learn02_cnnbasic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iP0zEPz4V5Ub"
   },
   "source": [
    "## 神经元入门+数据处理观察\n",
    "- 神经元\n",
    "- 正向传播\n",
    "- 反向传播\n",
    "- 激活函数 relu selu\n",
    "- 二分类sigmoid\n",
    "- 多分类softmax\n",
    "- 目标函数/损失函数 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x8XZ9AGtoc2b"
   },
   "source": [
    "## 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6oB6kbwiohiw"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "35juLaPqpCr7"
   },
   "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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-0348b2964bed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_default_graph\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[0m\u001b[0;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3072\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[0;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mNone\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[0;32m      4\u001b[0m \u001b[1;31m# [None], eg: [0,5,6,3]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mx_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\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;31mNameError\u001b[0m: name 'tf' is not defined"
     ]
    }
   ],
   "source": [
    "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 = 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')\n",
    "\n",
    "# 16 * 16\n",
    "pooling1 = tf.layers.max_pooling2d(conv1,\n",
    "                                   (2, 2), # kernel size\n",
    "                                   (2, 2), # stride\n",
    "                                   name = 'pool1')\n",
    "\n",
    "\n",
    "conv2 = tf.layers.conv2d(pooling1,\n",
    "                         32, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv2')\n",
    "\n",
    "# 8 * 8\n",
    "pooling2 = tf.layers.max_pooling2d(conv2,\n",
    "                                   (2, 2), # kernel size\n",
    "                                   (2, 2), # stride\n",
    "                                   name = 'pool2')\n",
    "\n",
    "conv3 = tf.layers.conv2d(pooling2,\n",
    "                         32, # output channel number\n",
    "                         (3,3), # kernel size\n",
    "                         padding = 'same',\n",
    "                         activation = tf.nn.relu,\n",
    "                         name = 'conv3')\n",
    "\n",
    "# 4 * 4 * 32\n",
    "pooling3 = tf.layers.max_pooling2d(conv3,\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",
    "# indices\n",
    "predict = tf.argmax(y_, 1)\n",
    "# [1,0,1,1,1,0,0,0]\n",
    "correct_prediction = tf.equal(predict, 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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Step: 100, loss: 2.09462, acc: 0.35000\n",
      "[Train] Step: 200, loss: 1.70411, acc: 0.40000\n",
      "[Train] Step: 300, loss: 1.72637, acc: 0.45000\n",
      "[Train] Step: 400, loss: 1.55578, acc: 0.40000\n",
      "[Train] Step: 500, loss: 1.25576, acc: 0.60000\n",
      "[Train] Step: 600, loss: 1.52830, acc: 0.45000\n",
      "[Train] Step: 700, loss: 1.68221, acc: 0.50000\n",
      "[Train] Step: 800, loss: 1.42715, acc: 0.55000\n",
      "[Train] Step: 900, loss: 1.27387, acc: 0.50000\n",
      "[Train] Step: 1000, loss: 1.53119, acc: 0.35000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 1000, acc: 0.52600\n",
      "[Train] Step: 1100, loss: 1.49806, acc: 0.65000\n",
      "[Train] Step: 1200, loss: 0.99467, acc: 0.65000\n",
      "[Train] Step: 1300, loss: 1.46154, acc: 0.55000\n",
      "[Train] Step: 1400, loss: 1.47188, acc: 0.55000\n",
      "[Train] Step: 1500, loss: 1.13636, acc: 0.55000\n",
      "[Train] Step: 1600, loss: 1.55550, acc: 0.35000\n",
      "[Train] Step: 1700, loss: 1.68211, acc: 0.55000\n",
      "[Train] Step: 1800, loss: 1.47786, acc: 0.35000\n",
      "[Train] Step: 1900, loss: 1.06810, acc: 0.65000\n",
      "[Train] Step: 2000, loss: 1.07895, acc: 0.60000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 2000, acc: 0.58700\n",
      "[Train] Step: 2100, loss: 0.96648, acc: 0.50000\n",
      "[Train] Step: 2200, loss: 1.16055, acc: 0.65000\n",
      "[Train] Step: 2300, loss: 1.15636, acc: 0.65000\n",
      "[Train] Step: 2400, loss: 0.95051, acc: 0.65000\n",
      "[Train] Step: 2500, loss: 1.62776, acc: 0.55000\n",
      "[Train] Step: 2600, loss: 0.74466, acc: 0.75000\n",
      "[Train] Step: 2700, loss: 1.07349, acc: 0.70000\n",
      "[Train] Step: 2800, loss: 0.95365, acc: 0.65000\n",
      "[Train] Step: 2900, loss: 0.98783, acc: 0.70000\n",
      "[Train] Step: 3000, loss: 0.66790, acc: 0.80000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 3000, acc: 0.63050\n",
      "[Train] Step: 3100, loss: 1.34383, acc: 0.40000\n",
      "[Train] Step: 3200, loss: 0.74760, acc: 0.65000\n",
      "[Train] Step: 3300, loss: 1.21417, acc: 0.50000\n",
      "[Train] Step: 3400, loss: 1.35727, acc: 0.45000\n",
      "[Train] Step: 3500, loss: 0.74372, acc: 0.65000\n",
      "[Train] Step: 3600, loss: 0.92027, acc: 0.70000\n",
