import tensorflow as tf

    'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')
# checkpoint related configuration
tf.app.flags.DEFINE_string(
    'checkpoint_path', './model',
    'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
    'checkpoint_model_scope', '',
    'Model scope in the checkpoint. None if the same as the trained model.')
tf.app.flags.DEFINE_string(
    #'blouse', 'dress', 'outwear', 'skirt', 'trousers', 'all'
    'model_scope', None,
    'Model scope name used to replace the name_scope in checkpoint.')
tf.app.flags.DEFINE_string(
    'checkpoint_exclude_scopes', None,
    'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint.')
tf.app.flags.DEFINE_boolean(
    'ignore_missing_vars', True,
    'When restoring a checkpoint would ignore missing variables.')
tf.app.flags.DEFINE_boolean(
    'run_on_cloud', False,
    'Wether we will train on cloud.')
tf.app.flags.DEFINE_boolean(
    'seq_train', False,
    'Wether we will train a sequence model.')
tf.app.flags.DEFINE_string(#
    'model_to_train', 'blouse, dress, outwear, skirt, trousers', #'all, blouse, dress, outwear, skirt, trousers', 'skirt, dress, outwear, trousers',
    'The sub-model to train (comma-separated list).')

FLAGS = tf.app.flags.FLAGS
#--model_scope=blouse --checkpoint_path=./logs/all --data_format=channels_last --batch_size=1
