lecture_2/5. Code - Non Linearity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2) (100, 2)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "circle_xs = np.load('circles_xs.npy')\n",
    "circle_one_hot = np.load('circle_one_hot.npy')\n",
    "print(circle_xs.shape, circle_one_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1167f7710>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160bc438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(circle_xs[:,0], circle_xs[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n",
      "100/100 [==============================] - 0s 4ms/step - loss: 0.7101 - acc: 0.5100\n",
      "Epoch 2/3000\n",
      "100/100 [==============================] - 0s 144us/step - loss: 0.7093 - acc: 0.5000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
      "Epoch 645/3000\n",
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     ]
    },
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     ]
    },
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     "text": [
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     ]
    },
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
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     ]
    },
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     ]
    },
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     ]
    },
    {
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     "text": [
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      "Epoch 2002/3000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 0s 149us/step - loss: 0.4604 - acc: 1.0000\n",
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     ]
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2479/3000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 2799/3000\n",
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      "Epoch 2878/3000\n"
     ]
    },
    {
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     "text": [
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      "100/100 [==============================] - 0s 134us/step - loss: 0.2872 - acc: 1.0000\n",
      "Epoch 2944/3000\n",
      "100/100 [==============================] - 0s 79us/step - loss: 0.2865 - acc: 1.0000\n",
      "Epoch 2945/3000\n",
      "100/100 [==============================] - 0s 122us/step - loss: 0.2862 - acc: 1.0000\n",
      "Epoch 2946/3000\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.2821 - acc: 1.000 - 0s 195us/step - loss: 0.2863 - acc: 1.0000\n",
      "Epoch 2947/3000\n",
      "100/100 [==============================] - 0s 114us/step - loss: 0.2865 - acc: 1.0000\n",
      "Epoch 2948/3000\n",
      "100/100 [==============================] - 0s 146us/step - loss: 0.2863 - acc: 1.0000\n",
      "Epoch 2949/3000\n",
      "100/100 [==============================] - 0s 162us/step - loss: 0.2858 - acc: 1.0000\n",
      "Epoch 2950/3000\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.2855 - acc: 1.0000\n",
      "Epoch 2951/3000\n",
      "100/100 [==============================] - 0s 191us/step - loss: 0.2850 - acc: 1.0000\n",
      "Epoch 2952/3000\n",
      "100/100 [==============================] - 0s 187us/step - loss: 0.2852 - acc: 1.0000\n",
      "Epoch 2953/3000\n",
      "100/100 [==============================] - 0s 144us/step - loss: 0.2846 - acc: 1.0000\n",
      "Epoch 2954/3000\n",
      "100/100 [==============================] - 0s 176us/step - loss: 0.2852 - acc: 1.0000\n",
      "Epoch 2955/3000\n",
      "100/100 [==============================] - 0s 139us/step - loss: 0.2847 - acc: 1.0000\n",
      "Epoch 2956/3000\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.2844 - acc: 1.0000\n",
      "Epoch 2957/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 0s 192us/step - loss: 0.2842 - acc: 1.0000\n",
      "Epoch 2958/3000\n",
      "100/100 [==============================] - 0s 145us/step - loss: 0.2839 - acc: 1.0000\n",
      "Epoch 2959/3000\n",
      "100/100 [==============================] - 0s 136us/step - loss: 0.2838 - acc: 1.0000\n",
      "Epoch 2960/3000\n",
      "100/100 [==============================] - 0s 178us/step - loss: 0.2838 - acc: 1.0000\n",
      "Epoch 2961/3000\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.2836 - acc: 1.0000\n",
      "Epoch 2962/3000\n",
      "100/100 [==============================] - 0s 214us/step - loss: 0.2834 - acc: 1.0000\n",
      "Epoch 2963/3000\n",
      "100/100 [==============================] - 0s 134us/step - loss: 0.2831 - acc: 1.0000\n",
      "Epoch 2964/3000\n",
      "100/100 [==============================] - 0s 139us/step - loss: 0.2830 - acc: 1.0000\n",
      "Epoch 2965/3000\n",
      "100/100 [==============================] - 0s 158us/step - loss: 0.2832 - acc: 1.0000\n",
      "Epoch 2966/3000\n",
      "100/100 [==============================] - 0s 158us/step - loss: 0.2828 - acc: 1.0000\n",
      "Epoch 2967/3000\n",
