cameronfr

// esm import p5
import p5 from 'https://cdn.skypack.dev/p5';
// esm import matter
import Matter from 'https://cdn.skypack.dev/matter-js';
import * as Tone from 'https://cdn.skypack.dev/tone';
// esm import tinycolor2
import tinycolor from 'https://cdn.skypack.dev/tinycolor2';

function changeHue(rgbColor, amount) {
  const color = tinycolor(rgbColor);

cameronfr

import sklearn
import sklearn.ensemble
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
from cifar10_models import *
import pickle
# https://github.com/huyvnphan/PyTorch-CIFAR10

cameronfr

import sklearn
import sklearn.ensemble
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline
plt.style.use("ggplot")

cameronfr

# standard GPLVM taken from following tutorial from https://pyro.ai/examples/gplvm.html
# Having problem where have to restart kernel because of shape mistmatch when re-instantiating gplbv obj

import os
os.environ['KMP_DUPLICATE_LIB_OK']='True' # need to remove MKL

import matplotlib.pyplot as plt
import pandas as pd
import torch
from torch.nn import Parameter

cameronfr

import torch
import torchvision
import numpy as np
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Distillation Experiments
# 1. Create a network A with structure X and random initializations
# 2. Get a bunch of data with random inputs from that network
# 3. Create networks B1, B2 ... BN with structure Y with random initializations
# 4. See if after 10 epochs with LR 0.001 if network A is able to distill network B

cameronfr

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from IPython.display import display
import numpy as np

cameronfr

// Add more babel plugins to https://raw.githubusercontent.com/hamilton/iodide-jsx/master/docs/evaluate-jsx.js
const REACT = 'https://cdnjs.cloudflare.com/ajax/libs/react/16.4.2/umd/react.production.min.js'
const REACT_DOM = 'https://unpkg.com/react-dom@16/umd/react-dom.production.min.js'
const BABEL_STANDALONE = 'https://unpkg.com/babel-standalone@6/babel.min.js'

const loadResource = url => new Promise((resolve) => {
  const head = document.getElementsByTagName('head')[0];
  const theScript = document.createElement('script');
  theScript.src = url;
  theScript.crossorigin = true;

cameronfr

Keybase proof

I hereby claim:

• I am cameronfr on github.
• I am cameronfr (https://keybase.io/cameronfr) on keybase.
• I have a public key whose fingerprint is F53C BA9C 6FB0 1B7C 74C5 454C A0F5 3421 AA8D A93C

To claim this, I am signing this object: