Image dimensions
Penguin learning project
The project uses 342 complete Palmer penguin records: 240 training-pool and 102 held-out rows. The selected training rows determine feature scaling and model parameters. Source CSV
This project trains weights with softmax, unlike the homepage lab, which uses nearest species averages. Same penguin measurements; different classification methods. Predictions can differ.
Model classes and structure · Describe families of rules, their size and structural properties.
The project supplies finite observations and fitted parameters. A held-out sample does not establish population bounds or prove a theorem.
Consider this concept alongside the project; its source assumptions may differ. These observations do not prove a theorem.
Input: 12 × 2; Held-out evaluation: 102 · #58 · 40.6 mm, 193 mm. Known answer: Adélie; Prediction: Adélie · [≈0.7383, ≈0.2444, ≈0.01737].
Used in these source sections: 1.3 The questions ahead
A 32 × 32 image has 32 rows and 32 columns. A 28 × 28 image has 784 pixel positions.
Dimensions determine how many input values a flattened image contains.
Source formula example
illustration · The counts are pixel positions, without any additional color channels.
Symbols
- Two image dimensions.
- Input dimension; 784 in the 28 × 28 example.
Source formula example
This source example uses its own inputs and assumptions. Editing it does not change the project.
Edit a value or the code, then run the example. This is a finite demonstration, not a proof of the paper’s theorem.
This example’s data flow
Code changed. Reset to restore this example’s data flow.
Calculated here
Result expression: { image32, image28 }
image32=32 * 32image28=28 * 28
Returns
image32- 1024
image28- 784
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. The counts are pixel positions, without any additional color channels.
Source: Inline notation