llmpass ,

I did tried both z-score and minmax scaler normalization, it showing almost similar results.

image

My apologies, DataGenerator function already performed z-score for the training datasets. I still couldnt understand why the model predict differently.

Ive go thru each Unet architecture and cross entrophy balanced but still couldnt figure it out.

Thank you for your help.

@kasyful Sir, i have met the same problem. Do you have any ideas?

@kasyful
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kasyful commented Apr 18, 2024

Thank you for your prompt response @llmpass ,
I did tried both z-score and minmax scaler normalization, it showing almost similar results.
image
My apologies, DataGenerator function already performed z-score for the training datasets. I still couldnt understand why the model predict differently.
Ive go thru each Unet architecture and cross entrophy balanced but still couldnt figure it out.
Thank you for your help.

@kasyful Sir, i have met the same problem. Do you have any ideas?

@suporange , you need to transpose the dataset before inference the model.

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