mcclux

import os
import sys
import dlib
import matplotlib.pyplot as plt
from PIL import Image

# adjust these variables as necessary

# dirname is the directory relative to the script where the files to detect a face and crop live
dirname = "source"

dirko

# Keras==1.0.6
import numpy as np
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, precision_recall_fscore_support

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

jexp

function import_extract_first {
  echo "name:ID(Concept)" > concepts.txt
  cat data-concept-instance-relations.txt | cut -d $'\t' -f 1 | sort | uniq >> concepts.txt
  
  echo "name:ID(Instance)" > instances.txt
  cat data-concept-instance-relations.txt | cut -d $'\t' -f 2 | sort | uniq >> instances.txt

  echo $':END_ID(Concept)\t:START_ID(Instance)	relations:int' > is_a.hdr
  
  $NEO4J_HOME/bin/neo4j-import --into concepts.db --id-type string --delimiter TAB --bad-tolerance 13000000 --skip-duplicate-nodes true --skip-bad-relationships true \

vickyqian

import tweepy
import csv
import pandas as pd
####input your credentials here
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)

mpociot

<?php

namespace App\BotMan\Middleware;

use Mpociot\BotMan\Message;
use Mpociot\BotMan\Http\Curl;
use Mpociot\BotMan\Interfaces\HttpInterface;
use Mpociot\BotMan\Interfaces\DriverInterface;
use Mpociot\BotMan\Interfaces\MiddlewareInterface;

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

aplz

import cv2 # opencv
import numpy as np

font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

# set the rectangle background to white
rectangle_bgr = (255, 255, 255)
# make a black image
img = np.zeros((500, 500))

lyshie

{
	"src": "/tmp/scratch-desktop/",
	"dest": "/tmp/",
	"arch": "i386",
	"icon": "/tmp/scratch-desktop/resources/Icon.png",
	"categories": [
		"Education"
	]
}