Lab6_2. Zoo_Softmax_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "tf.set_random_seed(222)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predicting animal type based on various features\n",
    "xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32)\n",
    "x_data = xy[:, 0:-1]\n",
    "y_data = xy[:, [-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 16) (101, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_data.shape, y_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_classes = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one hot:  Tensor(\"one_hot:0\", shape=(?, 1, 7), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "X = tf.placeholder(tf.float32, [None, 16])\n",
    "Y = tf.placeholder(tf.int32, [None, 1]) #0~6\n",
    "Y_one_hot = tf.one_hot(Y, nb_classes)\n",
    "print(\"one hot: \",Y_one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reshape Tensor(\"Reshape:0\", shape=(?, 7), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes])\n",
    "print(\"reshape\", Y_one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.random_normal([16, nb_classes]), name= 'weight')\n",
    "b = tf.Variable(tf.random_normal([nb_classes]), name = 'bias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tf.nn.softmax computes softmax activations\n",
    "# softmax = exp(logits) / reduce_sum(exp(logits), dim)\n",
    "\n",
    "logits = tf.matmul(X,W) + b\n",
    "hypothesis = tf.nn.softmax(logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Cross entropy cost/loss\n",
    "\n",
    "cost_ini = tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = Y_one_hot)\n",
    "cost = tf.reduce_mean(cost_ini)\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prediction = tf.argmax(hypothesis, 1)\n",
    "correct_prediction = tf.equal(prediction, tf.argmax(Y_one_hot, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step:     0\tLoss: 8.995\tAcc: 8.91%\n",
      "Step:   100\tLoss: 0.623\tAcc: 77.23%\n",
      "Step:   200\tLoss: 0.402\tAcc: 86.14%\n",
      "Step:   300\tLoss: 0.301\tAcc: 91.09%\n",
      "Step:   400\tLoss: 0.241\tAcc: 94.06%\n",
      "Step:   500\tLoss: 0.200\tAcc: 95.05%\n",
      "Step:   600\tLoss: 0.171\tAcc: 97.03%\n",
      "Step:   700\tLoss: 0.149\tAcc: 99.01%\n",
      "Step:   800\tLoss: 0.132\tAcc: 99.01%\n",
      "Step:   900\tLoss: 0.118\tAcc: 99.01%\n",
      "Step:  1000\tLoss: 0.107\tAcc: 99.01%\n",
      "Step:  1100\tLoss: 0.098\tAcc: 100.00%\n",
      "Step:  1200\tLoss: 0.090\tAcc: 100.00%\n",
      "Step:  1300\tLoss: 0.083\tAcc: 100.00%\n",
      "Step:  1400\tLoss: 0.078\tAcc: 100.00%\n",
      "Step:  1500\tLoss: 0.073\tAcc: 100.00%\n",
      "Step:  1600\tLoss: 0.068\tAcc: 100.00%\n",
      "Step:  1700\tLoss: 0.065\tAcc: 100.00%\n",
      "Step:  1800\tLoss: 0.061\tAcc: 100.00%\n",
      "Step:  1900\tLoss: 0.058\tAcc: 100.00%\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 1 True Y: 1\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    for step in range(2000):\n",
    "        sess.run(optimizer, feed_dict = {X: x_data, Y: y_data})\n",
    "        if step % 100 == 0:\n",
    "            loss, acc = sess.run([cost, accuracy], feed_dict = {X: x_data, Y: y_data})\n",
    "            print(\"Step: {:5}\\tLoss: {:.3f}\\tAcc: {:.2%}\".format(step, loss, acc))\n",
    "            \n",
    "    #predict output\n",
    "    pred = sess.run(prediction, feed_dict = {X:x_data})\n",
    "    \n",
    "    # y_data: (N,1) = flatten => (N, ) matches pred.shape\n",
    "    for p, y in  zip(pred, y_data.flatten()):\n",
    "         print(\"[{}] Prediction: {} True Y: {}\".format(p == int(y), p, int(y)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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