用均方误差来优化神经网络预测的结果
#coding=utf-8
# 预测酸奶日销量 y，x1 和 x2 是影响日销量的两个因素。
# 应提前采集的数据有:一段时间内，每日的 x1 因素、x2 因素和销量 y_。采集的数据尽量多。
# 在本例中用销量预测产量，最优的产量应该等于销量。由于目前没有数据集，所以拟造了一套数
# 据集。利用 Tensorflow 中函数随机生成 x1、 x2，制造标准答案 y_ = x1 + x2，为了更真实，求和后 还加了正负 0.05 的随机噪声。
import tensorflow as tf
import numpy as np
BATCH_SIZE= 8
SEED = 23455
rdm = np.random.RandomState(SEED)
X = rdm.rand(32,2)
Y_ = [[x1 + x2 + (rdm.rand()/10 - 0.05)] for (x1,x2) in X]
#定义神经网络的输入、参数和输出
x = tf.placeholder(tf.float32, shape=(None,2))
y_ = tf.placeholder(tf.float32,shape=(None,1))
w1 = tf.Variable(tf.random_normal([2,1],stddev=1,seed=1))
y = tf.matmul(x,w1)
#定义损失函数及反向传播方法
#定义损失函数为MSE,反向传播方法为梯度下降
loss_mse = tf.reduce_mean(tf.square(y_-y))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)
#生成会话并训练STEPS轮
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
STEPS = 20000
for i in range(STEPS):
start = (i * BATCH_SIZE) % 32
end = (i * BATCH_SIZE) % 32 + BATCH_SIZE
sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
if i % 500 == 0:
print "After %d training steps,w1 is: "%(i)
print sess.run(w1)
print "Final w1 is:\n",sess.run(w1)本案例输出的结果是:
After 0 training steps,w1 is:
[[-0.80974597]
[ 1.4852903 ]]
After 500 training steps,w1 is:
[[-0.46074435]
[ 1.641878 ]]
After 1000 training steps,w1 is:
[[-0.21939856]
[ 1.6984766 ]]
After 1500 training steps,w1 is:
[[-0.04415595]
[ 1.7003176 ]]
After 2000 training steps,w1 is:
[[0.08942621]
[1.673328 ]]
After 2500 training steps,w1 is:
[[0.19583555]
[1.6322677 ]]
After 3000 training steps,w1 is:
[[0.28375748]
[1.5854434 ]]
After 3500 training steps,w1 is:
[[0.35848638]
[1.5374472 ]]
After 4000 training steps,w1 is:
[[0.42332518]
[1.4907393 ]]
After 4500 training steps,w1 is:
[[0.48040026]
[1.4465574 ]]
After 5000 training steps,w1 is:
[[0.53113604]
[1.4054536 ]]
After 5500 training steps,w1 is:
[[0.5765325]
[1.3675941]]
After 6000 training steps,w1 is:
[[0.61732584]
[1.3329403 ]]
After 6500 training steps,w1 is:
[[0.6540846]
[1.3013426]]
After 7000 training steps,w1 is:
[[0.6872685]
[1.272602 ]]
After 7500 training steps,w1 is:
[[0.71725976]
[1.2465005 ]]
After 8000 training steps,w1 is:
[[0.7443861]
[1.2228197]]
After 8500 training steps,w1 is:
[[0.7689324]
[1.2013483]]
After 9000 training steps,w1 is:
[[0.79115134]
[1.1818889 ]]
After 9500 training steps,w1 is:
[[0.811267 ]
[1.1642567]]
After 10000 training steps,w1 is:
[[0.8294814]
[1.1482829]]
After 10500 training steps,w1 is:
[[0.84597576]
[1.1338125 ]]
After 11000 training steps,w1 is:
[[0.8609128]
[1.1207061]]
After 11500 training steps,w1 is:
[[0.87444043]
[1.1088346 ]]
After 12000 training steps,w1 is:
[[0.88669145]
[1.0980824 ]]
After 12500 training steps,w1 is:
[[0.8977863]
[1.0883439]]
After 13000 training steps,w1 is:
[[0.9078348]
[1.0795243]]
After 13500 training steps,w1 is:
[[0.91693527]
[1.0715363 ]]
After 14000 training steps,w1 is:
[[0.92517716]
[1.0643018 ]]
After 14500 training steps,w1 is:
[[0.93264157]
[1.0577497 ]]
After 15000 training steps,w1 is:
[[0.9394023]
[1.0518153]]
After 15500 training steps,w1 is:
[[0.9455251]
[1.0464406]]
After 16000 training steps,w1 is:
[[0.95107025]
[1.0415728 ]]
After 16500 training steps,w1 is:
[[0.9560928]
[1.037164 ]]
After 17000 training steps,w1 is:
[[0.96064115]
[1.0331714 ]]
After 17500 training steps,w1 is:
[[0.96476096]
[1.0295546 ]]
After 18000 training steps,w1 is:
[[0.9684917]
[1.0262802]]
After 18500 training steps,w1 is:
[[0.9718707]
[1.0233142]]
After 19000 training steps,w1 is:
[[0.974931 ]
[1.0206276]]
After 19500 training steps,w1 is:
[[0.9777026]
[1.0181949]]
Final w1 is:
[[0.98019385]
[1.0159807 ]]
Process finished with exit code 0本例中神经网络预测模型为 y = w1*x1 + w2*x2，损失函数采用均方误差。通过使 损失函数值(loss)不断降低，神经网络模型得到最终参数 w1=0.98，w2=1.02，销量预测结果为 y = 0.98*x1 + 1.02*x2。由于在生成数据集时，标准答案为 y = x1 + x2，因此，销量预测结果和标准答案已非常接近，说明该神经网络预测酸奶日销量正确。
