NeuralNet - loss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Output: \n",
      "[[0.44546438]\n",
      " [0.44420323]\n",
      " [0.42966785]]\n",
      "Actual Output: \n",
      "[[0.92]\n",
      " [0.86]\n",
      " [0.89]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# X = (hours sleeping, hours studying), y = score on test\n",
    "X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)\n",
    "y = np.array(([92], [86], [89]), dtype=float)\n",
    "\n",
    "# scale units\n",
    "X = X/np.amax(X, axis=0) # maximum of X array\n",
    "y = y/100 # max test score is 100\n",
    "\n",
    "class Neural_Network(object):\n",
    "  def __init__(self):\n",
    "    #parameters\n",
    "    self.inputSize = 2\n",
    "    self.outputSize = 1\n",
    "    self.hiddenSize = 3\n",
    "\n",
    "    #weights\n",
    "    self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # (3x2) weight matrix from input to hidden layer\n",
    "    self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # (3x1) weight matrix from hidden to output layer\n",
    "\n",
    "  def forward(self, X):\n",
    "    #forward propagation through our network\n",
    "    self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights\n",
    "    self.z2 = self.sigmoid(self.z) # activation function\n",
    "    self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights\n",
    "    o = self.sigmoid(self.z3) # final activation function\n",
    "    return o \n",
    "\n",
    "  def sigmoid(self, s):\n",
    "    # activation function \n",
    "    return 1/(1+np.exp(-s))\n",
    "\n",
    "NN = Neural_Network()\n",
    "\n",
    "#defining our output \n",
    "o = NN.forward(X)\n",
    "\n",
    "print \"Predicted Output: \\n\" + str(o) \n",
    "print \"Actual Output: \\n\" + str(y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: \n",
      "[[0.66666667 1.        ]\n",
      " [0.33333333 0.55555556]\n",
      " [1.         0.66666667]]\n",
      "Actual Output: \n",
      "[[0.92]\n",
      " [0.86]\n",
      " [0.89]]\n",
      "Predicted Output: \n",
      "[[0.61627345]\n",
      " [0.6171179 ]\n",
      " [0.66033032]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# X = (hours sleeping, hours studying), y = score on test\n",
    "X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)\n",
    "y = np.array(([92], [86], [89]), dtype=float)\n",
    "\n",
    "# scale units\n",
    "X = X/np.amax(X, axis=0) # maximum of X array\n",
    "y = y/100 # max test score is 100\n",
    "\n",
    "class Neural_Network(object):\n",
    "  def __init__(self):\n",
    "    #parameters\n",
    "    self.inputSize = 2\n",
    "    self.outputSize = 1\n",
    "    self.hiddenSize = 3\n",
    "\n",
    "    #weights\n",
    "    self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # (3x2) weight matrix from input to hidden layer\n",
    "    self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # (3x1) weight matrix from hidden to output layer\n",
    "\n",
    "  def forward(self, X):\n",
    "    #forward propagation through our network\n",
    "    self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights\n",
    "    self.z2 = self.sigmoid(self.z) # activation function\n",
    "    self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights\n",
    "    o = self.sigmoid(self.z3) # final activation function\n",
    "    return o \n",
    "\n",
    "  def sigmoid(self, s):\n",
    "    # activation function \n",
    "    return 1/(1+np.exp(-s))\n",
    "\n",
    "  def sigmoidPrime(self, s):\n",
    "    #derivative of sigmoid\n",
    "    return s * (1 - s)\n",
    "\n",
    "  def backward(self, X, y, o):\n",
    "    # backward propgate through the network\n",
    "    self.o_error = y - o # error in output\n",
    "    self.o_delta = self.o_error*self.sigmoidPrime(o) # applying derivative of sigmoid to error\n",
    "\n",
    "    self.z2_error = self.o_delta.dot(self.W2.T) # z2 error: how much our hidden layer weights contributed to output error\n",
    "    self.z2_delta = self.z2_error*self.sigmoidPrime(self.z2) # applying derivative of sigmoid to z2 error\n",
    "\n",
    "    self.W1 += X.T.dot(self.z2_delta) # adjusting first set (input --> hidden) weights\n",
    "    self.W2 += self.z2.T.dot(self.o_delta) # adjusting second set (hidden --> output) weights\n",
    "\n",
    "  def train (self, X, y):\n",
    "    o = self.forward(X)\n",
    "    self.backward(X, y, o)\n",
    "    \n",
    "lossHistory = []\n",
    "NN = Neural_Network()\n",
    "print \"Input: \\n\" + str(X) \n",
    "print \"Actual Output: \\n\" + str(y)  \n",
    "print \"Predicted Output: \\n\" + str(NN.forward(X)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "0.0004743799578786413\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00047411720182556773\n",
      "\n",
      "\n",
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      "0.0004738546792437981\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00047359238985018984\n",
      "\n",
      "\n",
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      "0.0004733303333619744\n",
      "\n",
      "\n",
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      "0.0004730685094967979\n",
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      "\n",
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      "0.00047280691797268434\n",
      "\n",
      "\n",
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      "0.0004725455585080529\n",
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      "0.000472284430821715\n",
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      "\n",
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      "0.00047202353463286876\n",
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      "\n",
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      "0.00047176286966109956\n",
      "\n",
      "\n",
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      "0.000471502435626394\n",
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      "\n",
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      "0.000471242232249119\n",
      "\n",
      "\n",
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      "\n",
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      "0.00047046300327134523\n",
      "\n",
      "\n",
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      "0.00047020371973520566\n",
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      "\n",
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      "0.0004699446654641358\n",
      "\n",
      "\n",
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      "0.0004696858401808227\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004694272436083406\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "0.00046891073549006735\n",
      "\n",
      "\n",
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      "0.00046865282339233127\n",
      "\n",
      "\n",
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      "0.00046839513890154123\n",
      "\n",
      "\n",
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      "0.0004681376817426835\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046788045164111437\n",
      "\n",
      "\n",
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      "0.0004676234483225892\n",
      "\n",
      "\n",
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      "\n",
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      "0.00046711012093954827\n",
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      "\n",
