"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML(\n",
+ "\"\"\"\n",
+ "\n",
+ "\"\"\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---------\n",
+ "### Congratulations, you're done with your pre-class assignment!\n",
+ "\n",
+ "Now, you just need to submit this assignment by uploading it to the course Desire2Learn web page for today's dropbox (Don't forget to add your name in the first cell)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "© Copyright 2017, Michigan State University Board of Trustees"
+ ]
+ }
+ ],
+ "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": "3.6.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/ANN/Ann2.ipynb b/doc/Programs/ANN/Ann2.ipynb
new file mode 100644
index 000000000..15ffa522e
--- /dev/null
+++ b/doc/Programs/ANN/Ann2.ipynb
@@ -0,0 +1,922 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Nicolas Dronchi
\n",
+ "\n",
+ "#### Yitian, Jake, David
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Day 23 In-Class Assignment: Artificial Neural Network\n",
+ "\n",
+ "\n",
+ "\n",
+ "![](\"https://ml4a.github.io/images/temp_fig_mnist.png\")
\n",
+ "From: Machine Learning for Artists - https://ml4a.github.io/
\n",
+ "\n",
+ "\n",
+ "\n",
+ "1. **Scientific motivation** \n",
+ " - Data Analysis / Pattern Recognition\n",
+ "2. **Modeling tools** \n",
+ " - Artificial Neural networks\n",
+ " - Error Calculations\n",
+ "3. **Programming concepts** \n",
+ " - More Debugging\n",
+ " - Selecting and using libraries\n",
+ "4. **Python Programming Concepts** \n",
+ " - More Understanding classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Agenda for today's class \n",
+ "\n",
+ "\n",
+ "1. Review pre-class assignment\n",
+ "1. Modify code to be more flexible\n",
+ "1. Use our ANN on the \"Digits\" dataset\n",
+ "1. Finding/Using Neural Networks Libraries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "# 1. Review pre-class assignment\n",
+ "\n",
+ "Below we summarize the steps involved in designing and training a feed-forward artificial neural network. We will use the [partSix.py](./partSix.py) file provided in the \"Neural Networks Demystified\" module which can be downloaded from github:\n",
+ "\n",
+ " git clone https://github.com/stephencwelch/Neural-Networks-Demystified\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# %load partSix.py\n",
+ "# Neural Networks Demystified\n",
+ "# Part 6: Training\n",
+ "#\n",
+ "# Supporting code for short YouTube series on artificial neural networks.\n",
+ "#\n",
+ "# Stephen Welch\n",
+ "# @stephencwelch\n",
+ "\n",
+ "\n",
+ "## ----------------------- Part 1 ---------------------------- ##\n",
+ "import numpy as np\n",
+ "\n",
+ "# X = (hours sleeping, hours studying), y = Score on test\n",
+ "X = np.array(([3,5], [5,1], [10,2]), dtype=float)\n",
+ "y = np.array(([75], [82], [93]), dtype=float)\n",
+ "\n",
+ "# Normalize\n",
+ "X = X/np.amax(X, axis=0)\n",
+ "y = y/100 #Max test score is 100\n",
+ "\n",
+ "## ----------------------- Part 5 ---------------------------- ##\n",
+ "\n",
+ "class Neural_Network(object):\n",
+ " def __init__(self):\n",
+ " #Define Hyperparameters\n",
+ " self.inputLayerSize = 2\n",
+ " self.outputLayerSize = 1\n",
+ " self.hiddenLayerSize = 3\n",
+ "\n",
+ " #Weights (parameters)\n",
+ " self.W1 = np.random.randn(self.inputLayerSize,self.hiddenLayerSize)\n",
+ " self.W2 = np.random.randn(self.hiddenLayerSize,self.outputLayerSize)\n",
+ "\n",
+ " def forward(self, X):\n",
+ " #Propogate inputs though network\n",
+ " self.z2 = np.dot(X, self.W1)\n",
+ " self.a2 = self.sigmoid(self.z2)\n",
+ " self.z3 = np.dot(self.a2, self.W2)\n",
+ " yHat = self.sigmoid(self.z3)\n",
+ " return yHat\n",
+ "\n",
+ " def sigmoid(self, z):\n",
+ " #Apply sigmoid activation function to scalar, vector, or matrix\n",
+ " return 1/(1+np.exp(-z))\n",
+ "\n",
+ " def sigmoidPrime(self,z):\n",
+ " #Gradient of sigmoid\n",
+ " return np.exp(-z)/((1+np.exp(-z))**2)\n",
+ "\n",
+ " def costFunction(self, X, y):\n",
+ " #Compute cost for given X,y, use weights already stored in class.\n",
+ " self.yHat = self.forward(X)\n",
+ " J = 0.5*sum((y-self.yHat)**2)\n",
+ " return J\n",
+ "\n",
+ " def costFunctionPrime(self, X, y):\n",
+ " #Compute derivative with respect to W and W2 for a given X and y:\n",
+ " self.yHat = self.forward(X)\n",
+ "\n",
+ " delta3 = np.multiply(-(y-self.yHat), self.sigmoidPrime(self.z3))\n",
+ " dJdW2 = np.dot(self.a2.T, delta3)\n",
+ "\n",
+ " delta2 = np.dot(delta3, self.W2.T)*self.sigmoidPrime(self.z2)\n",
+ " dJdW1 = np.dot(X.T, delta2)\n",
+ "\n",
+ " return dJdW1, dJdW2\n",
+ "\n",
+ " #Helper Functions for interacting with other classes:\n",
+ " def getParams(self):\n",
+ " #Get W1 and W2 unrolled into vector:\n",
+ " params = np.concatenate((self.W1.ravel(), self.W2.ravel()))\n",
+ " return params\n",
+ "\n",
+ " def setParams(self, params):\n",
+ " #Set W1 and W2 using single paramater vector.\n",
+ " W1_start = 0\n",
+ " W1_end = self.hiddenLayerSize * self.inputLayerSize\n",
+ " self.W1 = np.reshape(params[W1_start:W1_end], (self.inputLayerSize , self.hiddenLayerSize))\n",
+ " W2_end = W1_end + self.hiddenLayerSize*self.outputLayerSize\n",
+ " self.W2 = np.reshape(params[W1_end:W2_end], (self.hiddenLayerSize, self.outputLayerSize))\n",
+ "\n",
+ " def computeGradients(self, X, y):\n",
+ " dJdW1, dJdW2 = self.costFunctionPrime(X, y)\n",
+ " return np.concatenate((dJdW1.ravel(), dJdW2.ravel()))\n",
+ "\n",