      "[Train] Step: 3700, loss: 0.99751, acc: 0.75000\n",
      "[Train] Step: 3800, loss: 0.54787, acc: 0.85000\n",
      "[Train] Step: 3900, loss: 0.85944, acc: 0.60000\n",
      "[Train] Step: 4000, loss: 0.87295, acc: 0.75000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 4000, acc: 0.63900\n",
      "[Train] Step: 4100, loss: 0.86838, acc: 0.75000\n",
      "[Train] Step: 4200, loss: 0.60158, acc: 0.80000\n",
      "[Train] Step: 4300, loss: 1.04595, acc: 0.55000\n",
      "[Train] Step: 4400, loss: 0.88315, acc: 0.75000\n",
      "[Train] Step: 4500, loss: 0.78292, acc: 0.80000\n",
      "[Train] Step: 4600, loss: 1.02673, acc: 0.70000\n",
      "[Train] Step: 4700, loss: 1.11781, acc: 0.50000\n",
      "[Train] Step: 4800, loss: 1.12302, acc: 0.70000\n",
      "[Train] Step: 4900, loss: 0.76136, acc: 0.75000\n",
      "[Train] Step: 5000, loss: 1.25675, acc: 0.55000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 5000, acc: 0.65100\n",
      "[Train] Step: 5100, loss: 0.84338, acc: 0.65000\n",
      "[Train] Step: 5200, loss: 0.66870, acc: 0.75000\n",
      "[Train] Step: 5300, loss: 0.68353, acc: 0.70000\n",
      "[Train] Step: 5400, loss: 0.75815, acc: 0.80000\n",
      "[Train] Step: 5500, loss: 0.84902, acc: 0.65000\n",
      "[Train] Step: 5600, loss: 0.83528, acc: 0.65000\n",
      "[Train] Step: 5700, loss: 1.02410, acc: 0.60000\n",
      "[Train] Step: 5800, loss: 0.78019, acc: 0.70000\n",
      "[Train] Step: 5900, loss: 0.93050, acc: 0.60000\n",
      "[Train] Step: 6000, loss: 0.74457, acc: 0.75000\n",
      "(10000, 3072)\n",
      "(10000,)\n",
      "[Test ] Step: 6000, acc: 0.67950\n",
      "[Train] Step: 6100, loss: 0.91224, acc: 0.55000\n",
      "[Train] Step: 6200, loss: 1.00960, acc: 0.55000\n",
      "[Train] Step: 6300, loss: 1.28216, acc: 0.60000\n",
      "[Train] Step: 6400, loss: 0.86995, acc: 0.60000\n",
      "[Train] Step: 6500, loss: 1.30752, acc: 0.60000\n",
      "[Train] Step: 6600, loss: 0.56684, acc: 0.85000\n",
      "[Train] Step: 6700, loss: 0.70128, acc: 0.80000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-8842fb5a5ee8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m             feed_dict={\n\u001b[0;32m     16\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbatch_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m                 y: batch_labels})\n\u001b[0m\u001b[0;32m     18\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m100\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m             print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' \n",
      "\u001b[1;32mD:\\Anacoda_install\\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    927\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    930\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anacoda_install\\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   1150\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1153\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1154\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anacoda_install\\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   1326\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1329\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1330\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[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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anacoda_install\\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   1332\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\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[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1333\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1335\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1336\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anacoda_install\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1317\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\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[0;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1321\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\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;32mD:\\Anacoda_install\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1405\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m         run_metadata)\n\u001b[0m\u001b[0;32m   1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1409\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\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;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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 = 100000\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) % 100 == 0:\n",
    "            print('[Train] Step: %d, loss: %4.5f, acc: %4.5f' \n",
    "                  % (i+1, loss_val, acc_val))\n",
    "        if (i+1) % 1000 == 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": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "learn01_dataobserve_neuron.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "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": "3.6.7"
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