dangkhoasdc

-- LUA WARNINGS
-- Array starts from index 1
-- obj.func() is equivalent to obj:func() 
-- Loop:
--	for start_, end_ do
--	end
-- Condition:
--	if <condition> then
--	end
-- Function:

acamino

Shortcut — Action

• CTRL + A — Move to the beginning of the line
• CTRL + E — Move to the end of the line
• CTRL + [left arrow] — Move one word backward (on some systems this is ALT + B)
• CTRL + [right arrow] — Move one word forward (on some systems this is ALT + F)
• CTRL + U — (bash) Clear the characters on the line before the current cursor position
• CTRL + U —(zsh) If you're using the zsh, this will clear the entire line
• CTRL + K — Clear the characters on the line after the current cursor position
• ESC + [backspace] — Delete the word in front of the cursor

nvnhat95

#include <opencv\cv.h>
#include <opencv\highgui.h>
#include <iostream>
#include <string>
#include <cmath>

using namespace std;

// display video from array of frames, using left and right button.
void displayVideo(cv::Mat* frames, cv::Point2i* centralPoints, int numFrame, int FPS, string windowName) {

naotokui

mommi84

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

• 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding

chirag1992m

import numpy as np
import torch
import keras

def pyt_to_keras(pytorch_model, keras_model):
  """
    Given a PyTorch model, this method transfers the weight to
    a Keras Model (with backend TensorFlow) with the same architecture.
    Assumptions:
      1. The corresponding layer names in both the models will be the same

MarvinT

{
  "python": {
    "library": "import json\ndef black_reformat(cell_text):\n    import black\n    import re\n    cell_text = re.sub('^%', '#%#', cell_text, flags=re.M)\n    try:\n        reformated_text = black.format_str(cell_text, 88)\n    except TypeError:\n        reformated_text = black.format_str(cell_text, mode=black.FileMode(line_length=88))\n    return re.sub('^#%#', '%', reformated_text, flags=re.M)",
    "prefix": "print(json.dumps(black_reformat(u",
    "postfix": ")))"
  },
  "r": {
    "library": "library(formatR)\nlibrary(jsonlite)",
    "prefix": "cat(toJSON(paste(tidy_source(text=",
    "postfix": ", output=FALSE)[['text.tidy']], collapse='\n')))"

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

thomwolf

# Copyright (c) 2019-present, Thomas Wolf.
# All rights reserved. This source code is licensed under the MIT-style license.
""" A very small and self-contained gist to train a GPT-2 transformer model on wikitext-103 """
import os
from collections import namedtuple
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from ignite.engine import Engine, Events

akashpalrecha