我試圖從 lightaime 的 Github 頁面理解這段代碼。它是一種 vetorized softmax 方法。讓我困惑的是“softmax_output[range(num_train), list(y)]”這個表達是什么意思？def softmax_loss_vectorized(W, X, y, reg):    """    Softmax loss function, vectorize implementation    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    """    # Initialize the loss and gradient to zero.    loss = 0.0    dW = np.zeros_like(W)    num_classes = W.shape[1]    num_train = X.shape[0]    scores = X.dot(W)    shift_scores = scores - np.max(scores, axis = 1).reshape(-1,1)    softmax_output = np.exp(shift_scores)/np.sum(np.exp(shift_scores), axis = 1).reshape(-1,1)    loss = -np.sum(np.log(softmax_output[range(num_train), list(y)]))       loss /= num_train     loss +=  0.5* reg * np.sum(W * W)    dS = softmax_output.copy()    dS[range(num_train), list(y)] += -1    dW = (X.T).dot(dS)    dW = dW/num_train + reg* W    return loss, dW