cs501r_f2018:lab3
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cs501r_f2018:lab3 [2018/09/13 22:43] carr created |
cs501r_f2018:lab3 [2018/09/14 17:49] wingated |
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| ====Deliverable:==== | ====Deliverable:==== | ||
| - | For this lab, you will submit an ipython notebook via learningsuite. | + | For this lab, you will submit an ipython notebook via learningsuite. This is where you build your first deep neural network! |
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| + | For this lab, we'll be combining several different concepts that we've covered during class, including new layer types, initialization strategies, and an understanding of convolutions. | ||
| ---- | ---- | ||
| + | ====Grading standards:==== | ||
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| + | |||
| + | * 30% Part 0: Successfully followed lab video and typed in code | ||
| + | * 20% Part 1: Re-implement Conv2D and CrossEntropy loss function | ||
| + | * 20% Part 2: Implement different initialization strategies | ||
| + | * 10% Part 3: Print parameters, plot train/test accuracy | ||
| + | * 10% Part 4: Convolution parameters quiz | ||
| + | * 10% Tidy and legible figures, including labeled axes where appropriate | ||
| + | |||
| + | ---- | ||
| + | ====Detailed specs:==== | ||
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| + | |||
| + | **Part 0:** Watch and follow video tutorial | ||
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| **Part 1:** Re-implement a Conv2D module with parameters and a CrossEntropy loss function. | **Part 1:** Re-implement a Conv2D module with parameters and a CrossEntropy loss function. | ||
| + | |||
| You will need to use | You will need to use | ||
| https://pytorch.org/docs/stable/nn.html#torch.nn.Parameter | https://pytorch.org/docs/stable/nn.html#torch.nn.Parameter | ||
cs501r_f2018/lab3.txt · Last modified: 2021/06/30 23:42 (external edit)
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