Add F.normalize #1467
Add F.normalize #1467
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rename lp_normalize to normalize, and remove l2_normalize |
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Should default dim = -1? |
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@soumith pushed |
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yes, please put p=2 as the default (you will have to either have a default dim argument, or move p to 3rd argument) |
torch/nn/functional.py
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| .. math:: | ||
| v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)} | ||
| for each subtensor v over dimension dim of input. |
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torch/nn/functional.py
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| def normalize(input, p, dim, eps=1e-12): | ||
| r"""Performs l_p normalization of inputs over specified dimension. |
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torch/nn/functional.py
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| return loss | ||
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| def normalize(input, p, dim, eps=1e-12): |
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torch/nn/functional.py
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| for each subtensor v over dimension dim of input. Each subtensor is flattened into a vector, | ||
| i.e. :math:`\lVert v \rVert_p` is not a matrix norm. | ||
| With default arguments normalizes over the last dimension with Euclidean norm. |
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* master: Add F.normalize (pytorch#1467) Expose custom attributes from C++ functions (pytorch#1430) Add high order gradient support for Sigmoid (pytorch#1496)
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with gradcheck and doc.