TF之LiR:基于tensorflow实现机器学习之线性回归算法
目录
- -*- coding: utf-8 -*-
-
- TF之LiR:基于tensorflow实现机器学习之线性回归算法
- import tensorflow as tf
- import numpy
- import matplotlib.pyplot as plt
-
- rng =numpy.random
-
- 参数设定
- learning_rate=0.01
- training_epochs=10000
- display_step=50 每隔50次迭代输出一次
- 训练数据
- train_X=numpy.asarray([……])
- train_Y=numpy.asarray([……])
- n_samples=train_X.shape[0]
- print("train_X:",train_X)
- print("train_Y:",train_Y)
-
- 设置placeholder
- X=tf.placeholder("float")
- Y=tf.placeholder("float")
-
- 设置模型的权重和偏置,因为是不断更新的所以采用Variable定义
- W=tf.Variable(rng.randn(),name="weight")
- b=tf.Variable(rng.randn(),name="bias")
-
- 设置线性回归方程LiR:w*x+b
- pred=tf.add(tf.multiply(X,W),b)
- cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples) 设置cost为均方差即reduce_sum函数
- optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 梯度下降,minimize函数默认下自动修正w和b
-
- init=tf.global_variables_initializer() 在session运算时初始化所有变量
- 开始训练
- with tf.Session() as sess:
- sess.run(init) 运行一下初始化的变量
- for epoch in range(training_epochs): 输入所有训练数据
- for(x,y) in zip(train_X,train_Y):
- sess.run(optimizer,feed_dict={X:x,Y:y})
-
- 打印出每次迭代的log日志,每隔50个打印一次
- if (epoch+1) % display_step ==0:
- c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
- print("迭代次数Epoch:","%04d" % (epoch+1),"下降值cost=","{:.9f}".format(c),
- "W=",sess.run(W),"b=",sess.run(b))
- print("Optimizer Finished!")
- training_cost=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
- print("Training cost=",training_cost,"W=",sess.run(W),"b=",sess.run(b))
- 绘图
- plt.rcParams['font.sans-serif']=['SimHei']
- plt.subplot(121)
- plt.plot(train_X, train_Y, 'ro', label='Original data')
- plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
- plt.legend()
- plt.title("TF之LiR:Original data")
-
-
- 测试样本
- test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
- test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831,2.92, 3.24, 1.35, 1.03])
- print("Testing... (Mean square loss Comparison)")
- testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
- feed_dict={X:test_X,Y:test_Y}) same function as cost above
- print("Testing cost=", testing_cost)
- print("Absolute mean square loss difference:", abs( training_cost - testing_cost))
- 绘图
- plt.subplot(122)
- plt.plot(test_X, test_Y, 'bo', label='Testing data')
- plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
- plt.legend()
- plt.title("TF之LiR:Testing data")
- plt.show()
- 迭代次数Epoch: 6300 下降值cost= 0.076938324 W= 0.25199208 b= 0.8008495
- ……
- 迭代次数Epoch: 10000 下降值cost= 0.076965131 W= 0.24998894 b= 0.80145526
- 迭代次数Epoch: 10000 下降值cost= 0.076942705 W= 0.25047526 b= 0.80151606
- 迭代次数Epoch: 10000 下降值cost= 0.076929517 W= 0.25114807 b= 0.801635
- 迭代次数Epoch: 10000 下降值cost= 0.076958008 W= 0.25011322 b= 0.8015234
- 迭代次数Epoch: 10000 下降值cost= 0.076990739 W= 0.24960834 b= 0.80136055
- Optimizer Finished!
- Training cost= 0.07699074 W= 0.24960834 b= 0.80136055
- Testing... (Mean square loss Comparison)
- Testing cost= 0.07910849
- Absolute mean square loss difference: 0.002117753
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