Chapter 9 - Deep Learning .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = tf.constant(2,name='c')\n",
    "x = tf.Variable(3,name='x')\n",
    "y = tf.Variable(c*x,name='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'x:0' shape=() dtype=int32_ref>\n",
      "<tf.Variable 'y:0' shape=() dtype=int32_ref>\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(\"int32\")\n",
    "Y = tf.placeholder(\"int32\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "model = tf.global_variables_initializer()\n",
    "with tf.Session() as session:\n",
    "    session.run(model)\n",
    "    print(session.run(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "t = np.arange(9).reshape((3,3))\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tensor = tf.convert_to_tensor(t, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "   print(sess.run(tensor))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t0 = tf.zeros((3,3),'float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as session:\n",
    "    print(session.run(t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1 = tf.ones((3,3),'float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as session:\n",
    "    print(session.run(t1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tensorrand = tf.random_uniform((3, 3), minval=0, maxval=1, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.23529065  0.94629061  0.78508461]\n",
      " [ 0.1326803   0.34222937  0.07499647]\n",
      " [ 0.30431211  0.11213958  0.72429335]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as session:\n",
    "    print(session.run(tensorrand))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "norm = tf.random_normal((3, 3), mean=0, stddev=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.50575781  0.29254037 -4.7842865 ]\n",
      " [-1.10053539  1.82978404 -3.91513872]\n",
      " [ 1.072317   -0.42533118 -0.09797287]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as session:\n",
    "    print(session.run(norm))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1 = tf.random_uniform((3, 3), minval=0, maxval=1, dtype=tf.float32)\n",
    "t2 = tf.random_uniform((3, 3), minval=0, maxval=1, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t1 =  [[ 0.15637171  0.91241419  0.6265887 ]\n",
      " [ 0.4066335   0.76144099  0.18374431]\n",
      " [ 0.59261668  0.63377452  0.63247728]]\n",
      "t2 =  [[ 0.2456187   0.07070899  0.01038361]\n",
      " [ 0.89071929  0.5403806   0.13453126]\n",
      " [ 0.4072572   0.72879398  0.03496265]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    print('t1 = ',sess.run(t1))\n",
    "    print('t2 = ',sess.run(t2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sum = tf.add(t1,t2)\n",
    "mul = tf.matmul(t1,t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum = [[ 0.706038    1.28301334  0.19649673]\n",
      " [ 0.98367655  1.13871896  1.81580138]\n",
      " [ 1.08846235  1.81781852  0.35328352]]\n",
      "mul = [[ 1.01903796  1.12746143  1.07219756]\n",
      " [ 1.10262644  1.47867703  1.15581965]\n",
      " [ 1.05736542  1.34841466  1.14849663]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    print('sum =', sess.run(sum))\n",
    "    print('mul =', sess.run(mul))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "det = 0.171305\n"
     ]
    }
   ],
   "source": [
    "det = tf.matrix_determinant(t1)\n",
    "with tf.Session() as sess:\n",
    "    print('det =', sess.run(det))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Layer Perceptron (SLP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.   3. ]\n",
      " [ 1.   2. ]\n",
      " [ 1.   1.5]\n",
      " [ 1.5  2. ]\n",
      " [ 2.   3. ]\n",
      " [ 2.5  1.5]\n",
      " [ 2.   1. ]\n",
      " [ 3.   1. ]\n",
      " [ 3.   2. ]\n",
      " [ 3.5  1. ]\n",
