'1, 64, 256, 0\n', help='Configurations of different residual groups.') FLAGS.add('--max_pool', type=bool, default=True, help='whether to add max pooling before first residual unit.') FLAGS.add('--resgroup_len', type=int, default=0, help='whether to add max pooling before first residual unit.') FLAGS.add('--readout', type=int, default=4, help='the readout time for shared weights esidual group') FLAGS.overwrite_defaults(image_size=32, special_first=False, unit_type=1, residual_type=0) class Model(basic_resnet.Model): """Residual neural network model. classify web page only based on target html.""" def resnn(self, image_batch): """Build the resnn model. Args: image_batch: Sequences returned from inputs_train() or inputs_eval. Returns: Logits. """ # First convolution with tf.variable_scope('conv_layer1'):
logger.newline() logger.error("newline beneath~") logger.newline(2) logger.info("haha") # ================================ # test FLAGS # ================================ FLAGS.add("--aa", type=float, default=11., help="doc for dd") logger.info("aa: {}".format(FLAGS.get('aa'))) # for flag that should be overwrite later, don't set default FLAGS.add("--bb", type=int, default=None, help="doc for dd") if FLAGS.get('aa') == 11: FLAGS.overwrite_none(bb=15) FLAGS.add("--cc", type=bool, default=False, help="doc for dd") FLAGS.add("--dd", type=str, default="dddddd", help="doc for dd") # for flag that should be overwrite later, don't set default FLAGS.add("--ff", type=str, help="doc for dd") FLAGS.add("--gg", type=str, help="doc for dd") FLAGS.add("--hh", type=str, default="hhhhh", help="doc for dd") # overwrite or set new default values FLAGS.overwrite_defaults(dd="replaced dd", ee="an extra flag", ff="ff") FLAGS.overwrite_none(hh="this won't show", gg="gggg", ii="illigal") FLAGS.add("--jj", type=str, default="hhhhh", help="doc for dd") # parse FLAGS at the start of main() FLAGS.parse_and_log() logger.info(FLAGS.gg) logger.info(FLAGS.ii)
"""basic residual network class.""" import tensorflow as tf from tensorflow.contrib.layers import variance_scaling_initializer from tensorflow.contrib.layers import l2_regularizer from tensorflow.contrib.layers import fully_connected from models import basic_resnet # from models.basic_resnet import UnitsGroup # from utils import logger from utils import FLAGS FLAGS.overwrite_defaults(image_size=32) FLAGS.add('--groups_conf', type=str, default='' '12, 12, -1, 1\n' '12, 12, -1, 1\n' '12, 12, -1, 0', help='Configurations of different residual groups.') class Model(basic_resnet.Model): """Residual neural network model. classify web page only based on target html.""" def resnn(self, image_batch): """Build the resnn model. Args: image_batch: Sequences returned from inputs_train() or inputs_eval. Returns: Logits.