Project 5 Full Multi-threaded pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib.learn.python.learn.datasets import mnist\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_dir = \"mnist\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting mnist\\train-images-idx3-ubyte.gz\n",
      "Extracting mnist\\train-labels-idx1-ubyte.gz\n",
      "Extracting mnist\\t10k-images-idx3-ubyte.gz\n",
      "Extracting mnist\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "data_sets = mnist.read_data_sets(save_dir,\n",
    "                                 dtype=tf.uint8,\n",
    "                                 reshape=False,\n",
    "                                 validation_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving train\n",
      "saving test\n",
      "saving validation\n"
     ]
    }
   ],
   "source": [
    "data_splits = [\"train\",\"test\",\"validation\"]\n",
    "for d in range(len(data_splits)):\n",
    "    print(\"saving \" + data_splits[d])\n",
    "    data_set = data_sets[d]\n",
    "\n",
    "    filename = os.path.join(save_dir, data_splits[d] + '.tfrecords')\n",
    "    writer = tf.python_io.TFRecordWriter(filename)\n",
    "    \n",
    "    for index in range(data_set.images.shape[0]):\n",
    "        image = data_set.images[index].tostring()\n",
    "        example = tf.train.Example(features=tf.train.Features(feature={\n",
    "        'height': tf.train.Feature(int64_list=\n",
    "                                  tf.train.Int64List(value=\n",
    "                                  [data_set.images.shape[1]])),\n",
    "        'width': tf.train.Feature(int64_list=\n",
    "                                  tf.train.Int64List(value =\n",
    "                                  [data_set.images.shape[2]])),\n",
    "        'depth': tf.train.Feature(int64_list=\n",
    "                                  tf.train.Int64List(value =\n",
    "                                  [data_set.images.shape[3]])),\n",
    "        'label': tf.train.Feature(int64_list=\n",
    "                                  tf.train.Int64List(value =\n",
    "                                  [int(data_set.labels[index])])),\n",
    "        'image_raw': tf.train.Feature(bytes_list=\n",
    "                                   tf.train.BytesList(value =\n",
    "                                                     [image]))}))\n",
    "    writer.write(example.SerializeToString())\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#tf.train.string_input_producer() creates a queueRunner. It outputs filename strings to a\n",
    "#queue for our input  pipeline\n",
    "#num_epochs - produces each file name strings num_epoch times\n",
    "filename = os.path.join(save_dir ,\"train.tfrecords\")\n",
    "filename_queue = tf.train.string_input_producer(\n",
    "    [filename], num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reader = tf.TFRecordReader()\n",
    "_, serialized_example = reader.read(filename_queue)\n",
    "features = tf.parse_single_example(\n",
    "    serialized_example,\n",
    "    features={\n",
    "        'image_raw': tf.FixedLenFeature([], tf.string),\n",
    "        'label': tf.FixedLenFeature([], tf.int64),\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = tf.decode_raw(features['image_raw'], tf.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image.set_shape([784]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image = tf.cast(image, tf.float32) * (1. / 255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label = tf.cast(features['label'], tf.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "images_batch, labels_batch = tf.train.shuffle_batch(\n",
    "    [image, label], batch_size=128,\n",
    "    capacity=2000,\n",
    "    min_after_dequeue=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Variable W already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:\n\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-3613565ce762>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mW\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"W\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[1;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)\u001b[0m\n\u001b[0;32m   1063\u001b[0m       \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1064\u001b[0m       \u001b[0mpartitioner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1065\u001b[1;33m       use_resource=use_resource, custom_getter=custom_getter)\n\u001b[0m\u001b[0;32m   1066\u001b[0m get_variable_or_local_docstring = (\n\u001b[0;32m   1067\u001b[0m     \"\"\"%s\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[1;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)\u001b[0m\n\u001b[0;32m    960\u001b[0m           \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    961\u001b[0m           \u001b[0mpartitioner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 962\u001b[1;33m           use_resource=use_resource, custom_getter=custom_getter)\n\u001b[0m\u001b[0;32m    963\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    964\u001b[0m   def _get_partitioned_variable(self,\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[1;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter)\u001b[0m\n\u001b[0;32m    365\u001b[0m           \u001b[0mreuse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreuse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 367\u001b[1;33m           validate_shape=validate_shape, use_resource=use_resource)\n\u001b[0m\u001b[0;32m    368\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m   def _get_partitioned_variable(\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\variable_scope.py\u001b[0m in \u001b[0;36m_true_getter\u001b[1;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource)\u001b[0m\n\u001b[0;32m    350\u001b[0m           \u001b[0mtrainable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    351\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 352\u001b[1;33m           use_resource=use_resource)\n\u001b[0m\u001b[0;32m    353\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    354\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcustom_getter\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\variable_scope.py\u001b[0m in \u001b[0;36m_get_single_variable\u001b[1;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource)\u001b[0m\n\u001b[0;32m    662\u001b[0m                          \u001b[1;34m\" Did you mean to set reuse=True in VarScope? \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    663\u001b[0m                          \"Originally defined at:\\n\\n%s\" % (\n\u001b[1;32m--> 664\u001b[1;33m                              name, \"\".join(traceback.format_list(tb))))\n\u001b[0m\u001b[0;32m    665\u001b[0m       \u001b[0mfound_var\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vars\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfound_var\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Variable W already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:\n\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n"
