Put it all together

Chapter 6/4. Backpropagation in Code.ipynb

@@ -124,7 +124,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },

Chapter 6/6. Put it all together.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 4)\n",
+      "(4, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Input data \n",
+    "import numpy as np\n",
+    "np.random.seed(1)\n",
+    "streetlights=np.array([[1,0,1],\n",
+    "                      [0,1,1],\n",
+    "                      [0,0,1],\n",
+    "                      [1,1,1]])\n",
+    "\n",
+    "walk_vs_stop=np.array([[1,1,0,0]]).T\n",
+    "\n",
+    "alpha=0.2\n",
+    "hidden_size=4\n",
+    "\n",
+    "weights_0_1=2*np.random.random((3,hidden_size))-1\n",
+    "weights_1_2=2*np.random.random((hidden_size,1))-1\n",
+    "print weights_0_1.shape\n",
+    "print weights_1_2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def relu(x):\n",
+    "    return (x>0)\n",
+    "\n",
+    "def relu2deriv(output):\n",
+    "    return output>0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: 1.31255512793\n",
+      "Error: 0.857463601866\n",
+      "Error: 0.652266842474\n",
+      "Error: 0.203997142254\n",
+      "Error: 0.199295859099\n",
+      "Error: 0.0533385633385\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "\n",
+    "for iteration in xrange(60):\n",
+    "    layer_2_error=0\n",
+    "    for i in xrange(len(streetlights)):\n",
+    "        layer_0=streetlights[i:i+1]\n",
+    "        layer_1=relu(layer_0.dot(weights_0_1))\n",
+    "        layer_2=layer_1.dot(weights_1_2)\n",
+    "        \n",
+    "        layer_2_error+=np.sum((layer_2-walk_vs_stop[i:i+1])**2)\n",
+    "        layer_2_delta=layer_2-walk_vs_stop[i:i+1]\n",
+    "        layer_1_delta=(layer_2_delta.dot(weights_1_2.T))*relu2deriv(layer_1)\n",
+    "        \n",
+    "        weights_1_2-=alpha*layer_1.T.dot(layer_2_delta)\n",
+    "        weights_0_1-=alpha*layer_0.T.dot(layer_1_delta)\n",
+    "        \n",
+    "    if (iteration%10==9):\n",
+    "        print 'Error: '+str(layer_2_error)\n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}