Home_Made_ANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.special import expit, logit\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage import interpolation\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    # initialize the neural network\n",
    "    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):\n",
    "        \n",
    "        # set the number of nodes in each input, hidden and output layer\n",
    "        self.inodes = inputnodes #784\n",
    "        self.hnodes = hiddennodes #100\n",
    "        self.onodes = outputnodes #10\n",
    "        \n",
    "        # link weight matrices wih and who\n",
    "        # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer\n",
    "        # w12 w21\n",
    "        # w22 w22 etc\n",
    "        self.wih = np.random.normal(0., pow(self.hnodes, -0.5), (self.hnodes, self.inodes)) #100x784\n",
    "        self.who = np.random.normal(0., pow(self.onodes, -0.5), (self.onodes, self.hnodes)) #10x100\n",
    "        \n",
    "        # learning rate\n",
    "        self.lr = learningrate\n",
    "        \n",
    "        # activation function is the sigmoid function\n",
    "        self.activation_function = lambda x: expit(x)\n",
    "        self.inverse_activation_function = lambda x: logit(x)\n",
    "    \n",
    "    # train the neural network\n",
    "    def train(self, inputs_list, targets_list):\n",
    "        \n",
    "        # convert inputs and targets lists to 2d array\n",
    "        inputs = np.array(inputs_list, ndmin=2).T\n",
    "        targets = np.array(targets_list, ndmin=2).T\n",
    "        \n",
    "        # calculate signals into hidden layer\n",
    "        hidden_inputs = np.dot(self.wih, inputs) #100x784 * 784x6000\n",
    "        \n",
    "        # calculate signals emerging from hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs) #100x6000\n",
    "        \n",
    "        # calculate signals into final output layer\n",
    "        final_inputs = np.dot(self.who, hidden_outputs) #10x100 * 100x6000\n",
    "        \n",
    "        # calculate the signals emerging from the final output layer\n",
    "        final_outputs = self.activation_function(final_inputs) #10x6000\n",
    "        \n",
    "        # error is the (target - actual)\n",
    "        output_errors = targets - final_outputs #10x6000\n",
    "        \n",
    "        # hidden layer error is the output_errors, split by weights, recombined at hidden nodes\n",
    "        hidden_errors = np.dot(self.who.T, output_errors) #100x10 * 10x6000 = #100x6000\n",
    "        \n",
    "        # update the weights for the links between the hidden and output layers\n",
    "        self.who += self.lr * np.dot((output_errors * final_outputs * (1. - final_outputs)), hidden_outputs.T)\n",
    "        #10x6000 * 6000x100 = 10x100\n",
    "        # update the weights for the links between the input and hidden layers\n",
    "        self.wih += self.lr * np.dot((hidden_errors * hidden_outputs * (1. - hidden_outputs)), inputs.T)\n",
    "        #100x6000 * 6000x784 = 100x784\n",
    "    \n",
    "    # query the neural network\n",
    "    def query(self, inputs_list):\n",
    "        \n",
    "        # convert inputs list to 2d array\n",
    "        inputs = np.array(inputs_list, ndmin=2).T\n",
    "        \n",
    "        # calculate signals into hidden layer\n",
    "        hidden_inputs = np.dot(self.wih, inputs)\n",
    "        \n",
    "        # calculate signals emerging from hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs)\n",
    "        \n",
    "        # calculate signals into final output layer\n",
    "        final_inputs = np.dot(self.who, hidden_outputs)\n",
    "        \n",
    "        # calculate the signals emerging from the final output layer\n",
    "        final_outputs = self.activation_function(final_inputs)\n",
    "        \n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the mnist training data CSV file into a list\n",
    "training_data_file = open(\"input/train.csv\", 'r')\n",
    "training_data_list = training_data_file.readlines()[1:]\n",
    "training_data_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the mnist test data CSV file into a list\n",
    "test_data_file = open(\"input/test.csv\", 'r')\n",
    "test_data_list = test_data_file.readlines()[1:]\n",
    "test_data_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_NN(neural_network, training_set, output_nodes):\n",
    "    # train the neural network\n",
    "\n",
    "    # go through all records in the training data set\n",
    "    for record in training_data_list:\n",
    "        # split the record by the ',' commas\n",
    "        all_values = record.split(',')\n",
    "        # scale and shift the inputs\n",
    "        inputs = (np.asfarray(all_values[1:]) / 255. * .99) + .01\n",
    "        # create the target output value (all 0.01 except the desired label which is 0.99)\n",
    "        targets = np.zeros(output_nodes) + .01\n",
    "        # all_values[0] is the target label for this record\n",
