mhjensen
174 lines
7.6 KiB
Plaintext
174 lines
7.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "train() missing 1 required positional argument: 'testY'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-5-e816aa3fc208>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0mtrainX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestY\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miris\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miris\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0miris_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m: train() missing 1 required positional argument: 'testY'"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.datasets import load_iris\n",
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"iris=load_iris()\n",
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"\n",
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"from scipy import optimize\n",
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"\n",
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"class Neural_Network(object):\n",
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" def __init__(self, Lambda=0): \n",
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" #Define Hyperparameters\n",
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" self.inputLayerSize = 2\n",
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" self.outputLayerSize = 1\n",
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" self.hiddenLayerSize = 3\n",
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" \n",
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" #Weights (parameters)\n",
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" self.W1 = np.random.randn(self.inputLayerSize,self.hiddenLayerSize)\n",
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" self.W2 = np.random.randn(self.hiddenLayerSize,self.outputLayerSize)\n",
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" \n",
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" #Regularization Parameter:\n",
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" self.Lambda = Lambda\n",
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" \n",
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" def forward(self, X):\n",
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" #Propogate inputs though network\n",
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" self.z2 = np.dot(X, self.W1)\n",
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" self.a2 = self.sigmoid(self.z2)\n",
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" self.z3 = np.dot(self.a2, self.W2)\n",
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" yHat = self.sigmoid(self.z3) \n",
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" return yHat\n",
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" \n",
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" def sigmoid(self, z):\n",
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" #Apply sigmoid activation function to scalar, vector, or matrix\n",
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" return 1/(1+np.exp(-z))\n",
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" \n",
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" def sigmoidPrime(self,z):\n",
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" #Gradient of sigmoid\n",
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" return np.exp(-z)/((1+np.exp(-z))**2)\n",
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" \n",
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" def costFunction(self, X, y):\n",
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" #Compute cost for given X,y, use weights already stored in class.\n",
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" self.yHat = self.forward(X)\n",
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" J = 0.5*sum((y-self.yHat)**2)/X.shape[0] + (self.Lambda/2)*(np.sum(self.W1**2)+np.sum(self.W2**2))\n",
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" return J\n",
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" \n",
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" def costFunctionPrime(self, X, y):\n",
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" #Compute derivative with respect to W and W2 for a given X and y:\n",
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" self.yHat = self.forward(X)\n",
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" \n",
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" delta3 = np.multiply(-(y-self.yHat), self.sigmoidPrime(self.z3))\n",
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" #Add gradient of regularization term:\n",
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" dJdW2 = np.dot(self.a2.T, delta3)/X.shape[0] + self.Lambda*self.W2\n",
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" \n",
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" delta2 = np.dot(delta3, self.W2.T)*self.sigmoidPrime(self.z2)\n",
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" #Add gradient of regularization term:\n",
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" dJdW1 = np.dot(X.T, delta2)/X.shape[0] + self.Lambda*self.W1\n",
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" \n",
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" return dJdW1, dJdW2\n",
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" \n",
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" #Helper functions for interacting with other methods/classes\n",
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" def getParams(self):\n",
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" #Get W1 and W2 Rolled into vector:\n",
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" params = np.concatenate((self.W1.ravel(), self.W2.ravel()))\n",
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" return params\n",
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" \n",
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" def setParams(self, params):\n",
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" #Set W1 and W2 using single parameter vector:\n",
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" W1_start = 0\n",
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" W1_end = self.hiddenLayerSize*self.inputLayerSize\n",
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" self.W1 = np.reshape(params[W1_start:W1_end], \\\n",
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" (self.inputLayerSize, self.hiddenLayerSize))\n",
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" W2_end = W1_end + self.hiddenLayerSize*self.outputLayerSize\n",
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" self.W2 = np.reshape(params[W1_end:W2_end], \\\n",
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" (self.hiddenLayerSize, self.outputLayerSize))\n",
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" \n",
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" def computeGradients(self, X, y):\n",
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" dJdW1, dJdW2 = self.costFunctionPrime(X, y)\n",
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" return np.concatenate((dJdW1.ravel(), dJdW2.ravel()))\n",
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" \n",
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" \n",
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"class trainer(object):\n",
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" def __init__(self, N):\n",
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" #Make Local reference to network:\n",
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" self.N = N\n",
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" \n",
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" def callbackF(self, params):\n",
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" self.N.setParams(params)\n",
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" self.J.append(self.N.costFunction(self.X, self.y))\n",
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" self.testJ.append(self.N.costFunction(self.testX, self.testY))\n",
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" \n",
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" def costFunctionWrapper(self, params, X, y):\n",
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" self.N.setParams(params)\n",
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" cost = self.N.costFunction(X, y)\n",
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" grad = self.N.computeGradients(X,y)\n",
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" return cost, grad\n",
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" \n",
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" def train(self, trainX, trainY, testX, testY):\n",
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" #Make an internal variable for the callback function:\n",
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" self.X = trainX\n",
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" self.y = trainY\n",
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" \n",
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" self.testX = testX\n",
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" self.testY = testY\n",
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"\n",
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" #Make empty list to store training costs:\n",
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" self.J = []\n",
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" self.testJ = []\n",
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" \n",
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" params0 = self.N.getParams()\n",
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"\n",
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" options = {'maxiter': 200, 'disp' : True}\n",
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" _res = optimize.minimize(self.costFunctionWrapper, params0, jac=True, method='BFGS', \\\n",
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" args=(trainX, trainY), options=options, callback=self.callbackF)\n",
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"\n",
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" self.N.setParams(_res.x)\n",
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" self.optimizationResults = _res\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"trainX, testX, trainY, testY=train_test_split(iris['data'], iris['target'], random_state=0)\n",
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"\n",
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"iris_train=trainer.train(trainX, trainY, testX, testY)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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