Q1.ipynb

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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.9 64-bit"
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  "metadata": {
   "interpreter": {
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "Loading and Organizing Data from CIFAR10"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import datasets,layers,models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "x_train:  (50000, 32, 32, 3)\ny_train:  (50000, 1)\n"
     ]
    }
   ],
   "source": [
    "print('x_train: ', x_train.shape)\n",
    "print('y_train: ', y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Noof classes: 10\nNoof training images: 50000\nNoof test images: 10000\nNoof features: 3072\n"
     ]
    }
   ],
   "source": [
    "K = len(np.unique(y_train)) # Classes\n",
    "Ntr = x_train.shape[0]\n",
    "Nte = x_test.shape[0]\n",
    "Din = 3072 # CIFAR10\n",
    "print('Noof classes:',K)\n",
    "print('Noof training images:',Ntr)\n",
    "print('Noof test images:',Nte)\n",
    "print('Noof features:',Din)\n",
    "\n",
    "x_train=x_train[range(Ntr),:]\n",
    "y_train=y_train[range(Ntr),:]\n",
    "x_test=x_test[range(Nte),:]\n",
    "x_test=x_test[range(Nte),:]\n",
    "# Normalize pixel values\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "mean_image = np.mean(x_train, axis=0)\n",
    "x_train = x_train - mean_image\n",
    "x_test = x_test - mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "original labels: (50000, 1)\nlabels in class matrix: (50000, 10)\n"
     ]
    }
   ],
   "source": [
    "print('original labels:',y_train.shape)\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=K)\n",
    "print('labels in class matrix:',y_train.shape)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes=K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "x_train: (50000, 3072)\nx_test: (10000, 3072)\n"
     ]
    }
   ],
   "source": [
    "x_train = np.reshape(x_train,(Ntr,Din))\n",
    "x_test = np.reshape(x_test,(Nte,Din))\n",
    "print('x_train:', x_train.shape)\n",
    "print('x_test:', x_test.shape)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')"
   ]
  },
  {
   "source": [
    "Linear Classifier"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "w1: (3072, 10)\nb1: (10,)\nw1: [[-2.06717783e-05 -2.55990764e-06 -1.68321601e-06 ...  1.54153099e-05\n  -1.23645312e-05 -9.80608493e-06]\n [ 7.65636220e-07  3.10372587e-07  2.13940184e-06 ... -1.00063795e-05\n  -2.49607384e-05  3.40873914e-06]\n [-4.83187692e-06  3.04994980e-06 -9.39540504e-07 ...  1.70569124e-05\n   1.28107358e-06  8.82108609e-06]\n ...\n [-1.47980962e-05  4.00617852e-06 -1.22676334e-05 ...  1.44748422e-05\n  -5.20421474e-06  1.04301185e-05]\n [ 9.62096972e-06  5.99350227e-07  1.14947605e-05 ... -5.12862640e-06\n  -3.04470281e-05 -1.48305659e-05]\n [-2.17222215e-07  4.34442295e-06  2.30724807e-07 ... -3.34333016e-06\n   6.44217044e-06 -2.10713841e-05]]\n"
     ]
    }
   ],
   "source": [
    "std=1e-5\n",
    "w1 = std*np.random.randn(Din, K)\n",
    "b1 = np.zeros(K)\n",
    "print(\"w1:\", w1.shape)\n",
    "print(\"b1:\", b1.shape)\n",
    "print('w1:',w1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "epoch 0/300: train loss= 0.999983-- ,test loss= 0.999980--,train accuracy= 0.686826--, test accuracy= 0.689240\n",
      "epoch 10/300: train loss= 0.876637-- ,test loss= 0.876236--,train accuracy= 0.756394--, test accuracy= 0.758050\n",
      "epoch 20/300: train loss= 0.842540-- ,test loss= 0.842416--,train accuracy= 0.762628--, test accuracy= 0.767790\n",
      "epoch 30/300: train loss= 0.823616-- ,test loss= 0.823714--,train accuracy= 0.766536--, test accuracy= 0.770580\n",
      "epoch 40/300: train loss= 0.812388-- ,test loss= 0.812706--,train accuracy= 0.768630--, test accuracy= 0.771540\n",
      "epoch 50/300: train loss= 0.805425-- ,test loss= 0.805969--,train accuracy= 0.770364--, test accuracy= 0.772950\n",
      "epoch 60/300: train loss= 0.800905-- ,test loss= 0.801678--,train accuracy= 0.771520--, test accuracy= 0.773390\n",
      "epoch 70/300: train loss= 0.797821-- ,test loss= 0.798823--,train accuracy= 0.772740--, test accuracy= 0.773590\n",
      "epoch 80/300: train loss= 0.795605-- ,test loss= 0.796834--,train accuracy= 0.773750--, test accuracy= 0.774330\n",
      "epoch 90/300: train loss= 0.793930-- ,test loss= 0.795380--,train accuracy= 0.774368--, test accuracy= 0.774170\n",
      "epoch 100/300: train loss= 0.792605-- ,test loss= 0.794270--,train accuracy= 0.774972--, test accuracy= 0.773760\n",
      "epoch 110/300: train loss= 0.791515-- ,test loss= 0.793387--,train accuracy= 0.775342--, test accuracy= 0.773630\n",