+ "def computeNumericalGradient(N, X, y):\n",
+ " paramsInitial = N.getParams()\n",
+ " numgrad = np.zeros(paramsInitial.shape)\n",
+ " perturb = np.zeros(paramsInitial.shape)\n",
+ " e = 1e-4\n",
+ "\n",
+ " for p in range(len(paramsInitial)):\n",
+ " #Set perturbation vector\n",
+ " perturb[p] = e\n",
+ " N.setParams(paramsInitial + perturb)\n",
+ " loss2 = N.costFunction(X, y)\n",
+ "\n",
+ " N.setParams(paramsInitial - perturb)\n",
+ " loss1 = N.costFunction(X, y)\n",
+ "\n",
+ " #Compute Numerical Gradient\n",
+ " numgrad[p] = (loss2 - loss1) / (2*e)\n",
+ "\n",
+ " #Return the value we changed to zero:\n",
+ " perturb[p] = 0\n",
+ "\n",
+ " #Return Params to original value:\n",
+ " N.setParams(paramsInitial)\n",
+ "\n",
+ " return numgrad\n",
+ "\n",
+ "## ----------------------- Part 6 ---------------------------- ##\n",
+ "from scipy import optimize\n",
+ "\n",
+ "\n",
+ "class trainer(object):\n",
+ " def __init__(self, N):\n",
+ " #Make Local reference to network:\n",
+ " self.N = N\n",
+ "\n",
+ " def callbackF(self, params):\n",
+ " self.N.setParams(params)\n",
+ " self.J.append(self.N.costFunction(self.X, self.y))\n",
+ "\n",
+ " def costFunctionWrapper(self, params, X, y):\n",
+ " self.N.setParams(params)\n",
+ " cost = self.N.costFunction(X, y)\n",
+ " grad = self.N.computeGradients(X,y)\n",
+ " return cost, grad\n",
+ "\n",
+ " def train(self, X, y):\n",
+ " #Make an internal variable for the callback function:\n",
+ " self.X = X\n",
+ " self.y = y\n",
+ "\n",
+ " #Make empty list to store costs:\n",
+ " self.J = []\n",
+ "\n",
+ " params0 = self.N.getParams()\n",
+ "\n",
+ " options = {'maxiter': 200, 'disp' : True}\n",
+ " _res = optimize.minimize(self.costFunctionWrapper, params0, jac=True, method='BFGS', \\\n",
+ " args=(X, y), options=options, callback=self.callbackF)\n",
+ "\n",
+ " self.N.setParams(_res.x)\n",
+ " self.optimizationResults = _res\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input Data [[0.3 1. ]\n",
+ " [0.5 0.2]\n",
+ " [1. 0.4]]\n",
+ "Output Data [[0.75]\n",
+ " [0.82]\n",
+ " [0.93]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Input Data\", X)\n",
+ "print(\"Output Data\", y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Untrained Output [[0.40080444]\n",
+ " [0.43447789]\n",
+ " [0.42423465]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Untrained Random Network\n",
+ "NN = Neural_Network()\n",
+ "y1 = NN.forward(X)\n",
+ "print(\"Untrained Output\", y1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimization terminated successfully.\n",
+ " Current function value: 0.000000\n",
+ " Iterations: 51\n",
+ " Function evaluations: 56\n",
+ " Gradient evaluations: 56\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Training step\n",
+ "T = trainer(NN)\n",
+ "T.train(X,y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Trained Output [[0.75003127]\n",
+ " [0.81998936]\n",
+ " [0.92991079]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Trained Network\n",
+ "y2 = NN.forward(X)\n",
+ "print(\"Trained Output\",y2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ **DO THIS:** Calculate and compare the [mean squared error](https://en.wikipedia.org/wiki/Mean_squared_error) for untrained network (```y1```) and the trained network (```y2```). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.17545447]\n",
+ "[3.01663395e-09]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Put your code here\n",
+ "def MSE(y, yhat):\n",
+ " return (1/len(y1))*sum((y-yhat)**2)\n",
+ "\n",
+ "print(MSE(y, y1))\n",
+ "print(MSE(y, y2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "----\n",
+ "\n",
+ "# 2. Modify code to be more flexible\n",
+ "\n",
+ "The code for our Neural Network example above assumes an input layer size of 2, hidden layer size of 3 and an output layer size of 1. \n",
+ "\n",
+ "\n",
+ "✅ **DO THIS:** Modify the code in Section 1 above so that the user can specify these as inputs when creating the Neural_Network object. The default values should stay the same. Rerun the above example to make sure it still works. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "class Neural_Network(Neural_Network):\n",
+ " def __init__(self,insize, outsize, hiddensize):\n",
+ " #Define Hyperparameters\n",
+ " self.inputLayerSize = insize\n",
+ " self.outputLayerSize = outsize\n",
+ " self.hiddenLayerSize = hiddensize\n",
+ "\n",
+ " #Weights (parameters)\n",
+ " self.W1 = np.random.randn(self.inputLayerSize,self.hiddenLayerSize)\n",
+ " self.W2 = np.random.randn(self.hiddenLayerSize,self.outputLayerSize)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Untrained Output [[0.40867166]\n",
+ " [0.36286813]\n",
+ " [0.35807708]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Untrained Random Network\n",
+ "NN = Neural_Network(2,1,5)\n",
+ "y1 = NN.forward(X)\n",
+ "print(\"Untrained Output\", y1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimization terminated successfully.\n",
+ " Current function value: 0.000000\n",
+ " Iterations: 51\n",
+ " Function evaluations: 53\n",
+ " Gradient evaluations: 53\n"
+ ]
+ }
+ ],
+ "source": [
+ "T = trainer(NN)\n",
+ "T.train(X,y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Trained Output [[0.75000449]\n",
+ " [0.81997275]\n",
+ " [0.92997594]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Trained Network\n",
+ "y2 = NN.forward(X)\n",
+ "print(\"Trained Output\",y2)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.21752347]\n",
