MNIST with shape info TFrecord.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tfrecorder import TFrecorder\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST-data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST-data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST-data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST-data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Load training and eval data\n",
    "mnist = tf.contrib.learn.datasets.load_dataset(\"mnist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# how to write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nNote:\\n1. if the shape of particular feature for every example in your dataset varies, then you must set \"length_type\" as \\'var\\'\\n     example: \\'image\\' feature for the first example is 28x28; but for the second example is 64x64\\n2. \\'image_shape\\' decides the final shape of \\'image\\'\\n     example: the shape of \\'image\\' is (784,) when you write the tfrecord file, but the final shape after parsing is reshaped by the value of \\'image_shape\\'\\n3. by default, \\'_shape\\' info will not be returned after parsing, \\n   if you want to keep \\'_shape\\' info, please use: tfr = TFrecorder(retrieve_shape=True)\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# info of data\n",
    "df = pd.DataFrame({'name':['image','label','image_shape'],\n",
    "                  'type':['float32','int64','int64'],\n",
    "                  'shape':[(784,),(),(3,)],\n",
    "                  'isbyte':[True,False,False],\n",
    "                  \"length_type\":['fixed','fixed','fixed'],\n",
    "                  \"default\":[np.NaN,np.NaN,np.NaN]})\n",
    "'''\n",
    "Note:\n",
    "1. if the shape of particular feature for every example in your dataset varies, then you must set \"length_type\" as 'var'\n",
    "     example: 'image' feature for the first example is 28x28; but for the second example is 64x64\n",
    "2. 'image_shape' decides the final shape of 'image'\n",
    "     example: the shape of 'image' is (784,) when you write the tfrecord file, but the final shape after parsing is reshaped by the value of 'image_shape'\n",
    "3. by default, '_shape' info will not be returned after parsing, \n",
    "   if you want to keep '_shape' info, please use: tfr = TFrecorder(retrieve_shape=True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>isbyte</th>\n",
       "      <th>length_type</th>\n",
       "      <th>name</th>\n",
       "      <th>shape</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>fixed</td>\n",
       "      <td>image</td>\n",
       "      <td>(784,)</td>\n",
       "      <td>float32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>fixed</td>\n",
       "      <td>label</td>\n",
       "      <td>()</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>fixed</td>\n",
       "      <td>image_shape</td>\n",
       "      <td>(3,)</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   default  isbyte length_type         name   shape     type\n",
       "0      NaN    True       fixed        image  (784,)  float32\n",
       "1      NaN   False       fixed        label      ()    int64\n",
       "2      NaN   False       fixed  image_shape    (3,)    int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfr = TFrecorder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm -rf mnist_tfrecord/train mnist_tfrecord/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir -p mnist_tfrecord/train mnist_tfrecord/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved mnist_tfrecord/train/train0_10000.tfrecord\n",
      "saved mnist_tfrecord/train/train10000_20000.tfrecord\n",
      "saved mnist_tfrecord/train/train20000_30000.tfrecord\n",
      "saved mnist_tfrecord/train/train30000_40000.tfrecord\n",
      "saved mnist_tfrecord/train/train40000_50000.tfrecord\n",
      "saved mnist_tfrecord/train/train50000_60000.tfrecord\n"
     ]
    }
   ],
   "source": [
    "dataset = mnist.train\n",
    "path = 'mnist_tfrecord/train/train'\n",
    "num_examples_per_file = 10000\n",
    "num_so_far = 0\n",
    "\n",
    "writer = tf.python_io.TFRecordWriter('%s%s_%s.tfrecord' %(path, num_so_far, num_examples_per_file))\n",
    "print('saved %s%s_%s.tfrecord' %(path, num_so_far, num_examples_per_file))\n",
    "# write mutilple examples\n",
    "for i in np.arange(dataset.num_examples):\n",
    "    features = {}\n",
    "    # shape: 28,28,1\n",
    "    if i%2==0:\n",
    "        images = dataset.images[i].reshape((28,28,1))\n",
    "    # shape: 14,56,1\n",
    "    else:\n",
    "        images = dataset.images[i].reshape((14,56,1))\n",
    "    tfr.feature_writer(df.iloc[0], images, features)\n",
    "    # 写一个样本的标签信息存到字典features中\n",
    "    tfr.feature_writer(df.iloc[1], dataset.labels[i], features)\n",
    "    # ******************\n",
    "    # write shape info *\n",
    "    # ******************\n",
    "    tfr.feature_writer(df.iloc[2], images.shape, features)\n",
    "    \n",
    "    tf_features = tf.train.Features(feature= features)\n",
    "    tf_example = tf.train.Example(features = tf_features)\n",
    "    tf_serialized = tf_example.SerializeToString()\n",
    "    writer.write(tf_serialized)\n",
    "    if i%num_examples_per_file ==0 and i!=0:\n",
    "        writer.close()\n",
    "        num_so_far = i\n",
    "        writer = tf.python_io.TFRecordWriter('%s%s_%s.tfrecord' %(path, num_so_far, i+num_examples_per_file))\n",
