cs501r_f2018:lab2
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| For this lab, you will submit an ipython notebook via learningsuite. This lab will be mostly boilerplate code, but you will be required to implement a few extras. | For this lab, you will submit an ipython notebook via learningsuite. This lab will be mostly boilerplate code, but you will be required to implement a few extras. | ||
| + | |||
| + | **NOTE: you almost certainly will not understand most of what's going on in this lab! That's ok - the point is just to get you going with pytorch. We'll be working on developing a deeper understanding of every part of this code over the course of the next two weeks.** | ||
| A major goal of this lab is to help you become conversant in working through pytorch tutorials and documentation. So, you should feel free to google whatever you want and need! | A major goal of this lab is to help you become conversant in working through pytorch tutorials and documentation. So, you should feel free to google whatever you want and need! | ||
| Line 22: | Line 24: | ||
| The resulting image could, for example, look like this: | The resulting image could, for example, look like this: | ||
| - | {{ :cs501r_f2018:lab2.png?200|}} | + | {{:cs501r_f2018:lab2.png |}} |
| See the assigned readings for pointers to documentation on pytorch. | See the assigned readings for pointers to documentation on pytorch. | ||
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| assert torch.cuda.is_available() # You need to request a GPU from Runtime > Change Runtime Type | assert torch.cuda.is_available() # You need to request a GPU from Runtime > Change Runtime Type | ||
| - | </code python> | + | </code> |
| - | **Step 4.** Construct a model class that inherits from “nn.Module” | + | **Step 4.** Construct |
| - | Check out [[https://pytorch.org/docs/stable/nn.html#torch.nn.Module]] | + | - a model class that inherits from “nn.Module” |
| - | + | * Check out [[https://pytorch.org/docs/stable/nn.html#torch.nn.Module]] | |
| - | Your model can contain any submodules you wish -- nn.Linear is a good, easy, starting point | + | * Your model can contain any submodules you wish -- nn.Linear is a good, easy, starting point |
| - | a dataset class that inherits from “Dataset” and produces samples from | + | - a dataset class that inherits from “Dataset” and produces samples from [[https://pytorch.org/docs/stable/torchvision/datasets.html#fashion-mnist]] |
| - | https://pytorch.org/docs/stable/torchvision/datasets.html#fashion-mnist | + | * You may be tempted to use this dataset directly (as it already inherits from Dataset) but we want you to learn how a dataset is constructed. Your class should be pretty simple and output items from FashionMNIST |
| - | You may be tempted to use this dataset directly (as it already inherits from Dataset) but we want you to learn how a dataset is constructed. Your class should be pretty simple and output items from FashionMNIST | + | |
| **Step 5.** Create instances of the following objects: | **Step 5.** Create instances of the following objects: | ||
| Line 112: | Line 113: | ||
| * compute the loss between your model and the true value | * compute the loss between your model and the true value | ||
| * take a step on the optimizer | * take a step on the optimizer | ||
| - | |||
| - | ---- | ||
| - | ====Hints:==== | ||
| - | |||
| - | The following python functions might be helpful: | ||
| - | |||
| - | <code python> | ||
| - | |||
| - | |||
| - | </code> | ||
cs501r_f2018/lab2.txt · Last modified: 2021/06/30 23:42 (external edit)
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