这个程序为简单的三层结构组成:输入层、中间层、输出层
要理清各层间变量个数
import numpy as np import matplotlib.pyplot as plt import tensorflow as tf #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}) #获取预测值 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()
最终的运行结果图片