def run_networks():
 random.seed(12345678)
 np.random.seed(12345678)
 (training_data, validation_data, test_data) = mnist_loader.load_data_wrapper()
 results = []
 for eta in LEARNING_RATES:
  print ('\nTrain a network using eta = ' + str(eta))
  net = network2.Network([784, 30, 10])
  results.append(net.SGD(training_data, NUM_EPOCHS, 10, eta, lmbda=5.0, evaluation_data=validation_data, monitor_training_cost=True))
 f = open('multiple_eta.json', 'w')
 json.dump(results, f)
 f.close()

def run_networks ( ) : random . seed ( 12345678 ) np . random . seed ( 12345678 ) ( training_data , validation_data , test_data ) = mnist_loader . load_data_wrapper ( ) results = [ ] for eta in LEARNING_RATES : print ( '\nTrain a network using eta = ' + str ( eta ) ) net = network2 . Network ( [ 784 , 30 , 10 ] ) results . append ( net . SGD ( training_data , NUM_EPOCHS , 10 , eta , lmbda = 5.0 , evaluation_data = validation_data , monitor_training_cost = True ) ) f = open ( 'multiple_eta.json' , 'w' ) json . dump ( results , f ) f . close ( )
train networks using three different values for the learning rate .