TensorflowGD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "c= tf.constant([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensorflow.python.framework.ops.Tensor"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "g= tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "node {\n",
       "  name: \"Const\"\n",
       "  op: \"Const\"\n",
       "  attr {\n",
       "    key: \"dtype\"\n",
       "    value {\n",
       "      type: DT_INT32\n",
       "    }\n",
       "  }\n",
       "  attr {\n",
       "    key: \"value\"\n",
       "    value {\n",
       "      tensor {\n",
       "        dtype: DT_INT32\n",
       "        tensor_shape {\n",
       "          dim {\n",
       "            size: 3\n",
       "          }\n",
       "        }\n",
       "        tensor_content: \"\\001\\000\\000\\000\\002\\000\\000\\000\\003\\000\\000\\000\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "node {\n",
       "  name: \"Const_1\"\n",
       "  op: \"Const\"\n",
       "  attr {\n",
       "    key: \"dtype\"\n",
       "    value {\n",
       "      type: DT_INT32\n",
       "    }\n",
       "  }\n",
       "  attr {\n",
       "    key: \"value\"\n",
       "    value {\n",
       "      tensor {\n",
       "        dtype: DT_INT32\n",
       "        tensor_shape {\n",
       "          dim {\n",
       "            size: 3\n",
       "          }\n",
       "        }\n",
       "        tensor_content: \"\\001\\000\\000\\000\\002\\000\\000\\000\\003\\000\\000\\000\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "versions {\n",
       "  producer: 21\n",
       "}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.as_graph_def() #查看graph里的节点们"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    r=sess.run(c)\n",
    "    print(type(r))\n",
    "    print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "c2= c+c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "node {\n",
       "  name: \"Const\"\n",
       "  op: \"Const\"\n",
       "  attr {\n",
       "    key: \"dtype\"\n",
       "    value {\n",
       "      type: DT_INT32\n",
       "    }\n",
       "  }\n",
       "  attr {\n",
       "    key: \"value\"\n",
       "    value {\n",
       "      tensor {\n",
       "        dtype: DT_INT32\n",
       "        tensor_shape {\n",
       "          dim {\n",
       "            size: 3\n",
       "          }\n",
       "        }\n",
       "        tensor_content: \"\\001\\000\\000\\000\\002\\000\\000\\000\\003\\000\\000\\000\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "node {\n",
       "  name: \"Const_1\"\n",
       "  op: \"Const\"\n",
       "  attr {\n",
       "    key: \"dtype\"\n",
       "    value {\n",
       "      type: DT_INT32\n",
       "    }\n",
       "  }\n",
       "  attr {\n",
       "    key: \"value\"\n",
       "    value {\n",
       "      tensor {\n",
       "        dtype: DT_INT32\n",
       "        tensor_shape {\n",
       "          dim {\n",
       "            size: 3\n",
       "          }\n",
       "        }\n",
       "        tensor_content: \"\\001\\000\\000\\000\\002\\000\\000\\000\\003\\000\\000\\000\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "node {\n",
       "  name: \"add\"\n",
       "  op: \"Add\"\n",
       "  input: \"Const_1\"\n",
       "  input: \"Const_1\"\n",
       "  attr {\n",
       "    key: \"T\"\n",
       "    value {\n",
       "      type: DT_INT32\n",
       "    }\n",
       "  }\n",
       "}\n",
       "versions {\n",
       "  producer: 21\n",
       "}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.as_graph_def()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 6]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    print(sess.run(c2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01696086]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    r= tf.random_uniform([1],-1.0,1.0)\n",
    "    print(sess.run(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5d70e9e690>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d731db4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= np.arange(100)\n",
    "y = x+ np.random.normal(scale=5, size=100)\n",
    "plt.plot(x,y,'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "x_place= tf.placeholder('float')\n",
    "y_place= tf.placeholder('float')\n",
    "w= tf.Variable(tf.random_uniform([1],-1.0,1.0), name='w')\n",
    "b= tf.Variable(tf.zeros([1]), name='b')\n",
    "y_bar=w*x_place+b\n",
    "cost=tf.reduce_mean(tf.square(y_bar-y_place))\n",
    "train_step=tf.train.GradientDescentOptimizer(0.0001).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1417.94\n",
      "[array([ 0.33243752], dtype=float32), array([ 0.01872874], dtype=float32)]\n",
      "193.643\n",
      "[array([ 0.75935733], dtype=float32), array([ 0.02513698], dtype=float32)]\n",
      "49.4796\n",
      "[array([ 0.90585548], dtype=float32), array([ 0.02731744], dtype=float32)]\n",
      "32.504\n",
      "[array([ 0.95612675], dtype=float32), array([ 0.02804713], dtype=float32)]\n",
      "30.505\n",
      "[array([ 0.97337765], dtype=float32), array([ 0.02827898], dtype=float32)]\n",
      "30.2696\n",
      "[array([ 0.97929758], dtype=float32), array([ 0.02834001], dtype=float32)]\n",
      "30.2419\n",
      "[array([ 0.98132926], dtype=float32), array([ 0.02834241], dtype=float32)]\n",
      "30.2387\n",
      "[array([ 0.98202676], dtype=float32), array([ 0.0283247], dtype=float32)]\n",
      "30.2383\n",
      "[array([ 0.98226637], dtype=float32), array([ 0.02830009], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.98234886], dtype=float32), array([ 0.02827312], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.98237747], dtype=float32), array([ 0.02824533], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.98238754], dtype=float32), array([ 0.02821726], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.9823913], dtype=float32), array([ 0.0281891], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.98239285], dtype=float32), array([ 0.02816091], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.98239368], dtype=float32), array([ 0.02813271], dtype=float32)]\n",
      "30.2382\n",
      "[array([ 0.98239428], dtype=float32), array([ 0.0281045], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.9823947], dtype=float32), array([ 0.0280763], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239517], dtype=float32), array([ 0.0280481], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239559], dtype=float32), array([ 0.0280199], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239601], dtype=float32), array([ 0.0279917], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239642], dtype=float32), array([ 0.0279635], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.9823969], dtype=float32), array([ 0.0279353], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239726], dtype=float32), array([ 0.0279071], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239768], dtype=float32), array([ 0.02787891], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239815], dtype=float32), array([ 0.02785072], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239857], dtype=float32), array([ 0.02782252], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239899], dtype=float32), array([ 0.02779433], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.9823994], dtype=float32), array([ 0.02776614], dtype=float32)]\n",
