{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Networks - SVM Loss Function and Gradient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and Visualize CIFAR-10 Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# function to import CIFAR-10 data set\n",
    "def unpickle(file):\n",
    "    import pickle\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = pickle.load(fo, encoding='bytes')\n",
    "    return dict\n",
    "data_batch_1 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_1\")\n",
    "data_batch_2 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_2\")\n",
    "data_batch_3 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_3\")\n",
    "data_batch_4 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_4\")\n",
    "data_batch_5 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_5\")\n",
    "test_batch = unpickle(\"../Introduction_to_Image_Classification/data/test_batch\")\n",
    "\n",
    "# Let us concatenate the batch training data \n",
    "X_train=np.concatenate([data_batch_1[b'data'], \n",
    "                         data_batch_2[b'data'], \n",
    "                         data_batch_3[b'data'], \n",
    "                         data_batch_4[b'data'], \n",
    "                         data_batch_5[b'data']], \n",
    "                         axis = 0)\n",
    "\n",
    "\n",
    "# What is the shape of Xtr_rows ?\n",
    "X_train.shape\n",
    "\n",
    "\n",
    "# Let us concatenate the training labels\n",
    "y_train=np.concatenate([data_batch_1[b'labels'] , \n",
    "                data_batch_2[b'labels'],\n",
    "                data_batch_3[b'labels'],\n",
    "                data_batch_4[b'labels'],\n",
    "                data_batch_5[b'labels']], \n",
    "                axis = 0)\n",
    "\n",
    "# Let us define the test data as X_test\n",
    "X_test=test_batch[b'data']\n",
    "X_test.shape\n",
    "\n",
    "# Let us cast the test labels as ndarray\n",
    "y_test=np.array(test_batch[b'labels']) \n",
    "y_test.shape\n",
    "\n",
    "\n",
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "\n",
    "\n",
    "\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()\n",
    "\n",
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Preprocessing the Data : Subtract the Mean Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130 130 130 131 132 132 133 133 134 134]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# First: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0).astype('uint8')\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "# visualize the mean image\n",
    "plt.imshow(mean_image.reshape((3,32,32)).transpose((1,2,0))) \n",
    "plt.show()\n",
    "\n",
    "# Second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "\n",
    "# Third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Loss Function and Gradient (Not Vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 10.649423\n"
     ]
    }
   ],
   "source": [
    "from random import shuffle\n",
    "\n",
    "def svm_loss_naive(W, X, y, reg):\n",
    "  \"\"\"\n",
    "  Structured SVM loss function, naive implementation (with loops).\n",
    "\n",
    "  Inputs have dimension D, there are C classes, and we operate on minibatches\n",
    "  of N examples.\n",
    "\n",
    "  Inputs:\n",
    "  - W: A numpy array of shape (D, C) containing weights.\n",
    "  - X: A numpy array of shape (N, D) containing a minibatch of data.\n",
    "  - y: A numpy array of shape (N,) containing training labels; y[i] = c means\n",
    "    that X[i] has label c, where 0 <= c < C.\n",
    "  - reg: (float) regularization strength\n",
    "\n",
    "  Returns a tuple of:\n",
    "  - loss as single float\n",
    "  - gradient with respect to weights W; an array of same shape as W\n",
    "    To be precise: it is the Jacobian matrix of L with respect to all \n",
    "    matrix elements of W : dW is shorthand notation for dL/dW_ij\n",
    "  \"\"\"\n",
    "  \n",
    "\n",
    "  \n",
    "  # initialize the gradient as zero\n",
    "  dW = np.zeros(W.shape) \n",
    "  # compute the loss and the gradient\n",
    "  num_classes = W.shape[1]\n",
    "  num_train = X.shape[0]\n",
    "  loss = 0.0\n",
    "  for i in range(num_train):\n",
    "      scores = X[i].dot(W)\n",
    "      correct_class_score = scores[y[i]]\n",
    "      diff_count = 0  \n",
    "      for j in range(num_classes):\n",
    "          margin = scores[j] - correct_class_score + 1\n",
    "          if j == y[i]:\n",
    "              continue\n",
    "          if margin > 0:\n",
    "              diff_count += 1\n",
    "              # gradient update for incorrect rows\n",
    "              dW[:, j] += X[i] \n",
    "              loss += margin\n",
    "      # gradient update for correct row\n",
    "      dW[:, y[i]] += -diff_count * X[i]\n",
    "\n",
    "  # Right now the loss is a sum over all training examples, but we want it\n",
    "  # to be an average instead so we divide by num_train.\n",
    "  loss /= num_train\n",
    "  dW /= num_train\n",
    "  dW += reg*W # regularize the weights\n",
    "  # Add regularization to the loss.\n",
    "  loss += 0.5 * reg * np.sum(W * W)     \n",
    "  \n",
    "  # Add regularization to the loss.\n",
    "  loss += 0.5 * reg * np.sum(W * W)\n",
    "\n",
    "  return loss, dW\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "print('loss: %f' % (loss, ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Gradient Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute numerically the gradient along several randomly chosen \n",
    "dimensions, and compare them with our analytically computed gradient. \n",
    "The numbers should match almost exactly along all dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 27.222436 analytic: 27.132000, relative error: 1.663815e-03\n",
      "numerical: 1.851809 analytic: 1.744000, relative error: 2.998176e-02\n",
      "numerical: 72.183250 analytic: 72.012000, relative error: 1.187625e-03\n",
      "numerical: 84.852940 analytic: 85.228000, relative error: 2.205186e-03\n",
      "numerical: 78.516453 analytic: 78.556000, relative error: 2.517782e-04\n",
      "numerical: -7.425685 analytic: -7.556000, relative error: 8.698258e-03\n",
      "numerical: 65.614426 analytic: 65.438000, relative error: 1.346222e-03\n",
      "numerical: -70.012436 analytic: -69.910000, relative error: 7.320894e-04\n",
      "numerical: -19.432000 analytic: -19.432000, relative error: 3.065386e-12\n",
