02-00-usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 초기화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zeros\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "random_uniform\n",
      " [[ 0.3178966   0.41839838  0.44026852  0.5883038   0.35369873 -0.53501844\n",
      "  -0.77669096 -0.57965231 -0.64961147  0.93021321 -0.75844836  0.48704219\n",
      "   0.67768836  0.5846951  -0.02709985 -0.33059692  0.68793416  0.72179174\n",
      "   0.68701601  0.51686764  0.52032757  0.98338461  0.2529211  -0.21011686\n",
      "   0.2513566  -0.09390283 -0.51806521  0.40893364 -0.07989168 -0.18911266\n",
      "  -0.92840624 -0.95991397  0.09317946 -0.24730134  0.69835806  0.86380363\n",
      "   0.19039869 -0.57293296 -0.5268085   0.6061182   0.8000083   0.00871706\n",
      "   0.24027133 -0.24026513 -0.72809863 -0.06800508  0.80983329 -0.07835841\n",
      "   0.49372482 -0.25238824  0.46434784  0.56958437  0.99870801 -0.74601889\n",
      "   0.06452417 -0.5048902   0.09907055 -0.77903628 -0.81279778  0.75267506\n",
      "   0.87889361  0.5184381   0.48519897 -0.01480293  0.31088924  0.93293118\n",
      "   0.14138412  0.40126681  0.80337572 -0.19246149 -0.92375875  0.77870369\n",
      "   0.05208349  0.68791556  0.17682123  0.60949159 -0.14670825  0.21773219\n",
      "  -0.99038601 -0.81368804 -0.52164316 -0.41455102  0.69163346 -0.73164558\n",
      "  -0.22834992  0.51759744  0.12186933  0.85281372 -0.02466822  0.50734067\n",
      "  -0.72208261 -0.03344321 -0.33380342  0.03243852 -0.12953115  0.77309251\n",
      "   0.057899   -0.78676558  0.98779917  0.92022586 -0.26677561  0.46564484\n",
      "  -0.07001829  0.84150124 -0.56177926 -0.57434297  0.93821096  0.08025122\n",
      "  -0.89652085 -0.08585501  0.5357244  -0.44011474 -0.02070737  0.00460196\n",
      "  -0.96376038  0.31151295 -0.55624247  0.95609927 -0.90887189 -0.22695804\n",
      "  -0.90921855  0.90613413  0.02014804  0.69139051  0.67853928  0.546211\n",
      "  -0.98550177  0.09642291 -0.06663942 -0.41177654  0.55155373 -0.83752275\n",
      "   0.00397682 -0.38362551  0.54777503 -0.65898395 -0.41540074 -0.11334085\n",
      "   0.40938067 -0.30952907 -0.40836048 -0.01028633 -0.90125489  0.95479894\n",
      "  -0.73259425  0.28562927  0.54578233  0.53667188  0.01352167 -0.24417734\n",
      "  -0.93911147 -0.31766295 -0.94390202  0.46651578  0.83304787 -0.7556088\n",
      "  -0.11568451 -0.51901937  0.445647   -0.60304046 -0.8381815   0.39667487\n",
      "  -0.62169194 -0.41069794 -0.57068968 -0.23713899 -0.7942996  -0.67355251\n",
      "   0.76123071  0.07880926 -0.37868333 -0.41534829 -0.16308832  0.79043674\n",
      "  -0.31004167 -0.20067048 -0.98622608  0.11896443  0.71806169  0.47922373\n",
      "  -0.70454502  0.38486814  0.20697212  0.53321815 -0.611449   -0.4241271\n",
      "   0.44234848  0.37217569  0.22865057 -0.41853857 -0.98276973 -0.17243195\n",
      "   0.40060329 -0.37152982 -0.01216078  0.10031486 -0.8527844  -0.45574141\n",
      "   0.05178905 -0.12108135]]\n",
      "random_noraml\n",
      " [[-0.01287159  1.16133881 -0.82625645  1.25097871  0.16062222 -0.02554759\n",
      "   0.11066654  0.14024511  1.09182465 -0.4217881   0.98727679  0.52573925\n",
      "   1.32126153  0.04579427 -1.35190761 -1.48377752  0.51227206 -0.68037438\n",
      "   0.0067616  -0.13992797  0.97467923  0.32777166 -0.00571057 -1.29396117\n",
      "   0.52934718 -1.49183238 -1.24174511 -0.07132571 -0.19584724 -0.53180921\n",
      "   1.06336832 -1.03217053  2.14705133  1.09790671 -0.51667631  0.49274054\n",
      "  -1.27612972  1.45613754 -0.60262674 -0.69861388  1.00402772  0.35637707\n",
      "  -0.01082398  0.82336462 -0.68567461 -1.04225707  0.18607728  1.22669649\n",
