old_contents: import os
  
  import numpy as np
  
  import pylearn2
  from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
  from pylearn2.termination_criteria import EpochCounter
  from pylearn2.utils.serial import load_train_file
  
  def test_mnist():
      """
      Test the mnist.yaml file from the dropout
      paper on random input
      """
      train = load_train_file(os.path.join(pylearn2.__path__[0],
                                           "scripts/papers/maxout/mnist.yaml"))
      random_X = np.random.rand(10, 784)
      random_y = np.random.randint(0, 10, (10, 1))
      train.dataset = DenseDesignMatrix(X=random_X, y=random_y, y_labels=10)
  
      train.algorithm.termination_criterion = EpochCounter(max_epochs=1)
      train.main_loop()
  
new_contents: import os
  
  import numpy as np
  
  import pylearn2
  from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
  from pylearn2.termination_criteria import EpochCounter
  from pylearn2.utils.serial import load_train_file
  
  
  def test_mnist():
      """
      Test the mnist.yaml file from the dropout
      paper on random input
      """
      train = load_train_file(os.path.join(pylearn2.__path__[0],
                                           "scripts/papers/maxout/mnist.yaml"))
      random_X = np.random.rand(10, 784)
      random_y = np.random.randint(0, 10, (10, 1))
      train.dataset = DenseDesignMatrix(X=random_X, y=random_y, y_labels=10)
      train.algorithm.termination_criterion = EpochCounter(max_epochs=1)
      train.algorithm._set_monitoring_dataset(train.dataset)
      train.main_loop()
  
subject: Allow papers/maxout to be tested without MNIST data