'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.