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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