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  • 一元 非线性回归


    # coding: utf-8

    # In[1]:

    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt


    # In[6]:

    #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
    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]))
    Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
    L1 = tf.nn.tanh(Wx_plus_b_L1)

    #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]))
    Wx_plus_b_L2 = tf.matmul(L1,Weights_L2) + biases_L2
    prediction = tf.nn.tanh(Wx_plus_b_L2)

    #二次代价函数
    loss = tf.reduce_mean(tf.square(y-prediction))
    #使用梯度下降法训练
    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())
    for _ 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)
    plt.plot(x_data,prediction_value,'r-',lw=5)
    plt.show()


    # In[ ]:

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  • 原文地址:https://www.cnblogs.com/rongye/p/10002252.html
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