read_tfrecords_sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from meta import Meta\n",
    "%pylab inline\n",
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'data/test.tfrecords'\n",
    "filename_queue = tf.train.string_input_producer([filename], num_epochs=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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': tf.FixedLenFeature([], tf.string),\n",
    "      'length': tf.FixedLenFeature([], tf.int64),\n",
    "      'digits': tf.FixedLenFeature([5], tf.int64)\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = tf.decode_raw(features['image'], tf.uint8)\n",
    "image = tf.reshape(image, [64, 64, 3])\n",
    "length = features['length']\n",
    "digits = features['digits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "coord = tf.train.Coordinator()\n",
    "threads = tf.train.start_queue_runners(coord=coord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length: 1, digits: 4,0,0,0,0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb4483246d0>"
      ]
     },
     "execution_count": 6,
     "output_type": "execute_result",
     "metadata": {}
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV8AAAFiCAYAAABRUdvyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBCCCHWg/8POZHoLObUhzoAAAAASUVORK5CYII=\n"
     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "source": [
    "(image_val, length_val, digits_val) = sess.run([image, length, digits])\n",
    "\n",
    "print 'length: %d, digits: %d,%d,%d,%d,%d' % (\n",
    "    length_val, digits_val[0], digits_val[1], digits_val[2], digits_val[3], digits_val[4])\n",
    "\n",
    "imshow(image_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_train_examples: 30101\n",
      "num_val_examples: 3300\n",
      "num_test_examples: 13068\n"
     ]
    }
   ],
   "source": [
    "meta = Meta()\n",
    "meta.load('data/meta.json')\n",
    "print 'num_train_examples: %d' % meta.num_train_examples\n",
    "print 'num_val_examples: %d' % meta.num_val_examples\n",
    "print 'num_test_examples: %d' % meta.num_test_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "coord.request_stop()\n",
    "coord.join(threads)\n",
    "sess.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}