      "numerical: 72.136453 analytic: 72.214000, relative error: 5.372163e-04\n",
      "numerical: 83.964054 analytic: 84.352057, relative error: 2.305204e-03\n",
      "numerical: 77.114869 analytic: 77.281147, relative error: 1.076960e-03\n",
      "numerical: -40.186427 analytic: -40.202214, relative error: 1.963765e-04\n",
      "numerical: -46.241618 analytic: -46.248809, relative error: 7.774815e-05\n",
      "numerical: -7.937252 analytic: -8.083509, relative error: 9.129267e-03\n",
      "numerical: -87.263637 analytic: -87.255818, relative error: 4.479917e-05\n",
      "numerical: 43.166927 analytic: 43.066250, relative error: 1.167487e-03\n",
      "numerical: -12.293494 analytic: -12.292130, relative error: 5.544742e-05\n",
      "numerical: 73.631560 analytic: 73.687554, relative error: 3.800846e-04\n",
      "numerical: -85.680921 analytic: -85.620803, relative error: 3.509477e-04\n"
     ]
    }
   ],
   "source": [
    "def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5):\n",
    "  \"\"\"\n",
    "  sample a few random elements and only return numerical values\n",
    "  in this dimensions.\n",
    "  - f : is the loss function which will be passed to grad_check_sparse \n",
    "  as a lambda function\n",
    "  - x : is the array containing the weight matrix\n",
    "  - num_checks : how many elements of the array are randomly sampled\n",
    "  \"\"\"\n",
    "\n",
    "  for i in range(num_checks):\n",
    "    ix = tuple([np.random.randint(m) for m in x.shape])\n",
    "\n",
    "    oldval = x[ix]\n",
    "    # increment by h\n",
    "    x[ix] = oldval + h \n",
    "    # evaluate f(x + h)\n",
    "    fxph = f(x)\n",
    "    # increment by h\n",
    "    x[ix] = oldval - h \n",
    "    # evaluate f(x - h)\n",
    "    fxmh = f(x) \n",
    "    # reset\n",
    "    x[ix] = oldval \n",
    "\n",
    "    grad_numerical = (fxph - fxmh) / (2 * h)\n",
    "    grad_analytic = analytic_grad[ix]\n",
    "    rel_error = abs(grad_numerical - grad_analytic) / (abs(grad_numerical) + abs(grad_analytic))\n",
    "    print('numerical: %f analytic: %f, relative error: %e' % (grad_numerical, grad_analytic, rel_error))\n",
    "\n",
    "\n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Loss Function and Gradient (Vectorized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement the function `svm_loss_vectorized`; we compute\n",
    "the loss and the gradient by means of vectorized operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm_loss_vectorized(W, X, y, reg):\n",
    "  \"\"\"\n",
    "  Structured SVM loss function, vectorized implementation.\n",
    "\n",
    "  Inputs and outputs are the same as svm_loss_naive.\n",
    "    Inputs have dimension D, there are C classes, and we operate on minibatches\n",
    "  of N examples.\n",
    "\n",
    "  Inputs:\n",
    "  - W: A numpy array of shape (D, C) containing weights.\n",
    "  - X: A numpy array of shape (N, D) containing a minibatch of data.\n",
    "  - y: A numpy array of shape (N,) containing training labels; y[i] = c means\n",
    "    that X[i] has label c, where 0 <= c < C.\n",
    "  - reg: (float) regularization strength\n",
    "\n",
    "  Returns a tuple of:\n",
    "  - loss as single float\n",
    "  - gradient with respect to weights W; an array of same shape as W\n",
    "  \"\"\"\n",
    "  loss = 0.0\n",
    "  delta = 1\n",
    "  # initialize the gradient as zero\n",
    "  dW = np.zeros(W.shape) \n",
    "  # compute the loss \n",
    "  num_train = X.shape[0]\n",
    "  scores = X.dot(W)\n",
    "  correct_class_score = scores[np.arange(num_train), y]\n",
    "  margin = scores - correct_class_score[:, np.newaxis] + delta\n",
    "  margin[np.arange(num_train), y] = 0\n",
    "  margin = np.where(margin > 0, margin, 0)\n",
    "  loss = np.sum(margin)/num_train\n",
    "  # regularization\n",
    "  loss += 0.5 * reg * np.sum(W * W) \n",
    "  \n",
    "  # Compute the gradient : fully vectorized version \n",
    "  mask = np.zeros(margin.shape)\n",
    "  # column maps to class, row maps to sample; a value v in X_mask[i, j]\n",
    "  # adds a row sample i to column class j with multiple of v\n",
    "  mask[margin > 0] = 1\n",
    "  # for each sample, find the total number of classes where margin > 0\n",
    "  incorrect_counts = np.sum(mask, axis=1)\n",
    "  mask[np.arange(num_train), y] = -incorrect_counts\n",
    "  dW = X.T.dot(mask)\n",
    "\n",
    "  dW /= num_train # average out weights\n",
    "  dW += reg*W # regularize the weights\n",
    "  \n",
    "\n",
    "  return loss, dW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Correctness and Performance of Vectorized Gradient Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 1.064942e+01 computed in 0.783425s\n",
      "Vectorized loss: 1.064942e+01 computed in 0.006955s\n",
      "difference: 0.000000\n",
      "Naive loss and gradient: computed in 0.802595s\n",
      "Vectorized loss and gradient: computed in 0.004300s\n",
      "22.7 ms ± 4.24 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tic = time.time()\n",
    "loss_naive, _ = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))\n",
    "\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# Alternative time measurement with ipython : use %timeit\n",
    "%timeit svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Classifier with Stochastic Gradient Descent (SGD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 818.604090\n",
      "iteration 100 / 1500: loss 292.089905\n",
      "iteration 200 / 1500: loss 110.877384\n",
      "iteration 300 / 1500: loss 46.280911\n",
      "iteration 400 / 1500: loss 23.123541\n",
      "iteration 500 / 1500: loss 15.756814\n",
      "iteration 600 / 1500: loss 11.933461\n",
      "iteration 700 / 1500: loss 11.412081\n",
      "iteration 800 / 1500: loss 11.247968\n",
      "iteration 900 / 1500: loss 12.900388\n",
      "iteration 1000 / 1500: loss 13.957223\n",
      "iteration 1100 / 1500: loss 10.316672\n",
      "iteration 1200 / 1500: loss 9.824412\n",
      "iteration 1300 / 1500: loss 11.212222\n",
      "iteration 1400 / 1500: loss 11.595881\n",
      "That took 30.091362s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.157102\n",
      "validation accuracy: 0.145000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class LinearClassifier():\n",
    "\n",
    "  def __init__(self):\n",
    "    self.W = None\n",
    "\n",
    "  def train(self, X, y, learning_rate=1e-3, reg=1e-5, num_iters=100,\n",
    "            batch_size=200, verbose=False):\n",
    "    \"\"\"\n",
