def focal_loss():
 ...
 return xx
def fbeta_score():
 ...
 return yy
model.compile(optimizer=Adam(lr=0.0001), loss=[focal_loss],metrics=['accuracy',fbeta_score] )

#模型编译时加入自定义loss及评估函数
model.compile(optimizer = Adam(lr=1e-4), loss=[binary_focal_loss()],
    metrics=['accuracy',dice_coef])

#自定义loss及评估函数
def binary_focal_loss(gamma=2, alpha=0.25):
 """
 Binary form of focal loss.
 适用于二分类问题的focal loss
 focal_loss(p_t) = -alpha_t * (1 - p_t)**gamma * log(p_t)
  where p = sigmoid(x), p_t = p or 1 - p depending on if the label is 1 or 0, respectively.
 References:
  https://arxiv.org/pdf/1708.02002.pdf
 Usage:
  model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=["accuracy"], optimizer=adam)
 """
 alpha = tf.constant(alpha, dtype=tf.float32)
 gamma = tf.constant(gamma, dtype=tf.float32)

 def binary_focal_loss_fixed(y_true, y_pred):
  """
  y_true shape need be (None,1)
  y_pred need be compute after sigmoid
  """
  y_true = tf.cast(y_true, tf.float32)
  alpha_t = y_true * alpha + (K.ones_like(y_true) - y_true) * (1 - alpha)

  p_t = y_true * y_pred + (K.ones_like(y_true) - y_true) * (K.ones_like(y_true) - y_pred) + K.epsilon()
  focal_loss = - alpha_t * K.pow((K.ones_like(y_true) - p_t), gamma) * K.log(p_t)
  return K.mean(focal_loss)

 return binary_focal_loss_fixed

#'''
#smooth 参数防止分母为0
def dice_coef(y_true, y_pred, smooth=1):
 intersection = K.sum(y_true * y_pred, axis=[1,2,3])
 union = K.sum(y_true, axis=[1,2,3]) + K.sum(y_pred, axis=[1,2,3])
 return K.mean( (2. * intersection + smooth) / (union + smooth), axis=0)