+ "[4.47242084e-10]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(MSE(y, y1))\n",
+ "print(MSE(y, y2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "# 3. Use our ANN on the \"Digits\" dataset.\n",
+ "\n",
+ "Here is the code copied from out previous Machine Learning Module which downloads the \"digits\" dataset and separates it into training and testing sets. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDmEChzCBQ5jAIUzgECZwCPsfV3ODr+2GeukAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pylab as plt\n",
+ "import numpy as np\n",
+ "from sklearn.datasets import fetch_lfw_people, load_digits\n",
+ "from sklearn.cross_validation import train_test_split\n",
+ "\n",
+ "sk_data = load_digits();\n",
+ "\n",
+ "#Cool slider to browse all of the images.\n",
+ "from ipywidgets import interact\n",
+ "def browse_images(images, labels, categories):\n",
+ " n = len(images)\n",
+ " def view_image(i):\n",
+ " plt.imshow(images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
+ " plt.title('%s' % categories[labels[i]])\n",
+ " plt.axis('off')\n",
+ " plt.show()\n",
+ " interact(view_image, i=(0,n-1))\n",
+ "browse_images(sk_data.images, sk_data.target, sk_data.target_names)\n",
+ "\n",
+ "\n",
+ "feature_vectors = sk_data.data\n",
+ "class_labels = sk_data.target\n",
+ "categories = sk_data.target_names\n",
+ "\n",
+ "N, h, w = sk_data.images.shape\n",
+ "train_vectors, test_vectors, train_labels, test_labels = train_test_split(feature_vectors, class_labels, test_size=0.25, random_state=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following is copied and pasted from Section 1 and rewritten it to use the training and testing sets above. \n",
+ "\n",
+ "✅ **DO THIS:** Make changes to and finish the following code to work with the \"digits\" data. Some of the work has already been done for you. Please consider the following when making changes:\n",
+ "\n",
+ "* For this new input to work, you need to transform the training and testing data into a format that can work with the class that was developed. Use the example from above and the functions such as ```type``` and ```shape``` to figure out how to transform the data into inputs suitable for training the Neural Network. This will be the first step before you can run the example code below.\n",
+ "* Modify the number of Input, Output and Hidden layers to match the new problem. (I've supplied \"?\" for now, you should think about what these could/should be.)\n",
+ "* Make sure your inputs and outputs are normalized between zero (0) and one (1). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1347, 64)\n",
+ "(1347, 1)\n",
+ "[[0.22222222]\n",
+ " [0.66666667]\n",
+ " [0.66666667]\n",
+ " ...\n",
+ " [1. ]\n",
+ " [0.11111111]\n",
+ " [0.55555556]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_vectors = train_vectors/train_vectors.max()\n",
+ "\n",
+ "train_vectors = train_vectors\n",
+ "train_labels = train_labels.reshape(1347,1)\n",
+ "train_labels = train_labels/train_labels.max()\n",
+ "print(train_vectors.shape)\n",
+ "print(train_labels.shape)\n",
+ "print(train_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1.14594026e-02],\n",
+ " [1.83245292e-03],\n",
+ " [2.81387785e-03],\n",
+ " ...,\n",
+ " [1.97903197e-02],\n",
+ " [2.87355695e-02],\n",
+ " [8.32694764e-06]])"
+ ]
+ },
+ "execution_count": 138,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Run the training. \n",
+ "# X = np.array(([3,5], [5,1], [10,2]), dtype=float) 2,1,3\n",
+ "# y = np.array(([75], [82], [93]), dtype=float)\n",
+ "\n",
+ "NN = Neural_Network(64,1,10) #len(train_vectors)\n",
+ "NN.forward(train_vectors)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0moptions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'maxiter'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'disp'\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m \u001b[0m_res\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptimize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcostFunctionWrapper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'BFGS'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbackF\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetParams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_res\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32mC:\\Users\\Maxwell\\AppData\\Roaming\\Python\\Python36\\site-packages\\scipy\\optimize\\_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_minimize_cg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'bfgs'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 481\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0m_minimize_bfgs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 482\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton-cg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 483\u001b[0m return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,\n",
+ "\u001b[0;32mC:\\Users\\Maxwell\\AppData\\Roaming\\Python\\Python36\\site-packages\\scipy\\optimize\\optimize.py\u001b[0m in \u001b[0;36m_minimize_bfgs\u001b[0;34m(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)\u001b[0m\n\u001b[1;32m 1003\u001b[0m \u001b[0mA1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mI\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0msk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0myk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mrhok\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0mA2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mI\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0myk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0msk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mrhok\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m Hk = numpy.dot(A1, numpy.dot(Hk, A2)) + (rhok * sk[:, numpy.newaxis] *\n\u001b[0m\u001b[1;32m 1006\u001b[0m sk[numpy.newaxis, :])\n\u001b[1;32m 1007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "T = trainer(NN)\n",