    "    Train this linear classifier using stochastic gradient descent.\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (N, D) containing training data; there are N\n",
    "      training samples each of dimension D.\n",
    "    - y: A numpy array of shape (N,) containing training labels; y[i] = c\n",
    "      means that X[i] has label 0 <= c < C for C classes.\n",
    "    - learning_rate: (float) learning rate for optimization.\n",
    "    - reg: (float) regularization strength.\n",
    "    - num_iters: (integer) number of steps to take when optimizing\n",
    "    - batch_size: (integer) number of training examples to use at each step.\n",
    "    - verbose: (boolean) If true, print progress during optimization.\n",
    "    Outputs:\n",
    "    A list containing the value of the loss function at each training iteration.\n",
    "    \"\"\"\n",
    "    num_train, dim = X.shape\n",
    "    # assume y takes values 0...K-1 where K is number of classes\n",
    "    num_classes = np.max(y) + 1 \n",
    "    if self.W is None:\n",
    "      # lazily initialize W\n",
    "      self.W = 0.001 * np.random.randn(dim, num_classes)\n",
    "\n",
    "    # Run stochastic gradient descent to optimize W\n",
    "    loss_history = []\n",
    "    for it in range(num_iters):\n",
    "      X_batch = None\n",
    "      y_batch = None\n",
    "\n",
    "      \n",
    "      # Sample batch_size elements from the training data and their           \n",
    "      # corresponding labels to use in this round of gradient descent.        \n",
    "      # Store the data in X_batch and their corresponding labels in           \n",
    "      # y_batch; after sampling X_batch should have shape (dim, batch_size)   \n",
    "      # and y_batch should have shape (batch_size,)                           \n",
    "      #                                                                       \n",
    "      # Use np.random.choice to generate indices. Sampling with         \n",
    "      # replacement is faster than sampling without replacement.              \n",
    "     \n",
    "      sample_indices = np.random.choice(np.arange(num_train), batch_size)\n",
    "      X_batch = X[sample_indices]\n",
    "      y_batch = y[sample_indices]\n",
    "      \n",
    "\n",
    "      # evaluate loss and gradient\n",
    "      loss, grad = self.loss(X_batch, y_batch, reg)\n",
    "      loss_history.append(loss)\n",
    "\n",
    "      # perform parameter update\n",
    "      \n",
    "      # Update the weights using the gradient and the learning rate.          \n",
    "      \n",
    "      self.W += -learning_rate * grad\n",
    "     \n",
    "\n",
    "      if verbose and it % 100 == 0:\n",
    "        print('iteration %d / %d: loss %f' % (it, num_iters, loss))\n",
    "\n",
    "    return loss_history\n",
    "\n",
    "  def predict(self, X):\n",
    "    \"\"\"\n",
    "    Use the trained weights of this linear classifier to predict labels for\n",
    "    data points.\n",
    "    Inputs:\n",
    "    - X: D x N array of training data. Each column is a D-dimensional point.\n",
    "    Returns:\n",
    "    - y_pred: Predicted labels for the data in X. y_pred is a 1-dimensional\n",
    "      array of length N, and each element is an integer giving the predicted\n",
    "      class.\n",
    "    \"\"\"\n",
    "    y_pred = np.zeros(X.shape[1])\n",
    "   \n",
    "    # Implement this method. Store the predicted labels in y_pred.            \n",
    "    \n",
    "    y_pred = np.argmax(X.dot(self.W), axis=1)\n",
    "\n",
    "    return y_pred\n",
    "  \n",
    "  def loss(self, X_batch, y_batch, reg):\n",
    "    \"\"\"\n",
    "    Compute the loss function and its derivative. \n",
    "    Subclasses (child class) will override this.\n",
    "    Inputs:\n",
    "    - X_batch: A numpy array of shape (N, D) containing a minibatch of N\n",
    "      data points; each point has dimension D.\n",
    "    - y_batch: A numpy array of shape (N,) containing labels for the minibatch.\n",
    "    - reg: (float) regularization strength.\n",
    "    Returns: A tuple containing:\n",
    "    - loss as a single float\n",
    "    - gradient with respect to self.W; an array of the same shape as W\n",
    "    \"\"\"\n",
    "    pass\n",
    "\n",
    "class LinearSVM(LinearClassifier):\n",
    "  \"\"\" A subclass (child class) that uses the Multiclass SVM loss function \n",
    "      The function loss of the parent class LinearClassifier will be \n",
    "      overwritten by the following loss function.\n",
    "  \"\"\"\n",
    "\n",
    "  def loss(self, X_batch, y_batch, reg):\n",
    "    return svm_loss_vectorized(self.W, X_batch, y_batch, reg)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))\n",
    "\n",
    "\n",
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Tune the Hyperparameters Learning Rate and Regularization Strength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the validation set to tune hyperparameters (regularization strength and\n",
    "learning rate). You should experiment with different ranges for the learning\n",
    "rates and regularization strengths; if you are careful you should be able to\n",
    "get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [5e4, 1e5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rates = np.logspace(-5, 0, 5) \n",
    "# causes numeric issues: np.logspace(-5, 5, 8) #[-4, -3, -2, -1, 1, 2, 3, 4, 5, 6]\n",
    "regularization_strengths = np.logspace(-5, 2, 5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`results` is dictionary mapping tuples of the form\n",
    "(`learning_rate`, `regularization_strength`) to tuples of the form\n",
    "(training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "of data points that are correctly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest validation accuracy that we have seen so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_val = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LinearSVM object that achieved the highest validation rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_svm = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The corresponding learning rates and regularization strengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_l = np.min(learning_rates)\n",
    "best_r = np.min(regularization_strengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code that chooses the best hyperparameters by tuning on the validation \n",
    "set. For each combination of hyperparameters, we train a linear SVM on the      \n",
