cs501r_f2016:lab6v2
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| Like the previous lab, you must choose your topology. I have had good | 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 | ''conv'' helper functions that you implemented previously; you | ||
| may also need the tensorflow function ''tf.concat''. | 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** | **Part 1b: Implement a cost function** | ||
cs501r_f2016/lab6v2.txt · Last modified: 2021/06/30 23:42 (external edit)
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