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      "0.0004668537963284214\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046659769740710913\n",
      "\n",
      "\n",
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      "0.00046634182390325714\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "0.0004658307520603736\n",
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      "\n",
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      "0.00046557555317850124\n",
      "\n",
      "\n",
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      "0.0004653205786284182\n",
      "\n",
      "\n",
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      "0.000465065828139632\n",
      "\n",
      "\n",
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      "0.00046481130144205305\n",
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      "\n",
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      "0.0004645569982659337\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004643029183419166\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004640490614010251\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046379542717463895\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046354201539451845\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046328882579278963\n",
      "\n",
      "\n",
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      "0.0004630358581019488\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046278311205487\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00046253058738479207\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004622782838253128\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004620262011104141\n",
      "\n",
      "\n",
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      "0.0004617743389744396\n",
      "\n",
      "\n",
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      "0.00046152269715209627\n",
      "\n",
      "\n",
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      "0.0004612712753784642\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004610200733889792\n",
      "\n",
      "\n",
      "Loss: \n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Loss: \n",
      "0.0004602677834853449\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004600174579941901\n",
      "\n",
      "\n",
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      "0.0004597673509698763\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045951746215002354\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045926779127262963\n",
      "\n",
      "\n",
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      "0.00045901833807603776\n",
      "\n",
      "\n",
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      "0.0004587691022989587\n",
      "\n",
      "\n",
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      "0.0004585200836804743\n",
      "\n",
      "\n",
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      "0.00045827128196001084\n",
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      "\n",
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      "0.00045802269687736835\n",
      "\n",
      "\n",
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      "0.00045777432817269625\n",
      "\n",
      "\n",
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      "0.00045752617558649867\n",
      "\n",
      "\n",
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      "0.00045727823885965014\n",
      "\n",
      "\n",
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      "0.0004570305177333809\n",
      "\n",
      "\n",
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      "0.0004567830119492653\n",
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      "0.0004565357212492512\n",
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      "\n",
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      "0.00045628864537562323\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004560417840710424\n",
      "\n",
      "\n",
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      "0.00045579513707850783\n",
      "\n",
      "\n",
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      "0.00045554870414138\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045530248500337734\n",
      "\n",
      "\n",
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      "0.0004550564794085611\n",
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      "\n",
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      "0.0004548106871013499\n",
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      "\n",
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      "0.00045456510782651804\n",
      "\n",
      "\n",
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      "0.00045431974132918956\n",
      "\n",
      "\n",
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      "0.00045407458735484077\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004538296456492893\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045358491595871934\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004533403980296477\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004530960916089508\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004528519964438523\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045260811228192086\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045236443887107324\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045212097595958406\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004518777232960466\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004516346806294344\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004513918477090444\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004511492242845221\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004509068101058651\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045066460492339933\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00045042260848781533\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004501808205501297\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004499392408617126\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044969786917426017\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044945670523983174\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004492157488108074\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044897499963992307\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044873445748024623\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044849412208518004\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044825399320848406\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004480140706042272\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004477743540268455\n",
      "\n",
      "\n",
