def test_custom_default_value(self):
custom = {'default': 0.01}
parser = argparse.ArgumentParser()
net_args.add_training_settings_args(parser, learning_rate=custom,
excluded_args=['num_epochs'])
res = parser.parse_args([])
self.assertEqual(getattr(res, 'learning_rate'), 0.01)
def test_custom_short_alias(self):
custom = {'short_alias': 'o'}
parser = argparse.ArgumentParser()
net_args.add_training_settings_args(parser, optimizer=custom,
num_epochs={'required': False})
res = parser.parse_args(['-o', 'Adam'])
self.assertEqual(getattr(res, 'optimizer'), 'Adam')
def test_excluded_args(self):
excluded = set(net_args.TRAIN_SETTINGS_ARGS.keys())
excluded.remove('num_epochs')
parser = argparse.ArgumentParser()
net_args.add_training_settings_args(parser, excluded_args=excluded)
with self.assertRaises(SystemExit) as cm:
parser.parse_args(['--num_epochs', '10', '--optimizer', 'Adam'])
self.fail('Failed to raise exception on unknown arg passed')
def test_custom_required_arg(self):
custom = {'required': True}
parser = argparse.ArgumentParser()
net_args.add_training_settings_args(parser, loss_fn=custom,
excluded_args=['num_epochs'])
with self.assertRaises(SystemExit) as cm:
args = parser.parse_args([])
self.fail('Failed to raise exception on required arg not passed')
def __init__(self):
self.default_logdir = cutils.get_config_for_module(
"model_trainer")['default']['logdir']
parser = argparse.ArgumentParser(
description="Train network using provided dataset")
# dataset input
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
item_args = dargs.ItemTypeArgs()
atype = dargs.arg_type.INPUT
group = parser.add_argument_group(title="Input dataset")
dset_args.add_dataset_arg_double(group, atype)
item_args.add_item_type_args(group, atype)
group.add_argument('--test_items_count',
type=atypes.int_range(1),
help='Number of dataset items to include in the '
'test set. Overrides test_items_fraction.')
group.add_argument('--test_items_fraction',
type=float,
default=0.1,
help='Number of dataset items to include in the '
'test set, expressed as a fraction.')
modes = net_cons.DATASET_SPLIT_MODES
group.add_argument('--split_mode',
choices=modes,
required=True,
help='Method of splitting the test items subset '
'from the input dataset.')
# network to train
group = parser.add_argument_group(title="Network configuration")
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m')
group.add_argument('--tb_dir',
default=self.default_logdir,
help=('directory to store training logs for '
'tensorboard.'))
group.add_argument('--save',
action='store_true',
help=('save the model after training. Model files '
'are saved under tb_dir as net.network_name/'
'net.network_name.tflearn.*'))
# training settings
group = parser.add_argument_group(title="Training parameters")
net_args.add_training_settings_args(group,
num_epochs={
'required': False,
'default': 11,
'short_alias': 'e'
})
self.parser = parser
self.dset_args = dset_args
self.item_args = item_args
def __init__(self):
self.default_logdir = cutils.get_config_for_module(
"model_xvalidator")['default']['logdir']
print("default logdir set to {}".format(self.default_logdir))
parser = argparse.ArgumentParser(
description="Perform Kfold cross-validation on a given neural "
"network with the given dataset.")
# cross-validation settings
group = parser.add_argument_group(title="Cross-validation parameters")
group.add_argument('--num_crossvals',
type=atypes.int_range(1),
required=True,
help='number of cross validations to perform')
# network to train
group = parser.add_argument_group(title="Network to use")
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m')
# training_parameters
group = parser.add_argument_group(title="Training parameters to use")
net_args.add_training_settings_args(group,
num_epochs={
'required': False,
'default': 11,
'short_alias': 'e'
})
group.add_argument('--tb_dir',
default=self.default_logdir,
help=('directory to store training logs for '
'tensorboard.'))
# dataset input
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
item_args = dargs.ItemTypeArgs()
atype = dargs.arg_type.INPUT
group = parser.add_argument_group(title="Input dataset")
dset_args.add_dataset_arg_double(group, atype)
item_args.add_item_type_args(group, atype)
group.add_argument('--test_items_count',
type=atypes.int_range(1),
help='number of dataset items to include in the '
'test set. Overrides test_items_fraction.')
group.add_argument('--test_items_fraction',
type=float,
default=0.1,
help='number of dataset items to include in the '
'test set, expressed as a fraction.')
self.parser = parser
self.dset_args = dset_args
self.item_args = item_args
def test_custom_type(self):
custom = {'type': float}
parser = argparse.ArgumentParser()
net_args.add_training_settings_args(parser, num_epochs=custom)
res = parser.parse_args(['--num_epochs', '0.01'])
self.assertEqual(getattr(res, 'num_epochs'), 0.01)