text: from keras.models import Sequential
  from keras.layers import Dense
  from sklearn.model_selection import StratifiedKFold
  import numpy
  
  
  # Set a fix random number
  numpy.random.seed(42)
  
  # Load Pima Indians dataset
  dataset = numpy.loadtxt('../datasets/pima-indians-diabetes.csv', delimiter=',')
  
  # Split features (first 8 columns) and labels (9th column)
  X = dataset[:, 0:8]
  Y = dataset[:, 8]
  
  # Define 10 fold cross validation
  kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
  cv_scores = []
  
  for train, test in kfold.split(Y, Y):
      # Create model
      model = Sequential()
      model.add(Dense(12, input_dim=8, activation='relu'))
      model.add(Dense(8, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      # Compile the model
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # Fit the model
      model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)
  
      # Evaluate the model
      scores = model.evaluate(X[test], Y[test], verbose=0)
      print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
      cv_scores.append(scores[1] * 100)
  
  print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cv_scores), numpy.std(cv_scores)))
  