+ "T.train(train_vectors, train_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "pred_labels = NN.forward(train_vectors)\n",
+ "\n",
+ "print(\"Training Data error\", np.sum(np.sqrt((train_labels - pred_labels)*(train_labels-pred_labels)))/len(train_vectors))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "pred_labels = NN.forward(test_vectors)\n",
+ "\n",
+ "print(\"Testing Data error\", np.sum(np.sqrt((test_labels - pred_labels)*(test_labels-pred_labels)))/len(test_vectors))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Pay attention to how the plotting code rescales the data labels,\n",
+ "# if you scaled them differently, you may need to change this code.\n",
+ "def plot_gallery(images, true_titles, pred_titles, h, w, n_row=5, n_col=5):\n",
+ " \"\"\"Helper function to plot a gallery of portraits\"\"\"\n",
+ " plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))\n",
+ " plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n",
+ " for i in range(n_row * n_col):\n",
+ " plt.subplot(n_row, n_col, i + 1)\n",
+ " plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray_r)\n",
+ " plt.title(np.round(pred_titles[i]*10, 2)) \n",
+ " plt.xlabel('Actual='+str(true_titles[i]), size=9)\n",
+ " plt.xticks(())\n",
+ " plt.yticks(())\n",
+ "\n",
+ "plot_gallery(test_vectors, test_labels, pred_labels, h,w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ **DO THIS:** Modify the parameters of the neural network to get the best fit of the data. Consider also changing the training data you're providing to see how this changes your fit. Is it possible to change the number of input layers or output layers? If so, how you might you do it?\n",
+ "\n",
+ "Record your thoughts below along with your final best fit parameters/data. **Once you've come up with your best training data and neural network parameters, post your data/parameters the Slack channel for your section.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Picking large hidden layer values takes a long time to train. I found 25 works well, 10 is questionable, and 64 takes too long."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "# 4. Finding/Using Neural Networks Libraries\n",
+ "In this section we will repeat both examples from above (Grades and Digits) using a python neural network library. \n",
+ "\n",
+ "✅ Do This - As a group, find examples of neural network packages in python. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**✅ DO THIS** - Pick a package (or packages) you find interesting and get them working in this notebook. I suggest that each group member try to pick a different package and spend about 10 minutes trying to install and get it working. After about 10 minutes compare notes and pick the one the group will think is the easiest. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question :** What package did you pick? Please include any installation code needed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Put your installation code here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ **DO THIS** - Create an example to demonstrate that the Neural Network is working. Preferably using an example that comes with the provided NN Package. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Put your example code here \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ **DO THIS** - Reproduce the results from the \"Grade\" example above using ```X``` and ```y```:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Put your Grade example code here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ **DO THIS** - Reproduce the results from the \"Digits\" example above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Put your Digits example code here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question:** What settings worked the best for the 'Digits' data? How did you find these settings?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✎ Do This - Erase the contents of this cell and replace it with your answer to the above question! (double-click on this text to edit this cell, and hit shift+enter to save the text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question:** What part did you have the most trouble figuring out to get this assignment working?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✎ Do This - Erase the contents of this cell and replace it with your answer to the above question! (double-click on this text to edit this cell, and hit shift+enter to save the text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "----\n",
+ "# Assignment Wrap-up\n",
+ "\n",
+ "Fill out the following Google Form before submitting your assignment to D2L!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML(\n",
+ "\"\"\"\n",
+ "\n",
+ "\"\"\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "-----\n",
+ "### Congratulations, we're done!\n",
+ "\n",
