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View Code? Open in Web Editor NEWImplementation of "A Simple Neural Attentive Meta-Learner" (SNAIL, https://arxiv.org/pdf/1707.03141.pdf) in PyTorch
Implementation of "A Simple Neural Attentive Meta-Learner" (SNAIL, https://arxiv.org/pdf/1707.03141.pdf) in PyTorch
I guess the code in the file blocks.py
int(math.ceil(math.log(seq_length)))
is wrong? Because math.log(x)
return the natural logarithm of x, and I suppose we want a base-2 logarithm of x here?
In the original paper, "the number of shots K was chosen uniformly at random from 1 to 5." So the forward function of SNAIL should take an arbitrary K instead of defined at init, and training should follow the uniform random choosing as well.
Thanks!
Hi
have you finished the RL part? thanks in advance!!
For the Mini-ImageNet ResNet-based encoder, I believe the authors use mean pooling instead of max pooling, as done by this implementation. I am currently unable to reproduce the mini-imagenet results with your codebase (even train accuracy does not go up), and am hunting for discrepancies :)
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