text: import pandas as pd
  import numpy as np
  import tensorflow as tf
  from sklearn import metrics
  
  learning_rate = 0.01
  training_epochs = 10000
  
  column_names = ['pregnant', 'Plasma glucose concentration', 'Diastolic blood pressure', \
                  'Triceps skin fold thickness', 'serum insulin', 'BMI', 'pedigree', 'Age', 'Class']
  housing_data = pd.read_csv('./dataset/diabetes/content.txt', sep=',', names=column_names)
  
  train_dataset = housing_data.sample(frac=0.9, random_state=0)
  test_dataset = housing_data.drop(train_dataset.index)
  train_labels = train_dataset.pop('Class')
  test_labels = test_dataset.pop('Class')
  
  train_dataset_size = train_dataset.shape[0]
  
  def normalize(data):
      return data.apply(lambda column: (column - column.mean()) / column.std())
  
  normed_train_dataset = normalize(train_dataset)
  normed_test_dataset = normalize(test_dataset)
  
  X1 = tf.placeholder(tf.float32)
  X2 = tf.placeholder(tf.float32)
  X3 = tf.placeholder(tf.float32)
  X4 = tf.placeholder(tf.float32)
  X5 = tf.placeholder(tf.float32)
  X6 = tf.placeholder(tf.float32)
  X7 = tf.placeholder(tf.float32)
  X8 = tf.placeholder(tf.float32)
  
  rng = np.random
  w1 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w2 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w3 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w4 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w5 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w6 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w7 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  w8 = tf.Variable(rng.randn(), name="weights", dtype=tf.float32)
  
  b = tf.Variable(rng.randn(), name="biases", dtype=tf.float32)
  
  Y = tf.placeholder(tf.float32)
  
  linear = tf.multiply(X1, w1) + tf.multiply(X2, w2) + tf.multiply(X3, w3) + tf.multiply(X4, w4) + tf.multiply(X5, w5) + tf.multiply(X6, w6) +\
                  tf.multiply(X7, w7) + tf.multiply(X8, w8) + b
  model = tf.pow(1.0 + tf.exp(tf.multiply(linear, -1.0)), -1.0)
  
  loss = -1.0 * tf.reduce_sum(tf.add(tf.multiply(Y, tf.log(model)), tf.multiply(1.0 - Y, tf.log(1.0 - model)))) / tf.cast(train_dataset_size, dtype=tf.float32)
  
  optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
  
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
  
      for epoch in range(training_epochs):
          sess.run(optimizer, feed_dict={X1: normed_train_dataset['pregnant'], X2: normed_train_dataset['Plasma glucose concentration'], \
                                         X3: normed_train_dataset['Diastolic blood pressure'], X4: normed_train_dataset['Triceps skin fold thickness'], \
                                         X5: normed_train_dataset['serum insulin'], X6: normed_train_dataset['BMI'], \
                                         X7: normed_train_dataset['pedigree'], X8: normed_train_dataset['Age'],  Y: train_labels})
  
      predict_y = sess.run(model, feed_dict={X1: normed_test_dataset['pregnant'], X2: normed_test_dataset['Plasma glucose concentration'], \
                                            X3: normed_test_dataset['Diastolic blood pressure'], X4: normed_test_dataset['Triceps skin fold thickness'], \
                                            X5: normed_test_dataset['serum insulin'], X6: normed_test_dataset['BMI'], \
                                             X7: normed_test_dataset['pedigree'], X8: normed_test_dataset['Age']})
  
      print("predictions:")
      print(predict_y)
  
      auc = metrics.roc_auc_score(test_labels, predict_y)  # tf的tf.metrics.auc函数貌似第二个返回值才是auc？
      print("\nAUC(area under ROC curve): " + str(auc))
  