VGGNet-Implication.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGGNet 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 技巧+实现\n",
    "- 更深\n",
    "- 多实用3\\*3 两个->5 5 三个->7 7\n",
    "    - 视野域不变\n",
    "    - 参数减少\n",
    "    - 非线性变换因为层数增大而增多\n",
    "- 1\\*1 非线性变换\n",
    "    - 实现通道变换 降维/升维\n",
    "- 每次经过pooling 通道数目翻倍\n",
    "- 11->19 参数基本不变\n",
    "- 递进式训练：从浅层网络-> 深层网络\n",
    "- 数据增强进一步：多尺度(随机)\n",
    "    - 256 256 -> 放大 之后对于不同尺度进行抽样224\\*224 不同尺度对应不同分类器 最后ensemble\n",
    "- 与AlexNet 很相似 全连接层仍然在\n",
    "- A-LRN版本沿用Alex中的局部归一化 通道与相邻通道进行归一化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data prepocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['batches.meta', 'data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4', 'data_batch_5', 'readme.html', 'test_batch']\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "CIFAR_DIR = './cifar-10-batches-py'\n",
    "print(os.listdir(CIFAR_DIR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 3072)\n",
      "(50000,)\n",
      "(10000, 3072)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "def load_data(filename):\n",
    "    \"\"\"read data from data file.\"\"\"\n",
    "    with open(filename, 'rb') as f:\n",
    "        data = pickle.load(f, encoding='bytes')\n",
    "        return data[b'data'], data[b'labels']\n",
    "\n",
    "# tensorflow.Dataset.\n",
    "class CifarData:\n",
    "    def __init__(self, filenames, need_shuffle):\n",
    "        all_data = []\n",
    "        all_labels = []\n",
    "        for filename in filenames:\n",
    "            data, labels = load_data(filename)\n",
    "            all_data.append(data)\n",
    "            all_labels.append(labels)\n",
    "        # hstack vstack 用于合并二维数组变成矩阵形式\n",
    "        self._data = np.vstack(all_data)\n",
    "        self._data = self._data / 127.5 - 1\n",
    "        # 小心\n",
    "        self._labels = np.hstack(all_labels)\n",
    "        print(self._data.shape)\n",
    "        print(self._labels.shape)\n",
    "        \n",
    "        self._num_examples = self._data.shape[0]\n",
    "        self._need_shuffle = need_shuffle\n",
    "        self._indicator = 0\n",
    "        if self._need_shuffle:\n",
    "            self._shuffle_data()\n",
    "            \n",
    "    def _shuffle_data(self):\n",
    "        # [0,1,2,3,4,5] -> [5,3,2,4,0,1]\n",
    "        p = np.random.permutation(self._num_examples)\n",
    "        self._data = self._data[p]\n",
    "        self._labels = self._labels[p]\n",
    "    \n",
    "    def next_batch(self, batch_size):\n",
    "        \"\"\"return batch_size examples as a batch.\"\"\"\n",
    "        end_indicator = self._indicator + batch_size\n",
    "        if end_indicator > self._num_examples:\n",
    "            if self._need_shuffle:\n",
    "                self._shuffle_data()\n",
    "                self._indicator = 0\n",
    "                end_indicator = batch_size\n",
    "            else:\n",
    "                raise Exception(\"have no more examples\")\n",
    "        if end_indicator > self._num_examples:\n",
    "            raise Exception(\"batch size is larger than all examples\")\n",
    "        batch_data = self._data[self._indicator: end_indicator]\n",
    "        batch_labels = self._labels[self._indicator: end_indicator]\n",
    "        self._indicator = end_indicator\n",
    "        return batch_data, batch_labels\n",
    "\n",
    "train_filenames = [os.path.join(CIFAR_DIR, 'data_batch_%d' % i) for i in range(1, 6)]\n",
    "test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]\n",
    "\n",
    "train_data = CifarData(train_filenames, True)\n",
    "test_data = CifarData(test_filenames, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model\n",
    "- 可以达到70%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # 利用这个可清空defualt graph以及nodes\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, 3072])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "# [None], eg: [0,5,6,3]\n",
    "x_image = tf.reshape(x, [-1, 3, 32, 32])\n",
    "# 32*32\n",
    "x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])\n",
    "\n",
    "# conv1: 神经元图， feature_map, 输出图像\n",
    "conv1_1 = tf.layers.conv2d(x_image,\n",
    "                         32, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv1_1')\n",
    "\n",
    "conv1_2 = tf.layers.conv2d(conv1_1,\n",
    "                         32, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv1_2')\n",
    "# 16 * 16\n",
    "pooling1 = tf.layers.max_pooling2d(conv1_2,\n",
    "                                   (2, 2), # kernel size\n",
    "                                   (2, 2), # stride\n",
    "                                   name = 'pool1')\n",
    "\n",
    "\n",
    "conv2_1 = tf.layers.conv2d(pooling1,\n",
