MLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from sklearn import datasets\n",
    "import tensorflow.keras.datasets.mnist as mnist\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Layer Perceptron for Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE is 7.43 \n",
      "\n",
      "X=[ 0.56287507 -0.31214437], y=28.637 has a predicted output of 28.701\n",
      "X=[0.67890594 1.15284042], y=46.859 has a predicted output of 47.006\n",
      "X=[ 1.37913788 -0.40175494], y=73.091 has a predicted output of 73.170\n",
      "X=[ 1.13078355 -0.61817611], y=57.603 has a predicted output of 57.475\n",
      "X=[-0.48447315  0.66793423], y=-21.433 has a predicted output of -21.439\n",
      "X=[-1.19228511 -0.06513625], y=-66.510 has a predicted output of -66.588\n",
      "X=[0.56097055 1.12460254], y=40.105 has a predicted output of 40.061\n",
      "X=[0.38232592 0.02239105], y=21.340 has a predicted output of 21.319\n",
      "X=[ 2.36736081 -1.87513269], y=115.915 has a predicted output of 107.312\n",
      "X=[ 1.35964411 -1.85826425], y=60.280 has a predicted output of 59.853\n"
     ]
    }
   ],
   "source": [
    "# generate random data for regression\n",
    "X, y = datasets.make_regression(n_samples=100, n_features=2)\n",
    "\n",
    "# split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
    "\n",
    "# build the regression model and fit the training data\n",
    "reg = MLPRegressor(solver='adam', \n",
    "                    hidden_layer_sizes=(10),\n",
    "                    activation='logistic',\n",
    "                    max_iter=50000)\n",
    "\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    "# make prediction using the trained model\n",
    "y_pred = reg.predict(X_test)\n",
    "\n",
    "# print out the accuracy score\n",
    "print(\"The MSE is {:.2f} \\n\".format(mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "for i in range(len(y_test)):   \n",
    "    print(\"X={}, y={:.3f} has a predicted output of {:.3f}\".format(X_test[i], y_test[i], y_pred[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Layer Perceptron for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The prediction accuracy is 90% \n",
      "\n",
      "[-0.33658184 -0.93270487  3.00178024  0.59779242 -1.10272733] in category 0 is mistakenly classified to cateogry 1 with a probability of 0.921\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# generate a random dataset for classification\n",
    "X, y = datasets.make_classification(n_samples=100, n_features=5)\n",
    "\n",
    "# split the training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.1)\n",
    "\n",
    "# build and train the MLP classifier\n",
    "clf = MLPClassifier(solver='lbfgs', \n",
    "                    hidden_layer_sizes=(5, 5),\n",
    "                    activation='relu',\n",
    "                    max_iter=10000)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions using the trained MLP classifier\n",
    "y_pred = clf.predict(X_test)\n",
    "y_pred_proba = clf.predict_proba(X_test)\n",
    "\n",
    "# print out the accuracy score and test data results\n",
    "accu = accuracy_score(y_test, y_pred)\n",
    "print(\"The prediction accuracy is {:.0f}% \\n\".format(100*accu))\n",
    "\n",
    "# print out the test data that are mis-classified\n",
    "for i in range(len(y_test)):\n",
    "    if y_test[i] != y_pred[i]:\n",
    "        print(\"{} in category {} is mistakenly classified to cateogry {} with a probability of {:.3f}\\n\".format(X_test[i], y_test[i], y_pred[i], max(y_pred_proba[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The prediction accuracy is 93.33% \n",
      "\n",
      "Prediction of [6.  2.7 5.1 1.6] in category 1 was mistaken to cateogry 2\n"
     ]
    }
   ],
   "source": [
    "# load the iris dataset\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# split the training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
    "\n",
    "# develop and train a classification model based on multi-layer perceptron\n",
    "clf = MLPClassifier(solver='adam', \n",
    "                    hidden_layer_sizes=(10, 10),\n",
    "                    activation='relu',\n",
    "                    max_iter=10000)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# make prediction using the trained model\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# print out the accuracy score\n",
    "print(\"The prediction accuracy is {:.2f}% \\n\".format(100*accuracy_score(y_test, y_pred)))\n",
    "\n",
    "# print out the test data that are mis-classified\n",
    "for i in range(len(y_test)):   \n",
    "    if y_test[i] != y_pred[i]:\n",
    "        print(\"Prediction of {} in category {} was mistaken to cateogry {}\".format(X_test[i], y_test[i], y_pred[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Layer Perceptron Classifier for Image Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.42848015\n",
      "Iteration 2, loss = 0.20143446\n",
      "Iteration 3, loss = 0.15239314\n",
      "Iteration 4, loss = 0.12096471\n",
      "Iteration 5, loss = 0.10044422\n",
      "Iteration 6, loss = 0.08488432\n",
      "Iteration 7, loss = 0.07309715\n",
      "Iteration 8, loss = 0.06367090\n",
      "Iteration 9, loss = 0.05634828\n",
      "Iteration 10, loss = 0.04889265\n",
      "Iteration 11, loss = 0.04317030\n",
      "Iteration 12, loss = 0.03799462\n",
      "Iteration 13, loss = 0.03342435\n",
      "Iteration 14, loss = 0.03076240\n",
      "Iteration 15, loss = 0.02660936\n",
      "Iteration 16, loss = 0.02397118\n",
      "Iteration 17, loss = 0.02063346\n",
      "Iteration 18, loss = 0.01827249\n",
      "Iteration 19, loss = 0.01667057\n",
      "Iteration 20, loss = 0.01485539\n",
      "\n",
      "Training set score: 0.99825\n",
      "Test set score: 0.9783\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# load the MNIST data from TensorFlow/Keras\n",
    "(X_train , y_train), (X_test , y_test) = mnist.load_data()\n",
    "\n",
    "# flatten the images\n",
    "image_vector_size = X_train.shape[1]*X_train.shape[2]\n",
    "X_train = X_train.reshape(X_train.shape[0], image_vector_size)\n",
    "X_test = X_test.reshape(X_test.shape[0], image_vector_size)\n",
    "\n",
    "# normalize the data\n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255\n",
    "\n",
    "# build the MLP classifier\n",
    "clf = MLPClassifier(solver='adam',\n",
    "                   hidden_layer_sizes=(108),\n",
    "                    verbose=True,\n",
    "                    max_iter=20)\n",
    "\n",
    "# ignore the warning information\n",
    "with warnings.catch_warnings():\n",
    "    warnings.filterwarnings(\"ignore\", category=ConvergenceWarning, module=\"sklearn\")\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "# print out classification scores\n",
    "print(\"\\nTraining set score: \" + str(clf.score(X_train, y_train)))\n",
    "print(\"Test set score: \" + str(clf.score(X_test, y_test)))\n",
    "\n",
    "# use global min / max to ensure all weights are shown on the same scale\n",
    "vmin, vmax = clf.coefs_[0].min(), clf.coefs_[0].max()\n",
    "\n",
    "# make 4 x 4 subplots to visualize the coefficients\n",
    "fig, axes = plt.subplots(4, 4, figsize=(10,10))\n",
    "\n",
    "for coef, ax in zip(clf.coefs_[0].T, axes.ravel()):\n",
    "    ax.matshow(coef.reshape(28, 28), \n",
    "               cmap=plt.cm.gray, \n",
    "               vmin=0.5 * vmin,\n",
    "               vmax=0.5 * vmax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}