N8python

vec3 transmission = vec3(0.0);
float transmissionR, transmissionB, transmissionG;
float randomCoords = rand();
float thickness_smear = thickness * max(pow(roughness, 0.33), anisotropy);
vec3 distortionNormal = vec3(0.0);
vec3 temporalOffset = vec3(time, -time, -time) * temporalDistortion;
if (distortion > 0.0) {
  distortionNormal = distortion * vec3(snoiseFractal(vec3((pos * distortionScale + temporalOffset))), snoiseFractal(vec3(pos.zxy * distortionScale - temporalOffset)), snoiseFractal(vec3(pos.yxz * distortionScale + temporalOffset)));
}
for (float i = 0.0; i < ${samples}.0; i ++) {

N8python

const data = [
    [
        [0, 0], 1
    ],
    [
        [0, 1], 0
    ],
    [
        [1, 0], 0
    ],

N8python

const data = [
    [
        [0, 0], 1
    ],
    [
        [0, 1], 0
    ],
    [
        [1, 0], 0
    ],

N8python

(async() => {
    const model = await tf.loadLayersModel('file://./encoder-model/model.json');
    const input = tf.input({ shape: [2] });
    const dense1 = model.layers[4].apply(input);
    const dense2 = model.layers[5].apply(dense1);
    const dense3 = model.layers[6].apply(dense2);
    const dense4 = model.layers[7].apply(dense3);
    const decoder = tf.model({ inputs: input, outputs: dense4 });
    await decoder.save(`file://./decoder-model`);
    mnistToImage(decoder.predict(tf.tensor([

N8python

const tf = require("@tensorflow/tfjs-node");
const fs = require("fs");
const mnistToImage = require("./mnist-image.js");

function oneHotEncode(num) {
    const arr = Array(10).fill(0);
    arr[num] = 1;
    return arr;
}
const trainData = JSON.parse(fs.readFileSync("mnist_handwritten_train.json").toString());

N8python

const tf = require("@tensorflow/tfjs-node");
const fs = require("fs");
const { encode } = require("punycode");

function oneHotEncode(num) {
    const arr = Array(10).fill(0);
    arr[num] = 1;
    return arr;
}
const trainData = JSON.parse(fs.readFileSync("mnist_handwritten_train.json").toString());

N8python

async function main() {
    const fs = require("fs");
    const R = require("ramda");
    const tf = require("@tensorflow/tfjs-node");
    const fsExtra = require('fs-extra');
    const text = fs.readFileSync("input.txt").toString();
    const chars = Array.from(new Set(text.split("")));
    const encoding = Object.fromEntries(chars.map((x, i) => [x, i]));
    const decoding = Object.fromEntries(chars.map((x, i) => [i, x]));
    const sampleLength = 50;

N8python

const fs = require("fs");
const R = require("ramda");
const tf = require("@tensorflow/tfjs-node");
const fsExtra = require('fs-extra')
const text = fs.readFileSync("input.txt").toString();
const chars = Array.from(new Set(text.split("")));
const encoding = Object.fromEntries(chars.map((x, i) => [x, i]));
const decoding = Object.fromEntries(chars.map((x, i) => [i, x]));
const sampleLength = 20; // when I change this to 100, my lstm's loss goes to NaN
const epochSize = 5000;

N8python

const SQUARE_SIZE = 5;
let storage = [];

function setup() {
    createCanvas(600, 600);
    for (let x = 0; x < width / SQUARE_SIZE; x++) {
        storage[x] = [];
        for (let y = 0; y < height / SQUARE_SIZE; y++) {
            storage[x].push((Math.random() > 0.5) ? "red" : "blue")
        }

N8python

library(deSolve)
library(reshape2)
library(ggplot2)
N <- 1000
initial_state_values <- c(
  S = 0.999, 
  E = 0,
  IMI = 0.001,
  IM = 0,
  A = 0,