Matching residual 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.
Residual networks · Describe residual layer updates and their continuous analogue.
Convolution, recurrent and residual architectures are not part of this linear penguin model.
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: 6.2 Shortcuts between layers
Nℓ = Nℓ₋₁ means the output and previous layer have the same width.
Equal widths permit direct residual addition.
Source formula example
illustration · This checks one pair of widths; it does not establish a network-wide training result.
Symbols
- Current layer width.
- Previous layer width.
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.
Initial values in code
N=[3, 3]
Calculated here
Result expression: { previous: N[0], current: N[1], match: N[0] === N[1] }
Returns
previous- 3
current- 3
match- true
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented unless the source values are stated. This checks one pair of widths; it does not establish a network-wide training result.
Source: Inline notation