text: import matplotlib.pyplot as plt
  from numpy import arange, array, append
  from classification.fast_neural_network import FastNeuralNetwork as FNN
  from demo.classification.fast_neural_network.kaggle.store import getStoreInstance
  from utility import FeatureAugmentation as FA
  from normalization import MeanNormalization
  
  backoff1 = [2 ** i for i in arange(0, 11)]
  hidden_units = 128
  
  x, y = getStoreInstance().loadDataset('data/train.csv', isShuffle=True)
  m = len(x)
  idx_train = int(m * 0.60)
  idx_test = int(m * 0.80)
  x = FA.map2(x, degree=3)
  x = MeanNormalization().scale(x)
  x_train = x[:idx_train, :]
  y_train = y[:idx_train]
  x_val = x[idx_train:idx_test, :]
  y_val = y[idx_train:idx_test]
  x_test = x[idx_test:, :]
  y_test = y[idx_test:]
  
  fnn = FNN()
  error_train = array([])
  error_val = array([])
  
  for i in backoff1:
      fnn.fit(x_train, y_train, i, l=1.28)
      j_train = fnn.compute_cost(x_train, y_train)
      error_train = append(error_train, j_train)
      j_val = fnn.compute_cost(x_val, y_val)
      error_val = append(error_val, j_val)
  
  plt.plot(backoff1, error_train, linewidth=2)
  plt.plot(backoff1, error_val, linewidth=2)
  legend = ["Error Train", "Error CV"]
  plt.ylabel("Error")
  plt.xlabel("Number of hidden units (hu)")
  plt.legend(legend)
  plt.title("Learning curve for neural network.")
  plt.show()
  