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      "0.00044753484323109446\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004472955379720797\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004470564380052243\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004468175430863052\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004465788529714229\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004463403674170202\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004461020861798791\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044586400901709303\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004456261356861148\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044538846594471376\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044515099955100474\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004449137362634149\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044467667584072434\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004444398180420297\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004442031626267671\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004439667093546931\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000443730457985899\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004434944082808083\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004432585600001671\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004430229129050521\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004427874667568696\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044255222131734474\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004423171763485413\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000442082331612846\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044184768687295417\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004416132418919156\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004413789964330794\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004411449502601324\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004409111031370776\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004406774548282513\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044044400509829815\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00044021075371219776\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004399777004352459\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004397448450330611\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004395121872715777\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004392797269170572\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004390474637360784\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004388153974955417\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043858352796265904\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043835185490496915\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004381203780903188\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004378890972868885\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004376580122631577\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043742712278793455\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043719642863034096\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004369659295598083\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043673562534608687\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004365055157592508\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000436275600569672\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000436045879548042\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004358163524653765\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004355870190929843\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043535787920250495\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043512893256588206\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043490017895537314\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004346716181435352\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043444324990325143\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043421507400770795\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004339870902304018\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043375929834513823\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043353169812602603\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004333042893474983\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004330770717842756\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004328500452113995\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004326232094042119\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004323965641383645\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.000432170109189818\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004319438443348312\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004317177693499677\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043149188401210705\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043126618809841486\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043104068138638107\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004308153636537843\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00043059023467870947\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004303652942395445\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004301405421149801\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042991597808400664\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042969160192591623\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004294674134203041\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042924341234706594\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042901959848638203\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042879597161876135\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042857253152498\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042834927798613234\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.00042812621078361146\n",
      "\n",
      "\n",
      "Loss: \n",
      "0.0004279033296990923\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in xrange(1000): # trains the NN 1,000 times\n",
    "  loss = np.mean(np.square(y - NN.forward(X))) \n",
    "  lossHistory.append(loss)\n",
    "  print \"Loss: \\n\" + str(loss) # mean sum squared loss\n",
    "  print \"\\n\"\n",
    "  NN.train(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0004279033296990923\n"
     ]
    }
   ],
   "source": [
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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0.00044044400509829815, 0.00044021075371219776, 0.0004399777004352459, 0.0004397448450330611, 0.0004395121872715777, 0.0004392797269170572, 0.0004390474637360784, 0.0004388153974955417, 0.00043858352796265904, 0.00043835185490496915, 0.0004381203780903188, 0.0004378890972868885, 0.0004376580122631577, 0.00043742712278793455, 0.00043719642863034096, 0.0004369659295598083, 0.00043673562534608687, 0.0004365055157592508, 0.000436275600569672, 0.000436045879548042, 0.0004358163524653765, 0.0004355870190929843, 0.00043535787920250495, 0.00043512893256588206, 0.00043490017895537314, 0.0004346716181435352, 0.00043444324990325143, 0.00043421507400770795, 0.0004339870902304018, 0.00043375929834513823, 0.00043353169812602603, 0.0004333042893474983, 0.0004330770717842756, 0.0004328500452113995, 0.0004326232094042119, 0.0004323965641383645, 0.000432170109189818, 0.0004319438443348312, 0.0004317177693499677, 0.00043149188401210705, 0.00043126618809841486, 0.00043104068138638107, 0.0004308153636537843, 0.00043059023467870947, 0.0004303652942395445, 0.0004301405421149801, 0.00042991597808400664, 0.00042969160192591623, 0.0004294674134203041, 0.00042924341234706594, 0.00042901959848638203, 0.00042879597161876135, 0.00042857253152498, 0.00042834927798613234, 0.00042812621078361146, 0.0004279033296990923]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print(lossHistory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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, 1000), lossHistory)\n",
    "fig.suptitle(\"Training Loss\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}