這個程式碼不能在python熵執行，是官方給的程式碼，我只是按照我的意思理解了一下，並把自己的理解寫上去，如果想要找能執行的程式碼的同學請忽視，如果你找到了也可以和我分享

import numpy as np
from random import shuffle

def svm_loss_naive(W, X, y, reg):
  """
  Structured SVM loss function, naive implementation (with loops).

  Inputs have dimension D, there are C classes, and we operate on minibatches
  of N examples.

  Inputs:
  - W: A numpy array of shape (D, C) containing weights.
  - X: A numpy array of shape (N, D) containing a minibatch of data.
  - y: A numpy array of shape (N,) containing training labels; y[i] = c means
    that X[i] has label c, where 0 <= c < C.
  - reg: (float) regularization strength

  Returns a tuple of:
  - loss as single float
  - gradient with respect to weights W; an array of same shape as W
  """
  dW = np.zeros(W.shape) # 初始化梯度為零

  # compute the loss and the gradient
  num_classes = W.shape[1]
  num_train = X.shape[0]
  loss = 0.0
  for i in xrange(num_train):
    scores = X[i].dot(W)#做點乘
    correct_class_score = scores[y[i]]#最後的得分
    for j in xrange(num_classes):
      if j == y[i]:
        continue
      margin = scores[j] - correct_class_score + 1 #svm損失函式，Li=max(0,Sj-Syi+1)只要j!=yi
    
      if margin > 0:
        loss += margin#取最大值
        dW[:,j] += X[i].T
        dW[:,y[i]] += -X[i].T 

  # Right now the loss is a sum over all training examples, but we want it
  # to be an average instead so we divide by num_train.
  loss /= num_train#獲得損失的平均值
  dW /= num_train#梯度的平均值
  # 在損失中增加正規化。
  loss += 0.5 * reg * np.sum(W * W)
  dW += reg * W

  #############################################################################
  # TODO:                                                                     #
  # Compute the gradient of the loss function and store it dW.損失函式的梯度                #
  # Rather that first computing the loss and then computing the derivative,   #
  # it may be simpler to compute the derivative at the same time that the     #
  # loss is being computed. As a result you may need to modify some of the    #
  # code above to compute the gradient.                                       #
  #############################################################################


  return loss, dW


def svm_loss_vectorized(W, X, y, reg):
  """
  Structured SVM loss function, vectorized implementation.損失函式

  Inputs and outputs are the same as svm_loss_naive.
  """
  loss = 0.0
  dW = np.zeros(W.shape) #初始化梯度為零

  #############################################################################
  # TODO:                                                                     #
  # Implement a vectorized version of the structured SVM loss, storing the    #
  # result in loss.                                                           #
  #############################################################################
  num_train = X.shape[0]
  num_classes = W.shape[1]
  scores = X.dot(W)#WX
  correct_class_scores = scores[range(num_train), list(y)].reshape(-1,1) #(N, 1)
  margins = np.maximum(0, scores - correct_class_scores +1)
  margins[range(num_train), list(y)] = 0
  loss = np.sum(margins) / num_train + 0.5 * reg * np.sum(W * W)
  #pass
  #############################################################################
  #                             END OF YOUR CODE                              #
  #############################################################################


  #############################################################################
  # TODO:                                                                     #
  # Implement a vectorized version of the gradient for the structured SVM     #
  # loss, storing the result in dW.                                           #
  #                                                                           #
  # Hint: Instead of computing the gradient from scratch, it may be easier    #
  # to reuse some of the intermediate values that you used to compute the     #
  # loss.                                                                     #
  #############################################################################
  coeff_mat = np.zeros((num_train, num_classes))
  coeff_mat[margins > 0] = 1
  coeff_mat[range(num_train), list(y)] = 0
  coeff_mat[range(num_train), list(y)] = -np.sum(coeff_mat, axis=1)

  dW = (X.T).dot(coeff_mat)
  dW = dW/num_train + reg*W
  #pass
  #############################################################################
  #                             END OF YOUR CODE                              #
  #############################################################################

  return loss, dW