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

# Simplest case (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3)
fr = d[0]
print(type(fr))
print(fr.shape)

# Custom processing (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()})

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)