#TensorFlow实现线性回归算法import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt#使用numpy生成200个随机点x_data=np.linspace(-0.5,0.5,200)[:,np.newaxis]noise=np.random.normal(0,0.02,x_data.shape)y_data=np.square(x_data)+noise#定义两个placeholder存放输入数据x=tf.placeholder(tf.float32,[None,1])y=tf.placeholder(tf.float32,[None,1])#定义神经网络中间层Weights_L1=tf.Variable(tf.random_normal([1,10]))biases_L1=tf.Variable(tf.zeros([1,10]))    #加入偏置项Wx_plus_b_L1=tf.matmul(x,Weights_L1)+biases_L1L1=tf.nn.tanh(Wx_plus_b_L1)   #加入激活函数#定义神经网络输出层Weights_L2=tf.Variable(tf.random_normal([10,1]))biases_L2=tf.Variable(tf.zeros([1,1]))  #加入偏置项Wx_plus_b_L2=tf.matmul(L1,Weights_L2)+biases_L2prediction=tf.nn.tanh(Wx_plus_b_L2)   #加入激活函数#定义损失函数（均方差函数）loss=tf.reduce_mean(tf.square(y-prediction))#定义反向传播算法（使用梯度下降算法训练）train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)with tf.Session() as sess:    #变量初始化    sess.run(tf.global_variables_initializer())    #训练2000次    for i in range(2000):        sess.run(train_step,feed_dict={x:x_data,y:y_data})    #获得预测值    prediction_value=sess.run(prediction,feed_dict={x:x_data})    #画图    plt.figure()    plt.scatter(x_data,y_data)   #散点是真实值    plt.plot(x_data,prediction_value,'r-',lw=5)   #曲线是预测值    plt.show()

结果为：