{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Due date: Friday, 2 April 2021, 11:59 PM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "%config IPCompleter.greedy=True\n",
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import abspath\n",
    "path = 'F:\\\\UNIVERSITY\\\\4TH_SEM\\\\Assignments\\\\LaTeX Report\\\\figures\\\\'\n",
    "\n",
    "def saveto(filename):\n",
    "    plt.savefig(path+ filename)\n",
    "\n",
    "def saveimg(filename, image):\n",
    "    cv.imwrite(path+ filename,image)\n",
    "\n",
    "def sigmoid(hypothesis):\n",
    "    return 1/(1+ np.exp(-hypothesis))\n",
    "\n",
    "def getAccuracy(predictions,labels):\n",
    "    pred_class = np.argmax(predictions, axis=1)\n",
    "    real_class = np.argmax(labels, axis=1)\n",
    "    valid_pred = [pred_class == real_class]\n",
    "    return np.sum(valid_pred)/len(real_class) # 0-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (50000, 32, 32, 3)\n",
      "y_train:  (50000, 1)\n",
      "Pre-processing loaded data...\n",
      "\n",
      "Number of training samples: 50000\n",
      "Number of test samples:  10000 \n",
      "\n",
      "y_train:  (50000, 10)\n",
      "Reshaped x_train:  (50000, 3072)\n",
      "Reshaped x_test:  (10000, 3072)\n",
      "Pre-processing completed.\n"
     ]
    }
   ],
   "source": [
    "# Loading the Data Set\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "print('x_train: ', x_train.shape); print('y_train: ', y_train.shape)\n",
    "\n",
    "print(\"Pre-processing loaded data...\\n\")\n",
    "# y_train contains labels form 0 to 9 corresponding to 10 classes.\n",
    "K = len(np.unique(y_train)) # Number of Classes\n",
    "\n",
    "Ntr = x_train.shape[0]; print('Number of training samples:', Ntr) # Number of training samples 50,000\n",
    "Nte = x_test.shape[0];  print('Number of test samples: ',Nte,'\\n')# Number of test samples 10,000\n",
    "Din = 3072 # CIFAR10 # 32x32x3 = height x width x channel\n",
    "\n",
    "# Normalize pixel values: Image data preprocessing\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "mean_image = np.mean(x_train, axis=0) # axis=0: mean of a column; Mean of each pixel\n",
    "x_train = x_train - mean_image\n",
    "x_test = x_test - mean_image\n",
    "\n",
    "# Convert class vectors to binary class matrices.\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=K); print('y_train: ', y_train.shape); #print(y_train[0:10,:])\n",
    "y_test =  tf.keras.utils.to_categorical(y_test,  num_classes=K);\n",
    "\n",
    "x_train = np.reshape(x_train,(Ntr,Din)).astype('float32')\n",
    "x_test = np.reshape(x_test,(Nte,Din)).astype('float32')\n",
    "print('Reshaped x_train: ', x_train.shape)\n",
    "print('Reshaped x_test: ', x_test.shape)\n",
    "print(\"Pre-processing completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Reference1](https://cs231n.github.io/linear-classify/)\n",
    "\n",
    "A part of the code for a linear classifier for CIFAR10 given in listing 1. For our linear classifier, the score function is f (x) = Wx + b, and the loss function is the mean sum of squared errors function. [3 marks]\n",
    "1. Implement gradient descent and run for 300 epochs.\n",
    "2. Show the weights matrix W as 10 images.\n",
    "3. Report the (initial) learning rate, training and testing loss and accuracies.\n",
    "\n",
    "(Hint: If your loss explodes, reduce the leaning rate.)\n",
    "* [np.unique](https://numpy.org/doc/stable/reference/generated/numpy.unique.html), [np.mean](https://numpy.org/doc/stable/reference/generated/numpy.mean.html), [tf.keras.utils.to_categorical](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical), [np.random.randn](https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html), [numpy.argmax](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing the weight matrix with random weights...\n",
      "w1: (3072, 10)\n",
      "b1: (10,)\n",
      "Rearranging train and test samples...\n",
      "Rearranged x_train:  (50000, 3073)\n",
      "Rearranged w1:  (3073, 10)\n",
      "Rearranging completed.\n",
      "\n",
      "Running gradient descent...\n",
      "| Epoch 001 | Loss 0.5000 | accuracy: 0.0842 | val_loss: 0.4846 | val_accuracy: 0.2485 |\n",
      "| Epoch 030 | Loss 0.4297 | accuracy: 0.3648 | val_loss: 0.4287 | val_accuracy: 0.3640 |\n",
      "| Epoch 060 | Loss 0.4126 | accuracy: 0.3810 | val_loss: 0.4123 | val_accuracy: 0.3814 |\n",
      "| Epoch 090 | Loss 0.4049 | accuracy: 0.3896 | val_loss: 0.4049 | val_accuracy: 0.3889 |\n",
      "| Epoch 120 | Loss 0.4010 | accuracy: 0.3954 | val_loss: 0.4012 | val_accuracy: 0.3921 |\n",
      "| Epoch 150 | Loss 0.3987 | accuracy: 0.3993 | val_loss: 0.3992 | val_accuracy: 0.3938 |\n",
      "| Epoch 180 | Loss 0.3973 | accuracy: 0.4020 | val_loss: 0.3980 | val_accuracy: 0.3953 |\n",
      "| Epoch 210 | Loss 0.3964 | accuracy: 0.4047 | val_loss: 0.3972 | val_accuracy: 0.3962 |\n",
      "| Epoch 240 | Loss 0.3957 | accuracy: 0.4065 | val_loss: 0.3966 | val_accuracy: 0.3960 |\n",
