original_docstring: 
      Trains Softmax regression on digit (mod 3) classifications.
  
      See run_softmax_on_MNIST for more info.
      
docstring: Trains Softmax regression on digit (mod 3) classifications.
  See run_softmax_on_MNIST for more info.
docstring_tokens: Trains Softmax regression on digit ( mod 3 ) classifications . See run_softmax_on_MNIST for more info .
code: def run_softmax_on_MNIST_mod3(temp_parameter=1):
      """
      Trains Softmax regression on digit (mod 3) classifications.
  
      See run_softmax_on_MNIST for more info.
      """
      train_x, train_y, test_x, test_y = get_MNIST_data()
  
      train_y_mod3, test_y_mod3 = update_y(train_y, test_y)
  
      theta, cost_function_history = softmax_regression(
          train_x, train_y_mod3, temp_parameter, alpha=0.3, lambda_factor=1.0e-4, k=10, num_iterations=150)
  
      plot_cost_function_over_time(cost_function_history)
  
      test_error = compute_test_error(test_x, test_y_mod3, theta, temp_parameter)
  
      # Save the model parameters theta obtained from calling softmax_regression to disk.
      write_pickle_data(theta, "./theta_mod3.pkl.gz")
  
      return test_error
code_tokens: def run_softmax_on_MNIST_mod3 ( temp_parameter = 1 ) : train_x , train_y , test_x , test_y = get_MNIST_data ( ) train_y_mod3 , test_y_mod3 = update_y ( train_y , test_y ) theta , cost_function_history = softmax_regression ( train_x , train_y_mod3 , temp_parameter , alpha = 0.3 , lambda_factor = 1.0e-4 , k = 10 , num_iterations = 150 ) plot_cost_function_over_time ( cost_function_history ) test_error = compute_test_error ( test_x , test_y_mod3 , theta , temp_parameter ) write_pickle_data ( theta , "./theta_mod3.pkl.gz" ) return test_error
short_docstring: Trains Softmax regression on digit (mod 3) classifications.
short_docstring_tokens: Trains Softmax regression on digit ( mod 3 ) classifications .
comment: """
      Trains Softmax regression on digit (mod 3) classifications.
  
      See run_softmax_on_MNIST for more info.
      """$SEP$# Save the model parameters theta obtained from calling softmax_regression to disk.