deepanshululla

#include <exception>
#include <memory>
#include <mutex>
#include <queue>

struct EmptyQueueException : std::exception {
    const char *  what() const throw() {
        return "Empty queue";
    }
};

silvesthu

GPU Optimization for GameDev

Graphics Pipeline / GPU Architecture Overview

• 2011 - A trip through the Graphics Pipeline 2011
• 2015 - Life of a triangle - NVIDIA's logical pipeline
• 2015 - Render Hell 2.0
• 2016 - How bad are small triangles on GPU and why?
• 2017 - GPU Performance for Game Artists
• 2019 - Understanding the anatomy of GPUs using Pokémon
• 2020 - GPU ARCHITECTURE RESOURCES

xezpeleta

Ubiquiti EdgeRouter X: custom dynamic DNS

ssh ubnt@<your-ip>
configure

Obtain your public IP address behind a NAT: using ipinfo.io

stormraiser

Danbooru Faces v0.1

Discription

This dataset contains ~443k anime face images of size 256x256 drawn by ~7,000 artists, obtained from Danbooru

Collection

We first downloaded JSON files of all existing posts numbered from 1 to 2,800,000 using their API. We filtered the posts by the following criteria:

spro

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

ruoyu0088

sschnug

#include "Halide.h"
#include "Halide/tools/halide_image_io.h"
#include <iostream>

// This code calculates a block based mean on some a-priori known image-dimensions (1 uint8_t channel)
// An example image to process: http://i.imgur.com/Eyo0Xvc.png
// No customized scheduling within this code, but the SO-answer gives some recommendation!

int main(int argc, char **argv) {
  Halide::Buffer<uint8_t> input = Halide::Tools::load_image("TestImages/block_example.png");

shamatar

from keras.layers import Recurrent
import keras.backend as K
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer
from keras.engine import InputSpec

nigeljyng

class AttentionWithContext(Layer):
    """
    Attention operation, with a context/query vector, for temporal data.
    Supports Masking.
    Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
    "Hierarchical Attention Networks for Document Classification"
    by using a context vector to assist the attention
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape

andrewssobral

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize