text: import numpy as np
  import scipy
  import sklearn.decomposition
  import os
  from sklearn import neural_network
  from sklearn import cross_validation
  import random
  
  random.seed(2018)
  
  dat = np.load('../data2/data2.npz')
  X_test = dat['X_test']
  X_train = dat['X_train']
  Y_train = dat['Y_train']
  
  # Fully connected NN
  
  ## fisher 2000
  
  dat = np.load('fisher_2000.npz')
  X_train_fisher_2000 = dat['X_train']
  X_test_fisher_2000 = dat['X_test']
  
  fc_nn = neural_network.MLPClassifier(hidden_layer_sizes=(1000), learning_rate='adaptive', learning_rate_init=0.01)
  
  scores = cross_validation.cross_val_score(fc_nn, X_train_fisher_2000, Y_train, cv=5)
  print(scores)
  
  fc_nn.fit(X_train_fisher_2000, Y_train)
  Y_pred = fc_nn.predict(X_test_fisher_2000)
  np.savetxt('Y_pred_fc_nn_fisher_2000.txt', Y_pred, fmt='%d')
  
  
  