mpekalski

with tf.variable_scope("dataset"):
    train_values = tf.constant([[1,2,3],[2,3,4],
                                [3,4,2],[2,1,5],
                                [1,7,3],[2,2,7],
                                [0,1,0],[0,1,0],
                                [0,1,0],[0,1,0]])
    train_labels = tf.constant([1, 0, 0, 1, 1, 1,0,1,0,0])

    # 1) no shuffling, so the outcome would be deterministic
    # 2) make one dataset object from train_values and train_labels

mpekalski

"""Simple example on how to log scalars and images to tensorboard without tensor ops."""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

class Logger(object):
    """Logging in tensorboard without tensorflow ops."""

mpekalski

#!/bin/bash

IPT="/sbin/iptables"

### Interfaces ###
PUB_IF="eth0"   # public interface
PRV_IF="eth1"   # private interface
LO_IF="lo"      # loopback
SERVER_IP=$(ifconfig eth0 | grep 'inet addr:' | awk -F'inet addr:' '{ print $2}' | awk '{ print $1}')