      "| Epoch 270 | Loss 0.3951 | accuracy: 0.4086 | val_loss: 0.3962 | val_accuracy: 0.3970 |\n",
      "| Epoch 300 | Loss 0.3946 | accuracy: 0.4103 | val_loss: 0.3958 | val_accuracy: 0.3982 |\n",
      "Gradient Descent completed. Parameters were trained\n"
     ]
    }
   ],
   "source": [
    "print(\"Initializing the weight matrix with random weights...\")\n",
    "std=1e-5 # For random samples from N(\\mu, \\sigma^2), use: sigma * np.random.randn(...) + mu\n",
    "w1 = std*np.random.randn(Din, K) # Initializing the weight matrix with random weights\n",
    "b1 = np.zeros(K) # Initializing the bias vector\n",
    "print(\"w1:\", w1.shape);print(\"b1:\", b1.shape)\n",
    "\n",
    "# Keep track of two sets of parameters w1 and b1 seperately is not really efficient.\n",
    "# This can be eiliminated by combining both of them into one single matrix as follows.\n",
    "# Aditionally the bias term '1' must be added infront of each image row, for this to wrok.\n",
    "# i.e to enable matrix multiplication.\n",
    "\n",
    "print(\"Rearranging train and test samples...\")\n",
    "\n",
    "# Rearranging train and test samples: (ra=rearranged)\n",
    "x_train_ra = np.concatenate((np.ones((x_train.shape[0],1)),x_train), axis=1); print('Rearranged x_train: ', x_train_ra.shape)\n",
    "x_test_ra  = np.concatenate((np.ones((x_test.shape[0],1)),x_test), axis=1)\n",
    "\n",
    "# Rearranging weight matrix and bias matrix into single matrix\n",
    "w1 = np.concatenate((b1.reshape(1,K), w1), axis=0); print('Rearranged w1: ',w1.shape)\n",
    "\n",
    "print(\"Rearranging completed.\\n\")\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "iterations = 300  # Gradient descent interations\n",
    "lr = 1.4e-2 # Learninig rate\n",
    "lr_decay= 0.999\n",
    "reg = 5e-6\n",
    "\n",
    "loss_history = [] # Vlaues of loss function at each iteration \n",
    "test_loss = []\n",
    "train_acc_history = [] # Training accuracy\n",
    "val_acc_history = [] # Validation accuracy\n",
    "\n",
    "\n",
    "m = x_train.shape[0]  # Number of training examples\n",
    "m2 = x_test_ra.shape[0]\n",
    "\n",
    "# Running gradient descent number of times speciied in iterations\n",
    "print(\"Running gradient descent...\")\n",
    "\n",
    "for t in range(1,iterations+1):    \n",
    "    # Forward Propagation\n",
    "    hypothesis = x_train_ra.dot(w1)\n",
    "    loss = (1/(2*m))*np.sum(( hypothesis - y_train)**2) + (1/(2*m))*reg*np.sum(w1**2) \n",
    "    loss_history.append(loss)\n",
    "    \n",
    "    # Backward Propagation\n",
    "    dw1 = (1/m)*(x_train_ra.T.dot(hypothesis - y_train))  + (1/m)*reg*w1 \n",
    "    w1 = w1 - lr*dw1\n",
    "    \n",
    "    \n",
    "    # Training Accuracy and Validation Accuracy\n",
    "    train_acc = getAccuracy(hypothesis, y_train)\n",
    "    train_acc_history.append(train_acc)\n",
    "    valid_acc = getAccuracy(x_test_ra.dot(w1), y_test)\n",
    "    val_acc_history.append(valid_acc)\n",
    "   \n",
    "    # Test Loss    \n",
    "    t_loss = (1/(2*m2))*np.sum(( x_test_ra.dot(w1) - y_test)**2) + (1/(2*m2))*reg*np.sum(w1**2) \n",
    "    test_loss.append(t_loss)\n",
    "    \n",
    "    # Print details for selected iterations\n",
    "    if (t%30==0) or (t==1):\n",
    "        print(\"| Epoch {:03} | Loss {:.4f} | accuracy: {:.4f} | val_loss: {:.4f} | val_accuracy: {:.4f} |\"\\\n",
    "             .format(t, loss, train_acc, t_loss, valid_acc))\n",
    "    \n",
    "    \n",
    "    # Decaying learning rate\n",
    "    lr = lr*lr_decay\n",
    "    \n",
    "print(\"Gradient Descent completed. Parameters were trained\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------Plotting learning rate, training and testing loss and accuracies-------\n",
    "#fig, axes  = plt.subplots(1,2, sharex='all', sharey='all', figsize=(20,7))\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.plot(loss_history/np.max(loss_history), linewidth=3, label = 'Train Loss')\n",
    "plt.plot(test_loss/np.max(test_loss), linewidth=3, label = 'Test Loss')\n",
    "plt.plot(train_acc_history, linewidth=3, label = \"Training Accuracy\")\n",
    "plt.plot(val_acc_history,  linewidth=3, label =  \"Validation Accuracy\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy and Normalized Loss')\n",
    "plt.legend(loc='lower right')\n",
    "# saveto(\"part1plots.eps\")\n",
    "\n",
    "# items = {\"Train Loss\":loss_history, \"Test Loss\":test_loss, \"Training Accuracy\":train_acc_history,\\\n",
    "#          \"Validation Accuracy\": val_acc_history}\n",
    "\n",
    "# location = 1\n",
    "# for key in items.keys():\n",
    "#     plt.subplot(1,4,location);plt.plot(items[key], color='#0000ff', linewidth=4)\n",
    "#     plt.title(key)\n",
    "#     location+=1\n",
    "\n",
    "\n",
    "# -------------------Showing the weights matrix W1 as 10 images-----------------\n",
    "weights = w1[1:,] # Removing the row of bias terms.\n",
