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. |
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| **Part 3: implement L1 regularization** | **Part 3: implement L1 regularization** | ||
| - | For this part, you should implement L1 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). | + | For this part, you should implement L1 regularization on the weights. This will change your computation graph a bit, and specifically wil |
| - | + | ||
| - | 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. | + | |
cs501r_f2016/tmp.txt · Last modified: 2021/06/30 23:42 (external edit)
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