+ "Now, you just need to submit this assignment by uploading it to the course Desire2Learn web page for today's dropbox (Don't forget to add your names in the first cell).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "© Copyright 2017, Michigan State University Board of Trustees"
+ ]
+ }
+ ],
+ "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": "3.6.0"
+ },
+ "widgets": {
+ "state": {
+ "065d2168353641f29ca4ec30f7f110b9": {
+ "views": [
+ {
+ "cell_index": 18
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/ANN/Ann3.ipynb b/doc/Programs/ANN/Ann3.ipynb
new file mode 100644
index 000000000..4022f3fc5
--- /dev/null
+++ b/doc/Programs/ANN/Ann3.ipynb
@@ -0,0 +1,494 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Nicolas Dronchi
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Day 23 Pre-Class assignment: Back propagation\n",
+ "This pre-class assignment finishes out the videos from \"Neural Networks Demystified\" module. Please watch the videos. Again, you do not have to understand the equations but the math is included for completeness.\n",
+ "\n",
+ "If you are lost, I highly recommend reviewing the entire \"Neural Networks Demystified\" module which can be downloaded from github:\n",
+ "\n",
+ " git clone https://github.com/stephencwelch/Neural-Networks-Demystified\n",
+ "\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Goals for this pre-class assignment:\n",
+ "\n",
+ "1. Reviewing gradient descent\n",
+ "1. Performing Back Propagation\n",
+ "1. Training at network\n",
+ "\n",
+ "## Assignment instructions\n",
+ "\n",
+ "**This assignment is due by 11:59 p.m. the day before class** and should be uploaded into the appropriate \"Pre-class assignments\" dropbox folder in the Desire2Learn website.\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Gradient Decent\n",
+ "\n",
+ "✅ Do This - watch the following video:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import YouTubeVideo\n",
+ "YouTubeVideo('5u0jaA3qAGk',width=640,height=360)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question 1**: In simple terms, explain the \"Curse of Dimensionality\"?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to grid search/brute force solve for the best variables for all dimensions it would take N^D operations where D is the dimension. This can quickly reach amounts of time that aren't reasonably computable. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## 2. Back Propagation:\n",
+ "\n",
+ "Now watch the following video:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "YouTubeVideo('GlcnxUlrtek',width=640,height=360)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question 2**: The gradient decent algorithm in Neural Networks is often called \"back propagation.\" What is being passed back though the algorithm and causing the weights to be updated? "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The weights of how much they contribute to the error. The goal is to find out where the most error came from and then change that."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is a link to the entire code so far:\n",
+ "\n",
+ "https://raw.githubusercontent.com/stephencwelch/Neural-Networks-Demystified/master/partSix.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ Do This - Download and inspect the partSix.py file and run the following command:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0.3 1. ]\n",
+ " [0.5 0.2]\n",
+ " [1. 0.4]]\n",
+ "[[0.75]\n",
+ " [0.82]\n",
+ " [0.93]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from partSix import *\n",
+ "print(X)\n",
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ Do This - Create an instance of the Neural Network and apply forward function to estimate $\\hat{y}$:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.52155376],\n",
+ " [0.41215014],\n",
+ " [0.41576012]])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# put your code here\n",
+ "NN = Neural_Network()\n",
+ "NN.forward(X)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question 3**: How good is this initial estimation?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.24148593])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NN.costFunction(X, y )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "pretty bad. The cost function gives an error of 0.241. It only guesses variables around 0.5."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## 3. Training:\n",
+ "\n",
+ "Please watch the following video:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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amQ1ctpI3N/eGaWOK2YxRyQoJu9G7c1u8pVZWUOgjknGR2GMAZJ5ZNNu755U41ssMRWTc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\nSgFKUoBSlKAUpSgFKUoD/9k=\n",
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import YouTubeVideo\n",
+ "YouTubeVideo('9KM9Td6RVgQ',width=640,height=360)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ Do This - Now, create an instance of the ```trainer``` class from the partSix.py file. Call the objects ```train``` function by passing it the original ```X``` and ```y``` data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimization terminated successfully.\n",
+ " Current function value: 0.000000\n",
+ " Iterations: 36\n",
+ " Function evaluations: 39\n",
+ " Gradient evaluations: 39\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Put your code here\n",
+ "T = trainer(NN)\n",
+ "T.train(X,y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0.74998259]\n",
