def run_iris_comparison(num=25): """ Compare a few different test and training configurations """ print("Running neural network {} times each for three different sets of training and testing files".format(num)) test_files = ['iris_tes.txt', 'iris_tes50.txt',\ 'iris_tes30.txt'] train_files = ['iris_tra.txt', 'iris_tra100.txt',\ 'iris_tra120.txt'] for i in range(0, len(test_files)): print("trainfile = {} testfile = {}".format(train_files[i], test_files[i])) config_obj = openJsonConfig('conf/annconfig_iris.json') summary = {} for i in range(0, len(test_files)): config_obj['testing_file'] = test_files[i] config_obj['training_file'] = train_files[i] config_obj['plot_error'] = False config_obj['test'] = False crates = [] for j in range(0, num): nn = NeuralNetwork(config_obj) nn.back_propagation() cmat, crate, cout = nn.classification_test(nn.testing_data, nn.weights_best) crates.append(crate) summary[config_obj['testing_file']] =\ nn_stats(np.array(crates)) print print_stat_summary(summary)
def test_nn(config): nn = NeuralNetwork(config) nn.back_propagation() cmat, crate, cout = nn.classification_test(nn.testing_data, nn.weights_best) print cmat print crate