使用Tensorflow中的神经网络来拟合函数(y = x ^ 3 + 0.7)
# -*- coding:utf-8 -*-
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()
实验结果
