Created
January 28, 2017 01:30
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| def f(params,image): | |
| h1 = layer1(params,image) | |
| h2 = layer2(params,h1) | |
| with gradsthru(h2) as gh2: | |
| print("Only evaluated in the backwards pass!") | |
| print(np.linalg.norm(gh2)) | |
| print("Clipping just this gradient!") | |
| gh2 = np.clip(gh2,-10,10) | |
| return softmax(h2) |
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