pytorch常用损失函数–CrossEntropyLoss(交叉损失熵函数)

一.函数原理简介:
cross_entroy_loss(x, target):
x为torch.Size([m, classes_sum])，target为一维的tensor,维度为torch.Size([classes_sum])
target必须为long类型,x为float类型

softmax–>logsoftmax–>NLLLoss

二.代码直观演示:

''' 步骤: one-hot编码 softmax求解 logsoftmax求解 NLLLoss求解 '''
def cross_entroy_loss(x, target):
    rows = x.size(0)
    cols = x.size(1)
    one_hot =torch.zeros((rows, cols))
    for i in range(len(target)):
        cols = target[i]
        one_hot[i][cols] = 1

    #下面一句
    ''' torch.sum(torch.exp(x), dim = 1).reshape(-1,1) torch.sum(torch.exp(x), dim = 1)本身得到的是(1, 5)维度的数据 将torch.exp(x)的每一列求和之后并将其变为(5,1)维度的数据 '''
    #先进行softmax运算:
    ''' softmax = e(xi) / sum(e(xi))#对每行进行操作,即每行的值加起来为1 '''
    #再进行logsoftmax
    ''' logsoftmax = log(softmax) '''
    #最后进行NLLLoss
    ''' Nllloss = -1/N * (onehot * logsoftmax) #N代表target.shape[0] '''
    softmax = torch.exp(x) / torch.sum(torch.exp(x), dim = 1).reshape(-1,1)
    logsoftmax = torch.log(softmax)
    nllloss = - torch.sum(one_hot * logsoftmax) / target.shape[0]
    CrossEntroyLoss_value = nllloss

    return CrossEntroyLoss_value

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