      " [ 3.5  3. ]]\n",
      "[[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]\n"
     ]
    }
   ],
   "source": [
    "#Training set\n",
    "inputX = np.array([[1.,3.],[1.,2.],[1.,1.5],[1.5,2.],[2.,3.],[2.5,1.5],[2.,1.],[3.,1.],[3.,2.],[3.5,1.],[3.5,3.]])\n",
    "inputY = [[1.,0.]]*6+ [[0.,1.]]*5\n",
    "print(inputX)\n",
    "print(inputY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]\n"
     ]
    },
    {
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y49xhkDuymdW1nFqO+GcDZwBvk/RAcjtJ0kclfTTpcwvwBLAK+C7wDwARsQH4PHB/crs4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\nzcxSxsFvZpYyDn4zs5T5/3e7jUbaXdE7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abb8042b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = [0]*6 + [1]*5\n",
    "print(yc)\n",
    "plt.scatter(inputX[:,0],inputX[:,1],c=yc, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_epochs = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "display_step = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_samples = 11\n",
    "batch_size = 11\n",
    "total_batch = int(n_samples/batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_input = 2 # size data input (# size of each element of x)\n",
    "n_classes = 2 # n of classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tf Graph input\n",
    "x = tf.placeholder(\"float\", [None, n_input])\n",
    "y = tf.placeholder(\"float\", [None, n_classes])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set model weights\n",
    "W = tf.Variable(tf.zeros([n_input, n_classes])) \n",
    "b = tf.Variable(tf.zeros([n_classes]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "evidence = tf.add(tf.matmul(x, W), b) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_ = tf.nn.softmax(evidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_sum(tf.pow(y-y_,2))/ (2 * n_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer =tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_set = []\n",
    "epoch_set=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0000 cost= 0.249360308\n",
      "Epoch: 0050 cost= 0.221041128\n",
      "Epoch: 0100 cost= 0.198898271\n",
      "Epoch: 0150 cost= 0.181669712\n",
      "Epoch: 0200 cost= 0.168204829\n",
      "Epoch: 0250 cost= 0.157555178\n",
      "Epoch: 0300 cost= 0.149002746\n",
      "Epoch: 0350 cost= 0.142023861\n",
      "Epoch: 0400 cost= 0.136240512\n",
      "Epoch: 0450 cost= 0.131378993\n",
      "Epoch: 0500 cost= 0.127239138\n",
      "Epoch: 0550 cost= 0.123672642\n",
      "Epoch: 0600 cost= 0.120568059\n",
      "Epoch: 0650 cost= 0.117840447\n",
      "Epoch: 0700 cost= 0.115424201\n",
      "Epoch: 0750 cost= 0.113267884\n",
      "Epoch: 0800 cost= 0.111330733\n",
      "Epoch: 0850 cost= 0.109580085\n",
      "Epoch: 0900 cost= 0.107989430\n",
      "Epoch: 0950 cost= 0.106537104\n",
      "Epoch: 1000 cost= 0.105205171\n",
      "Epoch: 1050 cost= 0.103978693\n",
      "Epoch: 1100 cost= 0.102845162\n",
      "Epoch: 1150 cost= 0.101793952\n",
      "Epoch: 1200 cost= 0.100816071\n",
      "Epoch: 1250 cost= 0.099903718\n",
      "Epoch: 1300 cost= 0.099050261\n",
      "Epoch: 1350 cost= 0.098249927\n",
      "Epoch: 1400 cost= 0.097497642\n",
      "Epoch: 1450 cost= 0.096789025\n",
      "Epoch: 1500 cost= 0.096120209\n",
      "Epoch: 1550 cost= 0.095487759\n",
      "Epoch: 1600 cost= 0.094888613\n",
      "Epoch: 1650 cost= 0.094320126\n",
      "Epoch: 1700 cost= 0.093779817\n",
      "Epoch: 1750 cost= 0.093265578\n",
      "Epoch: 1800 cost= 0.092775457\n",
      "Epoch: 1850 cost= 0.092307687\n",
      "Epoch: 1900 cost= 0.091860712\n",
      "Epoch: 1950 cost= 0.091433071\n",
      "Training phase finished\n",
      "Training cost =  0.0910315 \n",
      "W= [[-0.70927787  0.70927781]\n",
      " [ 0.62999243 -0.62999237]] \n",
