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cs501r_f2018:lab4 [2018/09/25 17:23] shreeya |
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 | ||
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You are welcome (and encouraged) to use the built-in batch normalization and 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|}} |