import tensorflow as tf

tf.app.flags.DEFINE_integer('save-model', 1000,
                            'Number of steps between model saves (default: %(default)d)')

# Optimisation hyperparameters
tf.app.flags.DEFINE_integer('batch-size', 256, 'Number of examples per mini-batch (default: %(default)d)')
tf.app.flags.DEFINE_float('learning-rate', 1e-4, 'Learning rate (default: %(default)d)')
tf.app.flags.DEFINE_integer('img-width', 32, 'Image width (default: %(default)d)')
tf.app.flags.DEFINE_integer('img-height', 32, 'Image height (default: %(default)d)')
tf.app.flags.DEFINE_integer('img-channels', 3, 'Image channels (default: %(default)d)')
tf.app.flags.DEFINE_integer('num-classes', 10, 'Number of classes (default: %(default)d)')
tf.app.flags.DEFINE_string('log-dir', '{cwd}/logs/'.format(cwd=os.getcwd()),
                           'Directory where to write event logs and checkpoint. (default: %(default)s)')


run_log_dir = os.path.join(FLAGS.log_dir,
                           'exp_BN_bs_{bs}_lr_{lr}_aug_flip_brightness'.format(bs=FLAGS.batch_size,
                                                        lr=FLAGS.learning_rate))

