线性回归(linear

#回归 import numpy as np import tensorflow as tf import matplotlib.pyplot as plt n_observations=100 xs=np.linspace(-3,3,n_observations) ys=np.sin(xs)+np.random.uniform(-0.5,0.5,n_observations) plt.scatter(xs,ys) plt.show() X=tf.placeholder(tf.float32,name='X') Y=tf.placeholder(tf.float32,name='Y') # just state these two parameters W=tf.Variable(tf.random_normal([1]),name='weight') b=tf.Variable(tf.random_normal([1]),name='bias') Y_pred=tf.add(tf.multiply(X,W),b,name="y_pred") loss=tf.square(Y-Y_pred,name='loss') learning_rate=0.01 optimizer=tf.train.ProximalGradientDescentOptimizer(learning_rate).minimize(loss) n_samples=xs.shape[0] # inititalize these parameters.Only after this step, the para will have value init=tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) writer=tf.summary.FileWriter('./graphs/linear_reg',sess.graph) for i in range(50): total_loss=0 for x,y in zip(xs,ys): _,l=sess.run([optimizer,loss],feed_dict={X:x,Y:y}) total_loss+=l if i%5 ==0: print('Epoch:{0}: {1}'.format(l,total_loss/n_samples) ) writer.close(); # need run, and then you can get the content of w and b W,b=sess.run([W,b]) print(W,b) print ("W:"+str(W[0])) print ("b:"+str(b[0])) plt.plot(xs,ys,'bo',label='Real data') plt.plot(xs,xs*W+b,'r',label='Predicted data') plt.legend() plt.show()

#多项式回归 import numpy as np import tensorflow as tf import matplotlib.pyplot as plt n_observation=100 xs=np.linspace(-3,3,n_observation) ys=np.sin(xs)+np.random.uniform(-0.5,0.5,n_observation) plt.scatter(xs,ys) plt.show() X=tf.placeholder(tf.float32,name="X") Y=tf.placeholder(tf.float32,name="Y") W=tf.Variable(tf.random_uniform([1]),name="weights") b=tf.Variable(tf.random_uniform([1]),name="bias") Y_pred=tf.add(tf.multiply(X,W),b) W_2=tf.Variable(tf.random_uniform([1]),name="weights_2") Y_pred=tf.add(tf.multiply(tf.pow(X,2),W_2),Y_pred) W_3=tf.Variable(tf.random_uniform([1]),name="weights_3") Y_pred=tf.add(tf.multiply(tf.pow(X,2),W_3),Y_pred) sample_num=xs.shape[0] loss=tf.reduce_sum(tf.pow(Y_pred-Y,2))/sample_num learning_rate=0.01 optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) init=tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) writer=tf.summary.FileWriter('./graphs/polynomial_reg',sess.graph) for i in range(1000): total_loss=0 for x,y in zip(xs,ys): _,l=sess.run([optimizer,loss],feed_dict={X:x,Y:y}) total_loss+=l if i%20 ==0: print ('epoch {0}: {1}'.format(i,total_loss/sample_num)) writer.close() W, W_2, W_3, b=sess.run([W, W_2, W_3, b]) print(W, W_2, W_3, b) print ("W:" + str(W[0])) # print(W, b) print ("W:" + str(W[0])) print ("W_2:" + str(W_2[0])) print ("W_3:" + str(W_3[0])) print ("b:" + str(b[0])) plt.plot(xs,ys,'bo',label='real_data') plt.plot(xs,xs*W+np.power(xs,2)*W_2+np.power(xs,3)*W_3+b,'r',label='Predicted data') plt.legend() plt.show()