Product across layers
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.
Functions and derivatives · Describe maps and how their outputs change.
Project vectors have two features; the training matrix has n rows and two columns. Mathematical tools may operate on these values when their assumptions apply.
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
Multiply the real factors in order.
Products of layer effects can shrink or grow.
Source formula example
exact · This computes the product at one input; it does not characterize training gradients.
Symbols
- Real vector of L factors.
- Product function.
- Positive integer number of factors.
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.
You choose
input.first= 1
Calculated here
Result expression: { factors, product: factors.reduce((a, b) => a * b, 1) }
factors=[input.first, 2, 3]Uses:input.first
Returns
factors- array (3 items): [1, 2, 3]
product- 6
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
This finite example has L = 3.
Source: Source expression