diff --git a/notebooks/Linear_Classifier/Jupyter Notebook - Linear Classifier.ipynb b/notebooks/Linear_Classifier/Jupyter Notebook - Linear Classifier.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ace13f16eac527edb901bb0774ce00a4b728e958
--- /dev/null
+++ b/notebooks/Linear_Classifier/Jupyter Notebook - Linear Classifier.ipynb	
@@ -0,0 +1,1595 @@
+{
+ "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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\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
+}