Make your own neural network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plot\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy.special as sci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a= np.arange(6).reshape(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a[1,1]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10671c588>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1066bcb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.imshow(a, interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Dog:\n",
    "    def bark(self):\n",
    "        print(\"Wang, wang\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wang, wang\n"
     ]
    }
   ],
   "source": [
    "d= Dog()\n",
    "d.bark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing neuralNetwork.py\n"
     ]
    }
   ],
   "source": [
    "%%file neuralNetwork.py\n",
    "class neuralNetwork:\n",
    "    def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):\n",
    "        self.inodes=inputnodes\n",
    "        self.hnodes=hiddennodes\n",
    "        self.onodes=outputnodes\n",
    "        self.lr=learningrate\n",
    "        self.wih= np.random.rand(self.hnodes,self.inodes)-0.5\n",
    "        self.who= np.random.rand(self.onodes, self.hnodes)-0.5\n",
    "        \n",
    "        #这个lambda的意思：这里复制一个function，input是x，output是sci.expit(x)，而所谓的expit(x)其实就是sigmoid function\n",
    "        self.activation_function= lambda x: sci.expit(x)\n",
    "        pass\n",
    "    \n",
    "    def train(self,inputs_list, targets_list):\n",
    "        inputs= np.array(inputs_list, ndmin=2).T\n",
    "        targets= np.array(targets_list,ndmin=2).T\n",
    "        hidden_inputs= np.dot(self.wih, inputs)\n",
    "        hidden_outputs= self.activation_function(hidden_inputs)\n",
    "        final_inputs= np.dot(self.who, hidden_outputs)\n",
    "        final_outputs= self.activation_function(final_inputs)\n",
    "        output_errors= targets- final_outputs\n",
    "        hidden_errors= np.dot(self.who.T, output_errors)\n",
    "        self.who += self.lr*np.dot(output_errors*final_outputs*(1-final_outputs), np.transpose(hidden_outputs))\n",
    "        self.wih += self.lr*np.dot(hidden_errors*hidden_outputs*(1-hidden_outputs), np.transpose(inputs)) \n",
    "        pass\n",
    "    \n",
    "    #ndimin:Specifies the minimum number of dimensions that the resulting array should have. \n",
    "    #Ones will be pre-pended to the shape as needed to meet this requirement.\n",
    "    def query(self,inputs_list):\n",
    "        inputs= np.array(inputs_list, ndmin=2).T\n",
    "        \n",
    "        hidden_inputs= np.dot(self.wih,inputs)\n",
    "        hidden_outputs= self.activation_function(hidden_inputs)\n",
    "        \n",
    "        final_inputs= np.dot(self.who, hidden_outputs)\n",
    "        final_outputs= self.activation_function(final_inputs)\n",
    "        \n",
    "        return final_outputs\n",
    "    \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"mnist_train_100.csv\") as f:\n",
    "    data_list= f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "785\n",
      "784\n",
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "all_values= data_list[0].split(',')\n",
    "print(len(all_values))\n",
    "print(28**2)\n",
    "## asfarray:11 convert string to numbers, and copy an array of the numbers\n",
    "img_array= np.asfarray(all_values[1:]).reshape((28,28))\n",
    "print(img_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1085e1898>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SntTg28D/0+CxkSWSLpG0TdJHkl6QdHEb9fZfkt6R9LYGgzapoN6ma/At/duSdmQ/c4ve\ndom+CtlufMMPCIoDfkBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgvp/tGFqhedBhRoAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1083018d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.imshow(img_array, cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1087df198>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SfqDOW314q6TF2e3Fkl4osJe/0CkrN1dbWVoFP3Ydt+K1u7f9S9JVGrri/3+SHiiihyp9\n/ZWk/8q+dhfdm6RNGnoaeFRD10aWSDpL0jZJ70t6RdLkDurtXyXtkvS2hoI2taDe5mjoKf3bkt7K\nvq4q+rFL9FXI48Y7/ICguOAHBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/wfNDnvJ0xlPmwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10814bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_values= data_list[1].split(',')\n",
    "img_array= np.asfarray(all_values[1:]).reshape((28,28))\n",
    "print(img_array.shape)\n",
    "plot.imshow(img_array, cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.208       0.62729412  0.99223529  0.62729412  0.20411765\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.19635294  0.934       0.98835294  0.98835294  0.98835294\n",
      "  0.93011765  0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.21964706  0.89129412  0.99223529  0.98835294  0.93788235\n",
      "  0.91458824  0.98835294  0.23129412  0.03329412  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.04882353  0.24294118  0.87964706  0.98835294  0.99223529  0.98835294\n",
      "  0.79423529  0.33611765  0.98835294  0.99223529  0.48364706  0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.64282353  0.98835294  0.98835294  0.98835294  0.99223529\n",