      "epoch 120/300: train loss= 0.790590-- ,test loss= 0.792662--,train accuracy= 0.776090--, test accuracy= 0.773140\n",
      "epoch 130/300: train loss= 0.789785-- ,test loss= 0.792051--,train accuracy= 0.776574--, test accuracy= 0.773520\n",
      "epoch 140/300: train loss= 0.789072-- ,test loss= 0.791524--,train accuracy= 0.777038--, test accuracy= 0.773610\n",
      "epoch 150/300: train loss= 0.788432-- ,test loss= 0.791063--,train accuracy= 0.777208--, test accuracy= 0.773550\n",
      "epoch 160/300: train loss= 0.787851-- ,test loss= 0.790654--,train accuracy= 0.777314--, test accuracy= 0.773700\n",
      "epoch 170/300: train loss= 0.787319-- ,test loss= 0.790289--,train accuracy= 0.777746--, test accuracy= 0.773500\n",
      "epoch 180/300: train loss= 0.786828-- ,test loss= 0.789960--,train accuracy= 0.778034--, test accuracy= 0.773630\n",
      "epoch 190/300: train loss= 0.786374-- ,test loss= 0.789661--,train accuracy= 0.778228--, test accuracy= 0.773260\n",
      "epoch 200/300: train loss= 0.785952-- ,test loss= 0.789390--,train accuracy= 0.778558--, test accuracy= 0.773350\n",
      "epoch 210/300: train loss= 0.785557-- ,test loss= 0.789141--,train accuracy= 0.778624--, test accuracy= 0.773670\n",
      "epoch 220/300: train loss= 0.785187-- ,test loss= 0.788913--,train accuracy= 0.778848--, test accuracy= 0.774070\n",
      "epoch 230/300: train loss= 0.784840-- ,test loss= 0.788703--,train accuracy= 0.778966--, test accuracy= 0.773610\n",
      "epoch 240/300: train loss= 0.784512-- ,test loss= 0.788510--,train accuracy= 0.779224--, test accuracy= 0.773830\n",
      "epoch 250/300: train loss= 0.784203-- ,test loss= 0.788330--,train accuracy= 0.779436--, test accuracy= 0.774270\n",
      "epoch 260/300: train loss= 0.783910-- ,test loss= 0.788164--,train accuracy= 0.779590--, test accuracy= 0.773990\n",
      "epoch 270/300: train loss= 0.783633-- ,test loss= 0.788009--,train accuracy= 0.779692--, test accuracy= 0.774240\n",
      "epoch 280/300: train loss= 0.783369-- ,test loss= 0.787865--,train accuracy= 0.779730--, test accuracy= 0.774260\n",
      "epoch 290/300: train loss= 0.783117-- ,test loss= 0.787731--,train accuracy= 0.779956--, test accuracy= 0.774060\n"
     ]
    }
   ],
   "source": [
    "batch_size = Ntr\n",
    "iterations = 300\n",
    "lr = 1.4e-2\n",
    "lr_decay=0.999\n",
    "reg =5e-6\n",
    "train_loss_history = []\n",
    "train_acc_history = []\n",
    "test_loss_history = []\n",
    "test_acc_history = []\n",
    "seed = 0\n",
    "rng = np.random.default_rng(seed=seed)\n",
    "for t in range(iterations):\n",
    "    indices = np.arange(Ntr)\n",
    "    rng.shuffle(indices)\n",
    "    x=x_train[indices]\n",
    "    y=y_train[indices]\n",
    "\n",
    "    # Forward pass\n",
    "    y_pred=x.dot(w1) + b1\n",
    "    y_pred_test=x_test.dot(w1)+b1\n",
    "        \n",
    "    loss=1./batch_size*(np.square(y_pred-y)).sum() + reg * np.sum(w1*w1)\n",
    "    test_loss=1./Nte*(np.square(y_pred_test-y_test)).sum() + reg * np.sum(w1*w1)\n",
    "    train_loss_history.append(loss)\n",
    "    test_loss_history.append(test_loss)\n",
    "\n",
    "    train_acc = 1.0 - (1/(batch_size*K))*(np.abs(np.argmax(y,axis=1) - np.argmax(y_pred,axis=1))).sum()\n",
    "    train_acc_history.append(train_acc)\n",
    "    test_acc = 1.0 - (1/(Nte*K))*(np.abs(np.argmax(y_test,axis=1) - np.argmax(y_pred_test,axis=1))).sum()\n",
    "    test_acc_history.append(test_acc)\n",
    "\n",
    "    if t%10==0:\n",
    "        print('epoch %d/%d: train loss= %f-- ,test loss= %f--,train accuracy= %f--, test accuracy= %f' % (t,iterations,loss,test_loss,train_acc,test_acc))\n",
    "\n",
    "        \n",
    "    # Backward pass\n",
    "    dy_pred=1./batch_size*2.0*(y_pred-y)\n",
    "    dw1=x.T.dot(dy_pred) + reg * w1\n",
    "    db1=dy_pred.sum(axis=0)\n",
    "    w1 -= lr*dw1\n",
    "    b1 -= lr*db1\n",
    "    lr *= lr_decay\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "source": [
    "##### <html><h>Displaying trained weights for each class as images"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Displaying w as 10 images\n",
    "import cv2 as cv\n",
    "images=[]\n",
    "for i in range(w1.shape[1]):\n",
    "    temp=np.reshape(w1[:,i]*255,(32,32,3))\n",
    "    temp=cv.normalize(temp, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U)\n",
    "    images.append(temp)\n",
    "fig,ax=plt.subplots(2,5,figsize=(30,10))\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        ax[i,j].imshow(images[i*5+j],vmin=0,vmax=255)\n",
    "plt.savefig('ws.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}