tensorflow 非线性回归

#encoding:utf-8#encoding:utf-8import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt# 神经层函数参数， 输入值， 输入的大小， 输出的大小，激励函数（默认为空）def add_layer(inputs, in_size, out_size, activation_function=None):    # 生成初始参数时，随机变量回比全部为0要好很多，所以weights为一个in_size    # out_size列的随机变量矩阵    Weights = tf.Variable(tf.random_normal([in_size, out_size]))    # 在机器学习中biases的推荐值不为0，所以在0的基础上加了0.1    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)    # Wx_plus_b,即神经网络未激活的值。其中tf.matmul()是矩阵的乘法    Wx_plus_b = tf.matmul(inputs, Weights) + biases    # activation_function 激励函数为None，输出就是当前的预测值Wx_plus_b    # 不为空时就把Wx_plus_b传到activation_function()函数中得到    if activation_function == None:        outputs = Wx_plus_b    else:        outputs = activation_function(Wx_plus_b)    return outputs# 导入数据x_data = np.linspace(-1, 1, 300, dtype=np.float32)[:, np.newaxis]noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32)y_data = np.square(x_data) - 0.5 + noisexs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])# matplotlib 可视化fig = plt.figure()ax = fig.add_subplot(1, 1, 1)ax.scatter(x_data, y_data)plt.ion()#plt.ion()用于连续显示plt.show()# 搭建网络# 隐藏层 10个神经元l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)  # tf自带激励函数tf.nn.relu# 输出层prediction = add_layer(l1, 10, 1, activation_function=None)# 误差 二者差的平方和再取平均loss = tf.reduce_mean(tf.square(ys - prediction))# loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))# 梯度下降优化器  学习率为0.1 最小话损失函数train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    #     print sess.run(loss1, feed_dict= {xs:x_data, ys:y_data})    #     print sess.run(loss, feed_dict= {xs:x_data, ys:y_data})    for i in range(1000):        # 训练模型        sess.run(train_op, feed_dict={xs: x_data, ys: y_data})#        每隔50次训练刷新一次图形，用红色、宽度为5的线来显示我们的预测数据和输入之间的关系，#并暂停0.1s。        if i % 50 == 0:            try:                ax.lines.remove(lines[0])            except Exception:                pass            prediction_value = sess.run(prediction, feed_dict={xs: x_data, ys: y_data})            lines = ax.plot(x_data, prediction_value, 'r-', lw=5)            plt.pause(0.1)            # 每隔50输出误差#             print sess.run(loss, feed_dict= { xs :x_data, ys : y_data})

画出的图

每训练50次输出的误差

0.6859760.02003310.009531590.007653440.007193560.006942520.006724910.006480860.00622440.005982850.005725460.00548090.005247320.00500340.004777880.004578420.004401710.004240590.004098680.00397596