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ThrowThisShitAway10 t1_iqviiv2 wrote

What loss are you using? It seems to be around 0.1, yet in your image the predictions are clearly worse than 0.1 MAE. I'm guessing there's some bug in your code

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Imaginary_Carrot4092 OP t1_iqvj2lq wrote

I am using MSE. But the data points in the images are not predictions. They are the training data.

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sanderbaduk t1_iqvmins wrote

You have a single input and single output? It's likely to just learn something like the smoothed average, which is quite reasonable.
Also it seems your post is better in the subreddits mentioned under rule 4.

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ThrowThisShitAway10 t1_iqvtrz7 wrote

Oh... then I'm not sure what you're expecting to learn. There doesn't appear to be much (if any) correlation between your input and output values. If you provide a 0.0 as input to the network, how is it supposed to predict an output? There's no indication whether the value should be 3.0 or 4.0, so it will always just predict around the mean.

This one input feature is pretty useless. The ideal model is just y=3.5 and doesn't include x at all. If you're able to provide more input features that actually correlate with the output, then you'll get an interesting model.

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