深度学习第三篇线性回归

深度学习第三篇线性回归

import tensorflow as tfimport numpy as npimport 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placeholderx = 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_L1L1 = 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_L2prediction = 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()``

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