+ " [0.82002105]\n",
+ " [0.93000224]]\n",
+ "[3.75622733e-10]\n",
+ "[[0.75]\n",
+ " [0.82]\n",
+ " [0.93]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(NN.forward(X))\n",
+ "print(NN.costFunction(X, y ))\n",
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "✅ Do This - If done correctly, the ```NN``` object should now be trained. Apply the forward function again to see the new estimation of $\\hat{y}$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Question 4**: Hopefully this worked and the estimation is better than the previous one. How close are these values to the original grades? What shortcomings are there to testing using this approach?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These are very close values. I think some short comings is when there are local minimums in the data as well as when you have a low amount of data. I think it takes a large amount of data to train to be something useful and not just spit back out the same answers you trained it on."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "----\n",
+ "# Assignment wrap-up\n",
+ "\n",
+ "Please fill out the form that appears when you run the code below. **You must completely fill this out in order to receive credit for the assignment!**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML(\n",
+ "\"\"\"\n",
+ "\n",
+ "\"\"\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---------\n",
+ "### Congratulations, you're done with your pre-class assignment!\n",
+ "\n",
+ "Now, you just need to submit this assignment by uploading it to the course Desire2Learn web page for today's dropbox (Don't forget to add your name in the first cell)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "© Copyright 2017, Michigan State University Board of Trustees"
+ ]
+ }
+ ],
+ "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": "3.6.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/ANN/perceptron.ipynb b/doc/Programs/ANN/perceptron.ipynb
new file mode 100644
index 000000000..3852e4751
--- /dev/null
+++ b/doc/Programs/ANN/perceptron.ipynb
@@ -0,0 +1,16510 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Day 16 In-Class Assignment: Introduction to Machine Learning \n",
+ "\n",
+ "\n",
+ "![](\"https://goo.gl/FYHM5q\")
\n",
+ "Image from: https://goo.gl/ypY9G2
\n",
+ "\n",
+ "1. **Scientific motivation** \n",
+ " - Classifying data (iris types) \n",
+ "2. **Modeling tools** \n",
+ " - Machine Learning (Perceptron)\n",
+ "3. **Programming concepts** \n",
+ " - Creating Classes and re-usable code\n",
+ " - Pulling in data from outside sources \n",
+ " - Using external libraries \n",
+ " \n",
+ "\n",
+ "### Agenda for today's class\n",
+ "\n",
+ "\n",
+ "\n",
+ "1. Review of pre-class assignment\n",
+ "1. Problem Statement\n",
+ "1. Basics of the perceptron model\n",
+ "1. Loading and inspecting the data\n",
+ "1. Building the perceptron model\n",
+ "1. Plotting the decision boundary\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "----\n",
+ "# 1. Review of pre-class assignment\n",
+ "\n",
+ "Were there any specific questions that came up in the pre-class assignment?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Problem Statement\n",
+ " \n",
+ "\n",
+ "We want to build a model that can accurately classifying two types of flowers based off of measurements we have collected. Building this model will allow us to understand how basic machine learning models learn and what exactly is happening 'under the hood'. It will provide a slightly more intuitive view of machine learning and make it seem as less of a black box and more of a tool that we understand. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. The Basics of the Perceptron Model\n",
+ "\n",
+ "The perceptron is a what is known as a basic binary classifier. It takes in a set of training data that is linerally separable and then computes a *set of weights* and a *bias term* to apply to input data as to properly classify it. Perceptrons only works for data that contains two classes and is linearly separable. For data that does not have these properties, the classifier cannot properly learn the weights and bias term.\n",
+ "\n",
+ "Since the perceptron is based on linearly separable data, we can think of the model as trying to learn the slope of a line, $y = m~x + b$. However in machine learning, we usually define $X$ to be an input *vector*, which is a $1$ by $N$ matrix, where $N$ is the number of measurements or \"features\" for a given sample. Then, we can create a similar matrix of weights, $W$, also of length N, and re-write our equation to be:\n",
+ "\n",
+ "$$ Y = W \\cdot X + B$$\n",
+ "\n",
+ "where $Y$ represents the resulting classification, consisting of either -1 or 1, depending on the output of $W \\cdot X$, the dot product of $W$ and $X$, and $B$ represents the bias term. More explicitly, we can look at this in a matrix format:\n",
+ "\n",
+ "$$ Y = \\begin{bmatrix} w_1 & w_2 & \\dots & w_n \\end{bmatrix} \\cdot \\begin{bmatrix} x_1 \\\\ x_2 \\\\ \\vdots \\\\ x_n \\end{bmatrix} + B $$\n",
+ "\n",
+ "But, **how do we go about learning the weights of the model?**\n",
+ "\n",
+ "We learn the model weights by attempting to predict the class of our input data using an initial guess for the weights, and then update our weight values based on our prediction. We can define a \"step size\" for how much we change our weight between subsequent guess in the following way:\n",
+ "\n",
