If you sometimes find yourself needing to share a file over HTTP, there are not many file-sharing solutions you can use.
This action works by publishing files via GitHub Pages behind an obscure prefix path. It updates the repository README with the URLs, so that you have links to the published files.
You can keep the repository secret, and the files are public but semi-secret, so you can share them without concern that other files will be discovered.
| def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor: | |
| '''Calculate F1 score. Can work with gpu tensors | |
| The original implmentation is written by Michal Haltuf on Kaggle. | |
| Returns | |
| ------- | |
| torch.Tensor | |
| `ndim` == 1. 0 <= val <= 1 | |
| #!/bin/sh | |
| IP=192.168.2.200 | |
| docker run -d -p $IP:8019:8019 voxhub/silvius-worker:latest /bin/sh -c 'cd /root/silvius-backend ; python kaldigstserver/master_server.py' | |
| docker run -d voxhub/silvius-worker /root/worker.sh -u ws://$IP:8019/worker/ws/speech |
| 7 | |
| 2 | |
| 1 | |
| 0 | |
| 4 | |
| 1 | |
| 4 | |
| 9 | |
| 5 | |
| 9 |
| -- this program takes an original image, such as a photo, | |
| -- and a generated image, such as generated by jcjohnson/fast-neural-style | |
| -- and copies the original colors to the generated image | |
| -- like when using the original_colors param in jcjohnson/neural-style | |
| -- | |
| -- by hannu töyrylä @htoyryla 30 oct 2016 | |
| -- | |
| require 'torch' | |
| require 'image' |
| import urllib2, re, json, os, time, sys, HTMLParser | |
| html_parser = HTMLParser.HTMLParser() | |
| auth_address = "1KbV1e1u6P6AsY8XNBydgtbtN8iSB5WMyG" | |
| auth_privatekey = "xxxx" | |
| site = "1TaLkFrMwvbNsooF4ioKAY9EuxTBTjipT" | |
| zeronet_dir = ".." | |
| os.chdir(zeronet_dir) |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
The purpose of this document is to make recommendations on how to browse in a privacy and security conscious manner. This information is compiled from a number of sources, which are referenced throughout the document, as well as my own experiences with the described technologies.
I welcome contributions and comments on the information contained. Please see the How to Contribute section for information on contributing your own knowledge.
name: CaffeNet fine-tuned on the Oxford 102 category flower dataset
caffemodel: oxford102.caffemodel
caffemodel_url: https://s3.amazonaws.com/jgoode/oxford102.caffemodel
gist_id: 0179e52305ca768a601f
license: BSD-3
