Tensorflow实现酸奶销量预测分析


						本文实例为大家分享了Tensorflow酸奶销量预测分析的具体代码，供大家参考，具体内容如下





# coding:utf-8

# 酸奶成本为1元，利润为9元

# 预测少了相应的损失较大，故不要预测少

# 导入相应的模块

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

 

BATCH_SIZE=8

SEED=23455

COST=3

PROFIT=4

 

rdm=np.random.RandomState(SEED)

X=rdm.randn(100,2)

Y_=[[x1+x2+(rdm.rand()/10.0-0.05)] for (x1,x2) in X]

 

# 定义神经网络的输入、参数和输出，定义向前传播过程

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)

 

# 定义损失函数和反向传播过程

loss=tf.reduce_sum(tf.where(tf.greater(y,y_),(y-y_)*COST,(y_-y)*PROFIT)) #损失函数要根据不同的模型进行变换

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

 

# sess=tf.Session()

# STEPS=20000

# init_op=tf.global_variables_initializer()

# sess.run(init_op)

# for i in range(STEPS):

#  start=(i*BATCH_SIZE)%32

#  end=start+BATCH_SIZE

#  sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})

#  if i%500==0:

#

#   print("After %d steps,w1 is %f",(i,sess.run(w1)))

 

sess=tf.Session()

init_op=tf.global_variables_initializer()

sess.run(init_op)

STEPS=20000

for i in range(STEPS):

 start=(i*BATCH_SIZE)%100

 end=start+BATCH_SIZE

 sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})

 if i%500==0:

  print("After %d steps"%(i))

  # print(sess.run(loss_mse))

  # print("Loss is:%f",sess.run(loss_mse,feed_dict={y_:Y_,y:Y_}))

  print("w1 is:",sess.run(w1))

 print("Final is :",sess.run(w1))

 

xx,yy=np.mgrid[-3:3:.01,-3:3:.01]

grid=np.c_[xx.ravel(),yy.ravel()]

probs=sess.run(y,feed_dict={x:grid})

probs=probs.reshape(xx.shape)

 

plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_))

plt.contour(xx,yy,probs,[.9])

plt.show()



通过改变COST和PROFIT的值近而可以得出，当COST=1，PROFIT=9时，基于损失函数，模型的w1=1.02,w2=1.03说明模型会往多了预测；当COST=9,PROFIT=1时模型的w1=0.96,w2=0.97说明模型在往少了预测。





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