简单神经网络解决二分类问题示例(Tensorflow)(自定义损失函数)
在预测商品销量时,如果预测多了,商家损失的是生产商品的成本;而如果预测少了,损失的则是商品的利润。针对这种模型可以自己定义损失函数。
自定义损失函数为:
可使用如下代码实现:
loss_less = 10
loss_more = 1
loss = tf.reduce_sum(tf.where(tf.greater(y, y_),
(y - y_) * loss_more,
(y_ - y) * loss_less))
整体代码如下:
#coding:utf-8
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)
loss_less = 10
loss_more = 1
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(1)
dataset_size = 128
X = rdm.rand(dataset_size, 2)
Y = [[x1 + x2 + rdm.rand()/10.0-0.05] for (x1, x2) in X]
with tf.Session() as sess:
init_op = tf.initialize_all_variables()
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))