+ "`step_size = eta * (target class - predicted class)` \n",
+ "\n",
+ "Then, using our \"step size\", we then update all of our weights by multiplying our *step size times the corresponding feature values*:\n",
+ "\n",
+ "$$w_{1,new} = w_{1,old} + (\\mathrm{step~size} \\times x_{1,i})$$\n",
+ "$$w_{2,new} = w_{2,old} + (\\mathrm{step~size} \\times x_{2,i})$$\n",
+ "$$ \\vdots $$\n",
+ "$$w_{N,new} = w_{N,old} + (\\mathrm{step~size} \\times x_{N,i})$$\n",
+ "\n",
+ "We also have to update our bias term, but just use the step size for this update: $B_{new} = B_{old} + \\mathrm{step~size}$).\n",
+ "\n",
+ "In this model, we use `eta` to represent our \"learning_rate\", which takes on a value between 0 and 1.\n",
+ "\n",
+ "The step size should always be a positive or negative decimal value depending on `eta`. For example, if we set the learning rate, `eta`, to be .1 and our target is -1 and we predict 1 then the we get the following equation.\n",
+ "\n",
+ "`step_size = .1 * (-1-1) = -.2`\n",
+ " \n",
+ "Alternativey, if our target is 1 and we predict it as -1 then we will get the following.\n",
+ "\n",
+ "`step_size = .1 * (1 - -1) = .2`\n",
+ "\n",
+ "This process occurs iteratively. So, for a set number of iterations we calculate a step size and adjust the weights accordingly.\n",
+ "\n",
+ "**But, how do we handle the \"learning\" process when we have multiple samples?**\n",
+ "\n",
+ "In this case, we need to update the weights based on _all_ of the sample features. So our original equation above becomes:\n",
+ "\n",
+ "$$w_{1,new} = w_{1,old} + \\sum_{i=0}^{M} (\\mathrm{step~size} \\times x_{1,i})$$\n",
+ "$$w_{2,new} = w_{2,old} + \\sum_{i=0}^{M} (\\mathrm{step~size} \\times x_{2,i})$$\n",
+ "$$ \\vdots $$\n",
+ "$$w_{N,new} = w_{N,old} + \\sum_{i=0}^{M} (\\mathrm{step~size} \\times x_{N,i})$$\n",
+ "\n",
+ "where $M$ is our total number of samples and we compute new weights for every features value."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Loading and inspecting the data\n",
+ "\n",
+ "Before we build a machine learning model, we need data to base it off of. The data set we are going to use has been provided for you in the directory for this assignment, `irsi.csv`. This dataset contains measurements for the properties of two different iris varieties.\n",
+ "\n",
+ "**Load the data into python and visualize (with a plot) to get a sense for what it looks like. Use different colors to represent the two different iris classifications.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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dBh3/BcKJNe7DEHnTTfhFwpJ7H71033RI82rHcZQX734t634joQgv3//68t2P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\nXctQ/eBfN6d9avsUg1Qm1n7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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "100\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Do This: Load in the iris.csv file and plot the data based on the iris classifications\n",
+ "import csv\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "sepal_length = []\n",
+ "sepal_width = []\n",
+ "label = []\n",
+ "with open('iris.csv', 'r') as data:\n",
+ " datareader = csv.reader(data, delimiter=',', quotechar='|')\n",
+ " for i,row in enumerate(datareader):\n",
+ " if i == 0:\n",
+ " continue\n",
+ " sepal_length.append(float(row[0]))\n",
+ " sepal_width.append(float(row[1]))\n",
+ " label.append(row[2])\n",
+ "\n",
+ "colors = []\n",
+ "for i in label:\n",
+ " if i == 'Iris-setosa':\n",
+ " colors.append(-1)\n",
+ " elif i == 'Iris-versicolor':\n",
+ " colors.append(1)\n",
+ " else:\n",
+ " colors.append(2)\n",
+ "\n",
+ "dataset = np.vstack((np.asarray(sepal_length), np.asarray(sepal_width)))\n",
+ "dataset = dataset.T\n",
+ "\n",
+ "plt.scatter(sepal_length, sepal_width, c=colors)\n",
+ "plt.show()\n",
+ "print(len(colors))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Questions**: Is the data linearly separable? How many data points do we have? How many of each class?\n",
+ "\n",
+ "**Put your answers in the cell below**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Yes it is! There are exactly 100 data points."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Building the perceptron model\n",
+ "\n",
+ "Now that we have some data to work with, we want to start building a model, Part 3 outlines how to use the perceptron model to fit the data and properly update weights. Your job is to create a Python `perceptron` class that matches the following specifications:\n",
+ "\n",
+ "> \n",
+ "* Define the perceptron class with an `__init__` method\n",
+ " - The class should be initialized with the following attributes:\n",
+ " - user defined input value for `eta`, the learning rate for the perceptron.\n",
+ " - a number of interations to be used by the model, `n_iter`. This should also be an input parameter.\n",
+ " - an initial values for the bias (you can choose whether or not the user can set this values or if you want a standard default).\n",
+ "* Create two methods for the perceptron clas, a `fit` method that does the learning and a `predict` method that outputs the predicted class\n",
+ " - The `fit` method should:\n",
+ " - define an array of weights the same length as the input vector. You can choose how to initialize the weight values.\n",
+ " - go through a set number of iterations (based on `n_iter`) where it makes predictions and updates the weights vector accordingly for each subsequent round of predictions.\n",