      "b= [ 0.34513065 -0.34513068]\n",
      "Last result =  [[ 0.95485419  0.04514586]\n",
      " [ 0.85713255  0.14286745]\n",
      " [ 0.76163834  0.23836163]\n",
      " [ 0.74694741  0.25305259]\n",
      " [ 0.83659446  0.16340555]\n",
      " [ 0.27564839  0.72435158]\n",
      " [ 0.29175714  0.70824283]\n",
      " [ 0.090675    0.909325  ]\n",
      " [ 0.26010245  0.73989749]\n",
      " [ 0.04676624  0.95323378]\n",
      " [ 0.37878013  0.62121987]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "        \n",
    "    for i in range(training_epochs):\n",
    "        sess.run(optimizer, feed_dict = {x: inputX, y: inputY})\n",
    "        if i % display_step == 0:\n",
    "            c = sess.run(cost, feed_dict = {x: inputX, y: inputY})\n",
    "            print(\"Epoch:\", '%04d' % (i), \"cost=\", \"{:.9f}\".format(c))\n",
    "            avg_set.append(c)\n",
    "            epoch_set.append(i + 1)\n",
    "    \n",
    "    print(\"Training phase finished\")\n",
    "     \n",
    "    training_cost = sess.run(cost, feed_dict = {x: inputX, y: inputY})\n",
    "    print(\"Training cost = \", training_cost, \"\\nW=\", sess.run(W), \"\\nb=\", sess.run(b))\n",
    "    last_result = sess.run(y_, feed_dict = {x:inputX})\n",
    "    print(\"Last result = \",last_result)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1sKhpUry3mD99DJUDBxxUVjlwAPOnjylRRGZm2VNE0WmGPieXy0VNTU2PvqfPnjKzvk7S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\naZiZWWpOGmZmltr/B2ddpcSix/lJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abbf74240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_set,avg_set,'o',label = 'SLP Training phase')\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mZilTzad6zMysCAe/mVnKOPjNzFLGwW9mljIOfjOzlHHwm5mljIPfzCxlHPxm\nZinz/wHBEFn7P319LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc03c9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = last_result[:,1]\n",
    "plt.scatter(inputX[:,0],inputX[:,1],c=yc, s=50, alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.    2.25]\n",
      " [ 1.25  3.  ]\n",
      " [ 2.    2.5 ]\n",
      " [ 2.25  2.75]\n",
      " [ 2.5   3.  ]\n",
      " [ 2.    0.9 ]\n",
      " [ 2.5   1.2 ]\n",
      " [ 3.    1.25]\n",
      " [ 3.    1.5 ]\n",
      " [ 3.5   2.  ]\n",
      " [ 3.5   2.5 ]]\n",
      "[[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]\n"
     ]
    }
   ],
   "source": [
    "#Testing set\n",
    "testX = np.array([[1.,2.25],[1.25,3.],[2,2.5],[2.25,2.75],[2.5,3.],[2.,0.9],[2.5,1.2],[3.,1.25],[3.,1.5],[3.5,2.],[3.5,2.5]])\n",
    "testY = [[1.,0.]]*5 + [[0.,1.]]*6\n",
    "print(testX)\n",
    "print(testY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc053c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = [0]*5 + [1]*6\n",
    "print(yc)\n",
    "plt.scatter(testX[:,0],testX[:,1],c=yc, s=50, alpha=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "        \n",
    "    for i in range(training_epochs):\n",
    "        sess.run(optimizer, feed_dict = {x: inputX, y: inputY})\n",
    "    \n",
    "    pred = tf.nn.softmax(evidence) \n",
    "    result = sess.run(pred, feed_dict = {x: testX})\n",
    "    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(testY, 1))\n",
    "     \n",
    "    # Calculate accuracy\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "    print(\"Accuracy:\", accuracy.eval({x: testX, y: testY}))\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc068978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = result[:,1]\n",
    "plt.scatter(testX[:,0],testX[:,1],c=yc, s=50, alpha=1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi Layer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "#Training set\n",
    "inputX = np.array([[1.,3.],[1.,2.],[1.,1.5],[1.5,2.],[2.,3.],[2.5,1.5],[2.,1.],[3.,1.],[3.,2.],[3.5,1.],[3.5,3.]])\n",
