from tensorflow.examples.tutorials.mnist import input_data as mnist_feeder
import tensorflow as tf
[docs]class mnist(object):
"""
Class for the mnist objects
Args:
dir: Directory to cache at
Attributes:
images: This is the placeholder for images. This needs to be fed in using ``feed_dict``.
labels: This is the placeholder for images. This needs to be fed in using ``feed_dict``.
feed: This is a feeder from mnist tutorials of tensorflow. Use this for feeding in data.
"""
def __init__ (self, dir = 'data'):
"""
Class constructor
"""
self.feed = mnist_feeder.read_data_sets (dir, one_hot = True)
#Placeholders
with tf.variable_scope('dataset_inputs') as scope:
self.images = tf.placeholder(tf.float32, shape=[None, 784], name = 'images')
self.labels = tf.placeholder(tf.float32, shape = [None, 10], name = 'labels')
[docs]class fashion_mnist(object):
"""
Class for the fashion mnist objects.
Ensure that data is downloaded from
`here <https://github.com/zalandoresearch/fashion-mnist#get-the-data>`_
Args:
dir: Directory to cache at
Attributes:
images: This is the placeholder for images. This needs to be fed in using ``feed_dict``.
labels: This is the placeholder for images. This needs to be fed in using ``feed_dict``.
feed: This is a feeder from mnist tutorials of tensorflow. Use this for feeding in data.
"""
def __init__ (self, dir = 'data/fashion'):
"""
Class constructor
"""
self.feed = mnist_feeder.read_data_sets (dir, one_hot = True)
#Placeholders
with tf.variable_scope('dataset_inputs') as scope:
self.images = tf.placeholder(tf.float32, shape=[None, 784], name = 'images')
self.labels = tf.placeholder(tf.float32, shape = [None, 10], name = 'labels')