    "weights_pos =  weights- np.min(weights)# Making the minimum weight zero.\n",
    "images = ((weights_pos/np.max(weights_pos))*255).astype('uint8')\n",
    "CIFAR10 = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "fig, axes  = plt.subplots(2,5, sharex='all', sharey='all', figsize=(25,10))\n",
    "location = 1 # Location of the image in the grid of 2x5\n",
    "for i in range(K):\n",
    "    image = images[:,i].reshape(32,32,3)\n",
    "    plt.subplot(2,5,location),plt.imshow(image[:,:,::-1])\n",
    "    plt.title(\"Class: {}\".format(CIFAR10[i])),plt.xticks([]),plt.yticks([])    \n",
    "    #saveimg(\"Reg Image \"+ str(i)+\".jpg\", image)\n",
    "    location+=1\n",
    "# saveto(\"trainedWeightsp1.eps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2\n",
    "Code a two-layer fully connected network with H = 200 hidden nodes. Choose the sigmoid function as the activation function for the hidden nodes. The output layer has no activation function. [3 marks]\n",
    "\n",
    "1. Implement gradient descent and run for 300 epochs.\n",
    "2. Report the (initial) learning rate, training and testing loss and accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (50000, 32, 32, 3)\n",
      "y_train:  (50000, 1)\n",
      "Pre-processing loaded data...\n",
      "\n",
      "Number of training samples: 50000\n",
      "Number of test samples:  10000 \n",
      "\n",
      "y_train:  (50000, 10)\n",
      "Reshaped x_train:  (50000, 3072)\n",
      "Reshaped x_test:  (10000, 3072)\n",
      "Pre-processing completed.\n"
     ]
    }
   ],
   "source": [
    "# Loading the Data Set\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "print('x_train: ', x_train.shape); print('y_train: ', y_train.shape)\n",
    "#print(y_train[0:10])\n",
    "\n",
    "print(\"Pre-processing loaded data...\\n\")\n",
    "# y_train contains labels form 0 to 9 corresponding to 10 classes.\n",
    "K = len(np.unique(y_train)) # Number of Classes\n",
    "\n",
    "Ntr = x_train.shape[0]; print('Number of training samples:', Ntr) # Number of training samples 50,000\n",
    "Nte = x_test.shape[0]; print('Number of test samples: ',Nte,'\\n')      # Number of test samples 10,000\n",
    "Din = 3072 # CIFAR10 # 32x32x3 = height x width x channel\n",
    "\n",
    "# Image data preprocessing\n",
    "\"\"\"\n",
    "Remove the normalization. Otherwise the model will not learn.\n",
    "Because when the weights are extrememly small,\n",
    "weight matrix will consist of almost the same elements.\n",
    "and learning will stop.\n",
    "\"\"\"\n",
    "#x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "mean_image = np.mean(x_train, axis=0) # axis=0: mean of a column; Mean of each pixel\n",
    "x_train = x_train - mean_image\n",
    "x_test = x_test - mean_image\n",
    "\n",
    "# Convert class vectors to binary class matrices.\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=K); print('y_train: ', y_train.shape); #print(y_train[0:10,:])\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes=K); #print(y_test[0:10,:])\n",
    "\n",
    "x_train = np.reshape(x_train,(Ntr,Din)).astype('float32');# print(x_train[0:10, 0:20])\n",
    "x_test = np.reshape(x_test,(Nte,Din)).astype('float32')\n",
    "print('Reshaped x_train: ', x_train.shape)\n",
    "print('Reshaped x_test: ', x_test.shape)\n",
    "print(\"Pre-processing completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing the weight matrix with random weights...\n",
      "w1: (3072, 200)\n",
      "b1: (200,)\n",
      "w2: (200, 10)\n",
      "b2: (10,)\n",
      "Rearranging train and test samples...\n",
      "Rearranged x_train:  (50000, 3073)\n",
      "Rearranged w1:  (3073, 200)\n",
      "Rearranged w2:  (201, 10)\n",
      "Rearranging completed.\n",
      "Running gradient descent...\n",
      "| Epoch 001 | Loss 0.5000 | accuracy: 0.1000 | val_loss: 0.4541 | val_accuracy: 0.1005 |\n",
      "| Epoch 030 | Loss 0.4202 | accuracy: 0.2740 | val_loss: 0.4203 | val_accuracy: 0.2718 |\n",
      "| Epoch 060 | Loss 0.4101 | accuracy: 0.3362 | val_loss: 0.4097 | val_accuracy: 0.3352 |\n",
      "| Epoch 090 | Loss 0.4034 | accuracy: 0.3539 | val_loss: 0.4034 | val_accuracy: 0.3545 |\n",
      "| Epoch 120 | Loss 0.3943 | accuracy: 0.3891 | val_loss: 0.3955 | val_accuracy: 0.3832 |\n",
      "| Epoch 150 | Loss 0.3898 | accuracy: 0.4039 | val_loss: 0.3913 | val_accuracy: 0.4040 |\n",
      "| Epoch 180 | Loss 0.3880 | accuracy: 0.4071 | val_loss: 0.3897 | val_accuracy: 0.4013 |\n",
      "| Epoch 210 | Loss 0.3846 | accuracy: 0.4180 | val_loss: 0.3868 | val_accuracy: 0.4115 |\n",
      "| Epoch 240 | Loss 0.3821 | accuracy: 0.4263 | val_loss: 0.3853 | val_accuracy: 0.4178 |\n",
      "| Epoch 270 | Loss 0.3785 | accuracy: 0.4380 | val_loss: 0.3829 | val_accuracy: 0.4259 |\n",
      "| Epoch 300 | Loss 0.3777 | accuracy: 0.4379 | val_loss: 0.3827 | val_accuracy: 0.4233 |\n",
      "Gradient Descent completed. Parameters were trained\n"
     ]
    }
   ],
   "source": [
    "H = 200 # No of hidden nodes\n",
