Initial import of the project

Author: seloufian

README.md

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+<div>
+  <h2 align="center"><a href="https://cs231n.github.io">CS231n: Convolutional Neural Networks for Visual Recognition</a></h2>
+  <h2 align="center"><a href="https://cs231n.github.io/assignments2020/assignment1/">Assignment 1 (2020)</a></h3>
+</div>
+
+# Goals
+
+In this assignment you will practice putting together a simple image classification pipeline based on the k-Nearest Neighbor or the SVM/Softmax classifier. The goals of this assignment are as follows:
+
+- Understand the basic **Image Classification pipeline** and the data-driven approach (train/predict stages).
+- Understand the train/val/test **splits** and the use of validation data for **hyperparameter tuning**.
+- Develop proficiency in writing efficient **vectorized** code with numpy.
+- Implement and apply a k-Nearest Neighbor (**kNN**) classifier.
+- Implement and apply a Multiclass Support Vector Machine (**SVM**) classifier.
+- Implement and apply a **Softmax** classifier.
+- Implement and apply a **Two layer neural network** classifier.
+- Understand the differences and tradeoffs between these classifiers.
+- Get a basic understanding of performance improvements from using **higher-level representations** as opposed to raw pixels, e.g. color histograms, Histogram of Gradient (HOG) features, etc.
+
+# Questions
+
+## Q1: k-Nearest Neighbor classifier
+
+The notebook [``knn.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/knn.ipynb) will walk you through implementing the kNN classifier.
+
+## Q2: Training a Support Vector Machine
+
+The notebook [``svm.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/svm.ipynb) will walk you through implementing the SVM classifier.
+
+## Q3: Implement a Softmax classifier
+
+The notebook [``softmax.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/softmax.ipynb) will walk you through implementing the Softmax classifier.
+
+## Q4: Two-Layer Neural Network
+
+The notebook [``two_layer_net.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/two_layer_net.ipynb) will walk you through the implementation of a two-layer neural network classifier.
+
+## Q5: Higher Level Representations: Image Features
+
+The notebook [``features.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/features.ipynb) will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.

cs231n/assignment1/README.md

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+from cs231n.classifiers.k_nearest_neighbor import *
+from cs231n.classifiers.linear_classifier import *

