dniku

import multiprocessing, threading, time, Queue
import zmq


class RPCServer(threading.Thread):
    def __init__(self, rpc_address, queue):
        super(RPCServer, self).__init__(name="RPC server")
        self.queue = queue
        self.rpc_address = rpc_address
        self.stopped = False

dniku

import threading, multiprocessing, time, Queue

from spyne import MethodContext
from spyne.application import Application
from spyne.service import ServiceBase
from spyne.decorator import rpc
from spyne.protocol.msgpack import MessagePackRpc
from spyne.client.zeromq import ZeroMQClient
from spyne.auxproc import process_contexts
from spyne.server.zeromq import ZeroMQServer, ZmqMethodContext

dniku

import threading
import Queue
import random, time
import os, psutil

import gevent
import gevent.queue

proc = None

dniku

from __future__ import division
import pandas as pd
import datetime

time_format = '%d. %m. %Y %-H:%M'

def from_javatime(time):
	return datetime.datetime.fromtimestamp(time // 1000)

def quote(s):

dniku

import os, glob, shutil, bencode
from pprint import pprint

fail_path = 'failed'

def fix_resume(resume_path, old_destination, new_destination, old_incomplete, new_incomplete):
	for path in glob.glob(os.path.join(resume_path, '*.resume')):
		# print(path)
		with open(path, 'r') as f:
			try:

dniku

from __future__ import division, print_function

import os

import cv2
import numpy as np

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split

dniku

name: "LeNet"
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  data_param {

dniku

# The train/test net protocol buffer definition
net: "lenet_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 20 training iterations.
test_interval: 20
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01

dniku

from __future__ import division, print_function

import os

import cv2
import numpy as np

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split

dniku

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