Based on this article
ALL INSTALLATIONS ASSUME YES WHEN PROMPTED, that's what -y does
This script can be copy paste to ssh as is. No hands installation. :-)
yum install zsh -y
These commands are based on a askubuntu answer http://askubuntu.com/a/581497 | |
To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below. | |
USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING. | |
ABSOLUTELY NO WARRANTY. | |
If you are still reading let's carry on with the code. | |
sudo apt-get update && \ | |
sudo apt-get install build-essential software-properties-common -y && \ | |
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \ |
void demoExtractBackBufferPixels(LPDIRECT3DDEVICE9 d3d_device) { | |
// TODO: In your app, add FAILED() macros to check the HRESULTs passed back | |
// by each of the API calls. I leave these out for clarity. | |
// Grab the backbuffer from the Direct3D device | |
LPDIRECT3DSURFACE9 back_buffer = NULL; | |
d3d_device->GetBackBuffer(0, 0, D3DBACKBUFFER_TYPE_MONO, &back_buffer); | |
// Get the buffer's description and make an offscreen surface in system memory. |
#include "HMyClass.h" | |
#include <stdio.h> | |
void my_eh( const char * error_message, void * unused) | |
{ | |
printf("my_eh: %s\n", error_message); | |
} | |
int main() | |
{ |
ALL INSTALLATIONS ASSUME YES WHEN PROMPTED, that's what -y does
This script can be copy paste to ssh as is. No hands installation. :-)
yum install zsh -y
I hereby claim:
To claim this, I am signing this object:
from PIL import Image | |
import sys | |
import os | |
import math | |
import numpy as np | |
########################################################################################### | |
# script to generate moving mnist video dataset (frame by frame) as described in | |
# [1] arXiv:1502.04681 - Unsupervised Learning of Video Representations Using LSTMs | |
# Srivastava et al |
import tensorflow as tf | |
import numpy as np | |
import uuid | |
x = tf.placeholder(shape=[None, 3], dtype=tf.float32) | |
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid) | |
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid) | |
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid) | |
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid) | |
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid) |
#!/bin/bash | |
# | |
# Download the Large-scale CelebFaces Attributes (CelebA) Dataset | |
# from their Google Drive link. | |
# | |
# CelebA: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html | |
# | |
# Google Drive: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8 | |
python3 get_drive_file.py 0B7EVK8r0v71pZjFTYXZWM3FlRnM celebA.zip |