Tensorflow 一维回归练习

import tensorflow as tf
import numpy as np
import 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,x,y为一维数组
x = tf.placeholder(tf.float32,[None, 1])
y = tf.placeholder(tf.float32,[None, 1])

# 定义神经网络的中间层
# 一个输入，10个神经元输出
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)

# 输出层
# 10个神经元输入，一个输出
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)

# 二次函数代价
loss = tf.reduce_mean(tf.square(y-prediction))
# 使用梯度下降法
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()