+ " - The `predict` method should:\n",
+ " - take in a feature vector and return the predicted class based on the current weights\n",
+ " - *hint*: The prediction is just a dot product of the weights and the features plus the bias term. The resulting ouput should be in the range (-1,1), depending on the value of the dot product. If the prediction is less than 0 it should return -1, otherwise it should return 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "class perceptron():\n",
+ " def __init__(self, eta, n_iter, bias=0):\n",
+ " self.eta = eta\n",
+ " self.n_iter = n_iter\n",
+ " self.bias = bias\n",
+ " \n",
+ " def fit(self, data):\n",
+ " '''does the learning'''\n",
+ " self.weights = np.zeros(data.shape[1])\n",
+ " \n",
+ " for i in range(self.n_iter):\n",
+ " for j in range(len(self.weights)):\n",
+ " change = 0\n",
+ " for k in range(len(data)):\n",
+ " target = float(colors[k])\n",
+ " predicted = self.predict(data[k])\n",
+ " print(predicted)\n",
+ " ss = self.eta * (target - predicted)\n",
+ " change += ss*data[k][j]\n",
+ " self.bias += ss\n",
+ " self.weights[j] += change\n",
+ " \n",
+ " \n",
+ " def predict(self, values):\n",
+ " '''outputs the predicted class'''\n",
+ " prediction = np.dot(self.weights, values) + self.bias\n",
+ " if prediction >= 0:\n",
+ " return 1.0\n",
+ " else:\n",
+ " return -1.0\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([ 57.44, -92.64])"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "A = perceptron(0.1,100)\n",
+ "A.fit(dataset[0:80])\n",
+ "A.weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n",
+ "true\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(20):\n",
+ " a = A.predict(dataset[i+80])\n",
+ " b = colors[i+80]\n",
+ " if int(a) == b:\n",
+ " print(\"true\")\n",
+ " else:\n",
+ " print(\"false\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Testing the new perceptron class\n",
+ "\n",
+ "Now that we have a classifier built, we need to test it on data, but first we need to make sure our data is in the right format so we can properly train a classifier on it. This means, if you haven't already, that you need to make sure that you classes are either -1 or 1. You'll also need to make sure the features vectors can be fed into your perceptron class correctly.\n",
+ "\n",
+ "Also, remember, for a good model, we want the training data to have an even sample of both classes so it knows what to look for and doesn't end up biased towards a particular classifications.\n",
+ "\n",
+ "As a rule of thumb, you should use ~75% of your sample data as your training data and reserve the remaining ~25% as testing data.\n",
+ "\n",
+ "**Using your new perceptron class, train the model using your training and then test the results on your testing data.** You may want to come up with a method for computing how many predictions are right versus wrong."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# First, make sure you data is in the right format to be fed into your perceptron class and split your data into a training set and a testing set\n",
+ "\n",
+ "\n",
+ "# Then, train your model using your `fit` method.\n",
+ "\n",
+ "\n",
+ "# Finally, test your trained model on the testing data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 6. Plotting the decision boundary \n",
+ "\n",
+ "Finally, to better understand our classifier, it might help to plot what is known as the \"decision boundary\". The decision boundary is the line that seperates the classes in the classifier. The line is defined by the weights and the bias term that we calculated for our model.\n",
+ "\n",
+ "The slope of the decision boundary is defined as:\n",
+ "\n",
+ "$$ m = -\\frac{w_1}{w_2} $$\n",
+ "\n",
+ "And the $y$-intercept, $b$, is defined as:\n",
+ "\n",
+ "$$ b = -\\frac{B}{w_2} $$\n",
+ "\n",
+ "You should be able to generate a set of evenly spaced $x$-axis values and then used the equation for a line ($y = mx + b$) to compute the decision boundary for making a plot of the line. You should get something that looks like this:\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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cFWXKtlU7eOriV3A5XLnKbeE2fjzyGeFRxvRRwuY9PNT7WT9bp9Vu4bsDnxJd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\nuWHHp/nvL2u8uTuXEbb/Luj4EQRvAz3QtxF790q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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Compute the decision boundary and make a plot of it, along with the data\n",
+ "m = -A.weights[0]/A.weights[1]\n",
+ "b = -A.bias/A.weights[1]\n",
+ "\n",
+ "def line(x,m,b):\n",
+ " return m*x + b\n",
+ "x = np.linspace(4,7,num=50)\n",
+ "plt.plot(x,line(x,m,b))\n",
+ "plt.scatter(sepal_length, sepal_width, c=colors)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "----\n",
+ "# Assignment Wrap-up\n",
+ "\n",
+ "Fill out the following Google Form before submitting your assignment to D2L!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML(\n",
+ "\"\"\"\n",
+ "\n",
+ "\"\"\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "-----\n",
+ "### Congratulations, we're done!\n",
+ "\n",
+ "Now, you just need to submit this assignment by uploading it to the course Desire2Learn web page for today's dropbox (Don't forget to add your names in the first cell).\n"
+ ]
+ }
+ ],
+ "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": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}