数据为三十四所近四年分数线，结果预测277，由此推断为275或者280
#coding=utf-8 import tensorflow as tf import numpy as np #导入数据，数据 my_matrix = np.loadtxt(open("inputdata.csv","rb"),delimiter=",",skiprows=0) l1 = my_matrix[:,1] l2 = my_matrix[:,2] l3 = my_matrix[:,3] l4 = my_matrix[:,4] # 定义模型参数 W2 = tf.Variable([0.4], tf.float32) W3 = tf.Variable([0.15], tf.float32) W4 = tf.Variable([0.44], tf.float32) b = tf.Variable([0.0], tf.float32) # 定义模型 x2 = tf.placeholder(tf.float32) x3 = tf.placeholder(tf.float32) x4 = tf.placeholder(tf.float32) linear_model = W2*x2 + W3*x3 + W4*x4 + b #三元回归 y = tf.placeholder(tf.float32) # 设置损失函数 loss = tf.reduce_sum(tf.square(linear_model - y)/100) # sum of the squares # 设置优化方法 optimizer = tf.train.GradientDescentOptimizer(0.00001)#梯度下降 train = optimizer.minimize(loss)#最小化损失函数 损失函数可以试试其他的 # 输入训练数据 x2_train=l2.tolist() x3_train=l3.tolist() x4_train=l4.tolist() y_train=l1.tolist() # 初始化 init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # 循环训练 for i in range(1000000): sess.run(train,{x2:x2_train, x3:x3_train, x4:x4_train, y:y_train}) if(i%1000==0): print(sess.run([W2, W3, W4, b]),{x2:x2_train, x3:x3_train, x4:x4_train, y:y_train}) curr_W2, curr_W3, curr_W4, curr_b, curr_loss = sess.run([W2, W3, W4, b, loss],{x2: x2_train, x3: x3_train, x4: x4_train, y: y_train}) print("%d score:%d"%(i,curr_W2 * 265 + curr_W3 * 280 + curr_W4 * 285 + b)) print("%d W2: %s W3: %s W4: %s b: %s loss: %s" % (W2, W2, W2, b, loss)) print(sess.run(loss,{x2:x2_train,x3:x3_train,x4:x4_train, y:y_train})) # 输出 curr_W2, curr_W3, curr_W4, curr_b, curr_loss = sess.run([W2, W3, W4, b, loss], {x2:x2_train,x3:x3_train,x4:x4_train, y:y_train}) print("W2: %s W3: %s W4: %s b: %s loss: %s"%(curr_W2, curr_W3, curr_W4, curr_b, curr_loss)) print(curr_W2*265+curr_W3*280+curr_W4*285 + b)数据为（可用excel保存，格式为csv）
1,320,310,320,320 2,300,315,315,310 3,300,300,300,300 4,320,315,320,310 5,330,310,320,320 6,325,325,330,330 7,330,330,335,330 8,320,295,310,310 9,330,310,325,330 10,325,315,325,325 11,320,310,310,310 12,285,280,280,280 13,330,300,300,300 14,320,315,315,315 15,300,300,305,305 16,315,310,315,315 17,310,310,320,330 18,300,320,310,310 19,310,310,310,315 20,305,295,300,305 21,320,320,325,310 22,320,320,340,325 23,330,320,320,330 24,310,300,290,300 25,300,315,325,325 26,310,310,320,320 27,320,320,325,330 28,340,320,335,330 29,320,320,320,320 30,290,290,300,300 31,330,320,320,315 32,320,310,320,315