annanay25

#Backpropagation algorithm written in Python by annanay25.
import string
import math
import random

class Neural:
	def __init__(self, pattern):
		#
		# Lets take 2 input nodes, 3 hidden nodes and 1 output node.
		# Hence, Number of nodes in input(ni)=2, hidden(nh)=3, output(no)=1.

annanay25

import numpy as np
import pandas as pd
from sklearn import preprocessing

class decisionnode:
    def __init__(self,col=-1,value=None,results=None,tb=None,fb=None):
        self.col=col
        self.value=value
        self.results=results
        self.tb=tb

annanay25

p=3
m=1000

def stringHash(txt):
    # print len(txt)
    txtHashList = [0L] * (len(txt) - 100)
    for i in xrange(0, 100):
        # print i
        txtHashList[0] += ord(txt[i])*(p**i)

annanay25

package main

import (
	"fmt"
	"net"
	"os"
	"time"
	"strconv"
)

annanay25

package main

import (
	"fmt"
	"net"
	"os"
)

func sendResponse(conn *net.UDPConn, addr *net.UDPAddr){
	_,err := conn.WriteToUDP([]byte("Hello from Server. Message received. "), addr)

annanay25

diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl
index 48ef8df..717bfba 100644
--- a/tensorflow/core/platform/default/build_config.bzl
+++ b/tensorflow/core/platform/default/build_config.bzl
@@ -7,7 +7,7 @@ load("//tensorflow:tensorflow.bzl", "if_not_mobile")
 # configure may change the following lines
 WITH_GCP_SUPPORT = False
 WITH_HDFS_SUPPORT = False
-WITH_XLA_SUPPORT = False
+WITH_XLA_SUPPORT = True

annanay25

; ModuleID = 'test.ll'
source_filename = "bugpoint-output-b434f38.bc"
target datalayout = "e-p:64:64:64-S128-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f16:16:16-f32:32:32-f64:64:64-f128:128:128-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64"
target triple = "x86_64-unknown-linux-gnu"

module asm "\09.ident\09\22GCC: (GNU) 4.6.4 LLVM: 3.3.1\22"

; Function Attrs: nounwind uwtable
define void @quux() unnamed_addr #0 {
bb:

annanay25

void BlockGenerator::copyInstruction(...){
  .
  .
  .
    
  // Skip some special intrinsics for which we do not adjust the semantics to
  // the new schedule. All others are handled like every other instruction.
  if (isIgnoredIntrinsic(Inst)){
    IntrinsicInst *IT = dyn_cast<IntrinsicInst>(Inst);
    if(IT->getIntrinsicID() == llvm::Intrinsic::expect){

annanay25

from __future__ import print_function

import tensorflow as tf
import numpy
import time 
import os
from random import randint

# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

annanay25

@0 = private constant [1 x [28 x [28 x [1 x float]]]] [[28 x [28 x [1 x float]]] [[28 x [1 x float]] zeroinitializer, [28 x [1 x float]] zeroinitializer, [28 x [1 x float]] zeroinitializer, [28 x [1 x float]] zeroinitializer, [28 x [1 x float]] zeroinitializer, [28 x [1 x float]] zeroinitializer, [28 x [1 x float]] zeroinitializer, [28 x [1 x float]] [[1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] [float 0x3FD8585880000000], [1 x float] [float 0x3FD8181840000000], [1 x float] [float 0x3FD3535360000000], [1 x float] [float 0x3FDD9D9DC0000000], [1 x float] [float 0x3FCE9E9EC0000000], [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x float] zeroinitializer, [1 x