    "print(\"Initializing the weight matrix with random weights...\")\n",
    "std=1e-5 # For random samples from N(\\mu, \\sigma^2), use: sigma * np.random.randn(...) + mu\n",
    "\n",
    "# Hidden Layer \n",
    "w1 = std*np.random.randn(Din, H) # Initializing the weight matrix with random weights\n",
    "b1 = np.zeros(H) # Initializing the bias vector\n",
    "print(\"w1:\", w1.shape);print(\"b1:\", b1.shape)\n",
    "\n",
    "# Last Layer\n",
    "w2 = std*np.random.randn(H, K) # Initializing the weight matrix with random weights\n",
    "b2 = np.zeros(K) # Initializing the bias vector\n",
    "print(\"w2:\", w2.shape);print(\"b2:\", b2.shape)\n",
    "\n",
    "print(\"Rearranging train and test samples...\")\n",
    "# Rearranging train and test samples: (ra=rearranged)\n",
    "x_train_ra = np.concatenate((np.ones((x_train.shape[0],1)),x_train), axis=1); print('Rearranged x_train: ', x_train_ra.shape)\n",
    "x_test_ra  = np.concatenate((np.ones((x_test.shape[0],1)),x_test), axis=1)\n",
    "\n",
    "# Rearranging weight matrices and bias vectors into single matrices\n",
    "w1 = np.concatenate((b1.reshape(1,H), w1), axis=0); print('Rearranged w1: ',w1.shape)\n",
    "w2 = np.concatenate((b2.reshape(1,K), w2), axis=0); print('Rearranged w2: ',w2.shape)\n",
    "\n",
    "print(\"Rearranging completed.\")\n",
    "\n",
    "iterations = 300  # Gradient descent interations\n",
    "lr = 1.4e-2 # Learninig rate\n",
    "lr_decay= 0.999\n",
    "reg = 5e-6\n",
    "test_loss = []\n",
    "loss_history = [] # Vlaues of cost function at each iteration \n",
    "train_acc_history = []\n",
    "val_acc_history = []\n",
    "\n",
    "m = x_train.shape[0]  # Number of training examples\n",
    "m2 = x_test_ra.shape[0]\n",
    "# Running gradient descent number of times speciied in iterations\n",
    "print(\"Running gradient descent...\")\n",
    "\n",
    "for t in range(1,iterations+1):\n",
    "    # Forward Propagation\n",
    "    hypo = sigmoid(x_train_ra.dot(w1)) # Layer 1 with sigmoid activation\n",
    "    hypothesis = np.concatenate((np.ones((hypo.shape[0],1)),hypo), axis=1) # Rearranging for layer 2\n",
    "    predict = hypothesis.dot(w2) # Layer 2 \n",
    "    \n",
    "    loss = (1/(2*m))*np.sum(( predict - y_train)**2)\\\n",
    "         + (1/(2*m))*reg*np.sum(w1**2) + (1/(2*m))*reg*np.sum(w2**2)\n",
    "    loss_history.append(loss)\n",
    "    \n",
    "    # Back Propagation partial dertivatives of Loss function\n",
    "    # (dl/dw2) = (dl/dpredict)(dpredic/dw2)\n",
    "    dpredict =  (1/m)*(predict - y_train)\n",
    "    dw2 = hypothesis.T.dot(dpredict) + (1/m)*reg*w2\n",
    "    \n",
    "    # (dl/dw1) = (dl/dh)(dh/dw1)\n",
    "    # (dl/dw1) = (dl/dpredict)(dpredic/dh) * (dh/dw1x)(dw1x/dw1)\n",
    "    dh = dpredict.dot(w2[1:,].T) # Removing bias vector w2(201x10)--> 200x10\n",
    "    dhdxw1 = hypo*(1 - hypo) #using hypothesis 50000*200 before rearranging.\n",
    "    dw1 = x_train_ra.T.dot(dh*dhdxw1) + (1/m)*reg*w1\n",
    "    \n",
    "    # Gradient Descent\n",
    "    w1 = w1 - lr*dw1\n",
    "    w2 = w2 - lr*dw2\n",
    "    \n",
    "    # Training Accuracy \n",
    "    train_acc = getAccuracy(predict, y_train)\n",
    "    train_acc_history.append(train_acc)\n",
    "    \n",
    "    # Validation Accuracy\n",
    "    test_hypo = sigmoid(x_test_ra.dot(w1))\n",
    "    test_hypothesis = np.concatenate((np.ones((test_hypo.shape[0],1)),test_hypo), axis=1)# Rearranging for layer 2\n",
    "    test_predict = test_hypothesis.dot(w2)\n",
    "    valid_acc = getAccuracy(test_predict, y_test)\n",
    "    val_acc_history.append(valid_acc)\n",
    "    \n",
    "    # Test Loss    \n",
    "    t_loss = (1/(2*m2))*np.sum(( test_predict- y_test)**2)\\\n",
    "         + (1/(2*m2))*reg*np.sum(w1**2) + (1/(2*m2))*reg*np.sum(w2**2)\n",
    "    test_loss.append(t_loss)\n",
    "    \n",
    "    # Print details for selected iterations\n",
    "    if (t%30==0) or (t==1):\n",
    "        print(\"| Epoch {:03} | Loss {:.4f} | accuracy: {:.4f} | val_loss: {:.4f} | val_accuracy: {:.4f} |\"\\\n",
    "             .format(t, loss, train_acc, t_loss, valid_acc))\n",
    "        \n",
    "    # Decaying learning rate\n",
    "\n",
    "    lr = lr*lr_decay\n",
    "    \n",
    "print(\"Gradient Descent completed. Parameters were trained\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------Plotting learning rate, training and testing loss and accuracies-------\n",
    "# fig, axes  = plt.subplots(1,4, sharex='all', sharey='all', figsize=(30,6))\n",
    "# items = {\"Loss\":loss_history, \"Training Accuracy\":train_acc_history,\\\n",
    "#          \"Validation Accuracy\": val_acc_history, \"Learning Rate\":lr_hitory}\n",
    "# location = 1\n",
    "# for key in items.keys():\n",
    "#     plt.subplot(1,4,location);plt.plot(items[key], color='#0000ff', linewidth=3)\n",
    "#     plt.title(key)\n",
    "#     location+=1\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.plot(loss_history/np.max(loss_history), linewidth=3, label = 'Train Loss')\n",
