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--- cs501r_f2018:lab4 [2018/09/25 16:26]
wingated
+++ cs501r_f2018:lab4 [2018/09/26 00:36]
shreeya
@@ Line 56: / Line 56: @@
 ----
 ====Description:====
+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
@@ Line 102: / Line 104: @@
 <code python>
+import torchvision
 import os
 import gzip
@@ Line 139: / Line 142: @@
     img = self.dataset_folder[index]
     label = self.label_folder[index]
-    return img[0] * 255,label[0][0]
+    return img[0],label[0][0]
   def __len__(self):
@@ Line 188: / Line 191: @@
 </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).
-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|}}

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