mhjensen
85 lines
2.0 KiB
Plaintext
85 lines
2.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Output after training: [[ 6.55109972e-03 9.93684857e-01 9.93925710e-01 6.62304973e-03]\n",
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" [ 1.71082162e-03 9.97516440e-01 9.97766376e-01 1.82685927e-03]\n",
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" [ 2.05800960e-03 9.98268211e-01 9.97548919e-01 1.77362990e-03]\n",
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" [ 5.35659849e-04 9.99320839e-01 9.99100767e-01 4.87503198e-04]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"#sigmoid\n",
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"def nonlin(x, deriv=False):\n",
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" if (deriv==True):\n",
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" return x*(1-x)\n",
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" return 1/(1+np.exp(-x))\n",
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"\n",
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"#input data\n",
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"x=np.array([[0,0,1],[0,1,1],[1,0,1],[1,1,1]])\n",
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"\n",
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"#output data\n",
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"y=np.array([0,1,1,0]).T\n",
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"\n",
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"#seed random numbers to make calculation\n",
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"np.random.seed(1)\n",
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"\n",
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"#initialize weights with mean=0\n",
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"syn0=2*np.random.random((3,4))-1\n",
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"\n",
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"for iter in range(10000):\n",
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" #forward propogation\n",
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" l0=x\n",
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" l1=nonlin(np.dot(l0,syn0))\n",
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" l1_error=y-l1\n",
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" #multiply error by slope of sigmoid at values of l1\n",
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" l1_delta=l1_error*nonlin(l1,True)\n",
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" #update weights\n",
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" syn0+=np.dot(l0.T, l1_delta)\n",
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" \n",
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"print(\"Output after training: \",l1 )"
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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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