Comments (7)
Even using a learning rate 100x smaller than the default one still gives the same error (but now even further into the optimization, around iteration 2370).
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Hi @JulianoLagana, did you managed to solve the problem? I did try to clip all the np.exp
expressions to some value though still failing due to signal 8: SIGFPE (floating point error)
x = np.clip(x, -10, 10)
np.exp(x)
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Hi @leduckhc. No, unfortunately I didn't. These and other problems with this implementation led me to a different research direction. I hope you manage it, though.
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Hi @JulianoLagana . I just figured out that the weights of conv5_3
and lower (conv5_{2,1}, conv4_{1,2,3}, etc) contains NaNs. So the reason might be in bad initialization/loading of the network from caffemodel. I am gonna examine it in more depth.
I explored values and weights by going through
print {k: v.data for k, v in self.solver.net.blobs.items()}
print {k: v[0].data for k, v in self.solver.net.params.items()}
# v[0] is for weights, v[1] for biases
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Check #53 for solution
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Freezing layers is not a solution.
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Related Issues (20)
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