Network input dimension
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.
Prediction rules and layers · Describe or apply prediction rules, layers and parameters.
The project uses a linear rule with two features. The fitted weights produce scores or normalized mass.
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: 1.2 Learning from examples
N₀ = d states that the network input width equals the dimension of the input vector.
The first weight matrix must accept a vector of this length.
Source formula example
illustration · This checks one architecture and one input vector.
Symbols
- Input layer width.
- Input vector dimension.
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
d=2N=[2, 3, 1]x=[1, 2]
Calculated here
Result expression: { d, N, x, matches: N[0] === d && x.length === d }
Returns
d- 2
N- array (3 items): [2, 3, 1]
x- array (2 items): [1, 2]
matches- true
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented. This checks one architecture and one input vector.
Source: Inline notation