cs501r_f2018:lab6
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cs501r_f2018:lab6 [2018/10/02 16:01] carr |
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| - | ====Objective:==== | ||
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| - | Work with sequential data in Pytorch by building a Char-RNN for text generation | ||
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| - | ---- | ||
| - | ====Deliverable:==== | ||
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| - | For this lab, you will submit an ipython notebook via learningsuite. | ||
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| - | There are many resources for character level recurrent neural networks. This [[http://karpathy.github.io/2015/05/21/rnn-effectiveness/|Blog Post]] will be helpful in understanding the potential, and getting a basic understanding. | ||
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| - | This lab will have three parts: | ||
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| - | **Part 1:** Build RNN with built-in methods, train on _textfile.txt_ | ||
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| - | **Part 2:** Build your own LSTM Cell | ||
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| - | **Part 3:** Build your own GRU Cell | ||
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| - | **Part 4:** Generate awesome text with a dataset of your choice | ||
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| - | This is an example output from The Lord of the Rings, after only 20 minutes of training. | ||
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| - | "Who now further here the learnest and | ||
| - | south, looking slow you beastion, and that is all plainly day." | ||
| - | |||
| - | ---- | ||
| - | ====Grading standards:==== | ||
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| - | Your notebook will be graded on the following: | ||
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| - | * 100% Build something amazing | ||
| - | * 20% Modified code to include a test/train split | ||
| - | * 20% Modified code to include a visualization of train/test losses | ||
| - | * 10% Tidy and legible figures, including labeled axes where appropriate | ||
| - | |||
| - | ---- | ||
| - | ====Description:==== | ||
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| - | At this point in the semester, we have worked primarily with | ||
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| - | ---- | ||
| - | ====Part 1 detailed outline:==== | ||
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| - | **Step 1.** Get a colab notebook up and running with GPUs enabled. | ||
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| - | **Step 2.** Install pytorch and torchvision | ||
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| - | <code python> | ||
| - | !pip3 install torch | ||
| - | !pip3 install torchvision | ||
| - | !pip3 install tqdm | ||
| - | </code> | ||
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| - | **Step 3.** Import pytorch and other important classes | ||
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| - | <code python> | ||
| - | import torch | ||
| - | import torch.nn as nn | ||
| - | import torch.nn.functional as F | ||
| - | import torch.optim as optim | ||
| - | from torch.utils.data import Dataset, DataLoader | ||
| - | import numpy as np | ||
| - | import matplotlib.pyplot as plt | ||
| - | from torchvision import transforms, utils, datasets | ||
| - | from tqdm import tqdm | ||
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| - | assert torch.cuda.is_available() # You need to request a GPU from Runtime > Change Runtime Type | ||
| - | </code> | ||
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| - | **Step 4.** Construct | ||
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| - | - 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 | ||
| - | - a dataset class that inherits from “Dataset” and produces samples from [[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 | ||
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| - | **Step 5.** Create instances of the following objects: | ||
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| - | * SGD optimizer Check out [[https://pytorch.org/docs/stable/optim.html#torch.optim.SGD]] | ||
| - | * your model | ||
| - | * the DataLoader class using your dataset | ||
| - | * MSE loss function [[https://pytorch.org/docs/stable/nn.html#torch.nn.MSELoss]] | ||
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| - | **Step 6.** Loop over your training dataloader, inside of this loop you should | ||
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| - | * zero out your gradients | ||
| - | * compute the loss between your model and the true value | ||
| - | * take a step on the optimizer | ||
cs501r_f2018/lab6.txt · Last modified: 2021/06/30 23:42 (external edit)
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