    "inputY = [[1.,0.]]*6+ [[0.,1.]]*5\n",
    "\n",
    "learning_rate = 0.001\n",
    "training_epochs = 2000\n",
    "display_step = 50\n",
    "n_samples = 11\n",
    "batch_size = 11\n",
    "total_batch = int(n_samples/batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Network Parameters\n",
    "n_hidden_1 = 2 # 1st layer number of neurons\n",
    "#n_hidden_2 = 0 # 2nd layer number of neurons\n",
    "n_input = 2 # size data input\n",
    "n_classes = 2 # classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tf Graph input\n",
    "X = tf.placeholder(\"float\", [None, n_input])\n",
    "Y = tf.placeholder(\"float\", [None, n_classes])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store layers weight & bias\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    #'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    #'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create model\n",
    "def multilayer_perceptron(x):\n",
    "    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "    #layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "    # Output fully connected layer with a neuron for each class\n",
    "    out_layer = tf.matmul(layer_1, weights['out']) + biases['out']\n",
    "    return out_layer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Construct model\n",
    "evidence = multilayer_perceptron(X)\n",
    "y_ = tf.nn.softmax(evidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define cost and optimizer\n",
    "cost = tf.reduce_sum(tf.pow(Y-y_,2))/ (2 * n_samples)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_set = []\n",
    "epoch_set = []\n",
    "init = tf.global_variables_initializer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost=0.534337342\n",
      "Epoch: 0051 cost=0.500344753\n",
      "Epoch: 0101 cost=0.306632668\n",
      "Epoch: 0151 cost=0.105975978\n",
      "Epoch: 0201 cost=0.101429604\n",
      "Epoch: 0251 cost=0.098237276\n",
      "Epoch: 0301 cost=0.095216267\n",
      "Epoch: 0351 cost=0.092416212\n",
      "Epoch: 0401 cost=0.089862444\n",
      "Epoch: 0451 cost=0.087560482\n",
      "Epoch: 0501 cost=0.085503258\n",
      "Epoch: 0551 cost=0.083676644\n",
      "Epoch: 0601 cost=0.082062371\n",
      "Epoch: 0651 cost=0.080640934\n",
      "Epoch: 0701 cost=0.079392508\n",
      "Epoch: 0751 cost=0.078297973\n",
      "Epoch: 0801 cost=0.077339657\n",
      "Epoch: 0851 cost=0.076501206\n",
      "Epoch: 0901 cost=0.075768046\n",
      "Epoch: 0951 cost=0.075126931\n",
      "Epoch: 1001 cost=0.074566215\n",
      "Epoch: 1051 cost=0.074075654\n",
      "Epoch: 1101 cost=0.073646285\n",
      "Epoch: 1151 cost=0.073270097\n",
      "Epoch: 1201 cost=0.072940253\n",
      "Epoch: 1251 cost=0.072650768\n",
      "Epoch: 1301 cost=0.072396383\n",
      "Epoch: 1351 cost=0.072172567\n",
      "Epoch: 1401 cost=0.071975328\n",
      "Epoch: 1451 cost=0.071801312\n",
      "Epoch: 1501 cost=0.071647525\n",
      "Epoch: 1551 cost=0.071511410\n",
      "Epoch: 1601 cost=0.071390741\n",
      "Epoch: 1651 cost=0.071283557\n",
      "Epoch: 1701 cost=0.071188189\n",
      "Epoch: 1751 cost=0.071103193\n",
      "Epoch: 1801 cost=0.071027420\n",
      "Epoch: 1851 cost=0.070959546\n",
      "Epoch: 1901 cost=0.070898876\n",
      "Epoch: 1951 cost=0.070844449\n",
      "Training phase finished\n",
      "Training cost =  0.0707956\n",
      "Last result =  [[ 0.99322575  0.00677421]\n",
      " [ 0.97227752  0.02772249]\n",
      " [ 0.94491291  0.05508714]\n",
      " [ 0.90792239  0.09207766]\n",
      " [ 0.92056841  0.07943156]\n",
      " [ 0.27599165  0.72400832]\n",
      " [ 0.39872468  0.60127532]\n",
      " [ 0.04980643  0.95019352]\n",
      " [ 0.17974404  0.82025594]\n",
      " [ 0.01452302  0.98547703]\n",
