cs501r_f2017:lab7
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cs501r_f2017:lab7 [2017/10/27 19:08] wingated [Hints and implementation notes:] |
cs501r_f2017:lab7 [2021/06/30 23:42] (current) |
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| batch_norm epsilon=1e-5 | batch_norm epsilon=1e-5 | ||
| </code> | </code> | ||
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| + | Changing to number of critic steps from 5 to 1 didn't seem to matter; changing the alpha parameters to 0.0001 didn't seem to matter; but changing beta1 and beta2 to the values suggested in the paper (0.0 and 0.9, respectively) seemed to make things a lot worse. | ||
| ==Part 3: Generating the final face images== | ==Part 3: Generating the final face images== | ||
| - | Your final deliverable is two images. The first should be a set of randomly generated faces. This is as simple as generating random ''z'' variables, and then running them through your discriminator. | + | Your final deliverable is two images. The first should be a set of randomly generated faces. This is as simple as generating random ''z'' variables, and then running them through your generator. |
| For the second image, you must pick two random ''z'' values, then linearly interpolate between them (using about 8-10 steps). Plot the face corresponding to each interpolated ''z'' value. | For the second image, you must pick two random ''z'' values, then linearly interpolate between them (using about 8-10 steps). Plot the face corresponding to each interpolated ''z'' value. | ||
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