Difference between revisions of "Hyperparameters"

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(Created page with " - **choose size fixed-length representations** based on the distribution of the number of cloudy days in the training data: base length - augmentations...")
 
 
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    - **choose size fixed-length representations** based on the distribution of the number of cloudy days in the training data: base length
+
Chosen values are in bold.
−
        - augmentations
 
−
            - masking of season or some blocks
 
−
            - FFT on the pixels
 
−
    - 16 timeslot sub-sample
 
−
    - 25 timeslot sub-sample
 
  
−
- Representation dimension
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* '''Pixel''' not patch input data for training and inference.
−
    - 64, **128**, or 256
 
  
−
- ~~Representation length for each dimension~~
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* How many timeslots to sub-sample when creating d-pixel
−
    - ~~FP8~~
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** 16
−
    - ~~INT8~~
+
** 25
−
    - ~~Float16~~
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** '''40'''
−
    - ~~Bfloat16~~
 
−
    - **32 bits**
 
−
        - look at the distribution of representations for each dimension to see if they can be reduced
 
−
        - Matryoshka may change things
 
  
−
- Projector size
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* Representation dimension
−
    - 0, 256, 512, **1024**
+
** 64
 +
** '''128'''
 +
** 256
  
−
- Loss function
+
* Representation length for each dimension
−
    - Barlow twin (parameter lambda = 0.005)
+
** FP8
−
    - **MMCR (parameters alpha=0.005, lambda=0.005)**
+
** INT8
 +
** Float16
 +
** Bfloat16
 +
** '''32 bits''' However, we will need to look at the distribution of representations for each dimension to see if they can be reduced, and Matryoshka may change things
  
−
- Learning rate
+
*Projector size
−
    - **0.0001**
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** 0
−
    - others - chosen by Frank - depends on the data size
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** 256
 +
** 512
 +
** '''1024'''
  
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- Encoder type (each with its own parameters)
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* Loss function
−
    - MLP
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** Barlow twin (parameter lambda = 0.005)
−
    - ResNet
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** '''MMCR (parameters alpha=0.005, lambda=0.005)'''
−
    - **Transformer**
 
−
        - **8 attention heads**
 
−
        - Q, K, V same dimension as representation dimension = 128
 
−
        - **3 layers**
 
  
−
- How many augmentation pairs to use for each pixel
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* Learning rate
−
    - Training
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** '''0.0001'''
−
        - **1,**2
 
−
    - Testing (number of inferences for downstream task)
 
−
        - **1**
 
−
        - 10 (prioritise this)
 
−
            - majority vote
 
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            - **average**
 
  
−
- Downstream classifier
+
* Encoder type
−
    - **MLP**
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** MLP
−
        - Number of layers
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** ResNet50
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            - **3**
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** '''Transformer'''
−
    - Random Forest
+
***'''8 attention heads'''
−
    - XGBoost
+
***'''Q, K, V same dimension as representation dimension = 128'''
−
    - Linear regression
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*** '''3 layers'''
−
    - Logistic regression
 
  
−
- **Pixel** not patch
+
* Number of augmentation pairs to use for each pixel
 +
** Training
 +
*** '''1'''
 +
*** 2
 +
**Inferencing
 +
***1
 +
***10
 +
**** majority vote
 +
**** '''average'''
 +
 
 +
* Downstream classifier
 +
** '''MLP with 3 layers'''
 +
** Random Forest
 +
**XGBoost
 +
**Linear regression
 +
**Logistic regression
 +
 
 +
* Seasonal masking
 +
**Yes
 +
**No

Latest revision as of 16:03, 22 May 2025

Chosen values are in bold.

  • Pixel not patch input data for training and inference.
  • How many timeslots to sub-sample when creating d-pixel
    • 16
    • 25
    • 40
  • Representation dimension
    • 64
    • 128
    • 256
  • Representation length for each dimension
    • FP8
    • INT8
    • Float16
    • Bfloat16
    • 32 bits However, we will need to look at the distribution of representations for each dimension to see if they can be reduced, and Matryoshka may change things
  • Projector size
    • 0
    • 256
    • 512
    • 1024
  • Loss function
    • Barlow twin (parameter lambda = 0.005)
    • MMCR (parameters alpha=0.005, lambda=0.005)
  • Learning rate
    • 0.0001
  • Encoder type
    • MLP
    • ResNet50
    • Transformer
      • 8 attention heads
      • Q, K, V same dimension as representation dimension = 128
      • 3 layers
  • Number of augmentation pairs to use for each pixel
    • Training
      • 1
      • 2
    • Inferencing
      • 1
      • 10
        • majority vote
        • average
  • Downstream classifier
    • MLP with 3 layers
    • Random Forest
    • XGBoost
    • Linear regression
    • Logistic regression
  • Seasonal masking
    • Yes
    • No
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