      " [ 0.20480776  0.79519224]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(training_epochs):\n",
    "        avg_cost = 0.\n",
    "        # Loop over all batches\n",
    "        for i in range(total_batch):\n",
    "            #batch_x, batch_y = inputdata.next_batch(batch_size) TO BE IMPLEMENTED\n",
    "            batch_x = inputX\n",
    "            batch_y = inputY\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y})\n",
    "            # Compute average loss\n",
    "            avg_cost += c / total_batch\n",
    "        if epoch % display_step == 0:\n",
    "            print(\"Epoch:\", '%04d' % (epoch+1), \"cost={:.9f}\".format(avg_cost))\n",
    "            avg_set.append(avg_cost)\n",
    "            epoch_set.append(epoch + 1)\n",
    "     \n",
    "    print(\"Training phase finished\")\n",
    "    last_result = sess.run(y_, feed_dict = {X: inputX})\n",
    "    training_cost = sess.run(cost, feed_dict = {X: inputX, Y: inputY})\n",
    "    print(\"Training cost = \", training_cost)  \n",
    "    print(\"Last result = \", last_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AC0gcN3gJuNnd34m3vFBZWZlXVlbmerMiIh2amW1097JMl8+053AXcF3DJTOiT0rfA8zN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\nEREJKBxERCSgcBARkYDCQUREAv8fcFRqDNGF0YMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc28be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_set,avg_set,'o',label = 'MLP Training phase')\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Testing set\n",
    "testX = np.array([[1.,2.25],[1.25,3.],[2,2.5],[2.25,2.75],[2.5,3.],[2.,0.9],[2.5,1.2],[3.,1.25],[3.,1.5],[3.5,2.],[3.5,2.5]])\n",
    "testY = [[1.,0.]]*5 + [[0.,1.]]*6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Result =  [[ 0.99140078  0.0085992 ]\n",
      " [ 0.99558431  0.00441568]\n",
      " [ 0.90339947  0.09660055]\n",
      " [ 0.8767277   0.12327233]\n",
      " [ 0.84396356  0.15603639]\n",
      " [ 0.31285295  0.68714702]\n",
      " [ 0.15288183  0.84711814]\n",
      " [ 0.04270549  0.95729452]\n",
      " [ 0.0667615   0.93323845]\n",
      " [ 0.0397305   0.96026951]\n",
      " [ 0.09616365  0.90383631]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(training_epochs):\n",
    "        for i in range(total_batch):\n",
    "            batch_x = inputX\n",
    "            batch_y = inputY\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y})\n",
    "   \n",
    "    # Test model\n",
    "    pred = tf.nn.softmax(evidence) # Apply softmax to logits\n",
    "    result = sess.run(pred, feed_dict = {X: testX})\n",
    "    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))\n",
    "    \n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "    print(\"Accuracy:\", accuracy.eval({X: testX, Y: testY}))\n",
    "\n",
    "print(\"Result = \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.0085992   0.00441568  0.09660055  0.12327233  0.15603639  0.68714702\n",
      "  0.84711814  0.95729452  0.93323845  0.96026951  0.90383631]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc142cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = result[:,1]\n",
    "print(yc)\n",
    "plt.scatter(testX[:,0],testX[:,1],c=yc, s=50, alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Multi Layer Perceptron 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost=0.545439422\n",
      "Epoch: 0051 cost=0.545054793\n",
      "Epoch: 0101 cost=0.525345564\n",
      "Epoch: 0151 cost=0.361239076\n",
      "Epoch: 0201 cost=0.257665217\n",
      "Epoch: 0251 cost=0.199745610\n",
      "Epoch: 0301 cost=0.152912512\n",
      "Epoch: 0351 cost=0.122265637\n",
      "Epoch: 0401 cost=0.104842111\n",
      "Epoch: 0451 cost=0.094705105\n",
      "Epoch: 0501 cost=0.088475905\n",
      "Epoch: 0551 cost=0.084430106\n",
