自定义损失函数

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 19 08:49:34 2018

@author: tianmu
"""
"""
小记：
可以将此例假设为买卖东西。商品成本为1元，利润为10元。
为求利润最大化，即损失最小，需要用优化器AdamOptimizer来预测。
"""

import tensorflow as tf

from numpy.random import RandomState

batch_size = 8

x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_= tf.placeholder(tf.float32,shape=(None,1),name='y-input')

w1 = tf.Variable(tf.random_normal([2,1],stddev=1,seed=1))
y = tf.matmul(x,w1)

#成本和利润
loss_less=10
loss_more=1
#定义预测多了和预测少了的成本，此例需要loss尽量的少
loss=tf.reduce_sum(tf.where(tf.greater(y,y_),(y-y_)*loss_more,(y_-y)*loss_less))

train_step = tf.train.AdamOptimizer(0.001).minimize(loss)

rdm=RandomState()

dataset_size = 128

X=rdm.rand(dataset_size,2)

Y=[[x1+x2+rdm.rand()/10-0.05] for (x1,x2) in X]

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    STEPS= 5000
    for i in range(STEPS):
        start = (i*batch_size) % dataset_size
        end = min(start+batch_size,dataset_size)
        sess.run(train_step,
                 feed_dict={x:X[start:end],y_:Y[start:end]})
    print(sess.run(w1))