    "        targets[int(all_values[0])] = .99\n",
    "        n.train(inputs, targets)\n",
    "        \n",
    "        ## create rotated variations\n",
    "        x = 10\n",
    "        # rotated anticlockwise by x degrees\n",
    "        inputs_plusx_img = interpolation.rotate(inputs.reshape(28,28), x, cval=0.01, order=1, reshape=False)\n",
    "        n.train(inputs_plusx_img.reshape(784), targets)\n",
    "        # rotated clockwise by x degrees\n",
    "        inputs_minusx_img = interpolation.rotate(inputs.reshape(28,28), -x, cval=0.01, order=1, reshape=False)\n",
    "        n.train(inputs_minusx_img.reshape(784), targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_NN(neural_network, test_set):\n",
    "    # test the neural network\n",
    "\n",
    "    # scorecard for how well the network performs, initially empty\n",
    "    predictions = []\n",
    "\n",
    "    # go through all records in the test data set\n",
    "    for record in test_data_list:\n",
    "        # split the record by the ',' commas\n",
    "        all_values = record.split(',')\n",
    "        # correct answer is first value\n",
    "        # correct_label = int(all_values[0])\n",
    "        # print(correct_label, \"correct label\")\n",
    "        # scale and shift the inputs\n",
    "        inputs = (np.asfarray(all_values) / 255. * .99) + .01\n",
    "        # query the network\n",
    "        outputs = n.query(inputs)\n",
    "        # the index of the highest value corresponds to the label\n",
    "        label = np.argmax(outputs)\n",
    "        # print(label, \"network's answer\\n\")\n",
    "        # append correct or incorrect to the list\n",
    "        predictions.append(label)\n",
    "        \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "Epoch 2/8\n",
      "Epoch 3/8\n",
      "Epoch 4/8\n",
      "Epoch 5/8\n",
      "Epoch 6/8\n",
      "Epoch 7/8\n",
      "Epoch 8/8\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "shapes (200,784) and (783,1) not aligned: 784 (dim 1) != 783 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-1d4e40f1ef13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Check predictions vs test set (10000 examples) and convert to array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_NN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_data_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;31m# scorecard_array = np.asarray(scorecard)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# print(scorecard)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-21-603cb25d9fad>\u001b[0m in \u001b[0;36mtest_NN\u001b[0;34m(neural_network, test_set)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masfarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m.99\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m.01\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;31m# query the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;31m# the index of the highest value corresponds to the label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-0690158d992c>\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, inputs_list)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;31m# calculate signals into hidden layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0mhidden_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwih\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;31m# calculate signals emerging from hidden layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shapes (200,784) and (783,1) not aligned: 784 (dim 1) != 783 (dim 0)"
     ]
    }
   ],
   "source": [
    "# Run neural network with optimized parameters on test set\n",
    "input_nodes = 784\n",
    "hidden_nodes = 200\n",
    "output_nodes = 10\n",
    "learning_rate = .01\n",
    "epochs = 8\n",
    "\n",
    "n = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "for e in range(epochs):\n",
    "    print('Epoch {0}/{1}'.format(e + 1, epochs))\n",
    "    train_NN(n, training_data_list, output_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on test set\n",
    "predictions = test_NN(n, test_data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c7ec18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1144,
       "width": 1131
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df = pd.read_csv('input/test.csv', header=0)\n",
    "X_test = test_df.as_matrix()\n",
    "nrows = 5\n",
    "ncols = 5\n",
    "fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16))\n",
    "for i in range(nrows*ncols):\n",
    "    ax[i//ncols, i%ncols].imshow(X_test[i, :].reshape(28, 28), cmap='Greys')\n",
    "    ax[i//ncols, i%ncols].set_title('Prediction: {}'.format(int(predictions[i])), fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({\"ImageId\": list(range(1, len(predictions) + 1)), \n",
    "              \"Label\": predictions}\n",
    "            ).to_csv(\"submission.csv\", index=False, header=True)"
   ]
  }
 ],
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  "toc": {
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