dschaehi

if (Translator.BetterBibTeX) {
    if (tex.has.doi) {
        tex.remove("doi");
    }
    if (tex.has.url) {
        tex.remove("url");
    }
    if (tex.has.issn) {
        tex.remove("issn");
    }

dschaehi

type: ActionsTagsBackup
author: jaeheelee
platformVersion: 7.0.0-beta.68+c31a40c74
pluginVersion: 1.0.0-beta.35
timestamp: '2024-03-26T09:55:19.478Z'
actions:
  default0:
    event: 1
    operation: 1
    data: /unread

dschaehi

\NeedsTeXFormat{LaTeX2e}
\ProvidesPackage{commenting}[2022/10/29 by Jae Hee Lee (http://jaeheelee.de)]
\RequirePackage{hyperref}
\RequirePackage{ifdraft}
\RequirePackage{graphicx}
\RequirePackage{xcolor}
\RequirePackage{xspace}
\RequirePackage{environ}
\RequirePackage{soul}
\RequirePackage{twemojis}

dschaehi

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

dschaehi

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

                    

dschaehi

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]])

dschaehi

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:

dschaehi

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

dschaehi

#!/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

dschaehi

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