# Path: ops/network.py
# UPDATE_OPS_COLLECTION = tf.GraphKeys.UPDATE_OPS
# DEFAULT_PADDING = 'SAME'
# def layer(op):
#     def layer_decorated(self, *args, **kwargs):
#     def __init__(self, inputs, trainable=True):
#     def setup(self):
#     def feed(self, *args):
#     def add_layer(self, layer, name):
#     def get_output(self, layer_name=''):
#     def get_unique_name(self, prefix):
#     def make_var(self, name, shape, initializer, wd=None, trainable=True, collections=None):
#     def validate_padding(self, padding):
#     def conv(self, input, k_h, k_w, c_o, s_h, s_w, name,
#              weight_decay=None,
#              bias_decay=None,
#              relu=True,
#              padding=DEFAULT_PADDING,
#              group=1,
#              biased=True,
#              initializer=None,
#              trainable=True):
#     def relu(self, input, name):
#     def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
#     def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
#     def lrn(self, input, radius, alpha, beta, name, bias=1.0):
#     def dropout(self, input, name, keep_prob=0.5):
#     def concat(self, inputs, axis, name):
#     def add(self, inputs, name):
#     def batch_normalization(self, input, name,
#                             scale_offset=True,
#                             relu=False,
#                             decay=0.999,
#                             moving_vars='moving_vars'):
#     def fc(self, input, num_out, name, weight_decay=None, bias_decay=None, relu=True, initializer=None, trainable=True):
#     def softmax(self, input, name):
# class Network(object):


import tensorflow as tf
import numpy as np
from ops.network import networks_list
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


try:
    xrange
except NameError:
    xrange = range
slim = tf.contrib.slim
tf.app.flags.DEFINE_float('power', 1.0, 'The power parameter for polynomial decay.')
FLAGS = tf.app.flags.FLAGS


# Path: ops/network.py
# UPDATE_OPS_COLLECTION = tf.GraphKeys.UPDATE_OPS
# DEFAULT_PADDING = 'SAME'
# def layer(op):
#     def layer_decorated(self, *args, **kwargs):
#     def __init__(self, inputs, trainable=True):
#     def setup(self):
#     def feed(self, *args):
#     def add_layer(self, layer, name):
#     def get_output(self, layer_name=''):
#     def get_unique_name(self, prefix):
#     def make_var(self, name, shape, initializer, wd=None, trainable=True, collections=None):
#     def validate_padding(self, padding):
#     def conv(self, input, k_h, k_w, c_o, s_h, s_w, name,
#              weight_decay=None,
#              bias_decay=None,
#              relu=True,
#              padding=DEFAULT_PADDING,
#              group=1,
#              biased=True,
#              initializer=None,
#              trainable=True):
#     def relu(self, input, name):
#     def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
#     def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
#     def lrn(self, input, radius, alpha, beta, name, bias=1.0):
#     def dropout(self, input, name, keep_prob=0.5):
#     def concat(self, inputs, axis, name):
#     def add(self, inputs, name):
#     def batch_normalization(self, input, name,
#                             scale_offset=True,
#                             relu=False,
#                             decay=0.999,
#                             moving_vars='moving_vars'):
#     def fc(self, input, num_out, name, weight_decay=None, bias_decay=None, relu=True, initializer=None, trainable=True):
#     def softmax(self, input, name):
# class Network(object):


# Path: deployment/train_util.py
import tensorflow as tf
import numpy as np
from ops.network import networks_list

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


try:
    xrange
except NameError:
    xrange = range
slim = tf.contrib.slim
tf.app.flags.DEFINE_float('power', 1.0, 'The power parameter for polynomial decay.')
FLAGS = tf.app.flags.FLAGS


def _configure_learning_rate(global_step):