taichi-dev / taichi

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Kernel with ti.random() runtime error while using autodiff

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ljcc0930 commented Apr 30, 2021

import taichi as ti

loss = ti.field(dtype = ti.f32, shape = (), needs_grad = True)
x = ti.field(dtype = ti.f32, shape = (), needs_grad = True)

@ti.kernel
def work():
    loss[None] = x[None] * ti.random()
    # loss[None] = x[None] * 0.5 # this works

x[None] = 10
with ti.Tape(loss):
    work()
print(x.grad[None])

[Doc] Update doc on ti.Matrix (like zeros, ones, identity)

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[Lang] Deprecate ti.field(ti.f32), use ti.field(ti.f32, None) instead for advanced layouts

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I want to learn a Gaussian Mixture Model using Expectation-Maximization utilizing a Hypersphere manifold metric. I tried to adapt your KMeans example and run into some issue: the fit-method of RiemannianEM fails reporting

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Here are 164 public repositories matching this topic...

taichi-dev / taichi

Kernel with ti.random() runtime error while using autodiff

[Doc] Update doc on ti.Matrix (like zeros, ones, identity)

[Lang] Deprecate ti.field(ti.f32), use ti.field(ti.f32, None) instead for advanced layouts

taskflow / taskflow

plasma-umass / scalene

potential enhancement: incorporate flame graphs as in py-spy

EmbarkStudios / rust-gpu

QianMo / Game-Programmer-Study-Notes

uber / aresdb

calebwin / emu

QianMo / GPU-Gems-Book-Source-Code

geomstats / geomstats

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Clean Pytorch Backend

Uniformize the Error messages

brucefan1983 / CUDA-Programming

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andi611 / Apriori-and-Eclat-Frequent-Itemset-Mining

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