cs501r_f2016:lab6
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| ====Description:==== | ====Description:==== | ||
| - | This lab is a chance for you to start reading the literature on deep neural networks, and understand how to replicate methods from the literature. You will implement 4 different regularization methods, and will benchmark each one. | + | This lab is a chance for you to start reading the literature on deep neural networks, and understand how to replicate methods from the literature. You will implement 3 different regularization methods, and will benchmark each one. |
| To help ensure that everyone is starting off on the same footing, you should download the following scaffold code: | To help ensure that everyone is starting off on the same footing, you should download the following scaffold code: | ||
| - | [[http://liftothers.org/byu/lab6_scaffold.py|Lab 6 scaffold code]] | + | [[http://liftothers.org/byu/lab6_scaffold.py|Lab 6 scaffold code (UPDATED WITH RELUs)]] |
| For all 3 methods, we will run on a single, deterministic batch of the first 1000 images from the MNIST dataset. This will help us to overfit, and will hopefully be small enough not to tax your computers too much. | For all 3 methods, we will run on a single, deterministic batch of the first 1000 images from the MNIST dataset. This will help us to overfit, and will hopefully be small enough not to tax your computers too much. | ||
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| Note that you should **not** call your regularization variable "lambda" because that is a reserved keyword in python. | Note that you should **not** call your regularization variable "lambda" because that is a reserved keyword in python. | ||
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| + | Remember that the "masks" for both dropout and dropconnect change for **every** step in training. | ||
cs501r_f2016/lab6.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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