    "                         64, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv2_1')\n",
    "conv2_2 = tf.layers.conv2d(conv2_1,\n",
    "                         64, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv2_2')\n",
    "# 8 * 8\n",
    "pooling2 = tf.layers.max_pooling2d(conv2_2,\n",
    "                                   (2, 2), # kernel size\n",
    "                                   (2, 2), # stride\n",
    "                                   name = 'pool2')\n",
    "\n",
    "conv3_1 = tf.layers.conv2d(pooling2,\n",
    "                         128, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv3_1')\n",
    "conv3_2 = tf.layers.conv2d(conv3_1,\n",
    "                         128, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv3_2')\n",
    "\n",
    "# 4 * 4 * 32\n",
    "pooling3 = tf.layers.max_pooling2d(conv3_2,\n",
    "                                   (2, 2), # kernel size\n",
    "                                   (2, 2), # stride\n",
    "                                   name = 'pool3')\n",
    "# [None, 4 * 4 * 32]\n",
    "flatten = tf.layers.flatten(pooling3)\n",
    "y_ = tf.layers.dense(flatten, 10)\n",
    "\n",
    "loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)\n",
    "# y_ -> sofmax\n",
    "# y -> one_hot\n",
    "# loss = ylogy_\n",
    "\n",
    "# [1,0,1,1,1,0,0,0]\n",
    "correct_prediction = tf.equal(tf.argmax(y_, 1), y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))\n",
    "\n",
    "with tf.name_scope('train_op'):\n",
    "    train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Step: 50, loss: 1.96816, acc: 0.40000\n",
      "[Train] Step: 100, loss: 1.90950, acc: 0.15000\n",
      "[Train] Step: 150, loss: 2.09852, acc: 0.20000\n",
      "[Train] Step: 200, loss: 1.92543, acc: 0.30000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 200, acc: 0.32950\n",
      "[Train] Step: 250, loss: 1.61349, acc: 0.40000\n",
      "[Train] Step: 300, loss: 1.99049, acc: 0.40000\n",
      "[Train] Step: 350, loss: 1.56420, acc: 0.45000\n",
      "[Train] Step: 400, loss: 1.94742, acc: 0.35000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 400, acc: 0.39350\n",
      "[Train] Step: 450, loss: 1.10787, acc: 0.60000\n",
      "[Train] Step: 500, loss: 1.64498, acc: 0.50000\n",
      "[Train] Step: 550, loss: 1.61738, acc: 0.20000\n",
      "[Train] Step: 600, loss: 1.49076, acc: 0.40000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 600, acc: 0.43450\n",
      "[Train] Step: 650, loss: 1.54558, acc: 0.40000\n",
      "[Train] Step: 700, loss: 1.26109, acc: 0.50000\n",
      "[Train] Step: 750, loss: 1.16634, acc: 0.55000\n",
      "[Train] Step: 800, loss: 1.27678, acc: 0.50000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 800, acc: 0.47900\n",
      "[Train] Step: 850, loss: 1.37782, acc: 0.60000\n",
      "[Train] Step: 900, loss: 1.14719, acc: 0.60000\n",
      "[Train] Step: 950, loss: 1.73316, acc: 0.40000\n",
      "[Train] Step: 1000, loss: 1.13277, acc: 0.70000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 1000, acc: 0.52150\n"
     ]
    }
   ],
   "source": [
    "# cfg = tf.ConfigProto(allow_soft_placement=True )\n",
    "# cfg.gpu_options.allow_growth = True\n",
    "init = tf.global_variables_initializer()\n",
    "batch_size = 20\n",
    "train_steps = 1000\n",
    "test_steps = 100\n",
    "\n",
    "# train 10k: 71.35%\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for i in range(train_steps):\n",
    "        batch_data, batch_labels = train_data.next_batch(batch_size)\n",
    "        loss_val, acc_val, _ = sess.run(\n",
    "            [loss, accuracy, train_op],\n",
    "            feed_dict={\n",
    "                x: batch_data,\n",
    "                y: batch_labels})\n",
    "        if (i+1) % 50 == 0:\n",
    "            print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' \n",
    "                  % (i+1, loss_val, acc_val))\n",
    "        if (i+1) % 200 == 0:\n",
    "            test_data = CifarData(test_filenames, False)\n",
    "            all_test_acc_val = []\n",
    "            for j in range(test_steps):\n",
    "                test_batch_data, test_batch_labels \\\n",
    "                    = test_data.next_batch(batch_size)\n",
    "                test_acc_val = sess.run(\n",
    "                    [accuracy],\n",
    "                    feed_dict = {\n",
    "                        x: test_batch_data, \n",
    "                        y: test_batch_labels\n",
    "                    })\n",
    "                all_test_acc_val.append(test_acc_val)\n",
    "            test_acc = np.mean(all_test_acc_val)\n",
    "            print('[Test ] Step: %d, acc: %4.5f'\n",
    "                  % (i+1, test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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