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zO|V8K3*AjwlrVOhZI7RNr{)e~_(i*8a$D71g_1=kG8VbQq=!t+qje+tooDZ*9+WIc z-8ZWEAd;yjN=n>8k?=icBGOhUXLIzG^|v>gyS^)Y@l53G`{>rOw)2+PnAlx)8i}Bh zbwZWx9enZa9`=WMBb(0mIwm6P$vAzHC_bdPB` z&TcZe!FYgHjKHyoHU2=e{eI!T1By%scDz0;NOW#NH@Yty?c?OopezVJk*Vo_ZA?7k zr*e#p!q_f7Tkqn}4fxb&ux@nMp(-}w_P)$-SOtu5&nwltS*&E+)3=6v_oHW?J=Xqo zVJAb8^rUM2M^o?JI^}LML4pV7O)hI_d~hkVJCKW>hBOauSuX&D~NIeIqee*l(Dtl|Iw diff --git a/experiments/india/008_coarse4/readme.md b/experiments/india/008_coarse4/readme.md index 0946a59b..b3915361 100644 --- a/experiments/india/008_coarse4/readme.md +++ b/experiments/india/008_coarse4/readme.md @@ -23,6 +23,20 @@ https://wandb.ai/openclimatefix/india/runs/k4x1tunj Batch size 32, with 0.2 degree NWP data. Also kept the learning rate a bit higher https://wandb.ai/openclimatefix/india/runs/ktale7pa +### epochs +We set the early stopping epochs from 10 to 15. This should mean model will train a bit more +https://wandb.ai/openclimatefix/india/runs/8hfc83uv + +### small model +We made the model about 50% of the size by reduce the reducing the channels in the NWP encoder fomr 256 to 64 and reducing the hidden features in the output network fomr 1024 to 256 +https://wandb.ai/openclimatefix/india/runs/sk5ek3pk + + +### early stopping on MAE/val +Changing from quantile_loss to MAE/val to stop early on. This should mean the model does more training epochs, and the results we are interested int. +https://wandb.ai/openclimatefix/india/runs/a5nkkzj6 + + ### old Old experiment with 0.1 degree NWP data. https://wandb.ai/openclimatefix/india/runs/m46wdrr7. @@ -34,25 +48,28 @@ Need to check that 32 batches were being passed through. ## Results The coarsening data does seem to improve the experiments results in the first 10 hours of the forecast. -DA forecast looks very similar. +DA forecast looks very similar. Note the 0 hour forecast has a large amount of variation. -Still spike results in the individual runs +Still spike results in the individual runs. -| Timestep | b8_s1 MAE % | b8_s2 MAE % | b32_s3 MAE % | old MAE % | -| --- | --- | --- | --- | --- | -| 0-0 minutes | 0.052 | 0.047 | 0.027 | 0.066 | -| 15-15 minutes | 0.052 | 0.049 | 0.031 | 0.064 | -| 30-45 minutes | 0.052 | 0.051 | 0.037 | 0.063 | -| 45-60 minutes | 0.053 | 0.052 | 0.040 | 0.063 | -| 60-120 minutes | 0.056 | 0.054 | 0.048 | 0.063 | -| 120-240 minutes | 0.061 | 0.060 | 0.060 | 0.065 | -| 240-360 minutes | 0.061 | 0.062 | 0.063 | 0.065 | -| 360-480 minutes | 0.062 | 0.062 | 0.062 | 0.066 | -| 480-720 minutes | 0.063 | 0.063 | 0.062 | 0.065 | -| 720-1440 minutes | 0.065 | 0.066 | 0.065 | 0.066 | -| 1440-2880 minutes | 0.069 | 0.070 | 0.071 | 0.071 | +| Timestep | b8_s1 MAE % | b8_s2 MAE % | b32_s3 MAE % | epochs MAE % | small MAE % | mae/val MAE % | old MAE % | +| --- | --- | --- | --- | --- | --- | --- | --- | +| 0-0 minutes | 0.052 | 0.047 | 0.027 | 0.030 | 0.041 | 0.041 | 0.066 | +| 15-15 minutes | 0.052 | 0.049 | 0.031 | 0.033 | 0.041 | 0.041 | 0.064 | +| 30-45 minutes | 0.052 | 0.051 | 0.037 | 0.039 | 0.043 | 0.043 | 0.063 | +| 45-60 minutes | 0.053 | 0.052 | 0.040 | 0.043 | 0.044 | 0.044 | 0.063 | +| 60-120 minutes | 0.056 | 0.054 | 0.048 | 0.052 | 0.048 | 0.048 | 0.063 | +| 120-240 minutes | 0.061 | 0.060 | 0.060 | 0.064 | 0.057 | 0.057 | 0.065 | +| 240-360 minutes | 0.061 | 0.062 | 0.063 | 0.065 | 0.061 | 0.061 | 0.065 | +| 360-480 minutes | 0.062 | 0.062 | 0.062 | 0.063 | 0.063 | 0.063 | 0.066 | +| 480-720 minutes | 0.063 | 0.063 | 0.062 | 0.064 | 0.064 | 0.064 | 0.065 | +| 720-1440 minutes | 0.065 | 0.066 | 0.065 | 0.067 | 0.066 | 0.066 | 0.066 | +| 1440-2880 minutes | 0.069 | 0.070 | 0.071 | 0.071 | 0.071 | 0.071 | 0.071 | ![](mae_step.png "mae_steps") -![](mae_step_smooth.png "mae_steps") \ No newline at end of file +![](mae_step_smooth.png "mae_steps") + +I think its worth noting the model traing MAE is around `3`% and the validation MAE is about `7`%, so there is good reason to believe that the model is over fit to the trianing set. +It would be good to plot some of the trainin examples, to see if they are less spiky. \ No newline at end of file

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