def test_all(config: config_reader.CustomConfigParser):
all = {
'num_epochs': 2000,
'learning_rate': 0.001,
'batch_size': 32,
'validation_batch_size': 344,
'optimizer': 'Adagrad',
'l1_regularization': 0.002,
'l2_regularization': 0.002,
'dropout': 0.4,
'experiment_id': 'L1_H26_DO0.4_L10.002_L20.002_B32_LR0.001',
'save_checkpoints_steps': 5000,
'validation_interval': 10,
'initialize_with_checkpoint': '',
'save_summary_steps': 10,
'keep_checkpoint_max': 5,
'throttle': 50,
'type': 'classification',
'ground_truth_column': '-1',
'num_classes': '2',
'weight': '1',
'num_layers': '1',
'layer_size': '26',
'hidden_layers': [[32, 16, 16], [16, 8, 4]],
'batch_norm': 'True',
'residual': 'False',
'training_file': 'data/iris.csv',
'validation_file': 'data/iris.csv',
'checkpoint_dir': 'checkpoints/enigma',
'log_folder': 'log/enigma_Diag',
'model_name': 'DNNClassifier'
}
assert (config.all()) == all
def test_update(config: config_reader.CustomConfigParser):
a = config.all()
a.update({'num_epochs': 4000})
assert a['num_epochs'] == 4000