cs501r_f2018:lab4
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cs501r_f2018:lab4 [2018/09/25 16:26] wingated |
cs501r_f2018:lab4 [2021/06/30 23:42] (current) |
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| ====Description:==== | ====Description:==== | ||
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| + | For a video including some tips and tricks that can help with this lab: [[https://youtu.be/Ms19kgK_D8w|https://youtu.be/Ms19kgK_D8w]] | ||
| For this lab, you will implement a virtual radiologist. You are given | For this lab, you will implement a virtual radiologist. You are given | ||
| Line 102: | Line 104: | ||
| <code python> | <code python> | ||
| + | import torchvision | ||
| import os | import os | ||
| import gzip | import gzip | ||
| Line 139: | Line 142: | ||
| img = self.dataset_folder[index] | img = self.dataset_folder[index] | ||
| label = self.label_folder[index] | label = self.label_folder[index] | ||
| - | return img[0] * 255,label[0][0] | + | return img[0],label[0][0] |
| | | ||
| def __len__(self): | def __len__(self): | ||
| Line 188: | 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). This is the result I got after 1 hour or training. | ||
| - | 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. | + | {{:cs501r_f2016:training_accuracy.png?400|}} |
| + | {{:cs501r_f2016:training_loss.png?400|}} | ||
cs501r_f2018/lab4.1537892810.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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