Multihuntr

import numpy as np
import tensorflow as tf
import sys

from tensorflow.examples.tutorials.mnist import input_data

n_pseudo_batches =  int(sys.argv[1]) if len(sys.argv) > 1 else 128
actual_batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 32
iterations =        int(sys.argv[3]) if len(sys.argv) > 3 else 10

Multihuntr

import numpy as np
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

def simple_model(input):
  # This ensures that the model will always be instantiated the same, for comparison.
  hidden_initializer = tf.constant_initializer(np.random.uniform(-0.025, 0.025, size=[784,100]))
  hidden = tf.layers.dense(input, 100, kernel_initializer=hidden_initializer)
  out_initializer = tf.constant_initializer(np.random.uniform(-0.025, 0.025, size=[100,10]))

Multihuntr

import queue
import random

class RandomQueue(queue.Queue):
  def _put(self, item):
    n = len(self.queue)
    i = random.randint(0, n)
    self.queue.insert(i, item)