      "  0.98835294  0.98835294  0.38270588  0.74376471  0.99223529  0.65835294\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.208       0.934       0.99223529  0.99223529\n",
      "  0.74764706  0.45258824  0.99223529  0.89517647  0.19247059  0.31670588\n",
      "  1.          0.66223529  0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.19635294  0.934       0.98835294\n",
      "  0.98835294  0.70494118  0.05658824  0.30117647  0.47976471  0.09152941\n",
      "  0.01        0.01        0.99223529  0.95341176  0.20411765  0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.15752941  0.65058824\n",
      "  0.99223529  0.91458824  0.81752941  0.33611765  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.99223529  0.98835294  0.65058824\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.03717647\n",
      "  0.70105882  0.98835294  0.94176471  0.28564706  0.08376471  0.11870588\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.99223529  0.98835294  0.76705882  0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.23129412  0.98835294  0.98835294  0.25458824  0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.99223529  0.98835294  0.76705882  0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.77870588  0.99223529  0.74764706  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  1.          0.99223529  0.77094118  0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.30505882  0.96505882  0.98835294  0.44482353  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.99223529  0.98835294  0.58458824  0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.34        0.98835294  0.90294118  0.10705882  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.03717647\n",
      "  0.53411765  0.99223529  0.73211765  0.05658824  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.34        0.98835294  0.87576471  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.03717647\n",
      "  0.51858824  0.98835294  0.88352941  0.28564706  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.34        0.98835294  0.57294118  0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.19635294\n",
      "  0.65058824  0.98835294  0.68164706  0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.34388235  0.99223529  0.88352941\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.45258824  0.934       0.99223529  0.63894118  0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.34\n",
      "  0.98835294  0.97670588  0.57682353  0.19635294  0.12258824  0.34\n",
      "  0.70105882  0.88352941  0.99223529  0.87576471  0.65835294  0.22741176\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.34        0.98835294  0.98835294  0.98835294  0.89905882\n",
      "  0.84470588  0.98835294  0.98835294  0.98835294  0.77094118  0.51470588\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.11870588  0.78258824  0.98835294\n",
      "  0.98835294  0.99223529  0.98835294  0.98835294  0.91458824  0.57294118\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.10705882  0.50694118  0.98835294  0.99223529  0.98835294  0.55741176\n",
      "  0.15364706  0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01        0.01        0.01        0.01        0.01        0.01        0.01\n",
      "  0.01      ]\n"
     ]
    }
   ],
   "source": [
    "# scaled input和非scaled效果是一样哒\n",
    "scaled_input= (np.asfarray(all_values[1:])/255*0.99)+0.01\n",
    "print(scaled_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x108563668>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SfqDOW314q6TF2e3Fkl4osJe/0CkrN1dbWVoFP3Ydt+K1u7f9S9JVGrri/3+SHiiihyp9\n/ZWk/8q+dhfdm6RNGnoaeFRD10aWSDpL0jZJ70t6RdLkDurtXyXtkvS2hoI2taDe5mjoKf3bkt7K\nvq4q+rFL9FXI48Y7/ICguOAHBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/wfNDnvJ0xlPmwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10891b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scaled_input.reshape(28,28)\n",
    "plot.imshow(scaled_input.reshape(28,28), cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_nodes=784\n",
    "hidden_nodes=100\n",
    "output_nodes=10\n",
    "\n",
    "learning_rate=0.3\n",
    "n= neuralNetwork(input_nodes, hidden_nodes, output_nodes,learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for record in date_list:\n",
    "    all_values= record.split(',')\n",
    "    inputs=(np.asfarray(all_values[1:])/255*0.99)+0.01\n",
    "    targets=np.zeros(output_nodes)+0.01\n",
    "    targets[int(all_values[0])]=0.99\n",
    "    n.train(inputs, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('mnist_test_10.csv') as f:\n",
    "    test_data= f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "all_values= test_data[0].split(',')\n",
    "print(all_values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x108c0b780>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npP4Pc0oGVHoLWbQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ab9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_array= np.asfarray(all_values[1:]).reshape(28,28)\n",
    "plot.imshow(img_array, cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01956649],\n",
