text: import numpy as np
  from sklearn.model_selection import StratifiedKFold
  
  from examples.cnn.fashion.fashion import get_data
  from examples.cnn.cnn import cnn, select_gpu
  
  """
  Best result: 0.015133
  Job-id: 0
  Parameters: 
  name: "width1"
  int_val: 10
  name: "width2"
  int_val: 10
  name: "width3"
  int_val: 10
  name: "width4"
  int_val: 10
  name: "width5"
  int_val: 10
  name: "width6"
  int_val: 10
  name: "dropout1"
  dbl_val: 0.05
  name: "dropout2"
  dbl_val: 0.05
  name: "dropout3"
  dbl_val: 0.05
  name: "dropout4"
  dbl_val: 0.05
  name: "dropout5"
  dbl_val: 0.05
  name: "dropout6"
  dbl_val: 0.05
  name: "regularizer1"
  dbl_val: 0.0001
  name: "regularizer2"
  dbl_val: 0.0001
  name: "regularizer3"
  dbl_val: 0.0001
  name: "regularizer4"
  dbl_val: 0.0001
  """
  
  select_gpu()
  params = [10, 10, 10, 10, 10, 10,
            0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
            0.0001, 0.0001, 0.0001, 0.0001]
  
  x_train, y_train, x_test, y_test = get_data()
  X = np.concatenate((x_train, x_test))
  Y = np.concatenate((y_train, y_test))
  k_fold = StratifiedKFold(n_splits=10, shuffle=False, random_state=7)
  ret = []
  y_stub = np.random.randint(0, 10, X.shape[0])
  for train, test in k_fold.split(X, y_stub):
      ret.append(cnn(params, (X[train], Y[train], X[test], Y[test])))
  print(np.array(ret))
  