cs501r_f2018:lab4
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cs501r_f2018:lab4 [2018/09/25 16:48] rpottorff |
cs501r_f2018:lab4 [2021/06/30 23:42] (current) |
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| <code python> | <code python> | ||
| + | import torchvision | ||
| import os | import os | ||
| import gzip | import gzip | ||
| Line 190: | Line 191: | ||
| </code> | </code> | ||
| - | You are welcome (and encouraged) to use the built-in dropout layer. | + | You are welcome (and encouraged) to use the built-in batch normalization and dropout layer. |
| - | Guessing that the pixel is not cancerous every single time will give you an accuracy of ~ 85%. Your trained network should be able to do better than that (but you will not be graded on accuracy). I will post my accuracy and loss graph for training dataset soon so you can have a baseline for what your accuracy should be like. | + | Guessing that the pixel is not cancerous every single time will give you an accuracy of ~ 85%. Your trained network should be able to do better than that (but you will not be graded on accuracy). This is the result I got after 1 hour or training. |
| + | |||
| + | {{:cs501r_f2016:training_accuracy.png?400|}} | ||
| + | {{:cs501r_f2016:training_loss.png?400|}} | ||
cs501r_f2018/lab4.1537894127.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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