Tensorflow 实现线性回归（迭代方法）

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

from sklearn import datasets

from tensorflow.python.framework import ops

ops.reset_default_graph()

sess = tf.Session()

iris = datasets.load_iris()

x_vals = np.array([x[3] for x in iris.data])

y_vals = np.array([y[0] for y in iris.data])

learning_rate = 0.05

batch_size = 25

x_data = tf.placeholder(shape=[None,1],dtype=tf.float32)

y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)

A = tf.Variable(tf.random_normal(shape=[1,1]))

b = tf.Variable(tf.random_normal(shape=[1,1]))

model_output = tf.add(tf.matmul(x_data,A),b)

loss = tf.reduce_mean(tf.square(y_target - model_output))

init = tf.global_variables_initializer()

sess.run(init)

my_opt = tf.train.GradientDescentOptimizer(learning_rate)

train_step = my_opt.minimize(loss)

loss_vec = []

for i in range(100):

rand_index = np.random.choice(len(x_vals),size=batch_size)

rand_x = np.transpose([x_vals[rand_index]])

rand_y = np.transpose([y_vals[rand_index]])

sess.run(train_step,feed_dict={x_data:rand_x,y_target:rand_y})

temp_loss = sess.run(loss,feed_dict ={x_data:rand_x,y_target:rand_y})

loss_vec.append(temp_loss)

if(i + 1)%25 == 0:

print(‘step #’+str(i+1)+’ A= ‘+str(sess.run(A))+’ b = ‘+str(sess.run(b)))

print(‘Loss= ‘+str(temp_loss))

[slope] = sess.run(A)

[y_intercept] = sess.run(b)

print(slope)

print(y_intercept)

best_fit = []

for i in x_vals:

best_fit.append(slope*i + y_intercept)

plt.plot(x_vals,y_vals,’o’,color=’b’,label=’Data points’)

plt.plot(x_vals,best_fit,’-‘,color=’r’,label=’Best fit line’,linewidth=3)

plt.legend(loc=’upper left’)

plt.show()

plt.plot(loss_vec,’k-‘)

plt.title(‘L2 coss function’)

plt.xlabel(‘Generation’)

plt.ylabel(‘L2 Loss’)

plt.show()