logistic-regression-for-apple-detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd import Variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "images = np.load(\"./images.npy\")\n",
    "labels = np.load(\"./labels.npy\")\n",
    "\n",
    "input_labels = (labels > 0).astype(dtype=np.float)\n",
    "images = np.reshape(images, (images.shape[0], images.shape[1] * images.shape[2]))\n",
    "\n",
    "#normalize the input data.\n",
    "images = images/255\n",
    "images = images.astype(dtype=np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_images = torch.Tensor(images[0:40000, : ])\n",
    "cv_images = torch.Tensor(images[40000:45000, : ])\n",
    "test_images = torch.Tensor(images[45000:50000, : ])\n",
    "\n",
    "#split the labels in to train, validation and test data\n",
    "train_labels = torch.FloatTensor(input_labels[0:40000])\n",
    "cv_labels = torch.FloatTensor(input_labels[40000:45000])\n",
    "test_labels = torch .FloatTensor(input_labels[45000:50000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy(y,y_hat):\n",
    "    count = 0;\n",
    "    for i in range(y.shape[0]):\n",
    "        if y[i] == y_hat[i]:\n",
    "            count += 1\n",
    "    return count/y.shape[0]\n",
    "\n",
    "def accuracy_random_train():\n",
    "\n",
    "    import random\n",
    "    import numpy as np\n",
    "\n",
    "\n",
    "    x =random.sample(range(0,40000),1000)\n",
    "    y = np.empty(1000)\n",
    "    y_hat = np.empty(1000)\n",
    "\n",
    "\n",
    "    index=0\n",
    "    \n",
    "    for i in x:\n",
    "\n",
    "        images_random = Variable(train_images[i].view(1,676), requires_grad=False)\n",
    "        y[index] = train_labels[i]\n",
    "\n",
    "        y_pred = torch.sigmoid(torch.mm(images_random, W))\n",
    "\n",
    "        res = y_pred.data[0][0]\n",
    "    \n",
    "\n",
    "        if res > 0.5:\n",
    "            act_label = 1\n",
    "        else:\n",
    "            act_label = 0\n",
    "\n",
    "        y_hat[index] = act_label\n",
    "        index += 1\n",
    "\n",
    "    acc = accuracy(y,y_hat)\n",
    "   \n",
    "  \n",
    "    return acc\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "def accuracy_random_validation():\n",
    "\n",
    "    import random\n",
    "    import numpy as np\n",
    "\n",
    "\n",
    "    x =random.sample(range(0,5000),1000)\n",
    "    y = np.empty(1000)\n",
    "    y_hat = np.empty(1000)\n",
    "\n",
    "\n",
    "    index=0\n",
    "    \n",
    "    for i in x:\n",
    "\n",
    "        images_random = Variable(cv_images[i].view(1,676), requires_grad=False)\n",
    "        y[index] = cv_labels[i]\n",
    "\n",
    "       \n",
    "        y_pred = torch.sigmoid(torch.mm(images_random, W))\n",
    "\n",
    "        res = y_pred.data[0][0]\n",
    "    \n",
    "\n",
    "        if res > 0.5:\n",
    "            act_label = 1\n",
    "        else:\n",
    "            act_label = 0\n",
    "\n",
    "        y_hat[index] = act_label\n",
    "        index += 1\n",
    "\n",
    "    acc = accuracy(y,y_hat)\n",
    "   \n",
    "  \n",
    "    return acc\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtype = torch.FloatTensor\n",
    "input_size = 40000\n",
    "D_in = train_images.shape[1]\n",
    "online_learning_rate = 0.001\n",
    "online_training_iterations = 1\n",
    "\n",
    "\n",
    "\n",
    "size = (online_training_iterations * input_size) / 100\n",
    "index = 0\n",
    "\n",
    "# N dimensional arrays to store training and validation accuracies for every 100 steps\n",
    "accuracy_train = []\n",
    "accuracy_valid = []\n",
    "\n",
    "W = Variable(torch.randn(D_in, 1).type(dtype), requires_grad = True)\n",
    "\n",
    "while (online_training_iterations >= 0):\n",
    "    for t in range(input_size):\n",
    "        \n",
    "        x = Variable(train_images[t].view(1,676), requires_grad=False)\n",
    "        y = train_labels[t]\n",
    "        \n",
    "        y_pred = torch.sigmoid(torch.mm(x, W))\n",
    "        \n",
    "        #loss = -(y * torch.log(torch.sigmoid(torch.mm(x, W))) + \n",
    "         #        (1-y) * torch.log(torch.sigmoid(-torch.mm(x, W))))\n",
    "        \n",
    "        loss = -(y * torch.log(y_pred) +\n",
    "                 (1-y) * torch.log(1 - y_pred))\n",
    "        \n",
    "        if t%100 == 0:\n",
    "            accuracy_train.append(accuracy_random_train())\n",
    "            accuracy_valid.append(accuracy_random_validation())\n",
    "            #index += 1\n",
    "    \n",
    "        loss.backward()\n",
    "        W.data -= online_learning_rate * W.grad.data\n",
    "        W.grad.data.zero_()\n",
    "        W.data = W.data/torch.norm(W.data)\n",
    "    online_training_iterations -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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d+lpnheEX/ZuujyxgaMd6LMhJcS1/tcBd9Y2omczhqCauzxySbLXHigjjR3bh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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260e44160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "t = np.arange(0,400,1)\n",
    "\n",
    "s = accuracy_train[0:400]\n",
    "k = accuracy_valid[0:400]\n",
    "plt.plot(t, s, t, k)\n",
    "\n",
    "plt.xlabel('number of iteration')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Training/validation accuracy')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this over fitting:\n",
    "\n",
    "    From the plot above there is no over fitting here.The accuracies are similar both cases although there are minor fluctuations in the trend"
   ]
  }
 ],
 "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
}