cs231n/assignment1/cs231n/__init__.py

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+from builtins import range
+from builtins import object
+import numpy as np
+from past.builtins import xrange
+
+
+class KNearestNeighbor(object):
+    """ a kNN classifier with L2 distance """
+
+    def __init__(self):
+        pass
+
+    def train(self, X, y):
+        """
+        Train the classifier. For k-nearest neighbors this is just
+        memorizing the training data.
+
+        Inputs:
+        - X: A numpy array of shape (num_train, D) containing the training data
+          consisting of num_train samples each of dimension D.
+        - y: A numpy array of shape (N,) containing the training labels, where
+             y[i] is the label for X[i].
+        """
+        self.X_train = X
+        self.y_train = y
+
+    def predict(self, X, k=1, num_loops=0):
+        """
+        Predict labels for test data using this classifier.
+
+        Inputs:
+        - X: A numpy array of shape (num_test, D) containing test data consisting
+             of num_test samples each of dimension D.
+        - k: The number of nearest neighbors that vote for the predicted labels.
+        - num_loops: Determines which implementation to use to compute distances
+          between training points and testing points.
+
+        Returns:
+        - y: A numpy array of shape (num_test,) containing predicted labels for the
+          test data, where y[i] is the predicted label for the test point X[i].
+        """
+        if num_loops == 0:
+            dists = self.compute_distances_no_loops(X)
+        elif num_loops == 1:
+            dists = self.compute_distances_one_loop(X)
+        elif num_loops == 2:
+            dists = self.compute_distances_two_loops(X)
+        else:
+            raise ValueError('Invalid value %d for num_loops' % num_loops)
+
+        return self.predict_labels(dists, k=k)
+
+    def compute_distances_two_loops(self, X):
+        """
+        Compute the distance between each test point in X and each training point
+        in self.X_train using a nested loop over both the training data and the
+        test data.
+
+        Inputs:
+        - X: A numpy array of shape (num_test, D) containing test data.
+
+        Returns:
+        - dists: A numpy array of shape (num_test, num_train) where dists[i, j]
+          is the Euclidean distance between the ith test point and the jth training
+          point.
+        """
+        num_test = X.shape[0]
+        num_train = self.X_train.shape[0]
+        dists = np.zeros((num_test, num_train))
+        for i in range(num_test):
+            for j in range(num_train):
+                #####################################################################
+                # TODO:                                                             #
+                # Compute the l2 distance between the ith test point and the jth    #
+                # training point, and store the result in dists[i, j]. You should   #
+                # not use a loop over dimension, nor use np.linalg.norm().          #
+                #####################################################################
+                # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+                distance_ij = np.square(X[i] - self.X_train[j])
+                distance_ij = np.sqrt(np.sum(distance_ij))
+
+                dists[i, j] = distance_ij
+
+                # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+        return dists
+
+    def compute_distances_one_loop(self, X):
+        """
+        Compute the distance between each test point in X and each training point
+        in self.X_train using a single loop over the test data.
+
+        Input / Output: Same as compute_distances_two_loops
+        """
+        num_test = X.shape[0]
+        num_train = self.X_train.shape[0]
+        dists = np.zeros((num_test, num_train))
+        for i in range(num_test):
+            #######################################################################
+            # TODO:                                                               #
+            # Compute the l2 distance between the ith test point and all training #
+            # points, and store the result in dists[i, :].                        #
+            # Do not use np.linalg.norm().                                        #
+            #######################################################################
+            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            dists[i, :] = np.sqrt(np.sum(np.square(X[i] - self.X_train), axis=1))
+
+            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+        return dists
+
+    def compute_distances_no_loops(self, X):
+        """
+        Compute the distance between each test point in X and each training point
+        in self.X_train using no explicit loops.
+
+        Input / Output: Same as compute_distances_two_loops
+        """
+        num_test = X.shape[0]
+        num_train = self.X_train.shape[0]
+        dists = np.zeros((num_test, num_train))
+        #########################################################################
+        # TODO:                                                                 #
+        # Compute the l2 distance between all test points and all training      #
+        # points without using any explicit loops, and store the result in      #
+        # dists.                                                                #
+        #                                                                       #
+        # You should implement this function using only basic array operations; #
+        # in particular you should not use functions from scipy,                #
+        # nor use np.linalg.norm().                                             #
+        #                                                                       #
+        # HINT: Try to formulate the l2 distance using matrix multiplication    #
+        #       and two broadcast sums.                                         #
+        #########################################################################
+        # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+        train_matrix_tr = self.X_train.T
+
+        sum_1 = np.sum(np.square(X), axis=1)
+        sum_1 = sum_1.reshape((-1, sum_1.size)).T
+
+        sum_2 = np.sum(np.square(train_matrix_tr), axis=0)
+
+        dists = -2 * X.dot(train_matrix_tr) + sum_1 + sum_2
+        dists = np.sqrt(dists)
+
+        # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+        return dists
+
+    def predict_labels(self, dists, k=1):
+        """
+        Given a matrix of distances between test points and training points,
+        predict a label for each test point.
+
+        Inputs:
+        - dists: A numpy array of shape (num_test, num_train) where dists[i, j]
+          gives the distance betwen the ith test point and the jth training point.
+
+        Returns:
+        - y: A numpy array of shape (num_test,) containing predicted labels for the
+          test data, where y[i] is the predicted label for the test point X[i].
+        """
+        num_test = dists.shape[0]
+        y_pred = np.zeros(num_test)
+        for i in range(num_test):
+            # A list of length k storing the labels of the k nearest neighbors to
+            # the ith test point.
+            closest_y = []
+            #########################################################################
+            # TODO:                                                                 #
+            # Use the distance matrix to find the k nearest neighbors of the ith    #
+            # testing point, and use self.y_train to find the labels of these       #
+            # neighbors. Store these labels in closest_y.                           #
+            # Hint: Look up the function numpy.argsort.                             #
+            #########################################################################
+            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            closet_indexes = np.argsort(dists[i])
+            closet_indexes = closet_indexes[:k]
+
+            closest_y = self.y_train.take(closet_indexes)
+
+            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+            #########################################################################
+            # TODO:                                                                 #
+            # Now that you have found the labels of the k nearest neighbors, you    #
+            # need to find the most common label in the list closest_y of labels.   #
+            # Store this label in y_pred[i]. Break ties by choosing the smaller     #
+            # label.                                                                #
+            #########################################################################
+            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            y_pred[i] = np.bincount(closest_y).argmax()
+
+            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+        return y_pred

