用KNN算法分类CIFAR-10图片数据

　　KNN分类CIFAR-10，并且做Cross Validation，CIDAR-10数据库数据如下：

knn.py : 主要的试验流程

from cs231n.data_utils import     load_CIFAR10
from cs231n.classifiers import KNearestNeighbor
import random
import numpy as np
import     matplotlib.pyplot as plt
# set plt params
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
x_train,y_train,x_test,y_test = load_CIFAR10(cifar10_dir)
print'x_train : ',x_train.shape
print'y_train : ',y_train.shape
print'x_test : ',x_test.shape,'y_test : ',y_test.shape

#visual training example
classes = ['plane','car','bird','cat','deer','dog','forg','horse','ship','truck']
num_classes = len(classes)
samples_per_class = 7
for y,cls in enumerate(classes):
    #flaznonzero return indices_array of the none-zero elements
    # ten classes, y_train and y_test all in [1...10]
    idxs = np.flatnonzero(y_train == y)
    idxs = np.random.choice(idxs , samples_per_class, replace = False)
    for i,idx in enumerate(idxs):
        plt_idx = i*num_classes + y + 1
        # subplot(m,n,p)
        # m : length of subplot 
        # n : width of subplot
        # p : location of subplot
        plt.subplot(samples_per_class,num_classes,plt_idx)
        plt.imshow(x_train[idx].astype('uint8'))
        # hidden the axis info
        plt.axis('off')
        if i == 0:
            plt.title(cls)
plt.show()

# subsample data for more dfficient code execution 
num_training = 5000
#range(5)=[0,1,2,3,4]
mask = range(num_training)
x_train = x_train[mask]
y_train = y_train[mask]
num_test = 500
mask = range(num_test)
x_test = x_test[mask]
y_test = y_test[mask]
#the image data has three chanels
#the next two step shape the image size 32*32*3 to 3072*1
x_train = np.reshape(x_train,(x_train.shape[0],-1))
x_test = np.reshape(x_test,(x_test.shape[0],-1))
print 'after subsample and re shape:'
print 'x_train : ',x_train.shape," x_test : ",x_test.shape
#KNN classifier
classifier = KNearestNeighbor()
classifier.train(x_train,y_train)
# compute the distance between test_data and train_data 
dists = classifier.compute_distances_no_loops(x_test)
#each row is a single test example and its distances to training example
print 'dist shape : ',dists.shape
plt.imshow(dists , interpolation='none')
plt.show()
y_test_pred = classifier.predict_labels(dists,k = 5)
num_correct = np.sum(y_test_pred == y_test)
acc = float(num_correct)/num_test
print'k=5 ,The Accurancy is : ', acc

#Cross-Validation

#5-fold cross validation split the training data to 5 pieces
num_folds = 5
#k is params of knn
k_choice = [1,5,8,11,15,18,20,50,100]
x_train_folds = []
y_train_folds = []
x_train_folds = np.array_split(x_train,num_folds)
y_train_folds = np.array_split(y_train,num_folds)

k_to_acc={}

for k in k_choice:
    k_to_acc[k] =[]
for k in k_choice:
    print 'cross validation : k = ', k
    for j in range(num_folds):
        #vstack :stack the array to matrix
        #vertical
        x_train_cv = np.vstack(x_train_folds[0:j]+x_train_folds[j+1:])
        x_test_cv = x_train_folds[j]
        
        #>>> a = np.array((1,2,3))
        #>>> b = np.array((2,3,4))
        #>>> np.hstack((a,b))
        # horizontally    
        y_train_cv = np.hstack(y_train_folds[0:j]+y_train_folds[j+1:])
        y_test_cv = y_train_folds[j]
        
        classifier.train(x_train_cv,y_train_cv)
        dists_cv = classifier.compute_distances_no_loops(x_test_cv)
        y_test_pred = classifier.predict_labels(dists_cv,k)
        num_correct = np.sum(y_test_pred == y_test_cv)
        acc = float(num_correct)/ num_test
        k_to_acc[k].append(acc)
print k_to_acc

k_nearest_neighbor.py ： knn算法的实现：

import numpy as np
from collections import Counter
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.
    - each row is a training example
    - 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 xrange(num_test):
      for j in xrange(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.                                    #
        #####################################################################
    #Euclidean distance
    #dists[i,j] = np.sqrt(np.sum(X[i,:]-self.X_train[j,:])**2)
    # use linalg make it more easy
    dists[i,j] = np.linalg.norm(self.X_train[j,:]-X[i,:])
        #####################################################################
        #                       END OF YOUR CODE                            #
        #####################################################################
    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 xrange(num_test):
      #######################################################################
      # TODO:                                                               #
      # Compute the l2 distance between the ith test point and all training #
      # points, and store the result in dists[i, :].                        #
      #######################################################################
      #evevy row minus X[i,:] then norm it
      # axis = 1 imply operations by row 
      dist[i,:] = np.linalg.norm(self.X_train - X[i,:],axis = 1)      
      #######################################################################
      #                         END OF YOUR CODE                            #
      #######################################################################
    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.                #
    #                                                                       #
    # HINT: Try to formulate the l2 distance using matrix multiplication    #
    #       and two broadcast sums.                                         #
    #########################################################################
    M = np.dot(X , self.X_train.T)
    te = np.square(X).sum(axis = 1)
    tr = np.square(self.X_train).sum(axis = 1)
    dists = np.sqrt(-2*M +tr+np.matrix(te).T)
    #########################################################################
    #                         END OF YOUR CODE                              #
    #########################################################################
    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 xrange(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.                             #
      #########################################################################
      labels = self.y_train[np.argsort(dists[i,:])].flatten()
      closest_y = labels[0:k]
      #########################################################################
      # 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.                                                                #
      #########################################################################
      c = Counter(closest_y)
      y_pred[i] = c.most_common(1)[0][0]
      #########################################################################
      #                           END OF YOUR CODE                            # 
      #########################################################################

    return y_pred

data_utils.py ： CIFAR-10数据的读取

import cPickle as pickle
import numpy as np
import os
from scipy.misc import imread

def load_CIFAR_batch(filename):
  """ load single batch of cifar """
  with open(filename, 'rb') as f:
    datadict = pickle.load(f)
    X = datadict['data']
    Y = datadict['labels']
    X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
    Y = np.array(Y)
    return X, Y

def load_CIFAR10(ROOT):
  """ load all of cifar """
  xs = []
  ys = []
  for b in range(1,6):
    f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
    X, Y = load_CIFAR_batch(f)
    xs.append(X)
    ys.append(Y)    
  Xtr = np.concatenate(xs)
  Ytr = np.concatenate(ys)
  del X, Y
  Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
  return Xtr, Ytr, Xte, Yte

def load_tiny_imagenet(path, dtype=np.float32):
  """
  Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and
  TinyImageNet-200 have the same directory structure, so this can be used
  to load any of them.

  Inputs:
  - path: String giving path to the directory to load.
  - dtype: numpy datatype used to load the data.

  Returns: A tuple of
  - class_names: A list where class_names[i] is a list of strings giving the
    WordNet names for class i in the loaded dataset.
  - X_train: (N_tr, 3, 64, 64) array of training images
  - y_train: (N_tr,) array of training labels
  - X_val: (N_val, 3, 64, 64) array of validation images
  - y_val: (N_val,) array of validation labels
  - X_test: (N_test, 3, 64, 64) array of testing images.
  - y_test: (N_test,) array of test labels; if test labels are not available
    (such as in student code) then y_test will be None.
  """
  # First load wnids
  with open(os.path.join(path, 'wnids.txt'), 'r') as f:
    wnids = [x.strip() for x in f]

  # Map wnids to integer labels
  wnid_to_label = {wnid: i for i, wnid in enumerate(wnids)}

  # Use words.txt to get names for each class
  with open(os.path.join(path, 'words.txt'), 'r') as f:
    wnid_to_words = dict(line.split('\t') for line in f)
    for wnid, words in wnid_to_words.iteritems():
      wnid_to_words[wnid] = [w.strip() for w in words.split(',')]
  class_names = [wnid_to_words[wnid] for wnid in wnids]

  # Next load training data.
  X_train = []
  y_train = []
  for i, wnid in enumerate(wnids):
    if (i + 1) % 20 == 0:
      print 'loading training data for synset %d / %d' % (i + 1, len(wnids))
    # To figure out the filenames we need to open the boxes file
    boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid)
    with open(boxes_file, 'r') as f:
      filenames = [x.split('\t')[0] for x in f]
    num_images = len(filenames)
    
    X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype)
    y_train_block = wnid_to_label[wnid] * np.ones(num_images, dtype=np.int64)
    for j, img_file in enumerate(filenames):
      img_file = os.path.join(path, 'train', wnid, 'images', img_file)
      img = imread(img_file)
      if img.ndim == 2:
        ## grayscale file
        img.shape = (64, 64, 1)
      X_train_block[j] = img.transpose(2, 0, 1)
    X_train.append(X_train_block)
    y_train.append(y_train_block)
      
  # We need to concatenate all training data
  X_train = np.concatenate(X_train, axis=0)
  y_train = np.concatenate(y_train, axis=0)
  
  # Next load validation data
  with open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f:
    img_files = []
    val_wnids = []
    for line in f:
      img_file, wnid = line.split('\t')[:2]
      img_files.append(img_file)
      val_wnids.append(wnid)
    num_val = len(img_files)
    y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids])
    X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype)
    for i, img_file in enumerate(img_files):
      img_file = os.path.join(path, 'val', 'images', img_file)
      img = imread(img_file)
      if img.ndim == 2:
        img.shape = (64, 64, 1)
      X_val[i] = img.transpose(2, 0, 1)

  # Next load test images
  # Students won't have test labels, so we need to iterate over files in the
  # images directory.
  img_files = os.listdir(os.path.join(path, 'test', 'images'))
  X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype)
  for i, img_file in enumerate(img_files):
    img_file = os.path.join(path, 'test', 'images', img_file)
    img = imread(img_file)
    if img.ndim == 2:
      img.shape = (64, 64, 1)
    X_test[i] = img.transpose(2, 0, 1)

  y_test = None
  y_test_file = os.path.join(path, 'test', 'test_annotations.txt')
  if os.path.isfile(y_test_file):
    with open(y_test_file, 'r') as f:
      img_file_to_wnid = {}
      for line in f:
        line = line.split('\t')
        img_file_to_wnid[line[0]] = line[1]
    y_test = [wnid_to_label[img_file_to_wnid[img_file]] for img_file in img_files]
    y_test = np.array(y_test)
  
  return class_names, X_train, y_train, X_val, y_val, X_test, y_test


def load_models(models_dir):
  """
  Load saved models from disk. This will attempt to unpickle all files in a
  directory; any files that give errors on unpickling (such as README.txt) will
  be skipped.

  Inputs:
  - models_dir: String giving the path to a directory containing model files.
    Each model file is a pickled dictionary with a 'model' field.

  Returns:
  A dictionary mapping model file names to models.
  """
  models = {}
  for model_file in os.listdir(models_dir):
    with open(os.path.join(models_dir, model_file), 'rb') as f:
      try:
        models[model_file] = pickle.load(f)['model']
      except pickle.UnpicklingError:
        continue
  return models

 通过 cv，最优的 k 值为7，accurancy=0.282，太低了，明天用cnn重复这个实验...