Timeline for Understanding the behavior of a neural network when extrapolated
Current License: CC BY-SA 4.0
3 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 28, 2022 at 8:37 | comment | added | chrishmorris | Yes, but it can only be a linear continuation. A ReLu is non-linear only for ranges of data that cross its zero point, where the output switches from zero to linear. For sufficiently large values of the independent variables, all ReLus are bounded away from their zero point. Similar behaviour applies to any other neuron: the asymptotic behaviour is defined by the network architecture, not by training. | |
| Jan 26, 2022 at 22:42 | comment | added | Henrique | Hi @chrishmorris, thanks for the reply. I'm not sure if I understood it well. If the model when predicting extrapolated data is reflecting the assumptions made during the training phase, shouldn't it be some sort of continuation of the training range, at least while within the calibration zone (the left tail of the red curve)? | |
| Jan 26, 2022 at 19:49 | history | answered | chrishmorris | CC BY-SA 4.0 |