您好，请问您是想自定义一个loss么？可以参考 http://paddlepaddle.org/documentation/docs/zh/0.14.0/new_docs/advanced_usage/development/new_op.html 给我们贡献一个新的op

def batch_hard_triplet_loss(input, y_true, margin,batch_size):
"""
:param input: shape:[batch,feacture]
:param y_true:shape:[batch,1],y_true is label
:param margin:float
:param batch_size:batch大小
:return:tript_loss,shape:float
"""
# y_true shape [64, 1]
# y_true.t() shape [1, 64]
# pos_mask shape [64, 64], 矩阵中对角线上值为1
# pos_mask矩阵中 若[2,3]位置为1，代表labels中第2个值和第3个值得label相同
y_true_trans = fluid.layers.transpose(y_true, [1, 0])
y_true_trans_matrix = fluid.layers.concat([y_true_trans] * batch_size, axis=0)
pos_mask = fluid.layers.cast(
fluid.layers.equal(y_true_trans_matrix, y_true), "float32")

def _mask_max(input_tensor, mask, axis=None, keepdims=False):
# mask = 1(相同) 距离=input_tensor - 0
# mask = 0(不同) 距离=input_tensor - 1
# 距离最远的pos对
input_tensor = input_tensor-(1-mask)*1e6
_max = fluid.layers.reduce_max(input_tensor, dim=axis, keep_dim=keepdims)
return _max

def _mask_min(input_tensor, mask, axis=None, keepdims=False):
input_tensor=input_tensor+mask*1e6
_min = fluid.layers.reduce_min(input_tensor, dim=axis, keep_dim=keepdims)
return _min

a=fluid.layers.reduce_sum(fluid.layers.pow(input, 2.0), dim=1, keep_dim=True)
b=fluid.layers.reduce_sum(fluid.layers.pow(fluid.layers.transpose(input, [1, 0]), 2.0), dim=0,keep_dim=True)
b_matrix=fluid.layers.concat([b]*batch_size,axis=0)

dist_squared=(b_matrix+a)-2*fluid.layers.matmul(input, input,transpose_y=True)
dist=fluid.layers.elementwise_max(dist_squared,fluid.layers.fill_constant([1],dtype='float32',value=1e-16))
dist = fluid.layers.sqrt(dist)#欧式距离

pos_max = _mask_max(dist, pos_mask, axis=-1,keepdims=True)#pos_max.shape:(batch_size,1)
neg_min = _mask_min(dist, pos_mask, axis=-1,keepdims=True)#neg_min.shape:(batch_size,1)

basic_loss = (pos_max-neg_min)+margin#曼哈顿距离+margin
#截断triplet_loss损失***********************************************
# clip_loss=fluid.layers.relu(basic_loss)
# loss = fluid.layers.reduce_mean(clip_loss,dim=0)

#log(1+exp(x))triplet_loss损失**************************************
loss = fluid.layers.reduce_mean(
fluid.layers.log(1+fluid.layers.exp(basic_loss)), dim=0)
# loss=fluid.layers.reduce_mean(
# fluid.layers.log(fluid.layers.elementwise_add(
# fluid.layers.exp(basic_loss),fluid.layers.fill_constant([1],dtype='float32',value=1))),dim=0)

return loss