TensorFlow一元线性回归实践

import tensorflow as tf
import numpy as np

#定义超参数
learning_rate=0.01
max_train_steps=1000
log_step=17

#输入数据
train_X=np.array([[3.3],[4.4],[5.5],[6.71],[6.93],[4.168],[9.779],[6.182],[7.59],[2.167],[7.042],[10.791],[5.313],[7.997],[5.654],[9.27],[3.1]],dtype=np.float32)
train_Y=np.array([[1.7],[2.76],[2.09],[3.19],[1.694],[1.573],[3.366],[2.596],[2.53],[1.221],[2.827],[3.465],[1.65],[2.904],[2.42],[2.94],[1.3]],dtype=np.float32)
total_samples=train_X.shape[0]

#构建模型
X=tf.placeholder(tf.float32,[None,1])
W=tf.Variable(tf.random_normal([1,1]),name="weight")
b=tf.Variable(tf.zeros([1]),name="bias")
Y=tf.matmul(X,W)+b

#定义损失函数
Y_=tf.placeholder(tf.float32,[None,1])
loss=tf.reduce_sum(tf.pow(Y-Y_,2))/(total_samples)

#创建优化器
optimizer=tf.train.GradientDescentOptimizer(learning_rate)

#定义单步训练操作
train_op=optimizer.minimize(loss)

#创建会话
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    #迭代训练
    print("开始训练：")
    for step in range(max_train_steps):
        sess.run(train_op,feed_dict={X:train_X,Y_:train_Y})
        if step % log_step == 0:
            c=sess.run(loss,feed_dict={X:train_X,Y_:train_Y})
            print("Step:%d,loss==%.4f,W==%.4f,b==%.4f"%(step,c,sess.run(W),sess.run(b)))
    final_loss=sess.run(loss,feed_dict={X:train_X,Y_:train_Y})
    weight,bias=sess.run([W,b])
    print("Step:%d,loss==%.4f,W==%.4f,b==%.4f"%(max_train_steps,final_loss,sess.run(W),sess.run(b)))
    print("线性模型是：Y==%.4f*X+%.4f"%(weight,bias))

运行结果：

在这里插入图片描述
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来源：CSDN

作者：小白拉普拉斯

链接：https://blog.csdn.net/XiaobaiLaplace/article/details/103461628

标签

线性回归模型

tensorflow