mattjj

from __future__ import annotations
from contextlib import contextmanager
from typing import NamedTuple, Callable, Optional, Any
import numpy as np

Array = Any

class Node(NamedTuple):
  vjp: Optional[Callable]
  parents: List[Node]

roramigator

'use strict';

/**
 * TTTTTTTTTTTTTTTTTTTTTTTHHHHHHHHH     HHHHHHHHHEEEEEEEEEEEEEEEEEEEEEE
 * T:::::::::::::::::::::TH:::::::H     H:::::::HE::::::::::::::::::::E
 * T:::::::::::::::::::::TH:::::::H     H:::::::HE::::::::::::::::::::E
 * T:::::TT:::::::TT:::::THH::::::H     H::::::HHEE::::::EEEEEEEEE::::E
 * TTTTTT  T:::::T  TTTTTT  H:::::H     H:::::H    E:::::E       EEEEEE
 *         T:::::T          H:::::H     H:::::H    E:::::E
 *         T:::::T          H::::::HHHHH::::::H    E::::::EEEEEEEEEE

pdarragh

Approachable PL Papers for Undergrads

On September 28, 2021, I asked on Twitter:

PL Twitter:

you get to recommend one published PL paper for an undergrad to read with oversight by someone experienced. the paper should be interesting, approachable, and (mostly) self-contained.

what paper do you recommend?

shoyer

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

pnc

#!/bin/bash

set -e

CONTENTS=$(tesseract -c language_model_penalty_non_dict_word=0.8 --tessdata-dir /usr/local/share/tessdata/ "$1" stdout -l eng | xml esc)

hex=$((cat <<EOF
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">

stek29

import lldb
import shlex

FLAGS = [
	['CF', 'Carry Flag'],
	[None, 'Reserved'],
	['PF', 'Parity Flag'],
	[None, 'Reserved'],
	['AF', 'Adjust Flag'],
	[None, 'Reserved'],

johnmyleswhite

library("ggplot2")

n <- 10000
s_e <- 0.01

x1 <- runif(n, -10, 10)
x2 <- runif(n, -10, 10)
y <- sin(x1 * x2) + rnorm(n, 0, s_e)

df <- data.frame(y = y, x1 = x1, x2 = x2, z = x1 * x2)

falcondai

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops

@ops.RegisterGradient("GuidedRelu")
def _GuidedReluGrad(op, grad):
    return tf.select(0. < grad, gen_nn_ops._relu_grad(grad, op.outputs[0]), tf.zeros(grad.get_shape()))

if __name__ == '__main__':
    with tf.Session() as sess:

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without