      "Epoch: 0601 cost=0.081639968\n",
      "Epoch: 0651 cost=0.079590552\n",
      "Epoch: 0701 cost=0.077994257\n",
      "Epoch: 0751 cost=0.076691091\n",
      "Epoch: 0801 cost=0.075593010\n",
      "Epoch: 0851 cost=0.074652053\n",
      "Epoch: 0901 cost=0.073841967\n",
      "Epoch: 0951 cost=0.073147491\n",
      "Epoch: 1001 cost=0.072558440\n",
      "Epoch: 1051 cost=0.072066076\n",
      "Epoch: 1101 cost=0.071661890\n",
      "Epoch: 1151 cost=0.071336523\n",
      "Epoch: 1201 cost=0.071080148\n",
      "Epoch: 1251 cost=0.070882477\n",
      "Epoch: 1301 cost=0.070733428\n",
      "Epoch: 1351 cost=0.070623577\n",
      "Epoch: 1401 cost=0.070544362\n",
      "Epoch: 1451 cost=0.070488550\n",
      "Epoch: 1501 cost=0.070450030\n",
      "Epoch: 1551 cost=0.070423998\n",
      "Epoch: 1601 cost=0.070406795\n",
      "Epoch: 1651 cost=0.070395678\n",
      "Epoch: 1701 cost=0.070388608\n",
      "Epoch: 1751 cost=0.070384204\n",
      "Epoch: 1801 cost=0.070381515\n",
      "Epoch: 1851 cost=0.070379905\n",
      "Epoch: 1901 cost=0.070378967\n",
      "Epoch: 1951 cost=0.070378460\n",
      "Training phase finished\n",
      "Training cost =  0.0703782\n",
      "Last result =  [[ 0.99681216  0.00318782]\n",
      " [ 0.98395306  0.01604696]\n",
      " [ 0.96447945  0.0355206 ]\n",
      " [ 0.93584371  0.06415633]\n",
      " [ 0.94651353  0.05348643]\n",
      " [ 0.26769996  0.73230004]\n",
      " [ 0.40492818  0.59507179]\n",
      " [ 0.03708195  0.96291804]\n",
      " [ 0.1641494   0.8358506 ]\n",
      " [ 0.0090781   0.99092191]\n",
      " [ 0.19240776  0.80759221]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "#Training set\n",
    "inputX = np.array([[1.,3.],[1.,2.],[1.,1.5],[1.5,2.],[2.,3.],[2.5,1.5],[2.,1.],[3.,1.],[3.,2.],[3.5,1.],[3.5,3.]])\n",
    "inputY = [[1.,0.]]*6+ [[0.,1.]]*5\n",
    "\n",
    "learning_rate = 0.001\n",
    "training_epochs = 2000\n",
    "display_step = 50\n",
    "n_samples = 11\n",
    "batch_size = 11\n",
    "total_batch = int(n_samples/batch_size)\n",
    "\n",
    "# Network Parameters\n",
    "n_hidden_1 = 4 # 1st layer number of neurons\n",
    "n_hidden_2 = 2 # 2nd layer number of neurons\n",
    "n_input = 2 # size data input\n",
    "n_classes = 2 # classes\n",
    "\n",
    "# tf Graph input\n",
    "X = tf.placeholder(\"float\", [None, n_input])\n",
    "Y = tf.placeholder(\"float\", [None, n_classes])\n",
    "\n",
    "# Store layers weight & bias\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "# Create model\n",
    "def multilayer_perceptron(x):\n",
    "    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "    # Output fully connected layer with a neuron for each class\n",
    "    #out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
    "    out_layer = tf.add(tf.matmul(layer_2, weights['out']), biases['out'])\n",
    "    return out_layer\n",
    "\n",
    "# Construct model\n",
    "evidence = multilayer_perceptron(X)\n",
    "y_ = tf.nn.softmax(evidence)\n",
    "\n",
    "# Define cost and optimizer\n",
    "cost = tf.reduce_sum(tf.pow(Y-y_,2))/ (2 * n_samples)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
    "\n",
    "avg_set = []\n",
    "epoch_set = []\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(training_epochs):\n",
    "        avg_cost = 0.\n",
    "        # Loop over all batches\n",
    "        for i in range(total_batch):\n",
    "            #batch_x, batch_y = inputdata.next_batch(batch_size) TO BE IMPLEMENTED\n",
    "            batch_x = inputX\n",
    "            batch_y = inputY\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y})\n",
    "            # Compute average loss\n",
    "            avg_cost += c / total_batch\n",
    "        if epoch % display_step == 0:\n",
    "            print(\"Epoch:\", '%04d' % (epoch+1), \"cost={:.9f}\".format(avg_cost))\n",
