day2-basic-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BASIC MNIST USAGE\n",
    "- https://github.com/sjchoi86/advanced-tensorflow/blob/master/basic/basic_mnist.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TENSORFLOW VERSION IS [0.12.1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "%matplotlib inline\n",
    "print(\"TENSORFLOW VERSION IS [%s]\" % (tf.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DOWNLOAD MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting data/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting data/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting data/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('data/', one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HOW DOES IT LOOK LIKE?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_np(x):\n",
    "    print(\"SHAPE OF is %s\" % (x.shape,))\n",
    "    print(\"VALUES LOOK LIKE \\n %s\" % (x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHAPE OF is (55000, 784)\n",
      "VALUES LOOK LIKE \n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "SHAPE OF is (55000, 10)\n",
      "VALUES LOOK LIKE \n",
      " [[ 0.  0.  0. ...,  1.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  1.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print_np(mnist.train.images)\n",
    "print_np(mnist.train.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHAPE OF is (10000, 784)\n",
      "VALUES LOOK LIKE \n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "SHAPE OF is (10000, 10)\n",
      "VALUES LOOK LIKE \n",
      " [[ 0.  0.  0. ...,  1.  0.  0.]\n",
      " [ 0.  0.  1. ...,  0.  0.  0.]\n",
      " [ 0.  1.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print_np(mnist.test.images)\n",
    "print_np(mnist.test.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHAPE OF is (5000, 784)\n",
      "VALUES LOOK LIKE \n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "SHAPE OF is (5000, 10)\n",
      "VALUES LOOK LIKE \n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      " [ 1.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  0.]\n",
      " ..., \n",
      " [ 0.  0.  1. ...,  0.  0.  0.]\n",
      " [ 0.  1.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  1. ...,  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print_np(mnist.validation.images)\n",
    "print_np(mnist.validation.labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PLOT TRAIN IMAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "ntrain = mnist.train.images.shape[0]\n",
    "nsample = 3\n",
    "randidx = np.random.randint(ntrain, size=nsample)\n",
    "for i in randidx:\n",
    "    imgvec = mnist.train.images[i]\n",
    "    labelvec = mnist.train.labels[i, :]\n",
    "    img = np.reshape(imgvec, (28, 28))\n",
    "    label = np.argmax(labelvec)\n",
    "    plt.matshow(img, cmap=plt.get_cmap('gray'))\n",
    "    plt.title(\"[%d] DATA / LABEL IS [%d]\" % (i, label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FIRST, RANDOM PERMUTE\n",
    "- train data 가 순서대로 있기 때문에, 섞어줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10,)\n"
     ]
    }
   ],
   "source": [
    "ntrain = 10\n",
    "randindices = np.random.permutation(ntrain)\n",
    "print(randindices.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### THEN, SELECT MINIBATCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ITER: [0] BATCH INDEX: [5 1 6 7]\n",
      "  - SHAPE OF 'XBATCH IS (4, 784)\n",
      "  - SHAPE OF 'YBATCH IS (4, 10)\n",
      "ITER: [1] BATCH INDEX: [8 3 9 2]\n",
      "  - SHAPE OF 'XBATCH IS (4, 784)\n",
      "  - SHAPE OF 'YBATCH IS (4, 10)\n",
      "ITER: [2] BATCH INDEX: [0 4]\n",
      "  - SHAPE OF 'XBATCH IS (2, 784)\n",
      "  - SHAPE OF 'YBATCH IS (2, 10)\n"
     ]
    }
   ],
   "source": [
    "ntrain = 10\n",
    "nbatch = 4\n",
    "niter = ntrain // nbatch + 1\n",
    "for i in range(niter):\n",
    "    currindices = randindices[i*nbatch:(i+1)*nbatch]\n",
    "    print(\"ITER: [%d] BATCH INDEX: %s\" % (i, currindices))\n",
    "    \n",
    "    # GET BATCH\n",
    "    xbatch = mnist.train.images[currindices, :]\n",
    "    ybatch = mnist.train.labels[currindices, :]\n",
    "    print(\"  - SHAPE OF 'XBATCH IS %s\" % (xbatch.shape,)) \n",
    "    print(\"  - SHAPE OF 'YBATCH IS %s\" % (ybatch.shape,))"
   ]
  }
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