      "  -0.61326188 -1.03748107 -0.67428493  0.67764658 -1.16436052 -1.11027408\n",
      "  -2.17970204  0.89521289  0.14558014 -1.56009257  1.33403969  0.04670542\n",
      "  -0.3037307  -1.06583035 -1.15847981 -0.24359159  1.8512491  -0.44374499\n",
      "  -1.62585211  0.93021333 -0.00851085 -0.26684695 -2.27081776  0.74701232\n",
      "  -0.60842127  0.71935141  0.7708379  -0.46429113  0.97483885  0.72643965\n",
      "   0.59607571  1.30887377  0.14386764 -0.19881697 -0.59606928  0.21460208\n",
      "  -1.08606017  1.04431462 -1.22597337  0.58159482 -0.58116853 -0.40242484\n",
      "  -1.02994072 -0.52687448 -0.86102384 -0.30254129  1.36562431  0.99092859\n",
      "   0.20975094  0.39552361 -0.170303    1.0030303   0.71666759  0.1954813\n",
      "   0.68509281 -1.66112804  0.10203792  0.30008489  0.99959219  0.82215375\n",
      "   0.07166921 -0.27041313  0.94797748 -0.52931261  0.40169227 -0.09850006\n",
      "   0.95548296  1.4089421  -0.95417821  0.23096329 -0.19838265 -0.56338233\n",
      "   0.74905682 -0.41686451  1.37140548 -1.85437202  0.21768281 -0.26191148\n",
      "  -0.85794622  0.08630648  1.2935679  -0.41395253  1.37820065  0.21324688\n",
      "  -1.04099905  1.12462819 -1.42060244 -1.12174308 -1.53629446  1.67123449\n",
      "   0.26117009 -0.62967592 -1.00051343  0.18149258 -0.44337136  0.81048357\n",
      "  -0.09759605  1.91637003 -0.14034551  1.53860044 -0.18659252 -0.71832138\n",
      "  -0.75263929  0.26807305 -0.95287818  0.23990294  0.66743362  0.66757762\n",
      "   0.15910882 -1.43515408 -2.35989928  1.3223381   1.05735934  0.28106838\n",
      "  -0.30191222 -0.07780739  0.10920822  2.01687026 -0.42129061 -0.65914965\n",
      "   0.28946334  0.04618277  0.60222727  0.64539629 -0.45229614  1.85605335\n",
      "   0.3127507  -0.40469694 -1.44867015  0.40113133  0.74496675  1.08978522\n",
      "   1.16230023  0.17748652  0.10952535 -0.07716826  0.63939279  0.00481033\n",
      "  -1.17958438  0.37510952 -1.94531012  0.56278092 -0.63039935 -1.22303021\n",
      "  -0.86606997 -2.72297573 -1.21286118 -0.00607489  0.84225988  1.09130812\n",
      "   1.57859421  0.49818322]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "zeros = tf.zeros([10])\n",
    "random_uniform = tf.random_uniform([1, 200], -1, 1)\n",
    "random_normal = tf.random_normal([1, 200])\n",
    "\n",
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "print(\"zeros\\n\", sess.run(zeros))\n",
    "print(\"random_uniform\\n\", sess.run(random_uniform))\n",
    "print(\"random_noraml\\n\", sess.run(random_normal))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tf.random_uniform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZfTm7/7KZvXeu74rUvba2GKI6MRHNSMXrtrZal0ykIsmBwcwKwMPAPcBm4D4z2zztsXuA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\nfyDEcckhpX2JAAAAAElEQVQ1rwYeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd18cfcaa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.plot(sess.run(random_uniform), 'ro', alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tf.random_normal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd18cf07e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.plot(sess.run(random_normal), 'ro', alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([1, 2, 3, 4, 5, 6])\n",
    "print(a)\n",
    "\n",
    "a = x.reshape(2, 3)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  1.  1.]\n"
     ]
    }
   ],
   "source": [
    "b = np.ones(3)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.  3.  4.]\n",
      " [ 5.  6.  7.]]\n"
     ]
    }
   ],
   "source": [
    "print(a + b)"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}