NeuralNet from scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1.0/(1+ np.exp(-x))\n",
    "\n",
    "def sigmoid_derivative(x):\n",
    "    return x * (1.0 - x)\n",
    "\n",
    "class NeuralNetwork:\n",
    "    def __init__(self, x, y):\n",
    "        self.input      = x\n",
    "        self.weights1   = np.random.rand(self.input.shape[1],4) \n",
    "        self.weights2   = np.random.rand(4,1)                 \n",
    "        self.y          = y\n",
    "        self.output     = np.zeros(self.y.shape)\n",
    "       \n",
    "\n",
    "    def feedforward(self):\n",
    "        \n",
    "        self.layer1 = sigmoid(np.dot(self.input, self.weights1))\n",
    "        self.output = sigmoid(np.dot(self.layer1, self.weights2))\n",
    "        return self.output\n",
    "\n",
    "    def backprop(self):\n",
    "        # application of the chain rule to find derivative of the loss function with respect to weights2 and weights1\n",
    "        d_weights2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivative(self.output)))\n",
    "        d_weights1 = np.dot(self.input.T,  (np.dot(2*(self.y - self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))\n",
    "\n",
    "        # update the weights with the derivative (slope) of the loss function\n",
    "        self.weights1 += d_weights1\n",
    "        self.weights2 += d_weights2\n",
    "\n",
    "    def train(self):\n",
    "        o = self.feedforward()\n",
    "        self.backprop()\n",
    "        \n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    X = np.array([[0,0,1],\n",
    "                  [0,1,1],\n",
    "                  [1,0,1],\n",
    "                  [1,1,1]])\n",
    "    y = np.array([[0],[1],[1],[0]])\n",
    "    nn = NeuralNetwork(X,y)\n",
    "    for i in range(1500):\n",
    "        nn.feedforward()\n",
    "        nn.backprop()\n",
    "\n",
    " \n",
    "  \n",
    "\n",
    "    lossHistory = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1500):\n",
    "    nn.feedforward()\n",
    "    nn.backprop()\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00618173]\n",
      " [0.9810478 ]\n",
      " [0.98082453]\n",
      " [0.02361805]]\n"
     ]
    }
   ],
   "source": [
    "    print(nn.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1]\n",
      " [0 1 1]\n",
      " [1 0 1]\n",
      " [1 1 1]]\n",
      "[[0]\n",
      " [1]\n",
      " [1]\n",
      " [0]]\n"
     ]
    }
   ],
   "source": [
    "    print(X)\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "    X = np.array([[0,0,1],\n",
    "                  [0,1,1],\n",
    "                  [1,0,1],\n",
    "                  [1,1,1]])\n",
    "    y = np.array([[0],[1],[1],[0]])\n",
    "    nn = NeuralNetwork(X,y)\n",
    "    lossHistory = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "Loss: \n",
      "0.00038449581761024226\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00038415249430679115\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00038380975669621885\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003834676033125406\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000383126032694614\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003827850433860906\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003824446339354058\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003821048028957656\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00038176554882512624\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003814268702861637\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00038108876584626306\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000380751234077501\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00038041427355662823\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003800778828650338\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003797420605887488\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003794068053184212\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000379072115649283\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003787379901811505\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00037840442751839237\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0003780714262699235\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00037773898504917506\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000377407102474082\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lossHistory = []\n",
    "for i in xrange(1500): # trains the NN 1,000 times\n",
    "  loss = np.mean(np.square(y - nn.feedforward())) \n",
    "  lossHistory.append(loss)\n",
    "  print \"Loss: \\n\" + str(loss) # mean sum squared loss\n",
