import os
import time
import numpy as np
import tensorflow as tf
# from models import model
# import utils
from utils import CUR_TIME, logger, FLAGS
#########################################
# FLAGS
#########################################
# environmental parameters
FLAGS.add('--model',
type=str,
default='resnet',
help='the type of NN model. cnn, crnn, resnn, resrnn...')
FLAGS.add('--outputs_dir',
type=str,
default='outputs/',
help='Directory where to write event logs and checkpoint.')
FLAGS.add("--log_level",
type=int,
default=20,
help="numeric value of logger level, 20 for info, 10 for debug.")
# FLAGS.add('--if_eval',
# type=bool,
# default=True,
# help="Whether to log device placement.")
FLAGS.add('--debug',
type=bool,
import tensorflow as tf
# from tensorflow.models.image.cifar10 import cifar10
# from tensorflow.models.image.cifar10 import cifar10_input
# from tensorflow.models.image.cifar10.cifar10_input import _generate_image_and_label_batch
from tensorflow.models.image.cifar10.cifar10_input import read_cifar10
# import utils
from utils import logger
from utils import FLAGS
#########################################
# FLAGS
#########################################
FLAGS.add('--data_dir',
type=str,
default='data/',
help='directory for storing datasets and outputs.')
FLAGS.add('--num_read_threads',
type=int,
default=5,
help='number of reading threads to shuffle examples '
'between files.')
FLAGS.add("--max_images",
type=int,
help="save up to max_images number of images in summary.")
# dataset specific settings
FLAGS.add('--dataset',
type=str,
default='cifar-10',
help='dataset type, each dataset has its own default settings.')
"""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.add('--groups_conf',
type=str,
default=''
'3, 16, 64, 0\n'
'3, 32, 128, 1\n'
'16, 64, 256, 1',
help='Configurations of different residual groups.')
FLAGS.add('--ror_l1',
type=bool,
default=True,
help='RoR enable level 1 '
'requirement: every group is downsampling')
FLAGS.add('--ror_l2',
type=bool,
default=True,
help='RoR enable level 2, residual group shortcuts')
class Model(basic_resnet.Model):
# test logging
# ================================
# utils.set_logging(stream=False)
# utils.set_logger(stream=True)
# logger.info("logger111111")
logger.set_logger(stream=True)
logger.info(CUR_TIME)
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")
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.add(
'--groups_conf',
type=str,
default=''
'1, 16, 64, 0\n'
# '2, 32, 64, 1\n'
# '2, 64, 128, 1\n'
'1, 16, 64, 0\n'
'1, 32, 128, 0\n'
'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,
"""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.add('--groups_conf',
type=str,
default=''
'3, 16, 32, 0\n'
'4, 32, 64, 1\n'
'6, 64, 128, 1\n'
'3, 128, 256, 0',
help='Configurations of different residual groups.')
FLAGS.add('--max_pool',
type=bool,
default=True,
help='whether to add max pooling before first residual unit.')
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:
from collections import namedtuple
import tensorflow as tf
from tensorflow.contrib.layers import convolution2d
from tensorflow.contrib.layers import batch_norm
from tensorflow.contrib.layers import variance_scaling_initializer
from tensorflow.contrib.layers import l2_regularizer
from tensorflow.contrib.layers.python.layers import utils
from models import model
from utils import logger
from utils import FLAGS
FLAGS.add('--special_first',
type=bool,
default=False,
help='special first residual unit from P14 of '
'(arxiv.org/abs/1603.05027')
FLAGS.add('--shortcut',
type=int,
default=1,
help='shortcut connection type: (arXiv:1512.03385)'
'0: 0-padding and average pooling'
'1: convolution projection only for increasing dimension'
'2: projection for all shortcut')
FLAGS.add('--unit_type',
type=int,
default=0,
help='# the type of residual unit '
'# 0: post-activation; 1: pre-activation')
FLAGS.add('--residual_type',
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.
"""