nlgranger

# example of generate output using https://github.com/tianocore/edk2

sources=(
  "berkeley-softfloat-3::git+file:///home/ngranger/Downloads/edk2/src/berkeley-softfloat-3"
  "brotli::git+file:///home/ngranger/Downloads/edk2/src/brotli"
  "cmocka::git+file:///home/ngranger/Downloads/edk2/src/cmocka"
  "cryptography::git+file:///home/ngranger/Downloads/edk2/src/cryptography"
  "edk2-cmocka::git+file:///home/ngranger/Downloads/edk2/src/edk2-cmocka"
  "engine::git+file:///home/ngranger/Downloads/edk2/src/engine"
  "googletest::git+file:///home/ngranger/Downloads/edk2/src/googletest"

nlgranger

Computer Information:
Manufacturer: Gigabyte Technology Co., Ltd.
Model: B450M GAMING
Form Factor: Desktop
No Touch Input Detected
Processor Information:
CPU Vendor: AuthenticAMD
CPU Brand: AMD Ryzen 5 3600 6-Core Processor
CPU Family: 0x17
CPU Model: 0x71

nlgranger

/**
 * @file      sleep.ino
 * @author    Lewis He ([email protected])
 * @license   MIT
 * @copyright Copyright (c) 2023  Shenzhen Xin Yuan Electronic Technology Co.,
 * Ltd
 * @date      2023-05-24
 *
 */
#define TINY_GSM_MODEM_SIM7600

nlgranger

nlgranger

"""A reimplementeation of PyTorch's DataLoader to showcase seqtools.

:author: Nicolas Granger
:license: 0BSD (~public domain)
"""

import numbers
import random
from functools import singledispatch
from multiprocessing import sharedctypes

nlgranger

# Maintainer: Nicolas Granger <[email protected]>
pkgname=libnvidia-container
pkgver=1.0.0
pkgrel=1
pkgdesc="NVIDIA container runtime library"
arch=('x86_64')
url="https://nvidia.github.io/libnvidia-container/"
license=('BSD')
depends=()
makedepends=('bmake' 'rpcsvc-proto')

nlgranger

#include <stdlib.h>
#include <stdio.h>
#include <errno.h>
#include <libavutil/avutil.h>
#include <libavcodec/avcodec.h>
#include <libavformat/avformat.h>

char err_str[1024];

nlgranger

# like https://gmarti.gitlab.io/ml/2017/09/07/how-to-sort-distance-matrix.html
# but using just 4 lines of code thanks to 'optimal_ordering' argument 
# added to scipy.cluster.hierarchy.linkage

import matplotlib.pyplot as plt
import numpy as np
from scipy.cluster.hierarchy import linkage

# distance matrix
m = np.zeros((500, 500))

nlgranger

# Implementation of Matching Networks for Tensorflow.
# O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, and others,
# “Matching networks for one shot learning,” in Advances in Neural Information
# Processing Systems, 2016, pp. 3630–3638.

# Copyright 2018 Nicolas Granger <[email protected]>
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

nlgranger

import numpy as np
import tensorflow as tf

from procnet.utils import sensible_dir
from experiments.SpatialTransformerNetwork.model import spatial_transformer
from experiments.SpatialTransformerNetwork import dataset


def build_model_layers(inputs, nclasses, is_training):
    initializer = tf.contrib.layers.xavier_initializer()