text: from multiprocessing import freeze_support
  
  import matplotlib.pyplot as plt
  import numpy as np
  import scipy.interpolate
  import scipy.ndimage.filters
  
  import dataset.cifar10_dataset
  
  from network import activation, weight_initializer
  from network.layers.conv_to_fully_connected import ConvToFullyConnected
  from network.layers.convolution_im2col import Convolution
  from network.layers.dropout import Dropout
  from network.layers.fully_connected import FullyConnected
  from network.layers.max_pool import MaxPool
  from network.model import Model
  from network.optimizer import GDMomentumOptimizer
  
  if __name__ == '__main__':
      """
      """
  
      freeze_support()
  
      num_iteration = 20
      data = dataset.cifar10_dataset.load()
  
      layers = [
          # MaxPool(size=2, stride=2),
          Convolution((8, 3, 4, 4), stride=2, padding=2, dropout_rate=0, activation=activation.tanh),
          #MaxPool(size=2, stride=2),
          Convolution((16, 8, 3, 3), stride=2, padding=1, dropout_rate=0, activation=activation.tanh),
          #MaxPool(size=2, stride=2),
          Convolution((32, 16, 3, 3), stride=2, padding=1, dropout_rate=0, activation=activation.tanh),
          #MaxPool(size=2, stride=2),
          ConvToFullyConnected(),
          FullyConnected(size=64, activation=activation.tanh),
          FullyConnected(size=10, activation=None, last_layer=True)
      ]
  
      # -------------------------------------------------------
      # Train with BP
      # -------------------------------------------------------
  
      model = Model(
          layers=layers,
          num_classes=10,
          optimizer=GDMomentumOptimizer(lr=3*1e-2, mu=0.9),
      )
  
      print("\nRun training:\n------------------------------------")
  
      stats = model.train(data_set=data, method='bp', num_passes=num_iteration, batch_size=64)
      loss, accuracy = model.cost(*data.test_set())
  
      print("\nResult:\n------------------------------------")
      print('loss on test set: {}'.format(loss))
      print('accuracy on test set: {}'.format(accuracy))
  
      print("\nTrain statisistics:\n------------------------------------")
  
      print("time spend during forward pass: {}".format(stats['forward_time']))
      print("time spend during backward pass: {}".format(stats['backward_time']))
      print("time spend during update pass: {}".format(stats['update_time']))
      print("time spend in total: {}".format(stats['total_time']))
  
      plt.title('Loss function')
      plt.xlabel('epoch')
      plt.ylabel('loss')
      train_loss = scipy.ndimage.filters.gaussian_filter1d(stats['train_loss'], sigma=10)
      plt.plot(np.arange(len(stats['train_loss'])), train_loss)
      plt.plot(stats['valid_step'], stats['valid_loss'])
      plt.legend(['train loss bp', 'validation loss bp'], loc='upper right')
      plt.grid(True)
      plt.show()
  
      plt.title('Accuracy')
      plt.xlabel('epoch')
      plt.ylabel('accuracy')
      train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats['train_accuracy'], sigma=10)
      plt.plot(np.arange(len(stats['train_accuracy'])), train_accuracy)
      plt.plot(stats['valid_step'], stats['valid_accuracy'])
      plt.legend(['train accuracy bp', 'validation accuracy bp'], loc='lower right')
      plt.grid(True)
      plt.show()