assignments:assignment5
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
assignments:assignment5 [2016/10/17 19:30] asa [Part 2: Exploring neural networks for digit classification] |
assignments:assignment5 [2016/10/18 09:18] asa |
||
|---|---|---|---|
| Line 20: | Line 20: | ||
| Here's what you need to do: | Here's what you need to do: | ||
| - | * The code that was provided does not really have a bias for all but the first layer. Modify the code so that it correctly uses a bias for all layers. This part is only worth 5 points, and you can do the rest of the assignment with the original version of the code. | + | * The code that was provided does not really have a bias for all but the first layer. Modify the code so that it correctly uses a bias for all layers. This part is only worth 5 points, and you can do the rest of the assignment with the original version of the code. In an appendix of your report indicate what how you changed the code to accommodate a bias correctly. |
| * Plot network accuracy as a function of the number of hidden units for a single-layer network with a logistic activation function. Use a range of values where the network displays both under-fitting and over-fitting. | * Plot network accuracy as a function of the number of hidden units for a single-layer network with a logistic activation function. Use a range of values where the network displays both under-fitting and over-fitting. | ||
| * Plot network accuracy as a function of the number of hidden units for a two-layer network with a logistic activation function. Here, also demonstrate performance in a range of values where the network exhibits both under-fitting and over-fitting. Does this dataset benefit from the use of more than one layer? | * Plot network accuracy as a function of the number of hidden units for a two-layer network with a logistic activation function. Here, also demonstrate performance in a range of values where the network exhibits both under-fitting and over-fitting. Does this dataset benefit from the use of more than one layer? | ||
| - | * Add weight decay regularization to the neural network class you used (explain in your report how you did it). Does the network demonstrate less over-fitting on this dataset with the addition of weight decay? | + | * Add weight decay regularization to the neural network class (explain in your report how you did it). Does the network demonstrate less over-fitting on this dataset with the addition of weight decay? |
| * The provided implementation uses the same activation function in each layer. For solving regression problems we need to use a linear activation function to produce the output of the network. Explain why, and what changes need to be made in the code. | * The provided implementation uses the same activation function in each layer. For solving regression problems we need to use a linear activation function to produce the output of the network. Explain why, and what changes need to be made in the code. | ||
| Line 60: | Line 60: | ||
| Part 2: 85 points. | Part 2: 85 points. | ||
| + | ( 5 points): Fixing the code so it handles the bias term correctly | ||
| (25 points): Exploration of a network with a single hidden layer | (25 points): Exploration of a network with a single hidden layer | ||
| (25 points): Exploration of a network with two hidden layers | (25 points): Exploration of a network with two hidden layers | ||
| - | (15 points): How to add weight decay | + | (15 points): Regularization |
| (15 points): Linear activation function | (15 points): Linear activation function | ||
| - | ( 5 points): Fixing the code so it handles the bias term correctly | ||
| </code> | </code> | ||
assignments/assignment5.txt ยท Last modified: 2016/10/18 09:18 by asa
Page Tools
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 4.0 International
