damienpontifex

import argparse
import psutil
import tensorflow as tf
from typing import Dict, Any, Callable, Tuple

## Data Input Function
def data_input_fn(data_param,
                  batch_size:int=None,
                  shuffle=False) -> Callable[[], Tuple]:
    """Return the input function to get the test data.

ilblackdragon

import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

koaning

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)

raecoo

// e.g. console.save({hello: 'world'})
(function(console){
  console.save = function(data, filename){
    if(!data) {
      console.error('Console.save: No data')
      return;
    }
    if(!filename) filename = 'console.json'
    if(typeof data === "object"){
      data = JSON.stringify(data, undefined, 4)

xbb

Use this as an example on how to start the virtual console without the need of Java Web Start or accessing it from the web interface.
You can use the user and password that you use for the web interface.

You need an old JRE... I used 1.7.0_80 from the Server JRE package, also I have tested successfully 1.7.0_79 with MacOS.
You don't need to install it, just extract it or copy the files in "jre" folder.

Open the viewer.jnlp file that you get by launching the virtual console from the web interface with a text editor.
Note the urls to the jar files. Download the main jar file avctKVM.jar and the libs for your operating system and architecture.
Extract the dlls (.so Linux, .jnilib MacOS) from the jar libs.

mikoim

Parameters

Container: MP4

Parameter	YouTube recommends setting
-movflags faststart	moov atom at the front of the file (Fast Start)

Video codec: H.264

yyx990803

• New Features
• Dist Files Adjustments
• [email protected]
• JSX Improvements
• vue-server-renderer
• vue-template-compiler
• Fixed

New Features

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

keeferrourke

#!/bin/sh

# Written by: Keefer Rourke <https://krourke.org>
# Based on AUR package <https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=ttf-google-fonts-git>

# dependancies: fonts-cantarell, ttf-ubuntu-font-family, git
sudo apt-get install fonts-cantarell ttf-ubuntu-font-family git
srcdir="/tmp/google-fonts"
pkgdir="/usr/share/fonts/truetype/google-fonts"
giturl="git://github.com/google/fonts.git"

zaemiel

1. Go to Sublime Text to: Tools -> Build System -> New Build System 
and put the next lines:

{
    "cmd": ["python3", "-i", "-u", "$file"],
    "file_regex": "^[ ]File \"(...?)\", line ([0-9]*)",
    "selector": "source.python"
}

Then save it with a meaningful name like: python3.sublime-build