       "       [ 0.01418903],\n",
       "       [ 0.10708489],\n",
       "       [ 0.09304496],\n",
       "       [ 0.05500892],\n",
       "       [ 0.01714134],\n",
       "       [ 0.00258944],\n",
       "       [ 0.64980971],\n",
       "       [ 0.10843634],\n",
       "       [ 0.03501819]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.query(np.asfarray(all_values[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class neuralNetwork:\n",
    "    #initialisieren的时候需要的东西：每层点的个数，lr，activationFunction和最初的层之间的w\n",
    "    def __init__(self,inputNodes,hiddenNodes,outputNodes,learningRate):\n",
    "        self.iNodes= inputNodes\n",
    "        self.hNodes= hiddenNodes\n",
    "        self.oNodes= outputNodes\n",
    "        self.lr= learningRate\n",
    "        self.wih= np.random.rand(self.hNodes, self.iNodes)-0.5\n",
    "        self.who= np.random.rand(self.oNodes, self.hNodes)-0.5\n",
    "        self.activationFunction= lambda x: sci.expit(x)\n",
    "        pass\n",
    "    \n",
    "    def train(self,input_list, target_list):\n",
    "        inputs= np.array(input_list,ndmin=2).T\n",
    "        hidden_inputs= np.dot(self.wih,inputs)\n",
    "        hidden_outputs= self.activationFunction(hidden_inputs)\n",
    "        final_inputs= np.dot(self.who,hidden_outputs)\n",
    "        final_outputs= self.activationFunction(final_inputs)\n",
    "        \n",
    "        targets= np.array(target_list, ndmin=2).T\n",
    "        final_error= targets-final_outputs\n",
    "        hidden_error= np.dot(self.who.T, final_error)\n",
    "        self.who += self.lr* np.dot(final_error*final_outputs*(1-final_outputs),np.transpose(hidden_outputs))\n",
    "        self.wih += self.lr* np.dot(hidden_error*(hidden_outputs)*(1-hidden_outputs), np.transpose(inputs))\n",
    "        pass\n",
    "    \n",
    "    def query(self, input_list):\n",
    "        inputs= np.array(input_list, ndmin=2).T\n",
    "        hidden_inputs= np.dot(self.wih,inputs)\n",
    "        hidden_outputs= self.activationFunction(hidden_inputs)\n",
    "        final_inputs= np.dot(self.who, hidden_outputs)\n",
    "        final_outputs= self.activationFunction(final_inputs)\n",
    "        return final_outputs\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('mnist_train_100.csv') as f:\n",
    "    train_data= f.readlines()\n",
    "with open('mnist_test_10.csv') as f:\n",
    "    test_data= f.readlines()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1081160b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SntTg28D/0+CxkSWSLpG0TdJHkl6QdHEb9fZfkt6R9LYGgzapoN6ma/At/duSdmQ/c4ve\ndom+CtlufMMPCIoDfkBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgvp/tGFqhedBhRoAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c7f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allValues= train_data[0].split(',')\n",
    "m= np.asfarray(allValues[1:]).reshape(28,28)\n",
    "plot.imshow(m, cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n= neuralNetwork(784,100,10,0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(train_data)):\n",
    "    allValues= train_data[i].split(',')\n",
    "    inputs= np.asfarray(allValues[1:])/255*0.99+0.01\n",
    "    targets= np.zeros(10)+0.01\n",
    "    targets[int(allValues[0])]=0.99\n",
    "    n.train(inputs, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.017536  ],\n",
       "       [ 0.00392541],\n",
       "       [ 0.18650368],\n",
       "       [ 0.30520042],\n",
       "       [ 0.34766052],\n",
       "       [ 0.09699311],\n",
       "       [ 0.00483936],\n",
       "       [ 0.86264844],\n",
       "       [ 0.01643613],\n",
       "       [ 0.14878234]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testAll= test_data[0].split(',')\n",
    "print(testAll[0])\n",
    "n.query(np.asfarray(testAll[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 correct label\n",
      "7 network's answer\n",
      "2 correct label\n",
      "3 network's answer\n",
      "1 correct label\n",
      "1 network's answer\n",
      "0 correct label\n",
      "0 network's answer\n",
      "4 correct label\n",
      "4 network's answer\n",
      "1 correct label\n",
      "1 network's answer\n",
      "4 correct label\n",
      "4 network's answer\n",
      "9 correct label\n",
      "4 network's answer\n",
      "5 correct label\n",
      "4 network's answer\n",
      "9 correct label\n",
      "4 network's answer\n"
     ]
    }
   ],
   "source": [
    "scoreCard=[]\n",
    "for record in test_data:\n",
    "    allValues= record.split(',')\n",
    "    inputs= np.asfarray(allValues[1:])/255*0.99+0.01\n",
    "    outputs= n.query(inputs)\n",
    "    label= np.argmax(outputs)\n",
    "    correct_label=int(allValues[0])\n",
    "    print(correct_label,\"correct label\")\n",
    "    print(label, \"network's answer\")\n",
    "    if(label==correct_label):\n",
    "        scoreCard.append(1)\n",
    "    else:\n",
    "        scoreCard.append(0)\n",
    "    pass\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0, 1, 1, 1, 1, 1, 0, 0, 0]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoreCard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}