code: #!/usr/bin/python2.7
  # -*- coding:utf-8 -*-
  
  # Author: NetworkRanger
  # Date: 2018/11/4 下午12:04
  
  # 2.8 TensorFlow 实现创建张量
  
  # 1. 导入相应的工具库，初始化计算图
  import matplotlib.pyplot as plt
  import numpy as np
  from sklearn import datasets
  import tensorflow as tf
  sess = tf.Session()
  
  # 2. 导入iris数据集，根据目标数据是否为山鸢尾将其转换成1或者0。由于iris数据集将山鸢尾标记为0，我们将其从0置为1，同时把其他物种标记为0
  iris = datasets.load_iris()
  binary_target = np.array([1. if x==0 else 0. for x in iris.target])
  iris_2d = np.array([[x[2], x[3]] for x in iris.data])
  
  # 3. 声明变量训练大小，数据占位符和模型变量
  batch_size = 20
  x1_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)
  x2_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]))
  
  # 4. 定义线性模型
  my_mult = tf.matmul(x2_data, A)
  my_add = tf.add(my_mult, b)
  my_output = tf.subtract(x1_data, my_add)
  
  # 5. 增加TensorFlow 的 sigmoid 交叉熵损失函数 sigmoid_cross_entropy_with_logits()
  xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output, labels=y_target)
  
  # 6. 声明优化器方法，最小化交叉熵损失
  my_opt = tf.train.GradientDescentOptimizer(0.05)
  train_step = my_opt.minimize(xentropy)
  
  # 7. 创建一个变量初始化操作，然后让TensorFlow执行它
  init = tf.initialize_all_variables()
  sess.run(init)
  
  # 8. 现在迭代100次训练线性模型
  for i in range(1000):
      rand_index = np.random.choice(len(iris_2d), size=batch_size)
      rand_x = iris_2d[rand_index]
      rand_x1 = np.array([[x[0]] for x in rand_x])
      rand_x2 = np.array([[x[1]] for x in rand_x])
      rand_y = np.array([[y] for y in binary_target[rand_index]])
      sess.run(train_step, feed_dict={x1_data: rand_x1, x2_data: rand_x2, y_target: rand_y})
      if (i+1)%200 == 0:
          print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ', b = ' + str(sess.run(b)))
  
      """
      Step #200 A = [[8.574331]], b = [[-3.5213127]]
      Step #400 A = [[10.120312]], b = [[-4.6367807]]
      Step #600 A = [[11.085849]], b = [[-5.303544]]
      Step #800 A = [[11.831396]], b = [[-5.835288]]
      Step #1000 A = [[12.395876]], b = [[-6.260936]]
      """
  
  # 9. 下面的命令抽取模型变量并绘图
  [[slope]] = sess.run(A)
  [[intercept]] = sess.run(b)
  x = np.linspace(0, 3, num=50)
  ablineValues = []
  for i in x:
      ablineValues.append(slope+intercept)
  
  setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i] == 1]
  setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i] == 1]
  non_setosa_x = [a[1] for i,a in enumerate(iris_2d) if binary_target[i] == 0]
  non_setosa_y = [a[0] for i,a in enumerate(iris_2d) if binary_target[i] == 0]
  plt.plot(setosa_x, setosa_y, 'rx', ms=10, mew=2, label='setosa')
  plt.plot(non_setosa_x, non_setosa_y, 'ro', label='Non-setosa')
  plt.plot(x, ablineValues, 'b-')
  plt.xlim([0.0, 2.7])
  plt.ylim([0.0, 2.7])
  plt.suptitle('Linear Separator For I.setosa', fontsize=20)
  plt.xlabel('Petal Length')
  plt.ylabel('Petal Width')
  plt.legend(loc='lower right')
  plt.show()
apis: tensorflow.initialize_all_variables tensorflow.matmul matplotlib.pyplot.ylabel matplotlib.pyplot.plot tensorflow.random_normal sklearn.datasets.load_iris matplotlib.pyplot.xlim matplotlib.pyplot.suptitle numpy.linspace tensorflow.nn.sigmoid_cross_entropy_with_logits tensorflow.subtract tensorflow.Session matplotlib.pyplot.ylim tensorflow.placeholder matplotlib.pyplot.legend tensorflow.add matplotlib.pyplot.show tensorflow.train.GradientDescentOptimizer numpy.array matplotlib.pyplot.xlabel