使用神经网络来拟合函数y = x^3 +b

我们使用一个三层的小网络来，模拟函数y = x^3+b函数

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

#训练数据
x_data = np.linspace(-6.0,6.0,30)[:,np.newaxis]
y_data = np.power(x_data,3) + 0.7
#验证数据
t_data = np.linspace(-20.0,20.0,40)[:,np.newaxis]
ty_data = np.power(t_data,3) + 0.7
#占位符
x = tf.placeholder(tf.float32,[None,1])
y = tf.placeholder(tf.float32,[None,1])

#network 
#--layer one--
l_w_1 = tf.Variable(tf.random_normal([1,10]))
l_b_1 = tf.Variable(tf.zeros([1,10]))
l_fcn_1 = tf.matmul(x, l_w_1) + l_b_1
relu_1  = tf.nn.relu(l_fcn_1)
#---layer two----
l_w_2 = tf.Variable(tf.random_normal([10,20]))
l_b_2 = tf.Variable(tf.zeros([1,20]))
l_fcn_2 = tf.matmul(relu_1, l_w_2) + l_b_2
relu_2  = tf.nn.relu(l_fcn_2)


#---output---
l_w_3 = tf.Variable(tf.random_normal([20,1]))
l_b_3 = tf.Variable(tf.zeros([1,1]))
l_fcn_3 = tf.matmul(relu_2, l_w_3) + l_b_3
#relu_3  = tf.tanh(l_fcn_3)
# init
init = tf.global_variables_initializer()
#定义 loss func
loss = tf.reduce_mean(tf.square(y-l_fcn_3))
learn_rate =0.001
train_step = tf.train.GradientDescentOptimizer(learn_rate).minimize(loss)

with tf.Session() as sess:
    sess.run(init);
    for epoch in range(20):
        for step in range(5000):
            sess.run(train_step,feed_dict={x:x_data,y:y_data})
    y_pred = sess.run(l_fcn_3,feed_dict={x:t_data})
    print sess.run(l_fcn_3,feed_dict={x:[[10.]]})
    plt.figure()
    plt.scatter(t_data,ty_data)
    plt.plot(t_data,y_pred,'r-')
    plt.show()

 

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