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Chapter 5/6. Gradient Descent with Multiple Inputs & Outputs.ipynb
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|---|---|---|
| @@ -0,0 +1,136 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 215, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Create an empty network with multiple inputs & outputs\n", | ||
| "def nerual_network(inputs,weights):\n", | ||
| " pred=mat_mul(inputs,weights) # Matrix multiplication\n", | ||
| " return pred\n", | ||
| "\n", | ||
| "def mat_mul(inputs,weights):\n", | ||
| " assert len(inputs)==len(weights)\n", | ||
| " output=[]\n", | ||
| " for index,weight in enumerate(weights):\n", | ||
| " output.append(w_sum(inputs,weight))\n", | ||
| " return output\n", | ||
| "\n", | ||
| "def w_sum(inputs,weights):\n", | ||
| " sums=0\n", | ||
| " for i,v in enumerate(inputs):\n", | ||
| " sums+=inputs[i]*weights[i]\n", | ||
| " return sums\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 216, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Data information\n", | ||
| "\n", | ||
| "toes=[8.5,9.5,9.9,9.0]\n", | ||
| "wlrec=[0.65,0.8,0.8,0.9]\n", | ||
| "nfans=[1.2,1.3,0.5,1.0]\n", | ||
| "\n", | ||
| "hurt=[0.1,0.0,0.0,0.1]\n", | ||
| "win=[1,1,0,1]\n", | ||
| "sad=[0.1,0.0,0.1,0.2]\n", | ||
| "\n", | ||
| "inputs=[toes[0],wlrec[0],nfans[0]]\n", | ||
| "true=[hurt[0],win[0],sad[0]]\n", | ||
| "\n", | ||
| "weights=[[0.1,0.1,-0.3],[0.1,0.2,0.0],[0.0,1.3,0.1]]\n", | ||
| "alpha=0.01" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 217, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Prediction:[ 0.555 0.98 0.965]\n", | ||
| "Error:[ 2.07025000e-01 4.00000000e-04 7.48225000e-01]\n", | ||
| "Delta:[ 0.455 -0.02 0.865]\n", | ||
| "Weights_Delta: [[ 3.8675 -0.17 7.3525 ]\n", | ||
| " [ 0.29575 -0.013 0.56225]\n", | ||
| " [ 0.546 -0.024 1.038 ]]\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Run the model and make the prediciton\n", | ||
| "import numpy as np\n", | ||
| "pred=nerual_network(inputs,weights)\n", | ||
| "pred=np.array(pred)\n", | ||
| "weights=np.array(weights)\n", | ||
| "inputs=np.array(inputs)\n", | ||
| "print 'Prediction:'+str(pred)\n", | ||
| "true=np.array(true)\n", | ||
| "error=(pred-true)**2\n", | ||
| "print 'Error:'+str((error))\n", | ||
| "delta=pred-true\n", | ||
| "print 'Delta:'+str(delta)\n", | ||
| "\n", | ||
| "weights_delta=np.zeros(weights.shape)\n", | ||
| "for i,x in np.ndenumerate(inputs):\n", | ||
| " for j,v in np.ndenumerate(delta):\n", | ||
| " weights_delta[i][j]=inputs[i]*delta[j]\n", | ||
| "print 'Weights_Delta: '+str(weights_delta)\n", | ||
| " \n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 218, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[[ 0.061325 0.1017 -0.373525 ]\n", | ||
| " [ 0.0970425 0.20013 -0.0056225]\n", | ||
| " [-0.00546 1.30024 0.08962 ]]\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Update the weights\n", | ||
| "weights-=alpha*weights_delta\n", | ||
| "print weights" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python [conda root]", | ||
| "language": "python", | ||
| "name": "conda-root-py" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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