    "  print \"\\n\"\n",
    "  nn.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000377407102474082\n"
     ]
    }
   ],
   "source": [
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.00044540254091477853, 0.0004449479655531006, 0.00044449427423576856, 0.00044404146444390906, 0.00044358953366807754, 0.00044313847940820805, 0.0004426882991735775, 0.0004422389904827598, 0.00044179055086358496, 0.0004413429778530845, 0.0004408962689974715, 0.0004404504218520782, 0.0004400054339813251, 0.0004395613029586694, 0.0004391180263665761, 0.0004386756017964689, 0.0004382340268486919, 0.0004377932991324599, 0.0004373534162658361, 0.00043691437587567504, 0.0004364761755975828, 0.0004360388130758931, 0.0004356022859636129, 0.00043516659192238325, 0.00043473172862244964, 0.00043429769374261126, 0.0004338644849701898, 0.00043343210000098935, 0.0004330005365392625, 0.00043256979229765516, 0.0004321398649971921, 0.00043171075236721516, 0.0004312824521453707, 0.000430854962077548, 0.00043042827991786185, 0.00043000240342859925, 0.0004295773303801964, 0.00042915305855118857, 0.00042872958572819065, 0.00042830690970584097, 0.0004278850282867879, 0.000427463939281624, 0.0004270436405088817, 0.00042662412979498207, 0.0004262054049741943, 0.00042578746388861676, 0.00042537030438812365, 0.0004249539243303457, 0.000424538321580629, 0.00042412349401200317, 0.0004237094395051328, 0.00042329615594831647, 0.0004228836412374133, 0.00042247189327583904, 0.00042206090997451526, 0.0004216506892518511, 0.00042124122903369017, 0.00042083252725330064, 0.0004204245818513167, 0.00042001739077573817, 0.000419610951981867, 0.00041920526343229174, 0.00041880032309684836, 0.0004183961289526042, 0.00041799267898379384, 0.00041758997118182626, 0.0004171880035452211, 0.0004167867740795993, 0.00041638628079764553, 0.0004159865217190622, 0.0004155874948705583, 0.0004151891982858247, 0.00041479163000547413, 0.0004143947880770358, 0.0004139986705549158, 0.00041360327550037015, 0.0004132086009814758, 0.00041281464507309114, 0.00041242140585684385, 0.0004120288814210841, 0.00041163706986086803, 0.00041124596927792005, 0.0004108555777806137, 0.0004104658934839288, 0.00041007691450943947, 0.00040968863898526873, 0.00040930106504607306, 0.0004089141908330103, 0.0004085280144937103, 0.0004081425341822466, 0.0004077577480591089, 0.0004073736542911812, 0.00040699025105170794, 0.00040660753652026107, 0.0004062255088827302, 0.0004058441663312808, 0.0004054635070643369, 0.00040508352928653505, 0.0004047042312087335, 0.0004043256110479453, 0.0004039476670273393, 0.0004035703973762068, 0.0004031938003299287, 0.00040281787412996473, 0.0004024426170238086, 0.00040206802726497294, 0.00040169410311296746, 0.00040132084283326167, 0.0004009482446972753, 0.00040057630698233526, 0.00040020502797166345, 0.000399834405954349, 0.0003994644392253205, 0.0003990951260853259, 0.00039872646484090065, 0.0003983584538043505, 0.00039799109129372745, 0.00039762437563279946, 0.00039725830515102814, 0.00039689287818355103, 0.0003965280930711473, 0.0003961639481602288, 0.0003958004418027972, 0.000395437572356436, 0.00039507533818428173, 0.0003947137376549986, 0.00039435276914276266, 0.00039399243102723064, 0.000393632721693521, 0.00039327363953219, 0.00039291518293920965, 0.00039255735031594355, 0.0003922001400691292, 0.0003918435506108528, 0.00039148758035852143, 0.00039113222773485114, 0.0003907774911678383, 0.0003904233690907374, 0.00039006985994204525, 0.0003897169621654758, 0.00038936467420993116, 0.00038901299452949196, 0.0003886619215833925, 0.00038831145383599744, 0.00038796158975678186, 0.00038761232782030027, 0.00038726366650619127, 0.00038691560429912543, 0.0003865681396888129, 0.00038622127116995906, 0.00038587499724225706, 0.00038552931641037415, 0.0003851842271839059, 0.00038483972807738635, 0.00038449581761024226, 0.00038415249430679115, 0.00038380975669621885, 0.0003834676033125406, 0.000383126032694614, 0.0003827850433860906, 0.0003824446339354058, 0.0003821048028957656, 0.00038176554882512624, 0.0003814268702861637, 0.00038108876584626306, 0.000380751234077501, 0.00038041427355662823, 0.0003800778828650338, 0.0003797420605887488, 0.0003794068053184212, 0.000379072115649283, 0.0003787379901811505, 0.00037840442751839237, 0.0003780714262699235, 0.00037773898504917506, 0.000377407102474082]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print(lossHistory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(np.arange(0, 1500), lossHistory)\n",
    "fig.suptitle(\"Training Loss\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "keras_tf",
   "language": "python",
   "name": "keras_tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}