crypt3lx2k

#! /usr/bin/env python

import tensorflow as tf

def inference_fn(x, training=False):
    net = x

    net = tf.layers.flatten(net)
    net = tf.layers.dense(net, 512, activation=tf.nn.relu)
    net = tf.layers.dropout(net, 0.2, training=training)

EndingCredits

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.framework import ops
from tensorflow.python.training import optimizer
import tensorflow as tf

dfa1234

// XORCipher - Super simple encryption using XOR and Base64
// Usage
// --------
//
//     XORCipher.encode("test", "foobar");   // => "EgocFhUX"
//     XORCipher.decode("test", "EgocFhUX"); // => "foobar"
//
// Copyright © 2013 Devin Weaver <suki@tritarget.org>
//
// This program is free software. It comes without any warranty, to

shagunsodhani

Conditional Generative Adversarial Nets

Introduction

• Conditional version of Generative Adversarial Nets (GAN) where both generator and discriminator are conditioned on some data y (class label or data from some other modality).
• Link to the paper

Architecture

• Feed y into both the generator and discriminator as additional input layers such that y and input are combined in a joint hidden representation.

wiseodd

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os


def xavier_init(size):
    in_dim = size[0]

MateoV

var turf = require('turf'),
  flatten = require('geojson-flatten'),
  normalize = require('geojson-normalize'),
  tilebelt = require('tilebelt');

module.exports = function(tileLayers, tile, done) {
  // concat feature classes and normalize data
  var tigerRoads = normalize(tileLayers.tiger.tiger20062014);
  var osmData = normalize(tileLayers.osmdata.migeojson);