kerasカスタム損失関数は、単純な方法であり、

まず、我々はKerasは（MSEなど、メイ、など）方法によって定義される目的関数に使用を見て

from keras import backend as K

def mean_squared_error(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true), axis=-1)


def mean_absolute_error(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)


def mean_absolute_percentage_error(y_true, y_pred):
    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
    return 100. * K.mean(diff, axis=-1)

def categorical_crossentropy(y_true, y_pred):
    '''Expects a binary class matrix instead of a vector of scalar classes.
    '''
    return K.categorical_crossentropy(y_pred, y_true)

def sparse_categorical_crossentropy(y_true, y_pred):
    '''expects an array of integer classes.
    Note: labels shape must have the same number of dimensions as output shape.
    If you get a shape error, add a length-1 dimension to labels.
    '''
    return K.sparse_categorical_crossentropy(y_pred, y_true)

def binary_crossentropy(y_true, y_pred):
    return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)

def kullback_leibler_divergence(y_true, y_pred):
    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)

def poisson(y_true, y_pred):
    return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)


def cosine_proximity(y_true, y_pred):
    y_true = K.l2_normalize(y_true, axis=-1)
    y_pred = K.l2_normalize(y_pred, axis=-1)
    return -K.mean(y_true * y_pred, axis=-1)

だから、あなたは、自分の特定のタスクの目的関数を定義することができ、上記の方法に従ってください。例えば：予測値と実際の値との差の定義

from keras import backend as K
def new_loss(y_true,y_pred):
    return K.mean((y_pred-y_true),axis = -1)

次に、目的関数のコンパイルの独自の定義を使用

from keras import backend as K
def my_loss(y_true,y_pred):
    return K.mean((y_pred-y_true),axis = -1)
model.compile(optimizer=optimizers.RMSprop(lr)，loss=my_loss,
metrics=['accuracy'])