mnist_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['train_labels', 'train_images']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "train = np.load('../data/MNIST_train_100.npz')\n",
    "train.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 28, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['train_images'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T6XRp/xMmek2QbRj7/T5836dlO57n0c1Tw7NQqyB7mViWhWaziXq9jnq9vvSXbMJCnyNP\nXnSY8XiMRqOBIAgwGAxwenqKP//887trpcjsElngRr44ZJXGZDJ50mPg+4B7Cl0Rx3F3+hDhIRZ5\nJMOs24IxEnCF79VXN1xfR1T/EARBcKfEwE8lmnGdu4p+1pu+Mv6BiY4ITHREYKIjAhMdEZjoiPAk\nhleM9cNadERgoiMCEx0RmOiIwERHBCY6IjDREYGJjghMdERgoiMCEx0RmOiIwERHBCY6IjDREYGJ\njghMdERgoiMCEx0RmOiIwERHBCY6IjDREYGJjghMdERgoiMCEx0RmOiIwERHhP8BFSIrmavdsoIA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8278781c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(1,1))\n",
    "plt.axis('off')\n",
    "plt.imshow(train['train_images'][3], cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 784)\n",
      "(100, 2)\n"
     ]
    }
   ],
   "source": [
    "inputs = train['train_images'].reshape(100, 784)\n",
    "outputs = pd.get_dummies(train['train_labels']).iloc[:,:].values\n",
    "print(inputs.shape)\n",
    "print(outputs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.constant(inputs, dtype='float32', shape=[100, 784])\n",
    "y = tf.constant(outputs, dtype='float32', shape=[100, 2])\n",
    "\n",
    "w = tf.Variable(tf.truncated_normal([784,2], stddev=0.1))\n",
    "b = tf.Variable(tf.truncated_normal([1,2], stddev=0.1))\n",
    "\n",
    "MSE = tf.reduce_mean(tf.square(tf.nn.softmax(tf.matmul(x,w) + b) - y))\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer().minimize(MSE)\n",
    "\n",
    "y_pred = tf.nn.softmax(tf.matmul(x,w) + b)\n",
    "\n",
    "# create a graph session and initialize it\n",
    "init = tf.initialize_all_variables()\n",
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step = 0     MSE = 0.318325  \n",
      "step = 100   MSE = 0.045333  \n",
      "step = 200   MSE = 0.018782  \n",
      "step = 300   MSE = 0.010283  \n",
      "step = 400   MSE = 0.006527  \n",
      "step = 500   MSE = 0.004537  \n",
      "step = 600   MSE = 0.003351  \n",
      "step = 700   MSE = 0.002585  \n",
      "step = 800   MSE = 0.002058  \n",
      "step = 900   MSE = 0.001679  \n",
      "step = 1000  MSE = 0.001398  \n",
      "done !\n"
     ]
    }
   ],
   "source": [
    "MAXSTEPS = 1000\n",
    "for step in range(MAXSTEPS + 1):\n",
    "    (_,mse) = sess.run([optimizer,MSE])\n",
    "    if (step % (MAXSTEPS / 10)) == 0:\n",
    "        print('step = %-5d MSE = %-10f' % (step,mse))\n",
    "\n",
    "print('done !')\n",
    "\n",
    "tf_output_pred = sess.run(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf_mse = ((outputs - tf_output_pred)**2).mean()\n",
    "tf_std = tf_output_pred.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "LgR = LogisticRegression(solver='lbfgs', multi_class='multinomial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='multinomial',\n",
       "          n_jobs=1, penalty='l2', random_state=None, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LgR.fit(inputs,train['train_labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sk_outputs_pred = LgR.predict_proba(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# check Sklearn mean square error\n",
    "sk_mse = ((outputs - sk_outputs_pred)**2).mean()\n",
    "sk_std = sk_outputs_pred.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf mse:  0.00139507375488\n",
      "sk mse:  0.0192015802169\n",
      "\n",
      "tf rmse:  0.0373506861366\n",
      "sk rmse:  0.138569766605\n"
     ]
    }
   ],
   "source": [
    "print('tf mse: ', tf_mse)\n",
    "print('sk mse: ', sk_mse)\n",
    "print()\n",
    "print('tf rmse: ', np.sqrt(tf_mse))\n",
    "print('sk rmse: ', np.sqrt(sk_mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow error rate =  0.0\n",
      "sklearn error rate =  0.0\n"
     ]
    }
   ],
   "source": [
    "tf_acc = (tf_output_pred.argmax(axis=1) == outputs.argmax(axis=1)).sum() / outputs.shape[0]\n",
    "sk_acc = (sk_outputs_pred.argmax(axis=1) == outputs.argmax(axis=1)).sum() / outputs.shape[0]\n",
    "\n",
    "print('tensorflow error rate = ', 1 - tf_acc)\n",
    "print('sklearn error rate = ', 1 - sk_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test_images', 'test_labels']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.load('../data/MNIST_test_100.npz')\n",
    "test.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_in = test['test_images'].reshape(100, 784)\n",
    "test_out = pd.get_dummies(test['test_labels']).iloc[:,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf_test_outputs_pred = sess.run(y_pred, feed_dict={x: test_in})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sk_test_outputs_pred = LgR.predict_proba(test_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf_test_mse = ((test_out - tf_test_outputs_pred)**2).mean()\n",
    "tf_test_std = tf_test_outputs_pred.std()\n",
    "\n",
    "sk_test_mse = ((test_out - sk_test_outputs_pred)**2).mean()\n",
    "sk_test_std = sk_test_outputs_pred.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf mse:  0.0831381725287\n",
      "sk mse:  0.353527929652\n",
      "\n",
      "tf rmse:  0.288336908024\n",
      "sk rmse:  0.594582147102\n"
     ]
    }
   ],
   "source": [
    "print('tf mse: ', tf_test_mse)\n",
    "print('sk mse: ', sk_test_std)\n",
    "print()\n",
    "print('tf rmse: ', np.sqrt(tf_test_mse))\n",
    "print('sk rmse: ', np.sqrt(sk_test_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow error rate =  0.11\n",
      "sklearn error rate =  0.11\n"
     ]
    }
   ],
   "source": [
    "tf_test_acc = (tf_test_outputs_pred.argmax(axis=1) == test_out.argmax(axis=1)).sum() / test_out.shape[0]\n",
    "sk_test_acc = (sk_test_outputs_pred.argmax(axis=1) == test_out.argmax(axis=1)).sum() / test_out.shape[0]\n",
    "\n",
    "print('tensorflow error rate = ', 1 - tf_test_acc)\n",
    "print('sklearn error rate = ', 1 - sk_test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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