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View train_matrix_exp_estim.jl
using SimpleChains
function f(x)
N = Base.isqrt(length(x))
A = reshape(view(x, 1:N*N), (N,N))
expA = exp(A)
T = Float32;
D = 2 # 2x2 matrices
# install tinycudann via
# pip install git+
import torch
import tinycudann as tcnn
import time
class TCNNMatrixExponentEstimator1(torch.nn.Module):
def __init__(self, hidden=16) -> None:
rejuvyesh / stresstest_jax_dlpack.jl
Last active Feb 9, 2022
DLPACk segfault reproduce on CUDA+Jax
View stresstest_jax_dlpack.jl
using PyCall
using CUDA
using DLPack
using Test
#using Zygote
#using ChainRulesCore
jax = pyimport("jax")
rejuvyesh / stresstest_dlpack.jl
Created Feb 7, 2022
DLPack reproduce segfault
View stresstest_dlpack.jl
using PyCall
using DLPack
using Test
using Zygote
using ChainRulesCore
torch = pyimport("torch")
functorch = pyimport("functorch")
dlpack = pyimport("torch.utils.dlpack")
View debug_nested.jl
using Flux
qdim = 2
nn = Chain(Dense(qdim, 32, tanh), Dense(32, 2));
q = rand(2, 5);
function jac(x)
o = nn(x)
return reduce(hcat, [o[:, i] for i in 1:size(x)[end]])
#!/usr/bin/env python3
# File:
import numpy as np
import torch
from optimalcontrol.dircolproblem import DIRCOLProblem
from mechamodlearn import utils
View segfault_mujoco.jl
using MuJoCo
modelfile = "test/humanoid.xml"
pm = mj_loadXML(modelfile) # Raw C pointer to mjModel
pd = mj_makeData(pm) # Raw C pointer to mjData
m, d = mj.mapmujoco(pm, pd) # wrap with our jlModel, jlData types
# we can manipulate data in the raw C structs now
nq = mj.get(m, :nq)
rejuvyesh / notebook.ipynb
Created Jul 19, 2017 — forked from eamartin/notebook.ipynb
Understanding & Visualizing Self-Normalizing Neural Networks
View notebook.ipynb
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View bold_symbols.tex
View simple_multiwalker_spec.yaml
- idx: 0
status: 1
rate: 1.0
cls: SharedFFA3CLearner
lidx: 0
- idx: 1
status: 1
rate: 1.0