    "            avg_set.append(avg_cost)\n",
    "            epoch_set.append(epoch + 1)\n",
    "     \n",
    "    print(\"Training phase finished\")\n",
    "    last_result = sess.run(y_, feed_dict = {X: inputX})\n",
    "    training_cost = sess.run(cost, feed_dict = {X: inputX, Y: inputY})\n",
    "    print(\"Training cost = \", training_cost)  \n",
    "    print(\"Last result = \", last_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4+0XEzrhuySqgLM16REQkQumGwxxih7G+D7wHzAS+keIxG4DhZjbUzHoDs4h1TTUzs+Fx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\nDiIiEqJwEBGREIWDiIiEKBxERCTk/wNJclpo4X7h0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc0a3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_set,avg_set,'o',label = 'MLP Training phase')\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Testing set\n",
    "testX = np.array([[1.,2.25],[1.25,3.],[2,2.5],[2.25,2.75],[2.5,3.],[2.,0.9],[2.5,1.2],[3.,1.25],[3.,1.5],[3.5,2.],[3.5,2.5]])\n",
    "testY = [[1.,0.]]*5 + [[0.,1.]]*6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Result =  [[ 0.98936862  0.01063137]\n",
      " [ 0.99353611  0.00646387]\n",
      " [ 0.88723266  0.11276741]\n",
      " [ 0.85200977  0.1479902 ]\n",
      " [ 0.80816388  0.19183606]\n",
      " [ 0.36763456  0.63236541]\n",
      " [ 0.18352009  0.81647992]\n",
      " [ 0.05467953  0.94532043]\n",
      " [ 0.07995348  0.92004651]\n",
      " [ 0.04446219  0.9555378 ]\n",
      " [ 0.09504602  0.90495396]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(training_epochs):\n",
    "        for i in range(total_batch):\n",
    "            batch_x = inputX\n",
    "            batch_y = inputY\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y})\n",
    "\n",
    "   \n",
    "    # Test model\n",
    "    pred = tf.nn.softmax(evidence) # Apply softmax to logits\n",
    "    result = sess.run(pred, feed_dict = {X: testX})\n",
    "    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "    print(\"Accuracy:\", accuracy.eval({X: testX, Y: testY}))\n",
    "\n",
    "print(\"Result = \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVO4iIgFSuYuIBEjlLiISIJW7iEiA/h/jd4ReNEjJzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc03c4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = result[:,1]\n",
    "plt.scatter(testX[:,0],testX[:,1],c=yc, s=50, alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = 3*np.random.random((1000,2))\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(training_epochs):\n",
    "        for i in range(total_batch):\n",
    "            batch_x = inputX\n",
    "            batch_y = inputY\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y})\n",
    "\n",
    "   \n",
    "    # Test model\n",
    "    pred = tf.nn.softmax(evidence) \n",
    "    result = sess.run(pred, feed_dict = {X: test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7mF/e8zrO80CWZb4c+AvJCaanhN11kc5/CkBgkAG1B+IuCILj4MwQ0q0ANrtE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\nQL6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lolIVP0M6PU7Y3ElzdA26kk+ud++4cRtU+hYbKqfGGnc4jofAh0frRqfci1LO39zy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc01ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = result[:,1]\n",
    "plt.scatter(test[:,0],test[:,1],c=yc, s=50, alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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epdnA+rQa2jCBgM9RIAtzz/9GzQ5V4qJ41Do1WfI6EqAC0jv46gtXyM+KB7Np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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23abc03cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yc = np.round(result[:,1])\n",
    "plt.scatter(test[:,0],test[:,1],c=yc, s=50, alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}