from __future__ import absolute_import
import sugartensor as tf
from tensorflow.examples.tutorials.mnist import input_data
__author__ = 'mansour'
# constant sg_data to tensor conversion with queue support
def _data_to_tensor(data_list, batch_size, name=None):
r"""Returns batch queues from the whole data.
Args:
data_list: A list of ndarrays. Every array must have the same size in the first dimension.
batch_size: An integer.
name: A name for the operations (optional).
Returns:
A list of tensors of `batch_size`.
"""
# convert to constant tensor
const_list = [tf.constant(data) for data in data_list]
# create queue from constant tensor
queue_list = tf.train.slice_input_producer(const_list, capacity=batch_size*128, name=name)
# create batch queue
return tf.train.shuffle_batch(queue_list, batch_size, capacity=batch_size*128,
min_after_dequeue=batch_size*32, name=name)
[docs]class Mnist(object):
r"""Downloads Mnist datasets and puts them in queues.
"""
_data_dir = './asset/data/mnist'
def __init__(self, batch_size=128, reshape=False, one_hot=False):
# load sg_data set
data_set = input_data.read_data_sets(Mnist._data_dir, reshape=reshape, one_hot=one_hot)
self.batch_size = batch_size
# save each sg_data set
_train = data_set.train
_valid = data_set.validation
_test = data_set.test
# member initialize
self.train, self.valid, self.test = tf.sg_opt(), tf.sg_opt, tf.sg_opt()
# convert to tensor queue
self.train.image, self.train.label = \
_data_to_tensor([_train.images, _train.labels.astype('int32')], batch_size, name='train')
self.valid.image, self.valid.label = \
_data_to_tensor([_valid.images, _valid.labels.astype('int32')], batch_size, name='valid')
self.test.image, self.test.label = \
_data_to_tensor([_test.images, _test.labels.astype('int32')], batch_size, name='test')
# calc total batch count
self.train.num_batch = _train.labels.shape[0] // batch_size
self.valid.num_batch = _valid.labels.shape[0] // batch_size
self.test.num_batch = _test.labels.shape[0] // batch_size