    "plt.plot(test_loss/np.max(test_loss), linewidth=3, label = 'Test Loss')\n",
    "plt.plot(train_acc_history, linewidth=3, label = \"Training Accuracy\")\n",
    "plt.plot(val_acc_history,  linewidth=3, label =  \"Validation Accuracy\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy and Normalized Loss')\n",
    "plt.legend(loc='lower right')    \n",
    "# saveto(\"part2plots.eps\")\n",
    "\n",
    "# -------------------Showing the weights matrix W1.W2 as 10 images-----------------\n",
    "weights = w1[1:,].dot(w2[1:,]) # Removing the rows of bias terms.\n",
    "weights_pos =  weights- np.min(weights)# Making the minimum weight zero.\n",
    "images = ((weights_pos/np.max(weights_pos))*255).astype('uint8')\n",
    "CIFAR10 = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "fig, axes  = plt.subplots(2,5, sharex='all', sharey='all', figsize=(25,10))\n",
    "location = 1 # Location of the image in the grid of 2x5\n",
    "for i in range(K):\n",
    "    image = images[:,i].reshape(32,32,3)\n",
    "    plt.subplot(2,5,location),plt.imshow(image[:,:,::-1])\n",
    "    plt.title(\"Class: {}\".format(CIFAR10[i])),plt.xticks([]),plt.yticks([])    \n",
    "#     saveimg(\"Reg Image \"+ str(i)+\".jpg\", image)\n",
    "    location+=1\n",
    "# saveto(\"trainedWeightsnn2.eps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3\n",
    "\n",
    "Modify the code in item 2 to carry out stochastic gradient descent with a batch size of 500. [2 marks]\n",
    "1. Report training and testing loss and accuracies.\n",
    "2. Compare results with item2 (justify).\n",
    "\n",
    "[Reference](https://realpython.com/gradient-descent-algorithm-python/#minibatches-in-stochastic-gradient-descent)\n",
    "\n",
    "* Stochastic gradient descent randomly divides the set of observations into minibatches.\n",
    "* For each minibatch, the gradient is computed and the vector is moved.\n",
    "* Once all minibatches are used, you say that the iteration, or epoch, is finished and start the next one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (50000, 32, 32, 3)\n",
      "y_train:  (50000, 1)\n",
      "Pre-processing loaded data...\n",
      "\n",
      "Number of training samples: 50000\n",
      "Number of test samples:  10000 \n",
      "\n",
      "y_train:  (50000, 10)\n",
      "Reshaped x_train:  (50000, 3072)\n",
      "Reshaped x_test:  (10000, 3072)\n",
      "Pre-processing completed.\n"
     ]
    }
   ],
   "source": [
    "# Loading the Data Set\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "print('x_train: ', x_train.shape); print('y_train: ', y_train.shape)\n",
    "#print(y_train[0:10])\n",
    "\n",
    "print(\"Pre-processing loaded data...\\n\")\n",
    "# y_train contains labels form 0 to 9 corresponding to 10 classes.\n",
    "K = len(np.unique(y_train)) # Number of Classes\n",
    "\n",
    "Ntr = x_train.shape[0]; print('Number of training samples:', Ntr) # Number of training samples 50,000\n",
    "Nte = x_test.shape[0]; print('Number of test samples: ',Nte,'\\n')      # Number of test samples 10,000\n",
    "Din = 3072 # CIFAR10 # 32x32x3 = height x width x channel\n",
    "\n",
    "# Image data preprocessing\n",
    "\"\"\"\n",
    "Remove the normalization. Otherwise the model will not learn.\n",
    "Because when the weights are extrememly small,\n",
    "weight matrix will consist of almost the same elements.\n",
    "and learning will stop.\n",
    "\"\"\"\n",
    "#x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "mean_image = np.mean(x_train, axis=0) # axis=0: mean of a column; Mean of each pixel\n",
    "x_train = x_train - mean_image\n",
    "x_test = x_test - mean_image\n",
    "\n",
    "# Convert class vectors to binary class matrices.\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=K); print('y_train: ', y_train.shape); #print(y_train[0:10,:])\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes=K); #print(y_test[0:10,:])\n",
    "\n",
    "x_train = np.reshape(x_train,(Ntr,Din)).astype('float32');# print(x_train[0:10, 0:20])\n",
    "x_test = np.reshape(x_test,(Nte,Din)).astype('float32')\n",
    "print('Reshaped x_train: ', x_train.shape)\n",
    "print('Reshaped x_test: ', x_test.shape)\n",
    "print(\"Pre-processing completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing the weight matrix with random weights...\n",
      "w1: (3072, 200)\n",
      "b1: (200,)\n",
      "w2: (200, 10)\n",
      "b2: (10,)\n",
      "Rearranging train and test samples...\n",
      "Rearranged x_train:  (50000, 3073)\n",
      "Rearranged w1:  (3073, 200)\n",
      "Rearranged w2:  (201, 10)\n",
      "Rearranging completed.\n",
      "Running stochastic gradient descent...\n",
      "| Epoch 001 | Loss 0.4768 | accuracy: 0.1000 | val_loss: 0.4619 | val_accuracy: 0.1000 |\n",
      "| Epoch 030 | Loss 0.4182 | accuracy: 0.2812 | val_loss: 0.4176 | val_accuracy: 0.2813 |\n",
