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jblitzar
/
github-python-corpus

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def _sort_dict(self, dic):
result = {}
for key in sorted(dic.keys()):
result[key] = dic[key]
return result
def _sign(self, params):
stringToSign = 'GET&%2F&' + parse.quote(parse.urlencode(params))
h = hmac.new((_access_key_secret+'&').encode('utf-8'), stringToSign.encode('utf-8'), digestmod='sha1').digest()
signature = base64.b64encode(h).decode('utf-8')
return signature
# <FILESEP>
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
#from data import distorted_inputs
import re
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
TOWER_NAME = 'tower'
def get_checkpoint(checkpoint_path, requested_step=None, basename='checkpoint'):
if requested_step is not None:
model_checkpoint_path = '%s/%s-%s' % (checkpoint_path, basename, requested_step)
if os.path.exists(model_checkpoint_path) is None:
print('No checkpoint file found at [%s]' % checkpoint_path)
exit(-1)
print(model_checkpoint_path)
print(model_checkpoint_path)
return model_checkpoint_path, requested_step
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
# Restore checkpoint as described in top of this program
print(ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
return ckpt.model_checkpoint_path, global_step
else:
print('No checkpoint file found at [%s]' % checkpoint_path)
exit(-1)
def _activation_summary(x):
tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
tf.summary.histogram(tensor_name + '/activations', x)
tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
def inception_v3(nlabels, images, pkeep, is_training):
batch_norm_params = {
"is_training": is_training,
"trainable": True,
# Decay for the moving averages.
"decay": 0.9997,
# Epsilon to prevent 0s in variance.
"epsilon": 0.001,
# Collection containing the moving mean and moving variance.
"variables_collections": {
"beta": None,
"gamma": None,
"moving_mean": ["moving_vars"],
"moving_variance": ["moving_vars"],
}
}
weight_decay = 0.00004
stddev=0.1
weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope("InceptionV3", "InceptionV3", [images]) as scope:
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected],
weights_regularizer=weights_regularizer,
trainable=True):
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm,
normalizer_params=batch_norm_params):
net, end_points = inception_v3_base(images, scope=scope)
with tf.variable_scope("logits"):
shape = net.get_shape()
net = avg_pool2d(net, shape[1:3], padding="VALID", scope="pool")
net = tf.nn.dropout(net, pkeep, name='droplast')
net = flatten(net, scope="flatten")