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Problem of different predicted results #20
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Could you normalize your image, say minus mean and divide by standard deviation before you performing the inference? |
Thank you for your prompt response @llmpass , I did tried both z-score and minmax scaler normalization, it showing almost similar results. 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. |
Yes, that's strange. I'm sorry that I cannot help you too much this time. |
@kasyful Sir, i have met the same problem. Do you have any ideas? |
@suporange , you need to transpose the dataset before inference the model. |
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.
I am genuinely appreciate if you could provide any ideas or suggestion why the model work this way.
Thanks.
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