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--- cs501r_f2016:lab6v2 [2017/10/10 16:49]
wingated
+++ cs501r_f2016:lab6v2 [2021/06/30 23:42] (current)
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+=====BYU CS 501R - Deep Learning:Theory and Practice - Lab 6=====
 ====Objective:====
@@ Line 79: / Line 83: @@
 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
 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**

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