cs501r_f2016:tmp
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| ====Objective:==== | ====Objective:==== | ||
| - | To read current papers on DNN research and translate them into working models. To experiment with DNN-style regularization methods, including Dropout, Dropconnect, and L1/L2 weight regularization. | + | To read current papers on DNN research and translate them into working models. To experiment with DNN-style regularization methods, including Dropout, Dropconnect, and L1 weight regularization. |
| ---- | ---- | ||
| Line 12: | Line 12: | ||
| - You must implement dropout (NOT using the pre-defined Tensorflow layers) | - You must implement dropout (NOT using the pre-defined Tensorflow layers) | ||
| - You must implement dropconnect | - You must implement dropconnect | ||
| - | - You must experiment with L1/L2 weight regularization | + | - You must implement L1 weight regularization |
| You should turn in an iPython notebook that shows three plots, one for each of the regularization methods. | You should turn in an iPython notebook that shows three plots, one for each of the regularization methods. | ||
| Line 18: | Line 18: | ||
| - For dropout: a plot showing training / test performance as a function of the "keep probability". | - For dropout: a plot showing training / test performance as a function of the "keep probability". | ||
| - For dropconnect: the same | - For dropconnect: the same | ||
| - | - For L1/L2: a plot showing training / test performance as a function of the regularization strength, \lambda | + | - For L1 a plot showing training / test performance as a function of the regularization strength, \lambda |
| - | An example of my training/test performance is shown at the right. | + | An example of my training/test performance for dropout is shown at the right. |
| ---- | ---- | ||
| Line 29: | Line 29: | ||
| * 40% Correct implementation of Dropout | * 40% Correct implementation of Dropout | ||
| * 30% Correct implementation of Dropconnect | * 30% Correct implementation of Dropconnect | ||
| - | * 20% Correct implementation of L1/L2 regularization | + | * 20% Correct implementation of L1 regularization |
| * 10% Tidy and legible plots | * 10% Tidy and legible plots | ||
| Line 39: | Line 39: | ||
| 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]] | ||
| For all 4 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 4 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. | ||
| Line 47: | Line 47: | ||
| For the first part of the lab, you should implement dropout. The paper upon which you should base your implementation is found at: | For the first part of the lab, you should implement dropout. The paper upon which you should base your implementation is found at: | ||
| - | [[https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf|Dropout]] | + | [[https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf|The dropout paper]] |
| The relevant equations are found in section 4 (pg 1933). You may also refer to the slides. | The relevant equations are found in section 4 (pg 1933). You may also refer to the slides. | ||
| Line 64: | Line 64: | ||
| Once you understand dropout, implementing it is not hard; you should only have to add ~10 lines of code. | Once you understand dropout, implementing it is not hard; you should only have to add ~10 lines of code. | ||
| + | |||
| + | Also note that because dropout involves some randomness, your curve may not match mine exactly; this is expected. | ||
| **Part 2: implement dropconnect** | **Part 2: implement dropconnect** | ||
| Line 73: | Line 75: | ||
| **Important note**: the dropconnect paper has a somewhat more sophisticated inference method (that is, the method used at test time). **We will not use that method.** Instead, we will use the same inference approximation used by the Dropout paper -- we will simply scale things by the ''keep_probability''. | **Important note**: the dropconnect paper has a somewhat more sophisticated inference method (that is, the method used at test time). **We will not use that method.** Instead, we will use the same inference approximation used by the Dropout paper -- we will simply scale things by the ''keep_probability''. | ||
| - | You should scan across the same values of ''keep_probability'', and you should generate the same plot. | + | You should scan across the same values of ''keep_probability'', and you should generate a similar plot. |
| Dropconnect seems to want more training steps than dropout, so you should run the optimizer for 1500 iterations. | Dropconnect seems to want more training steps than dropout, so you should run the optimizer for 1500 iterations. | ||
| - | **Part 3: implement L1/L2 regularization** | + | **Part 3: implement L1 regularization** |
| - | + | ||
| - | For this part, you should implement both L1 and L2 regularization on the weights. This will change your computation graph a bit, and specifically will change your cost function -- instead of optimizing just ''cross_entropy'', you should optimize ''cross_entropy + lam*regularizers'', where ''lam'' is the \lambda regularization parameter from the slides. You should regularize all of the weights and biases (six variables in total). | + | |
| - | + | ||
| - | You should create a plot of test/training performance as you scan across values of lambda. You should test at least [0.1, 0.01, 0.001]. | + | |
| - | + | ||
| - | Note: unlike the dropout/dropconnect regularizers, you will probably not be able to improve test time performance! | + | |
| - | + | ||
| - | ---- | + | |
| - | ====Hints:==== | + | |
| - | + | ||
| - | To generate a random binary matrix, you can use ''np.random.rand'' to generate a matrix of random values between 0 and 1, and then only keep those above a certain threshold. | + | |
| + | For this part, you should implement L1 regularization on the weights. This will change your computation graph a bit, and specifically wil | ||
cs501r_f2016/tmp.txt · Last modified: 2021/06/30 23:42 (external edit)
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