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# with sigmoid function -> accuracy : 0.9108
W1 = tf.Variable(tf.random_normal([784, neurons]), name='weight1')
b1 = tf.Variable(tf.random_normal([neurons]), name='bias1')
logits1 = tf.matmul(X, W1) + b1
layer1 = tf.sigmoid(logits1)

W2 = tf.Variable(tf.random_normal([neurons, neurons]), name='weight2')
b2 = tf.Variable(tf.random_normal([neurons]), name='bias3')
logits2 = tf.matmul(layer1, W2) + b2
layer2 = tf.sigmoid(logits2)

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import time
import serial
import traceback
import signal
import sys
import codecs

# configure the serial connections (the parameters differs on the device you are connecting to)
ser = serial.Serial(
    port='COM6',

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from kafka import KafkaProducer
from confluent_kafka import Producer
import multiprocessing

Class KProducer(object):
    def __init__(self, bootstrap_servers, cbootstrap_servers, topic):
        self.producer = KafkaProducer(bootstrap_servers=bootstrap_servers)
        self.cproducer = Producer({'bootstrap.servers': cbootstrap_servers})
        self.topic = topic
        

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def checksum(msg):
    ck_a = 0
    ck_b = 0
    for i in msg:
        ck_a = ck_a + i
        ck_b = ck_b + ck_a
    ck_a = ck_a % 256
    ck_b = ck_b % 256

    return (ck_a, ck_b)

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#! /usr/bin/python
import socket
import time

client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
client_host = ('0.0.0.0', 0)
while True:
  try:
    client.sendto(b'1',client_host)
    time.sleep(1)

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