      "30.2381\n",
      "[array([ 0.98239982], dtype=float32), array([ 0.02773796], dtype=float32)]\n",
      "30.238\n",
      "[array([ 0.98240024], dtype=float32), array([ 0.02770977], dtype=float32)]\n",
      "30.238\n",
      "[array([ 0.98240066], dtype=float32), array([ 0.02768159], dtype=float32)]\n",
      "30.238\n",
      "[array([ 0.98240113], dtype=float32), array([ 0.0276534], dtype=float32)]\n",
      "30.238\n",
      "[array([ 0.98240155], dtype=float32), array([ 0.02762522], dtype=float32)]\n",
      "30.238\n",
      "[array([ 0.98240197], dtype=float32), array([ 0.02759704], dtype=float32)]\n",
      "30.238\n",
      "[array([ 0.98240238], dtype=float32), array([ 0.02756886], dtype=float32)]\n",
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      "[array([ 0.98277974], dtype=float32), array([ 0.00253646], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278022], dtype=float32), array([ 0.00250955], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278058], dtype=float32), array([ 0.00248264], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278099], dtype=float32), array([ 0.00245574], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278141], dtype=float32), array([ 0.00242884], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278183], dtype=float32), array([ 0.00240193], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278219], dtype=float32), array([ 0.00237503], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.9827826], dtype=float32), array([ 0.00234813], dtype=float32)]\n",
      "30.2311\n",
      "[array([ 0.98278302], dtype=float32), array([ 0.00232124], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278344], dtype=float32), array([ 0.00229434], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278385], dtype=float32), array([ 0.00226745], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278427], dtype=float32), array([ 0.00224055], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278463], dtype=float32), array([ 0.00221366], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278505], dtype=float32), array([ 0.00218677], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278546], dtype=float32), array([ 0.00215988], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278588], dtype=float32), array([ 0.00213299], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278624], dtype=float32), array([ 0.0021061], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278666], dtype=float32), array([ 0.00207922], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278707], dtype=float32), array([ 0.00205233], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278749], dtype=float32), array([ 0.00202545], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278791], dtype=float32), array([ 0.00199857], dtype=float32)]\n",
      "30.231\n",
      "[array([ 0.98278832], dtype=float32), array([ 0.00197169], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98278874], dtype=float32), array([ 0.00194481], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.9827891], dtype=float32), array([ 0.00191793], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98278952], dtype=float32), array([ 0.00189106], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98278993], dtype=float32), array([ 0.00186418], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279035], dtype=float32), array([ 0.00183731], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279071], dtype=float32), array([ 0.00181044], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279113], dtype=float32), array([ 0.00178356], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279154], dtype=float32), array([ 0.0017567], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279196], dtype=float32), array([ 0.00172983], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279232], dtype=float32), array([ 0.00170296], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279274], dtype=float32), array([ 0.0016761], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279315], dtype=float32), array([ 0.00164923], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279357], dtype=float32), array([ 0.00162237], dtype=float32)]\n",
      "30.2309\n",
      "[array([ 0.98279399], dtype=float32), array([ 0.00159551], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.9827944], dtype=float32), array([ 0.00156865], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279476], dtype=float32), array([ 0.00154179], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279518], dtype=float32), array([ 0.00151493], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.9827956], dtype=float32), array([ 0.00148808], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279601], dtype=float32), array([ 0.00146122], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279637], dtype=float32), array([ 0.00143437], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279679], dtype=float32), array([ 0.00140752], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279721], dtype=float32), array([ 0.00138067], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279762], dtype=float32), array([ 0.00135382], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279804], dtype=float32), array([ 0.00132697], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.9827984], dtype=float32), array([ 0.00130013], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279881], dtype=float32), array([ 0.00127328], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279923], dtype=float32), array([ 0.00124644], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98279965], dtype=float32), array([ 0.0012196], dtype=float32)]\n",
      "30.2308\n",
      "[array([ 0.98280001], dtype=float32), array([ 0.00119276], dtype=float32)]\n",
      "30.2307\n",
      "[array([ 0.98280042], dtype=float32), array([ 0.00116592], dtype=float32)]\n",
      "30.2307\n",
      "[array([ 0.98280084], dtype=float32), array([ 0.00113908], dtype=float32)]\n",
      "30.2307\n",
      "[array([ 0.98280126], dtype=float32), array([ 0.00111224], dtype=float32)]\n",
      "30.2307\n",
      "[array([ 0.98280168], dtype=float32), array([ 0.00108541], dtype=float32)]\n",
      "30.2307\n",
      "[array([ 0.98280209], dtype=float32), array([ 0.00105857], dtype=float32)]\n",
      "30.2307\n",
      "[array([ 0.98280245], dtype=float32), array([ 0.00103174], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for i in range(1000):\n",
    "        feed_dict={x_place:x.astype(float), y_place: y.astype(float)}\n",
    "        sess.run(train_step, feed_dict= feed_dict)\n",
    "        print(sess.run(cost, feed_dict= feed_dict))\n",
    "        print sess.run([w,b])"
   ]
  }
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