import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 非線性迴歸 # 使用numpy生成200個隨機點 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 # 定義兩個placeholder x=tf.placeholder(tf.float32,[None,1]) y=tf.placeholder(tf.float32,[None,1]) # 定義神經網絡的中間層 Weights_L1=tf.Variable(tf.random_normal([1,10])) # 權重,輸入層爲1,中間層爲10個 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) # 使用雙曲正切做爲激活函數 # 定義輸出層 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) # 二次代價函數 loss=tf.reduce_mean(tf.square(y-prediction)) # 使用梯度降低法 train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)# 最小化loss with tf.Session() as sess: # 變量初始化 sess.run(tf.global_variables_initializer()) for _ in range(2000): sess.run(train_step,feed_dict={x:x_data,y:y_data}) # 傳入參數 # 得到預測值 prediction_value=sess.run(prediction,feed_dict={x:x_data}) # 畫圖 plt.figure() plt.scatter(x_data,y_data) plt.plot(x_data,prediction_value,'r-',lw=5) plt.show()