cs231n/assignment1/cs231n/classifiers/__init__.py

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+from __future__ import print_function
+
+from builtins import range
+from builtins import object
+import numpy as np
+from cs231n.classifiers.linear_svm import *
+from cs231n.classifiers.softmax import *
+from past.builtins import xrange
+
+
+class LinearClassifier(object):
+
+    def __init__(self):
+        self.W = None
+
+    def train(self, X, y, learning_rate=1e-3, reg=1e-5, num_iters=100,
+              batch_size=200, verbose=False):
+        """
+        Train this linear classifier using stochastic gradient descent.
+
+        Inputs:
+        - X: A numpy array of shape (N, D) containing training data; there are N
+          training samples each of dimension D.
+        - y: A numpy array of shape (N,) containing training labels; y[i] = c
+          means that X[i] has label 0 <= c < C for C classes.
+        - learning_rate: (float) learning rate for optimization.
+        - reg: (float) regularization strength.
+        - num_iters: (integer) number of steps to take when optimizing
+        - batch_size: (integer) number of training examples to use at each step.
+        - verbose: (boolean) If true, print progress during optimization.
+
+        Outputs:
+        A list containing the value of the loss function at each training iteration.
+        """
+        num_train, dim = X.shape
+        num_classes = np.max(y) + 1 # assume y takes values 0...K-1 where K is number of classes
+        if self.W is None:
+            # lazily initialize W
+            self.W = 0.001 * np.random.randn(dim, num_classes)
+
+        # Run stochastic gradient descent to optimize W
+        loss_history = []
+        for it in range(num_iters):
+            X_batch = None
+            y_batch = None
+
+            #########################################################################
+            # TODO:                                                                 #
+            # Sample batch_size elements from the training data and their           #
+            # corresponding labels to use in this round of gradient descent.        #
+            # Store the data in X_batch and their corresponding labels in           #
+            # y_batch; after sampling X_batch should have shape (batch_size, dim)   #
+            # and y_batch should have shape (batch_size,)                           #
+            #                                                                       #
+            # Hint: Use np.random.choice to generate indices. Sampling with         #
+            # replacement is faster than sampling without replacement.              #
+            #########################################################################
+            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            batch_indexes = np.random.choice(num_train, batch_size)
+            X_batch = X[batch_indexes]
+            y_batch = y[batch_indexes]
+
+            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            # evaluate loss and gradient
+            loss, grad = self.loss(X_batch, y_batch, reg)
+            loss_history.append(loss)
+
+            # perform parameter update
+            #########################################################################
+            # TODO:                                                                 #
+            # Update the weights using the gradient and the learning rate.          #
+            #########################################################################
+            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            self.W += - learning_rate * grad
+
+            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+            if verbose and it % 100 == 0:
+                print('iteration %d / %d: loss %f' % (it, num_iters, loss))
+
+        return loss_history
+
+    def predict(self, X):
+        """
+        Use the trained weights of this linear classifier to predict labels for
+        data points.
+
+        Inputs:
+        - X: A numpy array of shape (N, D) containing training data; there are N
+          training samples each of dimension D.
+
+        Returns:
+        - y_pred: Predicted labels for the data in X. y_pred is a 1-dimensional
+          array of length N, and each element is an integer giving the predicted
+          class.
+        """
+        y_pred = np.zeros(X.shape[0])
+        ###########################################################################
+        # TODO:                                                                   #
+        # Implement this method. Store the predicted labels in y_pred.            #
+        ###########################################################################
+        # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+
+        predict_matrix = X.dot(self.W)
+        y_pred = predict_matrix.argmax(axis=1)
+
+        # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
+        return y_pred
+
+    def loss(self, X_batch, y_batch, reg):
+        """
+        Compute the loss function and its derivative.
+        Subclasses will override this.
+
+        Inputs:
+        - X_batch: A numpy array of shape (N, D) containing a minibatch of N
+          data points; each point has dimension D.
+        - y_batch: A numpy array of shape (N,) containing labels for the minibatch.
+        - reg: (float) regularization strength.
+
+        Returns: A tuple containing:
+        - loss as a single float
+        - gradient with respect to self.W; an array of the same shape as W
+        """
+        pass
+
+
+class LinearSVM(LinearClassifier):
+    """ A subclass that uses the Multiclass SVM loss function """
+
+    def loss(self, X_batch, y_batch, reg):
+        return svm_loss_vectorized(self.W, X_batch, y_batch, reg)
+
+
+class Softmax(LinearClassifier):
+    """ A subclass that uses the Softmax + Cross-entropy loss function """
+
+    def loss(self, X_batch, y_batch, reg):
+        return softmax_loss_vectorized(self.W, X_batch, y_batch, reg)