Keras loss函数

╰半夏微凉° 2022-05-27 09:13 218阅读 0赞

''' Created on 2018-4-16 '''
    
    def compile(
    self,
    optimizer, #优化器
    loss, #损失函数,可以为已经定义好的loss函数名称，也可以为自己写的loss函数
    metrics=None, #
    sample_weight_mode=None, #如果你需要按时间步为样本赋权（2D权矩阵），将该值设为“temporal”。默认为“None”，代表按样本赋权（1D权），和fit中sample_weight在赋值样本权重中配合使用
    weighted_metrics=None, 
    target_tensors=None,
    **kwargs #这里的设定的参数可以和后端交互。
    )
    
    实质调用的是Keras\engine\training.py 中的class Model中的def compile
    一般使用model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])
    
    # keras所有定义好的损失函数loss:
    # keras\losses.py
    # 有些loss函数可以使用简称:
    # mse = MSE = mean_squared_error
    # mae = MAE = mean_absolute_error
    # mape = MAPE = mean_absolute_percentage_error
    # msle = MSLE = mean_squared_logarithmic_error
    # kld = KLD = kullback_leibler_divergence
    # cosine = cosine_proximity
    # 使用到的数学方法:
    # mean:求均值
    # sum:求和
    # square:平方
    # abs:绝对值
    # clip:[裁剪替换](https://blog.csdn.net/qq1483661204/article/details)
    # epsilon:1e-7
    # log:以e为底
    # maximum(x,y):x与 y逐位比较取其大者
    # reduce_sum(x,axis):沿着某个维度求和
    # l2_normalize：l2正则化
    # softplus:softplus函数
    # 
    # import cntk as C
    # 1.mean_squared_error:
    # return K.mean(K.square(y_pred - y_true), axis=-1) 
    # 2.mean_absolute_error:
    # return K.mean(K.abs(y_pred - y_true), axis=-1)
    # 3.mean_absolute_percentage_error:
    # diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true),K.epsilon(),None))
    # return 100. * K.mean(diff, axis=-1)
    # 4.mean_squared_logarithmic_error:
    # first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
    # second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
    # return K.mean(K.square(first_log - second_log), axis=-1)
    # 5.squared_hinge:
    # return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)
    # 6.hinge(SVM损失函数):
    # return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)
    # 7.categorical_hinge:
    # pos = K.sum(y_true * y_pred, axis=-1)
    # neg = K.max((1. - y_true) * y_pred, axis=-1)
    # return K.maximum(0., neg - pos + 1.)
    # 8.logcosh:
    # def _logcosh(x):
    # return x + K.softplus(-2. * x) - K.log(2.)
    # return K.mean(_logcosh(y_pred - y_true), axis=-1)
    # 9.categorical_crossentropy:
    # output /= C.reduce_sum(output, axis=-1)
    # output = C.clip(output, epsilon(), 1.0 - epsilon())
    # return -sum(target * C.log(output), axis=-1)
    # 10.sparse_categorical_crossentropy:
    # target = C.one_hot(target, output.shape[-1])
    # target = C.reshape(target, output.shape)
    # return categorical_crossentropy(target, output, from_logits)
    # 11.binary_crossentropy:
    # return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)
    # 12.kullback_leibler_divergence:
    # y_true = K.clip(y_true, K.epsilon(), 1)
    # y_pred = K.clip(y_pred, K.epsilon(), 1)
    # return K.sum(y_true * K.log(y_true / y_pred), axis=-1)
    # 13.poisson:
    # return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)
    # 14.cosine_proximity:
    # y_true = K.l2_normalize(y_true, axis=-1)
    # y_pred = K.l2_normalize(y_pred, axis=-1)
    # return -K.sum(y_true * y_pred, axis=-1)