2018-03-17（多项式回归）

#多项式回归

import numpyas np

import tensorflowas tf

import matplotlib.pyplotas plt

plt.rcParams["figure.figsize"] = (14,8)

#准备好数据

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()

#准备好placeholder

X = tf.placeholder(tf.float32,name='X')

Y = tf.placeholder(tf.float32,name='Y')

#初始化权重和偏置

W = tf.Variable(tf.random_normal([1]),name='Weight')#权重一

W_2 = tf.Variable(tf.random_normal([1]),name='Weight_2')#权重二

W_3 = tf.Variable(tf.random_normal([1]),name='Weight_3')#权重三

b = tf.Variable(tf.random_normal([1]),name='bias')

#计算预测结果

Y_pred = tf.add(tf.multiply(X,W),b)#y1 = X*W+b

Y_pred = tf.add(tf.multiply(tf.pow(X,2),W_2),Y_pred)# y2 =X*X*W_2+y1

Y_pred = tf.add(tf.multiply(tf.pow(X,3),W_3),Y_pred)#y3 = X*X*X*W_3+y2

#y = X*X*X*W_3+X*X*W_2+X*W+b

#计算损失函数值

sample_num = xs.shape[0]

loss = tf.reduce_sum(tf.pow(Y_pred-Y,2))/sample_num#均方误差

#初始化optimizer

learning_rate =0.01

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

#制定迭代次数，并在session中执行graph

n_samples = xs.shape[0]

with tf.Session()as sess:

#初始化说有变量

    sess.run(tf.global_variables_initializer())

writer = tf.summary.FileWriter('./graphs/poplynomial_reg_2',sess.graph)

#训练模型

    for iin range(1000):

total_loss =0

        for x,yin zip(xs,ys):

#通过feed_dict将参数传入

            _,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/n_samples))

writer.close()

W,W_2,W_3,b = sess.run([W,W_2,W_3,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()

loss下降 训练可视化 graph可视化