    "training set, compute its accuracy on the training and validation sets, and  \n",
    "store these numbers in the results dictionary. In addition, we store the best   \n",
    "validation accuracy in `best_val` and the LinearSVM object that achieves this  \n",
    "accuracy in `best_svm`.                                                        \n",
    "                                                                             \n",
    "Hint: You should use a small value for `num_iters` as you develop your         \n",
    "validation code so that the SVMs don't take much time to train; once you are \n",
    "confident that your validation code works, you should rerun the validation   \n",
    "code with a larger value for `num_iters`.                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in double_scalars\n",
      "/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in multiply\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: RuntimeWarning: overflow encountered in subtract\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in subtract\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: overflow encountered in multiply\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:59: RuntimeWarning: invalid value encountered in add\n"
     ]
    }
   ],
   "source": [
    "for l in learning_rates:\n",
    "    for r in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train, y_train, learning_rate=l, reg=r, num_iters=1500, batch_size=200)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        training_accuracy = np.mean(y_train == y_train_pred)\n",
    "        validation_accuracy = np.mean(y_val == y_val_pred)\n",
    "        results[(l, r)] = (training_accuracy, validation_accuracy)\n",
    "        if validation_accuracy > best_val:\n",
    "            best_val = validation_accuracy\n",
    "            best_svm = svm\n",
    "            best_l = l\n",
    "            best_r = r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-05 reg 1.000000e-05 train accuracy: 0.216061 val accuracy: 0.213000\n",
      "lr 1.000000e-05 reg 5.623413e-04 train accuracy: 0.188796 val accuracy: 0.196000\n",
      "lr 1.000000e-05 reg 3.162278e-02 train accuracy: 0.199061 val accuracy: 0.195000\n",
      "lr 1.000000e-05 reg 1.778279e+00 train accuracy: 0.165388 val accuracy: 0.171000\n",
      "lr 1.000000e-05 reg 1.000000e+02 train accuracy: 0.142673 val accuracy: 0.127000\n",
      "lr 1.778279e-04 reg 1.000000e-05 train accuracy: 0.198490 val accuracy: 0.199000\n",
      "lr 1.778279e-04 reg 5.623413e-04 train accuracy: 0.183449 val accuracy: 0.194000\n",
      "lr 1.778279e-04 reg 3.162278e-02 train accuracy: 0.221286 val accuracy: 0.192000\n",
      "lr 1.778279e-04 reg 1.778279e+00 train accuracy: 0.219224 val accuracy: 0.179000\n",
      "lr 1.778279e-04 reg 1.000000e+02 train accuracy: 0.122224 val accuracy: 0.146000\n",
      "lr 3.162278e-03 reg 1.000000e-05 train accuracy: 0.196265 val accuracy: 0.182000\n",
      "lr 3.162278e-03 reg 5.623413e-04 train accuracy: 0.175286 val accuracy: 0.167000\n",
      "lr 3.162278e-03 reg 3.162278e-02 train accuracy: 0.166531 val accuracy: 0.166000\n",
      "lr 3.162278e-03 reg 1.778279e+00 train accuracy: 0.159265 val accuracy: 0.169000\n",
      "lr 3.162278e-03 reg 1.000000e+02 train accuracy: 0.105714 val accuracy: 0.122000\n",
      "lr 5.623413e-02 reg 1.000000e-05 train accuracy: 0.210959 val accuracy: 0.197000\n",
      "lr 5.623413e-02 reg 5.623413e-04 train accuracy: 0.236245 val accuracy: 0.227000\n",
      "lr 5.623413e-02 reg 3.162278e-02 train accuracy: 0.170571 val accuracy: 0.154000\n",
      "lr 5.623413e-02 reg 1.778279e+00 train accuracy: 0.147673 val accuracy: 0.157000\n",
      "lr 5.623413e-02 reg 1.000000e+02 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+00 reg 1.000000e-05 train accuracy: 0.179714 val accuracy: 0.167000\n",
      "lr 1.000000e+00 reg 5.623413e-04 train accuracy: 0.225020 val accuracy: 0.211000\n",
      "lr 1.000000e+00 reg 3.162278e-02 train accuracy: 0.134224 val accuracy: 0.110000\n",
      "lr 1.000000e+00 reg 1.778279e+00 train accuracy: 0.099959 val accuracy: 0.102000\n",
      "lr 1.000000e+00 reg 1.000000e+02 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.227000\n"
     ]
    }
   ],
   "source": [
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the cross-validation results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot training accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CIFAR-10 training accuracy')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c3YxC+Q4wCjvy4D+wkzl8L4uaDAaDIS9QhMaYr7nkGylb4E4C23+r6nHYU+kNBoPhc4M9jNBtyKjck1KZqkZFJCYilU72dIPBYPjcEENo1Pxrecdx82jZDSwVkReA5jmuqvrdrKkyGAyGPEBVCOfh+O84bgz4E05JJP/mlBoMBkOGiTk+8HzFjQGvahmnW0SuyJIeg8FgyBs0z10obkahXNTKuoszrMNgMBjyDgXC6mku+UbSFriInA98ERjcYup8BbAt28IMBoOhs4mp0FSgLpQ3gfVAd+C2hPW7gCXZFGUwGAz5gCKEY/nX8o6T1ICr6mpgtYicADSoakxEhgMjgKW5EthWorEYr32yitdXryYcizGqZ09OHbE/ZX5/+p0LlKiG+Gz3y2xtXAxAt+DB9CufjCX523LoKHWhELPfXcb7Gzbh83g4YvBAjhpag8cqnqTVLdlR18C/537AJxu2URrwcezo/Thkv35FGfslzua1W3nxwdfYuGozFd3KOfqciQwdMzhn51eEpjz2gbsNZnUUUI2dG3MuEFLVnGcHThcLZe7atXx71jM0hsPUhe3UW6U+H6rKD48+kovGjs2V1JyxdteLLNg8E0WJqp2dxCulgDC250z6l5/QuQKzwP3vLOC2l97AEqHeuc9lfh9Bn4/fn3Mqhw5sV2a+vCUWU343aw4P/XcRIkJTOIIAQb+P7hWl/O7rMxjSuzjCJMcJNYW57dI/MOfxdwAl3BTBsgRf0M+gkf254ekf0a1PdcpjZCIWSs+R3fTcB/ckob/z0H+kPaaITAV+B3iAP6vqL1tsvxL4KnZe4M3AJU6DGRG5CLjGqXpTutDdbporoqr1wJnAH1T1HOyp9XnF4vXr+cpjT7C1vr7ZeAPUh8M0RCLc8trr/HX+gk5UmHk+q3uF+ZuvIaJ1zcYbIKL1RLSO+Zt+xmd1r3Siwszz17fn85uX36AxEmk23gB1oTBb6+q55O9PsGTdhk5UmHl++ejLPDxnMaFIlKawnUZPgYZQmLVbarnoNw+zbkvxzLOLxWJcd/qveP3Jdwg3hQk3RZz1SlN9Ex8vWsXlh/2YXduzn0LO7sS0mks6nNnrdwHTgJHA+a0ka18IjHMCAj4G/NrZtytwHXAYMAG4TkRSPqVcGXARmQhcAPzbWZd3TqGrn3+RhkjyHJENkQi3znmdXU1NOVSVPVSjLNh0A1FNfj1RbWTBphtRTZ3hu1DY2djIb19+g4Zw8vvcGI5w3b9fyqGq7LJ603aefud9GpPkP1WgrjHE7bPm5FZYFln40lLefWMZoYbWE1hHI1FqN+/k8d/8K+taVIWmmLe5uGACsEJVV6pqCPgnMGPvY+orTqMY4G0gHq/2JOAFVd2mqtuBF4CppMCNAb8C+AnwpKq+JyJDgLxq1i3bvIVPtm9PW0+Ap94vjqz0G+rfIKbpM3fHtImN9cURPPLpJR9gufD3rtyyjRWbt+ZAUfb5x6sLiUZTp86LqfLfpSupzXKG9lzx6G3/onF36msJN0V4+q7niEaz2zhRhEjM01xc0A9Yk7C81lmXjEuB/7RzX1cp1V5T1dNU9VfO8spMTaMXkakiskxEVojIj9t7nGVbNuNx8Y/dEImwaP369p4mr6gNLSei6bPNR7SJ2tDyHCjKPovWrk/Z+o7jsSyWbdySA0XZZ+mq9URc5D71ez18ujl9I6YQ+HjRKlf1Qo0hdm7NrhtFgVDM21yA7iIyL6Fc1t5ji8iXgHHALe09RqeF2UrwFZ2I/aSZKyKzVPX9th7LEst1T7ybFlwhIAiChZK6BSKAuHrRyn+8bRhhUiyZiNyOqlEomtEoqfJ/JqKqWb/PqkJo75b3ljSdmOuAAQnL/Z11e+GM7rsaOEa12Q+6Dji2xb6vptLXmf/ZaX1FbjmkTx/CLl6lynw+jqoZ1J5T5B3dgodgSfqhkZb46VYyJvuCcsDEIQMp9acf0hWORhnTrzgytB9xwCD8LlIBRmMx9utTHCNRDjr6AFeGubJ7BRXdumRViwKRmNVcXDAXGCYig0XED5wH7JVDWEQOAf4EnKaqmxI2PQdMEZFqp/NyirMuKZ1pwNvs70l6oMoKxvbrm7Z1LSKcNGxYe06Rd3QLjiHg6Zq2XtDTja6Bg3OgKPtMGzl8n4TVLbFEOHRgX/pUZvcfO1ecfWT6zIU+j8X0CSMpcfFwKwTO/v5pabPSB0r8nP396Vl/64i3wOMlfX2NAJdjG94PgEecvsMbRCSeVf0WoBx41EnmPsvZdxtwI/ZDYC5wg7MuKW6y0vcQkZ+KyN0icm+8pL2SDCEil8X9TZs3b05a7xdTptDF709qxINeL785ZRoBb/4GZ28LIsKEXr/CIyVJ63ikhAm9flk0r9YBr5dbzphGMMk9tEToEghw0/QTc6wse/SoLOeKGUcS9LV+zV6PRfeKMr4zfVKOlWWP/cftx7RLJxMoDbS63R/0U3PgAKZ/86Ssa2lHCxxVna2qw1V1P1W92Vl3rarGDfUJqtpLVcc45bSEfe9V1aFO+Wu6c7lR9DRQCbyIPYwwXjqKK1+Rqt6tquNUdVyPHj2SHmxAVSVPXXgBY/v2JeDxUOrzUeLzUurz0b+ygj+dPoPJ++2XAdn5Q9fgKI7u92cq/EPxSBCPlDglSIV/GEf3+wvVwbwbst8hJu+/H3edexr9qioo9fsIOvc44PUwdkBfHv/aF+lfVdnZMjPKBceO5ZrzTqBreQllAeeaAz78Xg+TRtbwj6suoKI02NkyM8q3bv8KF153DmWVpZR2KSFQ4qekSxB/0Mdx50/i1leuxx/I/htHfCp9vOQbbmZiLlLVMRk/sYgX+AiYjG245wJfVNX3ku3jNiv9qu3bmbd2HRGNMaxbd8b27VM0rdBk7Ghaxo4mu/+3OjCKysDwTlaUXVSVhWvXs2LzVjyWxfiB/RjYtaqzZWWVaCzG3I/WsHZrLQGvl8NHDKRHZXlny8oq4VCYec8tZutn2ymtKGHCtEMorypztW8mZmKWD++tB9+1JyDrm1N+XXBZ6Z8RkZNVdXYmT6yqERGJ+4o8wL2pjHdbqKmupqY69TTbYqMqsD9Vgf07W0bOEBHGDujL2AF9O1tKzvBYFoePKI5OeLf4/D4mTu88e6kI4Wj+tbzjuDHgVwA/FZEQEJ8apapa0dGTOw+FjD4YDAaDIVOo4tr33RmkNeCqWhzd+QaDwdBmhGgsf92vroZkOMNfjnYWX1XVZ7InyWAwGPIDVYjksQvFzTDCX2K7Ud53yhUi8otsCzMYDIZ8IBaT5pJvuGmBnwyMUdUYgIjchx0O8SfZFGYwGAydjaoQzWMfuFtlVQmfi2uQrcFgMKQgFpXmkm+4aYH/AlgoIq9gx0Y6Gmh35ECDwWAoFFRB87gF7mYUykMi8iow3ln1I1UtrpQnBoPB0Cr52fKOk/TRIiIjnL9jgT7YwabWAn2ddQaDwVDcKGhMmku+kaoFfiVwGXBbK9sUOD4rijKAqrIrUkdUo1T4yvFI/g4DyhSqMUIxO3CZ3+qKSP6+9mWKSCzGtvp6vB4P1cFg0YdLAAhFo2xvbCDo8VIZLK74J8loagqzc3cjpSV+ypIEuMoqeWi44yQ14KoazzQxTVX3ym8kInn5y4nEorywcQ5PrXuBHeFdWCJ4xMOUXkcxo98JVPqKb05SJFbPJ7X3s2rng82JjT1SxuCKL1FTeSFeq7STFWaeLfX13DNvLg8tXUokFiOmSo/SUi4bN57zRo/G5ym+B/aanbXcOe8dnv7oAxA7LkpNZTWXjzuM04aNKMqH14pVm7nvsbd4fe7HWJYQjcYYsV9vLjrncCaOHZIbEQqaxy4UN8GsFqjq2HTrckGqYFbhWJjr37uDlXVraIrtnSvSKx7KvKX8cvRV9AwWR9B7gHC0ljc/u4CG6HpiLZIbWwQo8fbhiL5/x+cpnoFDa2trOeOfD7GzsZFwi1RjJV4vo3v14r4zzyqasMEASzdt5LynHqExEiba4v+1xOtlyuCh3H7iyUVlxN+av5Jrbp1FKBylpY0KBrycN30cXz3/yJTHyEQwq0BNf+19zRXNy59+7aq8CmaVygfeW0QOBUpE5BARGeuUY4G8a9bdt+oJPt796T7GGyCiUXaF67j5g7v2+TEUMgs3X0V9ZN0+xhsgRhP1kc9YtLl4BgypKhc/+QTbGxr2Md5g5zxdsnEjv3q9eDK0N0YiXDjrMerCoX2MN9jX/PwnK3hg6aLci8sSW7bv5me3zaIpFGn1/7WxKcI//zWPtxaszL4YFSS6p+QbqRylJwG3Ysfp/g22L/w2bN/4T7MvzT0N0UZe3vQWIQ0nrRMjxuambSzblYObngPqw+vY1jgXJfk1KyG2Nr5DQ6Q4EjnPXbeODbt3E0vxEG6MRHh46VLqw8m/l0Ji9scfEY6lThfYEIlw14J3iqZx8tRzi4jFUl9LY1OE+x57OzeCYgklz0hqwFX1PlU9DrhYVY9LKKep6hM51JiWpbXLsFx02oViYeZsSR9PvBDYWP+y3ZWcFmFj3cvZlpMT/rXsQxpcGGaPZfHWp5/mQFH2efSDd6lzcc27QiE+2rY1B4qyz3P/fZ9QOH2O2w9WbKC+Yd837oyi5HUL3M048MdF5BRgFBBMWH9DNoW1hfpIo6vWh6LsDtflQFH2icTqiKVofceJaYiI7s6Bouyzo7HR1TNLVdkVyvI/do7Y2bSve6w1PCLsDrmrm+/UN7p7e/J6LOobQ5SWpE/u3REkD1vecdwEs/ojcC7wHeyZmOcAeRVVvlugylUHjgcPvYqkEzPo7YHHxWAgjwQJeHrmQFH2GVBZiddyMzxS6F1eHJlq+nVxF3Y/FI3Rs6w4rrmby4w7sZjSpSzLA+IUexhhvOQZbv4bjlDVLwPbVfV6YCKQV/m6RlYMwyfpRx1YIhzf64gcKMo+vcumoC6cckqM3mXFkeT37FEH4nHxoA56PYzv1y8HirLPl0ePocyXPvfjsOquDKgojtFGZ007hJI0+S5FhKMmDCXgz/5oI4nuKfmGGwPe4PytF5G+2Fl5+nTkpCJyi4h8KCJLRORJEanqyPE8YnHewFMJWMlfpXzi45DqkfQOJk+MXEj4rC4M7HJuyla4JUEGdTkPn1UcLbMh1dUcOWgQgRTjvEu8Xv7viEl4XLXU858j+g9kQEUl3hR9PEGvlx8dcVQOVWWXKUcfQEmJn1TPar/Pw0VnH551LaK2CyVe8g03v/JnHAN7C7AAWAU81MHzvgAcqKoHYSc27nBo2im9juKUPsfht3xYLS4raAUY3qWG7w27pKOnySsO6Pp9epYej0dKsL1bcQSPlNC7dDIjul7ZWfKywh0nn8LBvXtT2qJV6hEh6PVyydhD+eJBB3WSusxjifD3GecwqKpqn5a4z7IIerzMPPI4jhpQ0zkCs0BJ0M8fbjqP6sqyfVrifp+HYMDHzVfNYL9BuWmM5XMnZtqJPHtVFgkAQVWtzZgAkTOAs1X1gnR13WSlX1W3ln999hJLaj8kqjEGlvRlRr8TOLjqAFcjVQoNVWV700I+qf0bO5qWAFAVOJjBlRdRHTikqCZ3xImpMmf1Ku6ZN49lW7fiFYtJgwZy6dhDOaBHcbxhtSQUjfL8yhX8edE81uyqJeDxMnXIMC4+6BAGVlZ1trys0NAY4qXXl/Ho7Pls3V5HSdDPtGNHMWPKwXSrTu8nz8REnmC/ATrw23saQcuvvjKvJvK4mYn5MXCLqv4xYd0zqnpqRgSI/At4WFUfTFfXjQE3GAwGyJwBH/SNPQb8o2vzy4C76QEIA8eJyGHA11U1BKTtIRKRF4HerWy6WlWfdupcDUSAv6c4zmXYQbUYOHCgC7kGg8GQITQ/fd9x3BjwelU9V0SuAuaIyDm4mEKiqiek2i4iFwOnApM1xWuAqt4N3A12C9yFXoPBYMgcBW7ABUBVfy0iC4Dnga4dOamITAWuAo5RdULoGQwGQ54hClYeDh+M46ZX79r4B1V9ETtGyp0dPO+dQBfgBRFZ5EwWMhgMhvyjjbFQRGSqiCwTkRUisk80ORE5WkQWiEhERM5usS3q2MRFIjIr3bmStsBFZISqfgisayUDzzPuLqV1VHVoR/Y3GAyGnKBtm8AjIh7gLuBE7Axmc0Vklqq+n1DtU+Bi4AetHKJBVce4PV9RZuQxGAyGTNFGF8oEYIWqrgQQkX8CM4BmA66qq5xtHfaup8zII3ZermtU9Y2OnshgMBgKDqWtnZj9gDUJy2uBw9qwf1BE5mGPzvulqj6VqnLKTkxVjYnIncAhbRBgMBgMRYEA1t4GvLtjYOPc7YyUyxSDVHWdiAwBXhaRpar6cbLKbkahvCQiZwFPpBruZzAYDEXJ3gZ8S5qJPOuAAQnL/Z11rlDVdc7flSLyKnbjOakBdzMK5evAo0CTiOwUkV0istOtIIPBYChYnGGE8eKCucAwERksIn7gPCDtaBIAEal2wpUgIt2BSST4zlvDTUKHgkrl3hQN8/z6d5mz6SMisSgjK/tx5sBD6Roojoh8rbEr3MhTq5fyv82rAJjQo4YzBh1EuS/QucKyyOaGOh7+aAlLtmzAZ3k4vv8QThk8gmARJTNuiUbWog2PQGQFSAkSnAqB4xAXoZQLlfrQR2za/U+aImvwWtV0L5tBRfCI3MX4aeMoFFWNiMjlwHOAB7hXVd8TkRuAeao6S0TGA08C1cB0EbleVUcBBwB/cjo3LWwfeEoD7iqYlYhUA8PYOyPPa+4vKzOki4Xy2sZl/HjhIwDUR+2MLAHLiwIXDZnEt4ZPLrrgTv9cOZ+bFz2PJUJD1M5kUuLxEVPlmjEnce6QliNACxtV5dYFc7jn3bkIQmM0AkCZ1wci3HnsdI4fsF8nq8wsqmF05zXQMBv7fd7JWCNlQADpejfiK54IjADRWB3LN3+TXU1ziWkYsK2oJaX4PN0Z0fN+gr7UeWUyEQulrMcAHXHGnlgoC+7Jr1gobjLyfBV4DfuJcr3zd2Z2ZbWduVtWctWCh6mPhpqNN0BTLEIoFuGBT97kj8uLIzdknCdWLebni1+gKRZpNt4ADdEwTbEINy9+jqdWL+lEhZnnNwtf5y/vzacpGm023gB1kTB14RDffPlp3lpfHPkw42jtj6DhP0ATJKbR0zrQbei2L6ORFZ0lL+OoRvlw44XsbHyHmDYSN94AMa2nKbKW9zacSTi6OQdiQKLaXPINNz7wK4DxwGonyfEhwI5simoPN7/7LxpjyXPpNUbD/PXj19kRKo6Z++FYlJsXP0djNNU1R7hp0XNps5oXCtsa6/njkv/REEl9zT9764UcqsouGl4GjS8CjSkqNaC7bsmZpmyzo+EV6sPLUJLlNY0Rje3is9pMDv5ITqEndGhU1Uaw44E7szP3z66stvFB7WdsbEwfotxCmLV2QQ4UZZ+X139EzIX7K6oxXl2/PAeKss+jHy3FcuECW7u7lve2bsyBouyj9fdDUkPWXAua3kBj23IhKeus33kPsTQhkpQwm3Y/hGokZb0O0/ZOzJzixoCvdTLyPIUdu+RpYHU2RbWVlbs3I6T/x26Mhfmgdn0OFGWflTu30BBJn3m9IRJmxc4tOVCUfd7dtnEvt0kyLLFYsWNrDhTlgPD7uJpJIgGI5NW/ZbtpCCcdNbcXSoRwbHtWtQj53QJ3MwrlDOfjTBF5BagEns2qqjbiFStl/rxEAlZx9Nh7LQ+WWMQ09a/KEsFvJc8hWUj4Xd47wf5+igJJn9DYRttQN7+xw4m4QGNYZPmaHR94vpK0BS4iXVsWYCnwOpBXY/LGdq1x5ect9fg5sufwHCjKPof3qMHnwkj5LA+H9azJvqAccPyAIfZokzSEY1HG9yqOrPQEJpMw+CsFCt7i+G1XBo/BHoGXGr+3Nx6rMut6rMiekm+kcqHMB+Y5f1uWvMpr1iPYhcO674cnjUfIa3k4tteIHKnKLqO79qVvaWVKx5EA/cuqOLC6T65kZZUpg4albVl7RDiqbw09S/OqjdFupNRN/hQ/lJyLPW+k8OlTcQlWmrcJS0roU/GN7A8LVpCYNpd8I6nFU9XBqjrE+duyDMmlSDdcO/p0qgKlSY140OPjtkPPL55Xa+COw8+izBto1YgLUOYN8LvDz8q1rKzhszz8cfIMgp7WXSlesegaLOUXk07KsbLsIVZXqLiJ5K1wP3gGIuXfzaWsrFLqH0Gfim9gSUmr2y0JUh4YS8/yL2Rdi6gW9jBCJ/j4PiUX4tpCj2AX/nnktzim1/74LS/l3gBl3gABy8vIyr7cc9hXGN9tcGfLzCjDKnvy2ORLmNBjEH7LQ7nXT7k3QMDycFiPGh6ffClDK4orS/sRfQbx0LTzGN2tF0GPl3Kfny4+PwGPhxMGDmX2jIuKpvUdxyqdgVTfAZ4akBKQcrsQhJIZSLdHEKu0s2VmlP5VVzCo+np8np5YUoYl5XikHEvK6NXlYvbv+deczUDN505MN1np/5WwGMSOdztfVXMeD9xtVvptTbtZsmMN0ViM/br0pKa8uIxYa6yr28H7OzYAMKq6D31Ls+8b7GxW7NjK8h1b8VrCIT360r2krLMlZRVVhcgHEF0D+ME/HrGK62HVEtUYu5sWEopuxGOVUxE8DEvchYjIxEzMLpX9deykPW83r/3nR3k1E9PNKJTpicsiMgC4PRMnF5HvA7cCPVQ1Y2PdugbKObbXAZk6XEHQr6yKfmVVnS0jpwyt6sbQqm6dLSNniAj4Rtrlc4KIRZfgoZ2qwcpD10mc9ryDrMUOutIhnAfBFOz0QgaDwZB3iNOJma+kNeAi8nv2dINbwBggE9MZf4udmf7pDBzLYDAYMo+CRArYgLP3kMEI8FBHU6yJyAxgnaouTjcMSEQuw87NycCBAztyWoPBYGgj+Tl8MI4bH/h97TmwiLwI9G5l09XAT7HdJ2lx0hXdDXYnZnu0GAwGQ7tQkEgeDj9xcONCWcq+MwlqsVvmN6lqq0EnVPWEJMcbDQwG4q3v/sACEZmgqhvaoN1gMBiyjkQL2IAD/8EOyPsPZ/k8oBTYAPwNmN76bq2jqkuBnvFlEVkFjMvkKBSDwWDIBPGJPPmKGwN+gqompnVZKiILVHWsiHwpW8IMBoOh01Egj10obsLJekRkQnzByecWn4/e4fAuqlpjWt8GgyFfkVisueQbblrgXwXuFZH4lK9dwFdFpAz4RdaUGQwGQ2ejmtctcDejUOYCo0Wk0llOTH3zSLaEdYSYRtgV/oyYRij39cZXZHEiWiMUC7Ox0X6R6RXsjt8qjtjQqWiINrGxcRsesehb0h2P2zjSBUxDdDe7wlvwip9qf29E3LxEFzZbGuvY3FBHF1+AfmUVuU1MriCRPEzF4+BmFEov4OdAX1WdJiIjgYmq+pesq2sjkVgjS7Y9yPs7HiemEQQhRoTBXY5nbLevUu5rbVRjYbMzvJtHPp3Ni5veak43pgqTe03kCwOmUeErvlgZGxu3cd8ns5mzZTEesVBVvJaXGf2O4twBkwl4iiOsaiKbGlfx6sb7WVm3EI94UY0R8JRxeLczGd9tOlYRPrzmblrDbYv/y8Itn+G3PEQ0Ru/SLnx39JGcXjMqR4ZcIQ9dJ3HcBLP6D/BX4GpVPVjsEGALVXV0LgQmkiqYVThWzzOffpOd4TVEde9UY4KFzyrl1AF/pCpQkwOluWFbqJYfLv4VO8O7iejerQSveKj0deGWg6+i2l88ga1W123g/xb+joZoI7EWo1v94qV/aU9+e8gVBD3uAh4VAqvrlvLw6pmENUTLEb0+CTCw7EC+MPDaojLiT33yHj95Z3arKfRKPD7OHHwgN044KaURz0Qwq8pALz2i7wXNy8+u+m1eBbNy8/7VXVUfwUnMp3YW0bx7p3h70++obcV4AygxQrE6nl/3Q9I9sAqJ25bdy47Qrn2MN0BEo+wI7eTWZfd2grLsENMY1yz9E/XRhn2MN0BII6yp38QfVjzRCeqyQyjWyCOf3khYm2gtsUNYm1hd9y5vb30y9+KyxNrdtUmNN0BDNMwTq97l2TXLsi9GgWhsT8kz3BjwOhHphvPrEZHDsSfy5A1N0V2s3PUCsVaM9x6UxugO1jcUR1b6dQ0bWbF7NbEUCW+jxFi+axXrGzblUFn2WLh9Obsi9Snz04Q1wiubFlAXacyZrmzy7o5X0TR5TyPaxDtbniTWyoO8ELn/o/lE0zS0GiJh7nr3zRyoUYhG95Q8w40BvxKYBewnIm8A9wPfyaqqNrK+fgGWiwE1EW3gk10v50BR9pm37V1iLt8m5m1/N8tqcsPrWxbREG1KW88rHhbvWJ4DRdnn3dpXCGv6h1FEm9jcVBxZ6f+9+gNXOW6X7djMzlCWH9RKXhvwlFZP7PTQxzhlf+xMXctUNZwDba6JaCOaNm+gTThWl2U1uaEpFiKi6YfhRzRKYzTVm0nh4LZVrWjRXHMo1uCqnmARjqV/uBUCyVwnLfFaFo3RCBXZFKOKRvIwm7FDyha4qkaB81U1oqrvqeq7+Wa8Acq9vZGU6X1tLHxU+IsjomGPQFeCVvqOuoDlp0egaw4UZZ/+JT3wueyo6xWszrKa3NDV3xdc/LajGqbC1z37gnJA39IuruopUOVvPW9mxlCFcGRPcYGITBWRZSKyQkR+3Mr2o0VkgYhEROTsFtsuEpHlTrko3bncuFDeEJE7ReQoERkbL66uJEf0KhmN10p/I0WE4RWn5EBR9pnYbYyrtw5FmdhtTPYF5YCT+hyOG2NW6gkysqI48p+O7zodn4sHde/gUCp8xZE68CsjxlPqTZOVHuHUgQfg92R/5I1Go80lHY7X4i5gGjASON8Zep3Ip8DF7IkvFd+3K3AdcBh26srrRCRlS8SNAR8DjAJuAG5zyq0u9ssZIhYTenwbryTL3A0eCTC4/HjKfb1yqCx7BD0Bzuh3IgEr+ZjngOXnzH4nFs246F7BrhzVYwyBFJOUApaPrw05LbeTPbJI/9KR9AoMxiPJr9krfo7v/ZUcqsoupww6gK6BUjwp7mHQ6+XbBx6RfTGqEInsKemZAKxQ1ZWqGgL+CczY+5C6SlWXwD4jEE4CXlDVbaq6HXgBmJrqZGkNuKoe10rJeULjdOxXMYWx3b+GR/xY7PmxCxZeCdK/7HCO7L3P20xB84UB0zih1xH4LR9Wwq20sPBbPqb0msQ5A6Z1osLMc+X+53Fo9QgCln8vt5lPPPgtL5cMPpXjenVuDsVMIiKcO2gmvYJD8Fl7N1C84scrfk7r930GlBZPnsyAx8ujUy6kf1klZd69Gx8lHh/lvgD3HXcegyuy7xpU1Ta1wIF+wJqE5bXOuqzsm3YiTz4hIpuBtnS1dwcKPVCWuYbOp9D1w+fzGgapaof8SiLyrHPeOEEgsTf9bifpTLz+2cBUVf2qs3whcJiqXt7Ksf8GPKOqjznLPwCCqnqTs/wzoEFVk3o82pPUuNNo680QkXn5NGuqPZhr6HwKXT+Ya2gvqprShdEK64ABCcv9nXVu9z22xb6vptqh+CPhGAwGQ+6YCwwTkcEi4sdOgDPL5b7PAVNEpNrpvJzirEuKm2BWZ7ayuhZYqqrFMcXPYDAYMoCqRkTkcmzD6wHuVdX3ROQGYJ6qznJyKjwJVAPTReR6VR2lqttE5EbshwDADaq6LdX53LhQLgUmAq84y8cC84HBInKDqj7Q1ovMIXenr5L3mGvofApdP5hryBmqOhuY3WLdtQmf52K7R1rb917AdQAjN9EInwO+rKobneVe2NPpzwdeU9UD3Z7MYDAYDJnDjQ98QNx4O2xy1m0D8m5WpsFgMHxecGPAXxWRZ5wpnhdhO+RfdVKq7ciqugwgIjNFZJ2ILHLKyZ2tqb2IyPdFREWkoOZMi8iNIrLE+f6fF5G+na2prYjILSLyoXMdT4pIVWdraisico6IvCciMREpmBEp6aamf55x40IR4EzgSGfVG8DjWiADyEVkJrA71VjKQkBEBgB/BkYAhxZSImgRqVDVnc7n7wIjVfUbnSyrTYjIFOBlp5PqVwCq+qNOltUmROQA7Nl/fwJ+oKqtZ0fJI5yp6R8BJ2JPbJmLHZ/p/U4Vlie4yYmpIvI6EE8H8r9CMd5Fxm+Bq4CnO1tIW4kbb4cyWstMkOeo6vMJi28DZyerm6+o6gdAoYUZaJ6aDiAi8anpxoDjwoUiIl8A/of9g/0C8E7LCFoFwOXOq++96YLD5CMiMgNYp6qLO1tLexGRm0VkDXABcG26+nnOJcB/OlvE54SOTE0vetwMI7waGB8f8y0iPYAXgceyKawtiMiLQGsZi68G/h9wI3ar70bsYFyX5E6dO9Jcw0+xB/XnLan0q+rTqno1cLWI/AS4HDvqWl6R7hqcOlcDEeDvudTmFjfXYCge3Bhwq8WEna3k2QxOVT3BTT0RuQd4Jsty2kWyaxCR0cBgYLHz6tsfWCAiE1R1Qw4lpsTtPcA2fLPJQwOe7hpE5GLgVGByvroR23AfCoWOTE0vetwY8GedseAPOcvn0mKQej4jIn1Udb2zeAZQUPnFVHUp0DO+LCKrgHEF1ok5TFXjOc5mAB92pp72ICJTsfsgjlHV+s7W8zmieWo6tuE+D/hi50rKH1xFIxSRs4BJzuIcVS2YFNgi8gB2THMFVgFfTzDoBUeBGvDHsVPyxbCjSX5DVQuqFSUiK4AA9hsowNsFOJLmDOD3QA/sIcCLVPWkThXlAmfo7+3smZp+c+cqyh8KKpyswWAwGPaQ1IUiIrtofbiXYI8uzGouUYPBYDCkxrTADQaDoUDJq9EkBoPBYHCPMeAGg8FQoBgDbjAYDAWKMeCGlIjI7gwd52/ZDsEgIn1FJKczhEWkRkTMuGRDp2AMuKGgEJGkI6dU9TNVzfhDItU5gRrMxBJDJ2EMuMEVYnOLiLwrIktF5FxnvSUif3BiZb8gIrPTtbRF5FAR+a+IzBeR50Skj7P+ayIyV0QWi8jjIlLqrP+biPxRRN4Bfu0s3yEib4rIyvj5nNbwu87ni0XkCRF5VkSWi8ivE85/qYh8JCL/E5F7ROTOVjTOFJEHROQN4AHn2HNEZIFTjnCq/hI4yol1/n8i4nG+p7lOALWvd/zbNxhax81UeoMB7JjwY4CDge7AXBF5DXuGbg0wEnvK/wekyOknIj7s2YAzVHWz8yC4GTvA2BOqeo9T7ybsfKy/d3btDxyhqlER+RvQBztG/QjsJCOtuU7GAIcATcAyEfk9EAV+BowFdgEvA8miPI4EjlTVBudhcqKqNorIMOzQEuOAH2PH1j7V0X0ZUKuq40UkALwhIs+r6ifJvhODob0YA25wy5HAQ6oaBTaKyH+B8c76R1U1BmwQkVdSHQR7Sv2BwAtOcC4PEA9tcKBjuKuAcuzM3nEedc4d5ynnnO+Lnae1NV5S1VoAEXkfGIT98PlvPNu3iDwKDE+y/yxVbXA++4A7RWQM9kMg2T5TgIMS3kIqgWGAMeCGjGMMuCHXCPCeqk5sZdvfgNNVdbET+e/YhG11Leo2tThmayTWidL233viOf8P2Ij9BmIBjUn2EeA7qvpcku0GQ8YwPnCDW+YA5zo+3h7A0diJPt4AznJ84b3Y2+i2xjKgh4hMBNulIiKjnG1dgPWOm+WCbFwEdnS7Y0Sk2umcPMvlfpXAeqfVfyH2mwPYbpguCfWeA77pXAMiMlzs/LEGQ8YxLXCDW54EJmL7ixW4SlU3OJEGJ2OnuFoDLABqkx1EVUOOe+EOEanE/g3eDryH7Zt+B9js/O2S7DjtRVXXicjPsR8+27BD2ybVm8AfgMdF5MvAs+xpnS8BoiKyGPsN4nfYfQILxPYRbQZOz+AlGAzNmFgohg4jIuWqultEumEbxkn5lGyiJQl6vdgPpnsLKUSywRDHtMANmeAZEakC/MCN+Wy8HWaKyAlAEHgeeKpz5RgM7cO0wA0Gg6FAMZ2YBoPBUKAYA24wGAwFijHgBoPBUKAYA24wGAwFijHgBoPBUKAYA24wGAwFyv8HEoiZ4QoO8pAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "marker_size = 100 # default size of markers is 20\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [results[x][1] for x in results] \n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the best svm on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.219000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rerun the training with larger value of num_iters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = LinearSVM()\n",
    "svm.train(X_train, y_train, learning_rate=best_l, reg=best_r, num_iters=3000, batch_size=200)\n",
    "y_train_pred = svm.predict(X_train)\n",
    "y_val_pred = svm.predict(X_val)\n",
    "training_accuracy = np.mean(y_train == y_train_pred)\n",
    "validation_accuracy = np.mean(y_val == y_val_pred)\n",
    "if validation_accuracy > best_val:\n",
    "    best_svm = svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the new best svm on test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.219000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the learned weights for each class.\n",