     ]
    }
   ],
   "source": [
    "W = tf.get_variable(\"W\",[28*28,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = tf.matmul(images_batch,W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y_pred,labels=labels_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_mean = tf.reduce_mean(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "sess.run(tf.local_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coord = tf.train.Coordinator()\n",
    "threads = tf.train.start_queue_runners(sess=sess,coord=coord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Thread(Thread-12, stopped daemon 12136)>,\n",
       " <Thread(Thread-13, started daemon 12348)>,\n",
       " <Thread(Thread-14, stopped daemon 12392)>,\n",
       " <Thread(Thread-15, started daemon 21800)>,\n",
       " <Thread(Thread-16, stopped daemon 9060)>,\n",
       " <Thread(Thread-17, started daemon 21156)>,\n",
       " <Thread(Thread-18, stopped daemon 19012)>,\n",
       " <Thread(Thread-19, started daemon 19932)>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    step = 0\n",
    "    while not coord.should_stop():\n",
    "        step += 1\n",
    "        print ('here')\n",
    "        sess.run([train_op])\n",
    "        if step%10:\n",
    "            loss_mean_val = sess.run([loss_mean])\n",
    "            print (step)\n",
    "            print (loss_mean_val)\n",
    "except:\n",
    "    print ('Done')\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "OutOfRangeError",
     "evalue": "RandomShuffleQueue '_1_shuffle_batch_1/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 5)\n\t [[Node: shuffle_batch_1 = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](shuffle_batch_1/random_shuffle_queue, shuffle_batch_1/n)]]\n\nCaused by op 'shuffle_batch_1', defined at:\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2802, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-12-c248ad991f31>\", line 4, in <module>\n    min_after_dequeue=10)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\training\\input.py\", line 1220, in shuffle_batch\n    name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\training\\input.py\", line 791, in _shuffle_batch\n    dequeued = queue.dequeue_many(batch_size, name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\data_flow_ops.py\", line 457, in dequeue_many\n    self._queue_ref, n=n, component_types=self._dtypes, name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\gen_data_flow_ops.py\", line 1342, in _queue_dequeue_many_v2\n    timeout_ms=timeout_ms, name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nOutOfRangeError (see above for traceback): RandomShuffleQueue '_1_shuffle_batch_1/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 5)\n\t [[Node: shuffle_batch_1 = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](shuffle_batch_1/random_shuffle_queue, shuffle_batch_1/n)]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOutOfRangeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1327\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1306\u001b[1;33m                                    status, run_metadata)\n\u001b[0m\u001b[0;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[1;34m()\u001b[0m\n\u001b[0;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[0;32m    467\u001b[0m   \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOutOfRangeError\u001b[0m: RandomShuffleQueue '_1_shuffle_batch_1/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 5)\n\t [[Node: shuffle_batch_1 = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](shuffle_batch_1/random_shuffle_queue, shuffle_batch_1/n)]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mOutOfRangeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-ea082b2869a4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_op\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    893\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 895\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    896\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1122\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1124\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1125\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1126\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[1;32m-> 1321\u001b[1;33m                            options, run_metadata)\n\u001b[0m\u001b[0;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1338\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1339\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1340\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1342\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOutOfRangeError\u001b[0m: RandomShuffleQueue '_1_shuffle_batch_1/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 5)\n\t [[Node: shuffle_batch_1 = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](shuffle_batch_1/random_shuffle_queue, shuffle_batch_1/n)]]\n\nCaused by op 'shuffle_batch_1', defined at:\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2802, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-12-c248ad991f31>\", line 4, in <module>\n    min_after_dequeue=10)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\training\\input.py\", line 1220, in shuffle_batch\n    name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\training\\input.py\", line 791, in _shuffle_batch\n    dequeued = queue.dequeue_many(batch_size, name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\data_flow_ops.py\", line 457, in dequeue_many\n    self._queue_ref, n=n, component_types=self._dtypes, name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\ops\\gen_data_flow_ops.py\", line 1342, in _queue_dequeue_many_v2\n    timeout_ms=timeout_ms, name=name)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 767, in apply_op\n    op_def=op_def)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2630, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1204, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nOutOfRangeError (see above for traceback): RandomShuffleQueue '_1_shuffle_batch_1/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 5)\n\t [[Node: shuffle_batch_1 = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](shuffle_batch_1/random_shuffle_queue, shuffle_batch_1/n)]]\n"
     ]
    }
   ],
   "source": [
    "sess.run(train_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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