import sys
from os import path
home_dir = path.expanduser('~')
proj_dir = path.join(home_dir, 'Projects')
data_dir = path.join(proj_dir, 'data')
yfcc_dir = path.join(data_dir, 'yfcc100m')
pypkg_dir = path.join(proj_dir, 'kdgan')
surv_dir = path.join(yfcc_dir, 'survey_data')
slim_dir = path.join(pypkg_dir, 'slim')
sys.path.insert(0, slim_dir)
kdgan_dir = path.join(pypkg_dir, 'kdgan')
logs_dir = path.join(kdgan_dir, 'logs')
temp_dir = path.join(kdgan_dir, 'temp')
ckpt_dir = path.join(kdgan_dir, 'checkpoints')
dataset = 'yfcc10k'
image_dir = path.join(yfcc_dir, 'images')
rawtag_file = path.join(yfcc_dir, 'sample_00')
sample_file = path.join(yfcc_dir, 'sample_09')
dataset_dir = path.join(yfcc_dir, 'yfcc10k')
raw_file = path.join(dataset_dir, '%s.raw' % dataset)
data_file = path.join(dataset_dir, '%s.data' % dataset)
train_file = path.join(dataset_dir, '%s.train' % dataset)
valid_file = path.join(dataset_dir, '%s.valid' % dataset)
label_file = path.join(dataset_dir, '%s.label' % dataset)
vocab_file = path.join(dataset_dir, '%s.vocab' % dataset)
image_data_dir = path.join(dataset_dir, 'ImageData')
train_tfrecord = '%s.tfrecord' % train_file
valid_tfrecord = '%s.tfrecord' % valid_file
user_key = 'user'
image_key = 'image'
text_key = 'text'
label_key = 'label'
file_key = 'file'
image_encoded_key = 'image/encoded'
image_format_key = 'image/format'
image_height_key = 'image/height'
image_width_key = 'image/width'
image_file_key = 'image/file'
unk_token = 'unk'
pad_token = ' '
num_threads = 4
channels = 3
num_label = 100
vocab_size = 7281
train_data_size = 8000
valid_data_size = 2000
train_batch_size = 32
valid_batch_size = 100
prerecord_dir = path.join(dataset_dir, 'Pretrained')
tfrecord_tmpl = '{0}_{1}_{2:03d}.{3}.tfrecord'

8232a67ac2fdb7ad7ecb822fc475a49cf4f30ac2
<mask token>

<mask token>
sys.path.insert(0, slim_dir)
<mask token>

<mask token>
home_dir = path.expanduser('~')
proj_dir = path.join(home_dir, 'Projects')
data_dir = path.join(proj_dir, 'data')
yfcc_dir = path.join(data_dir, 'yfcc100m')
pypkg_dir = path.join(proj_dir, 'kdgan')
surv_dir = path.join(yfcc_dir, 'survey_data')
slim_dir = path.join(pypkg_dir, 'slim')
sys.path.insert(0, slim_dir)
kdgan_dir = path.join(pypkg_dir, 'kdgan')
logs_dir = path.join(kdgan_dir, 'logs')
temp_dir = path.join(kdgan_dir, 'temp')
ckpt_dir = path.join(kdgan_dir, 'checkpoints')
dataset = 'yfcc10k'
image_dir = path.join(yfcc_dir, 'images')
rawtag_file = path.join(yfcc_dir, 'sample_00')
sample_file = path.join(yfcc_dir, 'sample_09')
dataset_dir = path.join(yfcc_dir, 'yfcc10k')
raw_file = path.join(dataset_dir, '%s.raw' % dataset)
data_file = path.join(dataset_dir, '%s.data' % dataset)
train_file = path.join(dataset_dir, '%s.train' % dataset)
valid_file = path.join(dataset_dir, '%s.valid' % dataset)
label_file = path.join(dataset_dir, '%s.label' % dataset)
vocab_file = path.join(dataset_dir, '%s.vocab' % dataset)
image_data_dir = path.join(dataset_dir, 'ImageData')
train_tfrecord = '%s.tfrecord' % train_file
valid_tfrecord = '%s.tfrecord' % valid_file
user_key = 'user'
image_key = 'image'
text_key = 'text'
label_key = 'label'
file_key = 'file'
image_encoded_key = 'image/encoded'
image_format_key = 'image/format'
image_height_key = 'image/height'
image_width_key = 'image/width'
image_file_key = 'image/file'
unk_token = 'unk'
pad_token = ' '
num_threads = 4
channels = 3
num_label = 100
vocab_size = 7281
train_data_size = 8000
valid_data_size = 2000
train_batch_size = 32
valid_batch_size = 100
prerecord_dir = path.join(dataset_dir, 'Pretrained')
tfrecord_tmpl = '{0}_{1}_{2:03d}.{3}.tfrecord'

import sys
from os import path
home_dir = path.expanduser('~')
proj_dir = path.join(home_dir, 'Projects')
data_dir = path.join(proj_dir, 'data')
yfcc_dir = path.join(data_dir, 'yfcc100m')
pypkg_dir = path.join(proj_dir, 'kdgan')
surv_dir = path.join(yfcc_dir, 'survey_data')
slim_dir = path.join(pypkg_dir, 'slim')
sys.path.insert(0, slim_dir)
kdgan_dir = path.join(pypkg_dir, 'kdgan')
logs_dir = path.join(kdgan_dir, 'logs')
temp_dir = path.join(kdgan_dir, 'temp')
ckpt_dir = path.join(kdgan_dir, 'checkpoints')
dataset = 'yfcc10k'
image_dir = path.join(yfcc_dir, 'images')
rawtag_file = path.join(yfcc_dir, 'sample_00')
sample_file = path.join(yfcc_dir, 'sample_09')
dataset_dir = path.join(yfcc_dir, 'yfcc10k')
raw_file = path.join(dataset_dir, '%s.raw' % dataset)
data_file = path.join(dataset_dir, '%s.data' % dataset)
train_file = path.join(dataset_dir, '%s.train' % dataset)
valid_file = path.join(dataset_dir, '%s.valid' % dataset)
label_file = path.join(dataset_dir, '%s.label' % dataset)
vocab_file = path.join(dataset_dir, '%s.vocab' % dataset)
image_data_dir = path.join(dataset_dir, 'ImageData')
train_tfrecord = '%s.tfrecord' % train_file
valid_tfrecord = '%s.tfrecord' % valid_file
user_key = 'user'
image_key = 'image'
text_key = 'text'
label_key = 'label'
file_key = 'file'
image_encoded_key = 'image/encoded'
image_format_key = 'image/format'
image_height_key = 'image/height'
image_width_key = 'image/width'
image_file_key = 'image/file'
unk_token = 'unk'
pad_token = ' '
num_threads = 4
channels = 3
num_label = 100
vocab_size = 7281
train_data_size = 8000
valid_data_size = 2000
train_batch_size = 32
valid_batch_size = 100
prerecord_dir = path.join(dataset_dir, 'Pretrained')
tfrecord_tmpl = '{0}_{1}_{2:03d}.{3}.tfrecord'