    "        print('saved %s%s_%s.tfrecord' %(path, num_so_far, i+num_examples_per_file))\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved mnist_tfrecord/test/test0_10000.tfrecord\n"
     ]
    }
   ],
   "source": [
    "# 用该方法写测试集的tfrecord文件\n",
    "dataset = mnist.test\n",
    "path = 'mnist_tfrecord/test/test'\n",
    "# 每个tfrecord文件写多少个样本\n",
    "num_examples_per_file = 10000\n",
    "# 当前写的样本数\n",
    "num_so_far = 0\n",
    "# 要写入的文件\n",
    "writer = tf.python_io.TFRecordWriter('%s%s_%s.tfrecord' %(path, num_so_far, num_examples_per_file))\n",
    "print('saved %s%s_%s.tfrecord' %(path, num_so_far, num_examples_per_file))\n",
    "# 写多个样本\n",
    "for i in np.arange(dataset.num_examples):\n",
    "    # 要写到tfrecord文件中的字典\n",
    "    features = {}\n",
    "    # 写一个样本的图片信息存到字典features中\n",
    "    # shape: 28,28,1\n",
    "    if i%2==0:\n",
    "        images=dataset.images[i].reshape((28,28,1))\n",
    "    # shape: 14,56,1\n",
    "    else:\n",
    "        images=dataset.images[i].reshape((14,56,1))\n",
    "    tfr.feature_writer(df.iloc[0], images, features)\n",
    "    # 写一个样本的标签信息存到字典features中\n",
    "    tfr.feature_writer(df.iloc[1], dataset.labels[i], features)\n",
    "    # ******************\n",
    "    # write shape info *\n",
    "    # ******************\n",
    "    # shape: 28,28,1\n",
    "    tfr.feature_writer(df.iloc[2], images.shape, features)\n",
    "    \n",
    "    tf_features = tf.train.Features(feature= features)\n",
    "    tf_example = tf.train.Example(features = tf_features)\n",
    "    tf_serialized = tf_example.SerializeToString()\n",
    "    writer.write(tf_serialized)\n",
    "    # 每写了num_examples_per_file个样本就令生成一个tfrecord文件\n",
    "    if i%num_examples_per_file ==0 and i!=0:\n",
    "        writer.close()\n",
    "        num_so_far = i\n",
    "        writer = tf.python_io.TFRecordWriter('%s%s_%s.tfrecord' %(path, num_so_far, i+num_examples_per_file))\n",
    "        print('saved %s%s_%s.tfrecord' %(path, num_so_far, i+num_examples_per_file))\n",
    "writer.close()\n",
    "# 把指定如何写成tfrecord文件的信息保存起来\n",
    "data_info_path = 'mnist_tfrecord/data_info.csv'\n",
    "df.to_csv(data_info_path,index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfr = TFrecorder(retrieve_shape=True)\n",
    "def input_fn_maker(path, data_info_path, shuffle=False, batch_size = 1, epoch = 1, padding = None):\n",
    "    def input_fn():\n",
    "        filenames = tfr.get_filenames(path=path, shuffle=shuffle)\n",
    "        dataset=tfr.get_dataset(paths=filenames, data_info=data_info_path, shuffle = shuffle, \n",
    "                            batch_size = batch_size, epoch = epoch, padding =padding)\n",
    "        iterator = dataset.make_one_shot_iterator()\n",
    "        return iterator.get_next()\n",
    "    return input_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read dataframe from mnist_tfrecord/test/test0_10000.tfrecord x 1\n"
     ]
    }
   ],
   "source": [
    "padding_info = ({'image':[None,None,1],'label':[],'image_shape':[None]})\n",
    "test_input_fn = input_fn_maker('mnist_tfrecord/test/',  'mnist_tfrecord/data_info.csv',batch_size = 1,\n",
    "                               padding = padding_info)\n",
    "train_input_fn = input_fn_maker('mnist_tfrecord/train/',  'mnist_tfrecord/data_info.csv', shuffle=True, batch_size = 1,\n",
    "                               padding = padding_info)\n",
    "train_eval_fn = input_fn_maker('mnist_tfrecord/train/',  'mnist_tfrecord/data_info.csv', batch_size = 1,\n",
    "                               padding = padding_info)\n",
    "test_inputs = test_input_fn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of image: (1, 28, 28, 1)\n",
      "value of label: [7]\n",
      "value of 'image_shape':\n",
      " [[28 28  1]]\n"
     ]
    }
   ],
   "source": [
    "sess =tf.InteractiveSession()\n",
    "exmaple_image, exmaple_label, exmaple_shape_image = sess.run([test_inputs['image'],test_inputs['label'],test_inputs['image_shape']])\n",
    "print(\"shape of image:\",exmaple_image.shape)\n",
    "print(\"value of label:\",exmaple_label)\n",
    "print(\"value of 'image_shape':\\n\",exmaple_shape_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess =tf.InteractiveSession()\n",
    "# Because test_inputs contain n_batch examples, and each example has different image shape.\n",
    "# executing tf.reshape once can not do the work.\n",
    "batch_size = 1\n",
    "list_image_examples = tf.reshape(test_inputs['image'][0],test_inputs['image_shape'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape of example (28, 28, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cdd03bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example= sess.run(list_image_examples)\n",
    "plt.imshow(example.reshape((28,28)),cmap=plt.cm.gray)\n",
    "print('image shape of example', example.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv = tf.layers.conv2d(\n",
    "        inputs=test_inputs['image'],\n",
    "        filters=64,\n",
    "        kernel_size=[5, 5],\n",
    "        padding=\"same\",\n",
    "        activation=tf.nn.relu,\n",
    "        name = 'conv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 14, 56, 64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.global_variables_initializer().run()\n",
    "sess.run(conv).shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}