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class MaxNorm(object):
    def __init__(self, max_value=1, frequency=5):
        self.frequency = frequency
        self.max_value = max_value
        self.tiny = _finfo(torch.FloatTensor([])).tiny

    def __call__(self, module):
        if hasattr(module, "weight"):
            w = module.weight.data
            norms = w.norm(p=2, dim=w.dim() - 1, keepdim=True)

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#!/usr/local/miniconda3/bin/python
# A snippet for reading a Unicode string from clipboard, convert the string to one that is ASCII compatible, and then paste it to the clipboard. This converts for example "ß" to "ss"..
# The code is based on the answer https://stackoverflow.com/a/25802742/3706018.
import pasteboard
import subprocess
from unidecode import unidecode

pb = pasteboard.Pasteboard()
asciidata = unidecode(pb.get_contents())

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@font-face {
 font-family: "DejaVu Sans Mono";
 font-weight: normal;
 font-style: normal;
 src: local('"DejaVu Sans Mono"'), url('fonts/dejavu.ttf') format('truetype');
}
@font-face {
 font-family: "DejaVu Sans Mono";
 font-weight: normal;
 font-style: italic;

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;;; emacs-zotero-bib-fetch.el --- Manage Zotero collections from emacs
;;
;; Filename: emacs-zotero-bib-fetch.el
;; Author: Anders Johansson, based on zotelo by Spinu Vitalie
;; Maintainer: Anders Johansson
;; Copyright (C) 2011-2014, Anders Johansson and Spinu Vitalie
;; Created: 1 Jul 2014
;; Version: 1.2
;; URL: https://github.com/andersjohansson/emacs-zotero-bib-fetch
;; Keywords: zotero, emacs, reftex, bibtex, bibliography manager

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#!/bin/bash

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo apt update
sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

sudo apt update
sudo apt install -y cuda

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{
    "title": "Emacs keys",
    "rules": [
	{
            "description": "Change right_option + a to right_control + a",
	    "manipulators": [
		{
		    "type": "basic",
		    "from": {
			"key_code": "a",

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import torch


def hamming_score(pred, answer):
    out = ((pred & answer).sum(dim=1) / (pred | answer).sum(dim=1)).mean()
    if out.isnan():
        out = torch.tensor(1.0)
    return out

answer = torch.tensor([[0, 1, 0], [0, 1, 1], [1, 0, 1], [0, 0, 1]])

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def flatten_json(json):
    if type(json) == dict:
        for k, v in list(json.items()):
            if type(v) == dict:
                flatten_json(v)
                json.pop(k)
                for k2, v2 in v.items():
                    json[k+"."+k2] = v2

                    

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from collections import OrderedDict

from torchvision import models


def gen_feature_extractor(model, output_layer):
    layers = OrderedDict()
    for (k, v) in model._modules.items():
        layers[k] = v
        if k == output_layer:

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{
    "shortcuts": [
        {
            "command": "notebook:toggle-all-cell-line-numbers",
            "keys": [
                "Alt L"
            ],
            "selector": ".jp-Notebook:focus"
        },        
        // Moving cells