      "| Epoch 060 | Loss 0.4061 | accuracy: 0.3500 | val_loss: 0.4061 | val_accuracy: 0.3491 |\n",
      "| Epoch 090 | Loss 0.3966 | accuracy: 0.3836 | val_loss: 0.3972 | val_accuracy: 0.3837 |\n",
      "| Epoch 120 | Loss 0.3894 | accuracy: 0.4057 | val_loss: 0.3909 | val_accuracy: 0.4007 |\n",
      "| Epoch 150 | Loss 0.3844 | accuracy: 0.4207 | val_loss: 0.3869 | val_accuracy: 0.4130 |\n",
      "| Epoch 180 | Loss 0.3804 | accuracy: 0.4325 | val_loss: 0.3841 | val_accuracy: 0.4239 |\n",
      "| Epoch 210 | Loss 0.3770 | accuracy: 0.4425 | val_loss: 0.3819 | val_accuracy: 0.4307 |\n",
      "| Epoch 240 | Loss 0.3740 | accuracy: 0.4514 | val_loss: 0.3800 | val_accuracy: 0.4339 |\n",
      "| Epoch 270 | Loss 0.3712 | accuracy: 0.4590 | val_loss: 0.3783 | val_accuracy: 0.4384 |\n",
      "| Epoch 300 | Loss 0.3686 | accuracy: 0.4663 | val_loss: 0.3769 | val_accuracy: 0.4433 |\n",
      "Stochastic Gradient Descent completed in 23.0 minutes 60.00 seconds.Parameters were trained.\n"
     ]
    }
   ],
   "source": [
    "H = 200 # No of hidden nodes\n",
    "print(\"Initializing the weight matrix with random weights...\")\n",
    "std=1e-5 # For random samples from N(\\mu, \\sigma^2), use: sigma * np.random.randn(...) + mu\n",
    "\n",
    "# Hidden Layer \n",
    "w1 = std*np.random.randn(Din, H) # Initializing the weight matrix with random weights\n",
    "b1 = np.zeros(H) # Initializing the bias vector\n",
    "print(\"w1:\", w1.shape);print(\"b1:\", b1.shape)\n",
    "\n",
    "# Last Layer\n",
    "w2 = std*np.random.randn(H, K) # Initializing the weight matrix with random weights\n",
    "b2 = np.zeros(K) # Initializing the bias vector\n",
    "print(\"w2:\", w2.shape);print(\"b2:\", b2.shape)\n",
    "\n",
    "print(\"Rearranging train and test samples...\")\n",
    "# Rearranging train and test samples: (ra=rearranged)\n",
    "x_train_ra = np.concatenate((np.ones((x_train.shape[0],1)),x_train), axis=1); print('Rearranged x_train: ', x_train_ra.shape)\n",
    "x_test_ra  = np.concatenate((np.ones((x_test.shape[0],1)),x_test), axis=1)\n",
    "\n",
    "# Rearranging weight matrices and bias vectors into single matrices\n",
    "w1 = np.concatenate((b1.reshape(1,H), w1), axis=0); print('Rearranged w1: ',w1.shape)\n",
    "w2 = np.concatenate((b2.reshape(1,K), w2), axis=0); print('Rearranged w2: ',w2.shape)\n",
    "\n",
    "print(\"Rearranging completed.\")\n",
    "\n",
    "iterations = 300  # Gradient descent interations\n",
    "lr = 1.4e-2 # Learninig rate\n",
    "lr_decay= 0.999\n",
    "reg = 5e-6\n",
    "test_loss = []\n",
    "loss_history = [] # Vlaues of cost function at each iteration \n",
    "train_acc_history = []\n",
    "val_acc_history = []\n",
    "mini_batch_loss = []\n",
    "\n",
    "m = x_train.shape[0]  # Number of training examples\n",
    "m2 = x_test.shape[0]\n",
    "# Running gradient descent number of times speciied in iterations\n",
    "print(\"Running stochastic gradient descent...\")\n",
    "\n",
    "beginat = time.time()\n",
    "\n",
    "batch_size = 500 \n",
    "seed = 0\n",
    "rng = np.random.default_rng(seed=seed)\n",
    "for t in range(1,iterations+1):\n",
    "    indices = np.arange(Ntr)\n",
    "    rng.shuffle(indices)\n",
    "    x_train_3 = x_train_ra[indices]\n",
    "    y_train_3 = y_train[indices]\n",
    "    \n",
    "    batch_loss = 0\n",
    "    for start in range(0,Ntr,batch_size):\n",
    "        stop = start + batch_size\n",
    "        # Forward Propagation\n",
    "        hypo = sigmoid(x_train_3[start:stop].dot(w1)) # Layer 1 with sigmoid activation\n",
    "        hypothesis = np.concatenate((np.ones((hypo.shape[0],1)),hypo), axis=1) # Rearranging for layer 2\n",
    "        predict = hypothesis.dot(w2) # Layer 2 \n",
    "\n",
    "        minibatch_loss = (1/(2*m))*np.sum(( predict - y_train_3[start:stop])**2)\\\n",
    "             + (1/(2*m))*reg*np.sum(w1**2) + (1/(2*m))*reg*np.sum(w2**2)\n",
    "        \n",
    "        mini_batch_loss.append(minibatch_loss)\n",
    "        batch_loss+= minibatch_loss\n",
    "\n",
    "        # Back Propagation partial dertivatives of Loss function\n",
    "        # (dl/dw2) = (dl/dpredict)(dpredic/dw2)\n",
    "        dpredict =  (1/m)*(predict - y_train_3[start:stop])\n",
    "        dw2 = hypothesis.T.dot(dpredict) + (1/m)*reg*w2\n",
    "\n",
    "        # (dl/dw1) = (dl/dh)(dh/dw1)\n",
    "        # (dl/dw1) = (dl/dpredict)(dpredic/dh) * (dh/dw1x)(dw1x/dw1)\n",
    "        dh = dpredict.dot(w2[1:,].T) # Removing bias vector w2(201x10)--> 200x10\n",
    "        dhdxw1 = hypo*(1 - hypo) #using hypothesis 50000*200 before rearranging.\n",
    "        dw1 = x_train_3[start:stop].T.dot(dh*dhdxw1) + (1/m)*reg*w1\n",
    "\n",
    "        # Gradient Descent\n",
    "        w1 = w1 - lr*dw1\n",
    "        w2 = w2 - lr*dw2\n",
    "    \n",
    "    loss_history.append(batch_loss)\n",
    "    \n",
    "    # Training Accuracy\n",
    "    hypo = sigmoid(x_train_3.dot(w1)) # Layer 1 with sigmoid activation\n",