    "Depending on your choice of learning rate and regularization strength, these may\n",
    "or may not be nice to look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.T.reshape(10,3,32,32).transpose(2,3,1,0)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax Linear Classifier : Not Vectorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 2.462197\n",
      "sanity check: 2.302585\n",
      "numerical: 3.211194 analytic: 3.211189, relative error: 8.060887e-07\n",
      "numerical: -0.539907 analytic: -0.539912, relative error: 4.825723e-06\n",
      "numerical: 3.701203 analytic: 3.701196, relative error: 8.716703e-07\n",
      "numerical: 14.517206 analytic: 14.517198, relative error: 2.613311e-07\n",
      "numerical: -9.800780 analytic: -9.800784, relative error: 1.827418e-07\n",
      "numerical: 15.496042 analytic: 15.496034, relative error: 2.499581e-07\n",
      "numerical: 0.374921 analytic: 0.374916, relative error: 7.011827e-06\n",
      "numerical: 17.383515 analytic: 17.383508, relative error: 2.126607e-07\n",
      "numerical: -7.557805 analytic: -7.557808, relative error: 2.332168e-07\n",
      "numerical: 4.524942 analytic: 4.524937, relative error: 6.025743e-07\n",
      "numerical: -9.262434 analytic: -9.262438, relative error: 2.185417e-07\n",
      "numerical: -6.892215 analytic: -6.892219, relative error: 2.433884e-07\n",
      "numerical: -2.417390 analytic: -2.417395, relative error: 1.076355e-06\n",
      "numerical: 0.174921 analytic: 0.174916, relative error: 1.541490e-05\n",
      "numerical: -0.754994 analytic: -0.755000, relative error: 4.155306e-06\n",
      "numerical: -0.795812 analytic: -0.795817, relative error: 3.283622e-06\n",
      "numerical: 0.789450 analytic: 0.789444, relative error: 3.708386e-06\n",
      "numerical: 1.307015 analytic: 1.307010, relative error: 2.032914e-06\n",
      "numerical: -7.209426 analytic: -7.209430, relative error: 3.010362e-07\n",
      "numerical: 0.347822 analytic: 0.347818, relative error: 7.047705e-06\n"
     ]
    }
   ],
   "source": [
    "def softmax_loss_naive(W, X, y, reg):\n",
    "  \"\"\"\n",
    "  Softmax loss function, naive implementation (with loops)\n",
    "  Inputs have dimension D, there are C classes, and we operate on minibatches\n",
    "  of N examples.\n",
    "  Inputs:\n",
    "  - W: A numpy array of shape (D, C) containing weights.\n",
    "  - X: A numpy array of shape (N, D) containing a minibatch of data.\n",
    "  - y: A numpy array of shape (N,) containing training labels; y[i] = c means\n",
    "    that X[i] has label c, where 0 <= c < C.\n",
    "  - reg: (float) regularization strength\n",
    "  Returns a tuple of:\n",
    "  - loss as single float\n",
    "  - gradient with respect to weights W; an array of same shape as W\n",
    "  \"\"\"\n",
    "  # Initialize the loss and gradient to zero.\n",
    "  loss = 0.0\n",
    "  dW = np.zeros_like(W)\n",
    "\n",
    "  \n",
    "  # Compute the softmax loss and its gradient using explicit loops.     \n",
    "  # Store the loss in loss and the gradient in dW. If you are not careful     \n",
    "  # here, it is easy to run into numeric instability. Don't forget the        \n",
    "  # regularization!                                                           \n",
    "  \n",
    "  num_train = X.shape[0]\n",
    "  num_classes = W.shape[1]\n",
    "  loss = 0.0\n",
    "  for i in range(num_train):\n",
    "    # Compute vector of scores\n",
    "    f_i = X[i].dot(W)\n",
    "\n",
    "    # Normalization trick to avoid numerical instability\n",
    "    f_i -= np.max(f_i)\n",
    "\n",
    "    # Compute loss (and add to it, divided later)\n",
    "    sum_j = np.sum(np.exp(f_i))\n",
    "    p = lambda k: np.exp(f_i[k]) / sum_j\n",
    "    loss += -np.log(p(y[i]))\n",
    "\n",
    "    # Compute gradient\n",
    "    # Here we are computing the contribution to the inner sum for a given i.\n",
    "    for k in range(num_classes):\n",
    "      p_k = p(k)\n",
    "      dW[:, k] += (p_k - (k == y[i])) * X[i]\n",
    "\n",
    "  loss /= num_train\n",
    "  loss += 0.5 * reg * np.sum(W * W)\n",
    "  dW /= num_train\n",
    "  dW += reg*W\n",
    "\n",
    "\n",
    "  return loss, dW\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import time\n",
    "\n",
    "# Generate a random softmax weight matrix and use it \n",
    "# to compute the loss.\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As a rough sanity check, our loss should be something close to -log(0.1).\n",
    "print('loss: %f' % loss)\n",
    "print('sanity check: %f' % (-np.log(0.1)))\n",
    "\n",
    "\n",
    "# Loss and gradient computed in none vectorized fashion\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As we did for the SVM, use numeric gradient checking as a debugging tool.\n",
    "# The numeric gradient should be close to the analytic gradient.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
    "\n",
    "# similar to SVM case, do another gradient check with regularization\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax Loss and Gradient : Vectorized Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a naive implementation of the softmax loss function and its gradient,\n",
    "we implement a vectorized version in `softmax_loss_vectorized`.\n",
    "The two versions should compute the same results, but the vectorized version should be\n",
    "much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_loss_vectorized(W, X, y, reg):\n",
    "  \"\"\"\n",
    "  Softmax loss function, vectorized version.\n",
    "  Inputs and outputs are the same as softmax_loss_naive.\n",
    "  \"\"\"\n",
    "  # Initialize the loss and gradient to zero.\n",
    "  loss = 0.0\n",
    "  dW = np.zeros_like(W)\n",
    "\n",
    "  \n",
    "  # Compute the softmax loss and its gradient using no explicit loops.  \n",
    "  # Store the loss in loss and the gradient in dW. If you are not careful     \n",
    "  # here, it is easy to run into numeric instability. Don't forget the        \n",
    "  # regularization!                                                           \n",
    " \n",
    "  num_train = X.shape[0]\n",
    "  f = X.dot(W)\n",
    "  f -= np.max(f, axis=1, keepdims=True) # max of every sample\n",
    "  sum_f = np.sum(np.exp(f), axis=1, keepdims=True)\n",
    "  p = np.exp(f)/sum_f\n",
    "\n",
    "  loss = np.sum(-np.log(p[np.arange(num_train), y]))\n",
    "\n",
    "  ind = np.zeros_like(p)\n",
