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
ּ_בּ | |
בּ_בּ | |
טּ_טּ | |
כּ‗כּ | |
לּ_לּ | |
מּ_מּ | |
סּ_סּ | |
תּ_תּ | |
٩(×̯×)۶ | |
٩(̾●̮̮̃̾•̃̾)۶ |
#!/bin/bash | |
### Command log to install Cuda Toolkit 6.5, driver 343.22, and ccminer. | |
## Update the system | |
sudo apt-get update && sudo apt-get -y dist-upgrade | |
# All the dependencies for Cuda & ccminer (I think) | |
sudo apt-get -y install gcc g++ build-essential automake linux-headers-$(uname -r) git gawk libcurl4-openssl-dev libjansson-dev xorg libc++-dev libgmp-dev python-dev | |
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
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.
""" | |
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) |
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) |
-- 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' |
7 | |
2 | |
1 | |
0 | |
4 | |
1 | |
4 | |
9 | |
5 | |
9 |
#!/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 |
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 | |