使用scikit learn的内建iris数据集。用数据点（x代表花瓣宽度，y代表花瓣长度）找到最优直线。
1.导入必要的编程库，创建计算图，加载数据集。
>>> 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])
2.声明学习率、批量大小、占位符和模型变量
>>> 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]))
3.增加线性模型，y=Ax+b
>>> model_output=tf.add(tf.matmul(x_data,A),b)
4.声明L2损失函数，其为批量损失的平均值。初始化变量，声明优化器。
>>> 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)
5.遍历迭代，并在随机选择的批量数据上进行模型训练。迭代100次，每25次迭代输出变量值和损失值。
注意：保存每次迭代的损失值，将其用于后续的可视化
>>> 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))
...
Step #25A=[[2.1689417]]b=[[2.9067767]]
Loss=0.9575598
Step #50A=[[1.6556607]]b=[[3.6350703]]
Loss=0.5116888
Step #75A=[[1.3509697]]b=[[4.1133633]]
Loss=0.39227816
Step #100A=[[1.1751562]]b=[[4.356544]]
Loss=0.31387034
6.抽取系数，创建最佳拟合直线
>>> [slope]=sess.run(A)
>>> [y_intercept]=sess.run(b)
>>> best_fit=[]
>>> for i in x_vals:
... best_fit.append(slope*i+y_intercept)
...
7.绘制你喝的直线和L2正则损失函数
>>> plt.plot(x_vals,y_vals,'o',label='Data Points')
[<matplotlib.lines.Line2D object at 0x000002216124B518>]
>>> plt.plot(x_vals,best_fit,'r-',label='Best fit line',linewidth=3)
[<matplotlib.lines.Line2D object at 0x0000022159E49128>]
>>> plt.legend(loc='upper left')
<matplotlib.legend.Legend object at 0x000002216124BD30>
>>> plt.title('Sepal Length vs Pedal Width')
Text(0.5,1,'Sepal Length vs Pedal Width')
>>> plt.xlabel('Pedal Width')
Text(0.5,0,'Pedal Width')
>>> plt.ylabel('Sepal Length')
Text(0,0.5,'Sepal Length')
>>> plt.show()
>>> plt.plot(loss_vec,'k-')
[<matplotlib.lines.Line2D object at 0x0000022160D0AEF0>]
>>> plt.title('L2 Loss per Generation')
Text(0.5,1,'L2 Loss per Generation')
>>> plt.xlabel('Generation')
Text(0.5,0,'Generation')
>>> plt.ylabel('L2 Loss')
Text(0,0.5,'L2 Loss')
>>> plt.show()
