import tensorflow as tf

tf.app.flags.DEFINE_float(
    'neg_threshold', 0.4, 'Matching threshold for the negtive examples in the loss function.')
# optimizer related configuration
tf.app.flags.DEFINE_float(
    'weight_decay', 0.0005, 'The weight decay on the model weights.')
tf.app.flags.DEFINE_float(
    'momentum', 0.9,
    'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
tf.app.flags.DEFINE_float(
    'end_learning_rate', 0.00005,
    'The minimal end learning rate used by a polynomial decay learning rate.')
# for learning rate exponential_decay
tf.app.flags.DEFINE_float(
    'learning_rate_decay_factor', 0.96, 'Learning rate decay factor.')
tf.app.flags.DEFINE_float(
    'decay_steps', 1000,
    'Number of epochs after which learning rate decays.')
# for learning rate piecewise_constant decay
tf.app.flags.DEFINE_string(
    'decay_boundaries', '60000, 800000',
    'Learning rate decay boundaries by global_step (comma-separated list).')
tf.app.flags.DEFINE_string(
    'lr_decay_factors', '1, 0.6, 0.1',
    '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/resnet50',#None,
    'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
