text: import numpy as np
  import tensorflow as tf
  
  from yadlt.models.rbm_models import dbn
  from yadlt.utils import datasets, utilities
  
  # #################### #
  #   Flags definition   #
  # #################### #
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  
  # Global configuration
  flags.DEFINE_string('dataset', 'mnist', 'Which dataset to use. ["mnist", "cifar10", "custom"]')
  flags.DEFINE_string('train_dataset', '', 'Path to train set .npy file.')
  flags.DEFINE_string('train_labels', '', 'Path to train labels .npy file.')
  flags.DEFINE_string('valid_dataset', '', 'Path to valid set .npy file.')
  flags.DEFINE_string('valid_labels', '', 'Path to valid labels .npy file.')
  flags.DEFINE_string('test_dataset', '', 'Path to test set .npy file.')
  flags.DEFINE_string('test_labels', '', 'Path to test labels .npy file.')
  flags.DEFINE_string('cifar_dir', '', 'Path to the cifar 10 dataset directory.')
  flags.DEFINE_string('name', 'dbn', 'Name of the model.')
  flags.DEFINE_string('save_predictions', '', 'Path to a .npy file to save predictions of the model.')
  flags.DEFINE_string('save_layers_output_test', '', 'Path to a .npy file to save test set output from all the layers of the model.')
  flags.DEFINE_string('save_layers_output_train', '', 'Path to a .npy file to save train set output from all the layers of the model.')
  flags.DEFINE_boolean('do_pretrain', True, 'Whether or not pretrain the network.')
  flags.DEFINE_integer('seed', -1, 'Seed for the random generators (>= 0). Useful for testing hyperparameters.')
  flags.DEFINE_integer('verbose', 0, 'Level of verbosity. 0 - silent, 1 - print accuracy.')
  flags.DEFINE_float('momentum', 0.5, 'Momentum parameter.')
  
  # RBMs layers specific parameters
  flags.DEFINE_string('rbm_layers', '256,', 'Comma-separated values for the layers in the sdae.')
  flags.DEFINE_boolean('rbm_gauss_visible', False, 'Whether to use Gaussian units for the visible layer.')
  flags.DEFINE_float('rbm_stddev', 0.1, 'Standard deviation for Gaussian visible units.')
  flags.DEFINE_string('rbm_learning_rate', '0.01,', 'Initial learning rate.')
  flags.DEFINE_string('rbm_num_epochs', '10,', 'Number of epochs.')
  flags.DEFINE_string('rbm_batch_size', '10,', 'Size of each mini-batch.')
  flags.DEFINE_string('rbm_gibbs_k', '1,', 'Gibbs sampling steps.')
  
  # Supervised fine tuning parameters
  flags.DEFINE_string('finetune_act_func', 'sigmoid', 'Activation function.')
  flags.DEFINE_float('finetune_learning_rate', 0.01, 'Learning rate.')
  flags.DEFINE_float('finetune_momentum', 0.7, 'Momentum parameter.')
  flags.DEFINE_integer('finetune_num_epochs', 10, 'Number of epochs.')
  flags.DEFINE_integer('finetune_batch_size', 10, 'Size of each mini-batch.')
  flags.DEFINE_string('finetune_opt', 'gradient_descent', '["gradient_descent", "ada_grad", "momentum", "adam"]')
  flags.DEFINE_string('finetune_loss_func', 'softmax_cross_entropy', 'Loss function. ["mean_squared", "softmax_cross_entropy"]')
  flags.DEFINE_float('finetune_dropout', 1, 'Dropout parameter.')
  
