tensorflow拟合曲线

单值法：

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt# Prepare train datatrain_X = np.linspace(0, np.pi, 100)train_Y=np.sin(train_X)# Define the modelX1 = tf.placeholder("float")X2 = tf.placeholder("float")X3 = tf.placeholder("float")X4 = tf.placeholder("float")Y = tf.placeholder("float")w1 = tf.Variable(0.0, name="weight1")w2 = tf.Variable(0.0, name="weight2")w3 = tf.Variable(0.0, name="weight3")w4 = tf.Variable(0.0, name="weight4")b = tf.Variable(0.0, name="bias")y1=w1*X1+(w2)*(X2)**3+w3*(X3)**5+(w4)*(X4)**7loss = tf.reduce_mean(tf.square(Y - y1))c1=[]c2=[]c3=[]c4=[]loss1=[]##loss = tf.reduce_mean(tf.square(Y - w1*X1 -(w2)*(X2)**3-(w3)*(X3)**5))optimizer =tf.train.AdamOptimizer()train_op=optimizer.minimize(loss)## Create session to runwith tf.Session() as sess:    init=tf.global_variables_initializer()    sess.run(init)    epoch = 1    for i in range(100):        for (x, y) in zip(train_X, train_Y):                     _,ww1, ww2,ww3,ww4,loss_= sess.run([train_op, w1, w2,w3,w4,loss],feed_dict={X1:x,X2:x,X3:x,X4:x,Y: y})        print("Epoch: {}, w1: {}, w2: {},w3:{},loss:{}".format(epoch, ww1,ww2,ww3,loss_))        epoch += 1        c1=c1+[ww1]        c2=c2+[ww2]        c3=c3+[ww3]        c4=c4+[ww4]        loss1=loss1+[loss_]plt.plot(train_X,train_Y,"+")plt.plot(train_X,ww1*train_X+(ww2)*(train_X**3)+ww3*(train_X**5)+ww4*(train_X**7))plt.show()

向量法：

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt# Prepare train datatrain_X = np.linspace(0, np.pi, 100)train_Y=np.sin(train_X)# Define the modelX1 = tf.placeholder(tf.float32,shape=(100,))X2 = tf.placeholder(tf.float32,shape=(100,))X3 = tf.placeholder(tf.float32,shape=(100,))X4 = tf.placeholder(tf.float32,shape=(100,))Y = tf.placeholder(tf.float32,shape=(100,))w1 = tf.Variable(0.0,name="weight1")w2 = tf.Variable(0.0, name="weight2")w3 = tf.Variable(0.0, name="weight3")w4 = tf.Variable(0.0, name="weight4")y1=w1*X1+w2*X2+w3*X3+w4*X4loss = tf.reduce_mean(tf.square(Y - y1))optimizer =tf.train.AdamOptimizer()train_op=optimizer.minimize(loss)## Create session to runwith tf.Session() as sess:    init=tf.global_variables_initializer()    sess.run(init)    epoch = 1    for i in range(10000):        _,ww1, ww2,ww3,ww4,loss_= sess.run([train_op, w1, w2,w3,w4,loss],feed_dict={X1:train_X,X2:train_X**3,X3:train_X**5,X4:train_X**7,Y: train_Y})        print("Epoch: {}, w1: {}, w2: {},w3:{},w4:{},loss:{}".format(epoch, ww1,ww2,ww3,ww4,loss_))        epoch += 1plt.plot(train_X,train_Y,"+",label='data')plt.plot(train_X,ww1*train_X+(ww2)*(train_X**3)+ww3*(train_X**5)+ww4*(train_X**7),label='curve')plt.savefig('1.png',dpi=200)plt.axis([0,np.pi,-2,2])plt.legend(loc=1)plt.show()