    "    hypothesis = np.concatenate((np.ones((hypo.shape[0],1)),hypo), axis=1) # Rearranging for layer 2\n",
    "    predict = hypothesis.dot(w2) # Layer 2 \n",
    "    train_acc = getAccuracy(predict, y_train_3)\n",
    "    train_acc_history.append(train_acc)\n",
    "    \n",
    "    # Validation Accuracy\n",
    "    test_hypo = sigmoid(x_test_ra.dot(w1))\n",
    "    test_hypothesis = np.concatenate((np.ones((test_hypo.shape[0],1)),test_hypo), axis=1)# Rearranging for layer 2\n",
    "    test_predict = test_hypothesis.dot(w2)\n",
    "    valid_acc = getAccuracy(test_predict, y_test)\n",
    "    val_acc_history.append(valid_acc)\n",
    "    \n",
    "    # Test Loss    \n",
    "    t_loss = (1/(2*m2))*np.sum(( test_predict- y_test)**2)\\\n",
    "         + (1/(2*m2))*reg*np.sum(w1**2) + (1/(2*m2))*reg*np.sum(w2**2)\n",
    "    test_loss.append(t_loss)\n",
    "    \n",
    "    # Print details for selected iterations\n",
    "    if (t%30==0) or (t==1):\n",
    "        print(\"| Epoch {:03} | Loss {:.4f} | accuracy: {:.4f} | val_loss: {:.4f} | val_accuracy: {:.4f} |\"\\\n",
    "             .format(t, batch_loss, train_acc, t_loss, valid_acc))\n",
    "    #loss: 1.8916 - accuracy: 0.2991 - val_loss: 1.2849 - val_accuracy: 0.5374\n",
    "    # Decaying learning rate\n",
    " \n",
    "    lr = lr*lr_decay\n",
    "\n",
    "endat = time.time()    \n",
    "duration = endat - beginat\n",
    "print(\"Stochastic Gradient Descent completed in {} minutes {:.2f} seconds.Parameters were trained.\"\\\n",
    "      .format(duration//60, duration%60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ----------------------------Plotting Minibatch loss--------------------------\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.plot(mini_batch_loss); plt.title('Minibatch Loss')\n",
    "plt.xlabel(\"Total Number of Iterations\"); #saveto('part3minibloss.eps')\n",
    "\n",
    "# ------Plotting learning rate, training and testing loss and accuracies-------\n",
    "# fig, axes  = plt.subplots(1,4, sharex='all', sharey='all', figsize=(30,6))\n",
    "# items = {\"Loss\":loss_history, \"Training Accuracy %\":train_acc_history,\\\n",
    "#          \"Validation Accuracy %\": val_acc_history, \"Learning Rate\":lr_hitory}\n",
    "# location = 1\n",
    "# for key in items.keys():\n",
    "#     plt.subplot(1,4,location);plt.plot(items[key], color='#0000ff', linewidth=3)\n",
    "#     plt.title(key); plt.xlabel('Number of Iterations')\n",
    "#     location+=1\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.plot(loss_history/np.max(loss_history), linewidth=3, label = 'Train Loss')\n",
    "plt.plot(test_loss/np.max(test_loss), linewidth=3, label = 'Test Loss')\n",
    "plt.plot(train_acc_history, linewidth=3, label = \"Training Accuracy\")\n",
    "plt.plot(val_acc_history,  linewidth=3, label =  \"Validation Accuracy\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy and Normalized Loss')\n",
    "plt.legend(loc='lower right')\n",
    "# saveto(\"part3plots.eps\")\n",
    "\n",
    "# -------------------Showing the weights matrix W1.W2 as 10 images-----------------\n",
    "weights = w1[1:,].dot(w2[1:,]) # Removing the rows of bias terms.\n",
    "weights_pos =  weights- np.min(weights)# Making the minimum weight zero.\n",
    "images = ((weights_pos/np.max(weights_pos))*255).astype('uint8')\n",
    "CIFAR10 = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "fig, axes  = plt.subplots(2,5, sharex='all', sharey='all', figsize=(25,10))\n",
    "location = 1 # Location of the image in the grid of 2x5\n",
    "for i in range(K):\n",
    "    image = images[:,i].reshape(32,32,3)\n",
    "    plt.subplot(2,5,location),plt.imshow(image[:,:,::-1])\n",
    "    plt.title(\"Class: {}\".format(CIFAR10[i])), plt.xticks([]),plt.yticks([]) \n",
    "#     saveimg(CIFAR10[i] +\".jpg\", image)\n",
    "    location+=1\n",
    "# saveto(\"trainedWeightsnn2stochastic.eps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4 \n",
    "Construct a CNN using Keras.models.Sequential (with the following configuration: C32, C64, C64, F64, F10. All three convolutions layers are 3x3. Max pooling (2x2) follows each convolution layer. Use SDG (with momentum) with a batch size of 50 and CategoricalCrossentropy as the loss. [2\n",
    "marks]\n",
    "1. How many learnable parameters are there in this network?\n",
    "2. Report the parameters such as the learning rate and momentum.\n",
    "3. Report training and testing loss and accuracies.\n",
    "\n",
    "[Code Reference](https://www.tensorflow.org/tutorials/images/cnn),\n",
    "[model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit),\n",
    "[CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy),\n",
    "[sgd](https://keras.io/api/optimizers/sgd/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "C32 (Conv2D)                 (None, 30, 30, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "C64_1 (Conv2D)               (None, 13, 13, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "C64_2 (Conv2D)               (None, 4, 4, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "F64 (Dense)                  (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "F10 (Dense)                  (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 73,418\n",