  # Conversion of Autoencoder layers parameters from string to their specific type
  rbm_layers = utilities.flag_to_list(FLAGS.rbm_layers, 'int')
  rbm_learning_rate = utilities.flag_to_list(FLAGS.rbm_learning_rate, 'float')
  rbm_num_epochs = utilities.flag_to_list(FLAGS.rbm_num_epochs, 'int')
  rbm_batch_size = utilities.flag_to_list(FLAGS.rbm_batch_size, 'int')
  rbm_gibbs_k = utilities.flag_to_list(FLAGS.rbm_gibbs_k, 'int')
  
  # Parameters validation
  assert FLAGS.dataset in ['mnist', 'cifar10', 'custom']
  assert FLAGS.finetune_act_func in ['sigmoid', 'tanh', 'relu']
  assert len(rbm_layers) > 0
  
  if __name__ == '__main__':
  
      utilities.random_seed_np_tf(FLAGS.seed)
  
      if FLAGS.dataset == 'mnist':
  
          # ################# #
          #   MNIST Dataset   #
          # ################# #
  
          trX, trY, vlX, vlY, teX, teY = datasets.load_mnist_dataset(mode='supervised')
  
      elif FLAGS.dataset == 'cifar10':
  
          # ################### #
          #   Cifar10 Dataset   #
          # ################### #
  
          trX, trY, teX, teY = datasets.load_cifar10_dataset(FLAGS.cifar_dir, mode='supervised')
          vlX = teX[:5000]  # Validation set is the first half of the test set
          vlY = teY[:5000]
  
      elif FLAGS.dataset == 'custom':
  
          # ################## #
          #   Custom Dataset   #
          # ################## #
  
          def load_from_np(dataset_path):
              if dataset_path != '':
                  return np.load(dataset_path)
              else:
                  return None
  
          trX, trY = load_from_np(FLAGS.train_dataset), load_from_np(FLAGS.train_labels)
          vlX, vlY = load_from_np(FLAGS.valid_dataset), load_from_np(FLAGS.valid_labels)
          teX, teY = load_from_np(FLAGS.test_dataset), load_from_np(FLAGS.test_labels)
  
      else:
          trX, trY, vlX, vlY, teX, teY = None, None, None, None, None, None
  
      # Create the object
      finetune_act_func = utilities.str2actfunc(FLAGS.finetune_act_func)
  
      srbm = dbn.DeepBeliefNetwork(
          name=FLAGS.name, do_pretrain=FLAGS.do_pretrain,
          rbm_layers=rbm_layers,
          finetune_act_func=finetune_act_func, rbm_learning_rate=rbm_learning_rate,
          verbose=FLAGS.verbose, rbm_num_epochs=rbm_num_epochs, rbm_gibbs_k = rbm_gibbs_k,
          rbm_gauss_visible=FLAGS.rbm_gauss_visible, rbm_stddev=FLAGS.rbm_stddev,
          momentum=FLAGS.momentum, rbm_batch_size=rbm_batch_size, finetune_learning_rate=FLAGS.finetune_learning_rate,
          finetune_num_epochs=FLAGS.finetune_num_epochs, finetune_batch_size=FLAGS.finetune_batch_size,
          finetune_opt=FLAGS.finetune_opt, finetune_loss_func=FLAGS.finetune_loss_func,
          finetune_dropout=FLAGS.finetune_dropout)
  
      # Fit the model (unsupervised pretraining)
      if FLAGS.do_pretrain:
          srbm.pretrain(trX, vlX)
  
      # finetuning
      print('Start deep belief net finetuning...')
      srbm.fit(trX, trY, vlX, vlY)
  
      # Test the model
      print('Test set accuracy: {}'.format(srbm.compute_accuracy(teX, teY)))
  
      # Save the predictions of the model
      if FLAGS.save_predictions:
          print('Saving the predictions for the test set...')
          np.save(FLAGS.save_predictions, srbm.predict(teX))
  
  
      def save_layers_output(which_set):
          if which_set == 'train':
              trout = srbm.get_layers_output(trX)
              for i, o in enumerate(trout):
                  np.save(FLAGS.save_layers_output_train + '-layer-' + str(i + 1) + '-train', o)
  
          elif which_set == 'test':
              teout = srbm.get_layers_output(teX)
              for i, o in enumerate(teout):
                  np.save(FLAGS.save_layers_output_test + '-layer-' + str(i + 1) + '-test', o)
  
  
      # Save output from each layer of the model
      if FLAGS.save_layers_output_test:
          print('Saving the output of each layer for the test set')
          save_layers_output('test')
  
      # Save output from each layer of the model
      if FLAGS.save_layers_output_train:
          print('Saving the output of each layer for the train set')
          save_layers_output('train')
  