    "  ind[np.arange(num_train), y] = 1\n",
    "  dW = X.T.dot(p - ind)\n",
    "\n",
    "  loss /= num_train\n",
    "  loss += 0.5 * reg * np.sum(W * W)\n",
    "  dW /= num_train\n",
    "  dW += reg*W\n",
    "\n",
    "\n",
    "  return loss, dW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Classifier : Softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive loss: 2.462197e+00 computed in 1.393167s\n",
      "vectorized loss: 2.462197e+00 computed in 0.006959s\n",
      "Loss difference: 0.000000\n",
      "Gradient difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "class Softmax(LinearClassifier):\n",
    "  \"\"\" Softmax is a\n",
    "  \n",
    "  subclass (child class) that uses the \n",
    "      Softmax + Cross-entropy loss function and \n",
    "      overrides the loss function of the superclass \n",
    "      (parent class) LinearClassifier.\n",
    "   \"\"\"\n",
    "\n",
    "  def loss(self, X_batch, y_batch, reg):\n",
    "      return softmax_loss_vectorized(self.W, X_batch, y_batch, reg)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "\n",
    "tic = time.time()\n",
    "loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# As we did for the SVM, we use the Frobenius norm to compare the two versions\n",
    "# of the gradient.\n",
    "grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))\n",
    "print('Gradient difference: %f' % grad_difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune the Hyperparameters : Learning Rate and Regularization Strength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the validation set to tune hyperparameters (regularization strength and\n",
    "learning rate). You should experiment with different ranges for the learning\n",
    "rates and regularization strengths; if you are careful you should be able to\n",
    "get a classification accuracy of over 0.35 on the validation set.\n",
    "\n",
    "Use the validation set to set the learning rate and regularization strength. \n",
    "This should be identical to the validation that you did for the SVM; save    \n",
    "the best trained softmax classifer in best_softmax.                          \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:22: RuntimeWarning: divide by zero encountered in log\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: overflow encountered in double_scalars\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: overflow encountered in multiply\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:31: RuntimeWarning: overflow encountered in multiply\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in subtract\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:59: RuntimeWarning: overflow encountered in multiply\n"
     ]
    }
   ],
   "source": [
    "results = {}\n",
    "best_val = -1\n",
    "best_softmax = None\n",
    "# np.logspace(-10, 10, 8) #-10, -9, -8, -7, -6, -5, -4\n",
    "learning_rates = np.logspace(-10, 10, 5) \n",
    "# causes numeric issues: np.logspace(-5, 5, 8) #[-4, -3, -2, -1, 1, 2, 3, 4, 5, 6]\n",
    "regularization_strengths = np.logspace(-3, 6, 5) \n",
    "\n",
    "iters = 1500\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        softmax = Softmax()\n",
    "        softmax.train(X_train, y_train, learning_rate=lr, reg=rs, num_iters=iters)\n",
    "        y_train_pred = softmax.predict(X_train)\n",
    "        acc_train = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = softmax.predict(X_val)\n",
    "        acc_val = np.mean(y_val == y_val_pred)\n",
    "        results[(lr, rs)] = (acc_train, acc_val)\n",
    "        if best_val < acc_val:\n",
    "            best_val = acc_val\n",
    "            best_softmax = softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Print out results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-10 reg 1.000000e-03 train accuracy: 0.099449 val accuracy: 0.096000\n",
      "lr 1.000000e-10 reg 1.778279e-01 train accuracy: 0.095551 val accuracy: 0.097000\n",
      "lr 1.000000e-10 reg 3.162278e+01 train accuracy: 0.110163 val accuracy: 0.135000\n",
      "lr 1.000000e-10 reg 5.623413e+03 train accuracy: 0.098367 val accuracy: 0.079000\n",
      "lr 1.000000e-10 reg 1.000000e+06 train accuracy: 0.091612 val accuracy: 0.105000\n",
      "lr 1.000000e-05 reg 1.000000e-03 train accuracy: 0.170224 val accuracy: 0.161000\n",
      "lr 1.000000e-05 reg 1.778279e-01 train accuracy: 0.245673 val accuracy: 0.266000\n",
      "lr 1.000000e-05 reg 3.162278e+01 train accuracy: 0.195918 val accuracy: 0.180000\n",
      "lr 1.000000e-05 reg 5.623413e+03 train accuracy: 0.099612 val accuracy: 0.119000\n",
      "lr 1.000000e-05 reg 1.000000e+06 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+00 reg 1.000000e-03 train accuracy: 0.135204 val accuracy: 0.135000\n",
      "lr 1.000000e+00 reg 1.778279e-01 train accuracy: 0.099857 val accuracy: 0.107000\n",
      "lr 1.000000e+00 reg 3.162278e+01 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+00 reg 5.623413e+03 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+00 reg 1.000000e+06 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+05 reg 1.000000e-03 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+05 reg 1.778279e-01 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+05 reg 3.162278e+01 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+05 reg 5.623413e+03 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+05 reg 1.000000e+06 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+10 reg 1.000000e-03 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+10 reg 1.778279e-01 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+10 reg 3.162278e+01 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+10 reg 5.623413e+03 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 1.000000e+10 reg 1.000000e+06 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.266000\n"
     ]
    }
   ],
   "source": [
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on test set\n",
    "Evaluate the best softmax on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "softmax on raw pixels final test set accuracy: 0.221000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = best_softmax.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the learned weights for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = best_softmax.W[:-1,:] # strip out the bias\n",
    "w = w.T.reshape(10, 3, 32, 32).transpose(2,3,1,0)\n",
    "\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "  \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}