      "100/100 [==============================] - 0s 147us/step - loss: 0.2823 - acc: 1.0000\n",
      "Epoch 2968/3000\n",
      "100/100 [==============================] - 0s 146us/step - loss: 0.2830 - acc: 1.0000\n",
      "Epoch 2969/3000\n",
      "100/100 [==============================] - 0s 191us/step - loss: 0.2821 - acc: 1.0000\n",
      "Epoch 2970/3000\n",
      "100/100 [==============================] - 0s 103us/step - loss: 0.2822 - acc: 1.0000\n",
      "Epoch 2971/3000\n",
      "100/100 [==============================] - 0s 184us/step - loss: 0.2819 - acc: 1.0000\n",
      "Epoch 2972/3000\n",
      "100/100 [==============================] - 0s 168us/step - loss: 0.2819 - acc: 1.0000\n",
      "Epoch 2973/3000\n",
      "100/100 [==============================] - 0s 115us/step - loss: 0.2814 - acc: 1.0000\n",
      "Epoch 2974/3000\n",
      "100/100 [==============================] - 0s 118us/step - loss: 0.2815 - acc: 1.0000\n",
      "Epoch 2975/3000\n",
      "100/100 [==============================] - 0s 186us/step - loss: 0.2810 - acc: 1.0000\n",
      "Epoch 2976/3000\n",
      "100/100 [==============================] - 0s 114us/step - loss: 0.2813 - acc: 1.0000\n",
      "Epoch 2977/3000\n",
      "100/100 [==============================] - 0s 223us/step - loss: 0.2811 - acc: 1.0000\n",
      "Epoch 2978/3000\n",
      "100/100 [==============================] - 0s 100us/step - loss: 0.2809 - acc: 1.0000\n",
      "Epoch 2979/3000\n",
      "100/100 [==============================] - 0s 204us/step - loss: 0.2809 - acc: 1.0000\n",
      "Epoch 2980/3000\n",
      "100/100 [==============================] - 0s 168us/step - loss: 0.2804 - acc: 1.0000\n",
      "Epoch 2981/3000\n",
      "100/100 [==============================] - 0s 117us/step - loss: 0.2802 - acc: 1.0000\n",
      "Epoch 2982/3000\n",
      "100/100 [==============================] - 0s 188us/step - loss: 0.2801 - acc: 1.0000\n",
      "Epoch 2983/3000\n",
      "100/100 [==============================] - 0s 115us/step - loss: 0.2800 - acc: 1.0000\n",
      "Epoch 2984/3000\n",
      "100/100 [==============================] - 0s 176us/step - loss: 0.2799 - acc: 1.0000\n",
      "Epoch 2985/3000\n",
      "100/100 [==============================] - 0s 122us/step - loss: 0.2799 - acc: 1.0000\n",
      "Epoch 2986/3000\n",
      "100/100 [==============================] - 0s 154us/step - loss: 0.2795 - acc: 1.0000\n",
      "Epoch 2987/3000\n",
      "100/100 [==============================] - 0s 164us/step - loss: 0.2795 - acc: 1.0000\n",
      "Epoch 2988/3000\n",
      "100/100 [==============================] - 0s 144us/step - loss: 0.2790 - acc: 1.0000\n",
      "Epoch 2989/3000\n",
      "100/100 [==============================] - 0s 191us/step - loss: 0.2790 - acc: 1.0000\n",
      "Epoch 2990/3000\n",
      "100/100 [==============================] - 0s 125us/step - loss: 0.2789 - acc: 1.0000\n",
      "Epoch 2991/3000\n",
      "100/100 [==============================] - 0s 115us/step - loss: 0.2791 - acc: 1.0000\n",
      "Epoch 2992/3000\n",
      "100/100 [==============================] - 0s 163us/step - loss: 0.2786 - acc: 1.0000\n",
      "Epoch 2993/3000\n",
      "100/100 [==============================] - 0s 109us/step - loss: 0.2784 - acc: 1.0000\n",
      "Epoch 2994/3000\n",
      "100/100 [==============================] - 0s 172us/step - loss: 0.2784 - acc: 1.0000\n",
      "Epoch 2995/3000\n",
      "100/100 [==============================] - 0s 131us/step - loss: 0.2781 - acc: 1.0000\n",
      "Epoch 2996/3000\n",
      "100/100 [==============================] - 0s 141us/step - loss: 0.2781 - acc: 1.0000\n",
      "Epoch 2997/3000\n",
      "100/100 [==============================] - 0s 173us/step - loss: 0.2780 - acc: 1.0000\n",
      "Epoch 2998/3000\n",
      "100/100 [==============================] - 0s 126us/step - loss: 0.2787 - acc: 1.0000\n",
      "Epoch 2999/3000\n",
      "100/100 [==============================] - 0s 185us/step - loss: 0.2786 - acc: 1.0000\n",
      "Epoch 3000/3000\n",
      "100/100 [==============================] - 0s 97us/step - loss: 0.2780 - acc: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11679b320>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras # import the framework\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(10, input_dim=2))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(2))\n",
    "\n",
    "# linear separator bits\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(loss='categorical_crossentropy', # a standard measure of error for 2-category problems, used for knob tweaking\n",
    "              optimizer='sgd', # this is the knob-tuning portion. also standard.\n",
    "              metrics=['accuracy']) # this is just so we can see how good the predictions are\n",
    "\n",
    "model.fit(circle_xs, circle_one_hot, epochs=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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