      "Trainable params: 73,418\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/10\n",
      "1000/1000 [==============================] - 58s 14ms/step - loss: 1.8814 - accuracy: 0.2973 - val_loss: 1.2735 - val_accuracy: 0.5427\n",
      "Epoch 2/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 1.2343 - accuracy: 0.5598 - val_loss: 1.0891 - val_accuracy: 0.6179\n",
      "Epoch 3/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 1.0269 - accuracy: 0.6372 - val_loss: 1.0110 - val_accuracy: 0.6507\n",
      "Epoch 4/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.8947 - accuracy: 0.6852 - val_loss: 0.9615 - val_accuracy: 0.6698\n",
      "Epoch 5/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.8072 - accuracy: 0.7176 - val_loss: 0.9093 - val_accuracy: 0.6863\n",
      "Epoch 6/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.7252 - accuracy: 0.7482 - val_loss: 0.8807 - val_accuracy: 0.7078\n",
      "Epoch 7/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.6745 - accuracy: 0.7618 - val_loss: 0.9267 - val_accuracy: 0.6909\n",
      "Epoch 8/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.6278 - accuracy: 0.7780 - val_loss: 0.8539 - val_accuracy: 0.7137\n",
      "Epoch 9/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.5793 - accuracy: 0.7968 - val_loss: 0.8795 - val_accuracy: 0.7131\n",
      "Epoch 10/10\n",
      "1000/1000 [==============================] - 8s 8ms/step - loss: 0.5406 - accuracy: 0.8113 - val_loss: 0.8898 - val_accuracy: 0.7134\n",
      "313/313 - 9s - loss: 0.8898 - accuracy: 0.7134\n",
      "0.7134000062942505\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
    "K = len(np.unique(y_train)) # Number of Classes\n",
    "\n",
    "# Normalize pixel values: Image data preprocessing\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "mean_image = np.mean(x_train, axis=0) # axis=0: mean of a column; Mean of each pixel\n",
    "x_train = x_train - mean_image\n",
    "x_test = x_test - mean_image\n",
    "\n",
    "# Convert class vectors to binary class matrices.\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=K)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes=K)\n",
    "\n",
    "#number of output channels for each Conv2D layer is controlled by the first argument\n",
    "model = models.Sequential()\n",
    "\n",
    "# As we go deeper into the model height and widht shrinks\n",
    "# So we can increase the convolution channels\n",
    "\n",
    "# 32, 3x3 convolutions\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3), name='C32'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# 64, 3x3 convolutions\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu', name='C64_1'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# 64, 3x3 convolutions\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu', name='C64_2'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "#feeding the last output tensor from the convolutional base \n",
    "#  (of shape (None, 2, 2, 64)) into one or more Dense layers\n",
    "# Dense layers take vectors as input\n",
    "\n",
    "model.add(layers.Flatten()) # Make the (None, 2, 2, 64) tensor flat\n",
    "model.add(layers.Dense(64, activation='relu', name='F64'))\n",
    "\n",
    "# CIFAR has 10 output classes, final Dense layer should have 10 outputs\n",
    "model.add(layers.Dense(10, name='F10'))\n",
    "\n",
    "# Complete architecture of the model\n",
    "model.summary()\n",
    "\n",
    "# An optimizer is one of the two arguments required for compiling a Keras model:\n",
    "# hyperparameter - whose value is used to control the learning process\n",
    "# momentum: float hyperparameter() >= 0 that accelerates gradient descent \n",
    "#  in the relevant direction and dampens oscillations.\n",
    "# Defaults to 0, i.e., vanilla gradient descent.\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=1.4e-2, momentum=0.9),\n",
    "              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=50, epochs=10, \n",
    "                    validation_data=(x_test, y_test))\n",
    "\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.plot(history.history['loss']/np.max(history.history['loss']), linewidth=3, label = 'Train Loss')\n",
    "plt.plot(history.history['val_loss']/np.max(history.history['val_loss']), linewidth=3, label = 'Test Loss')\n",
    "plt.plot(history.history['accuracy'], linewidth=3, label = \"Training Accuracy\")\n",
    "plt.plot(history.history['val_accuracy'],  linewidth=3, label =  \"Validation Accuracy\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy and Normalized Loss')\n",
    "plt.legend(loc='lower right')\n",
    "#saveto(\"part4plots.eps\")\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}