cs501r_f2016:lab6v2
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
cs501r_f2016:lab6v2 [2017/10/10 16:47] wingated |
cs501r_f2016:lab6v2 [2021/06/30 23:42] (current) |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| + | =====BYU CS 501R - Deep Learning:Theory and Practice - Lab 6===== | ||
| + | |||
| + | |||
| + | |||
| ====Objective:==== | ====Objective:==== | ||
| Line 28: | Line 32: | ||
| * 30% Proper design, creation and debugging of a dense prediction network | * 30% Proper design, creation and debugging of a dense prediction network | ||
| * 30% Proper design of a loss function and test set accuracy measure | * 30% Proper design of a loss function and test set accuracy measure | ||
| - | * 20% Proper experimentation with regularization | + | * 20% Proper experimentation with two different regularizers |
| * 20% Tidy visualization of the output of your dense predictor | * 20% Tidy visualization of the output of your dense predictor | ||
| Line 51: | Line 55: | ||
| overfitting is a real possibility. | overfitting is a real possibility. | ||
| - | [[http://liftothers.org/cancer_data.tar.gz|The data can be downloaded here.]] | + | [[http://liftothers.org/cancer_data.tar.gz|The data can be downloaded here.]] //Please note that this dataset is not publicly available, and should not be redistributed.// |
| As in the previous lab, you are welcome to sub-sample the data if your computer is not powerful enough to fit it all in RAM. However, if you do, please clearly report how much data you used in your final report. | As in the previous lab, you are welcome to sub-sample the data if your computer is not powerful enough to fit it all in RAM. However, if you do, please clearly report how much data you used in your final report. | ||
| Line 79: | Line 83: | ||
| Like the previous lab, you must choose your topology. I have had good | Like the previous lab, you must choose your topology. I have had good | ||
| - | luck implementing the "Deep Convolution U-Net" from this paper: | + | luck implementing the "Deep Convolution U-Net" from this paper: [[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks for Biomedical Image Segmentation]] (See figure 1, replicated at the right). This should be fairly easy to implement given the |
| - | + | ||
| - | [[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks | + | |
| - | for Biomedical Image Segmentation]] | + | |
| - | + | ||
| - | (See figure 1). This should be fairly easy to implement given the | + | |
| ''conv'' helper functions that you implemented previously; you | ''conv'' helper functions that you implemented previously; you | ||
| may also need the tensorflow function ''tf.concat''. | may also need the tensorflow function ''tf.concat''. | ||
| + | //Note that the simplest network you could implement (with all the desired properties) is just a single convolution layer with two filters and no relu! Why is that? (of course it wouldn't work very well!)// | ||
| **Part 1b: Implement a cost function** | **Part 1b: Implement a cost function** | ||
cs501r_f2016/lab6v2.1507654075.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
Page Tools
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 4.0 International
