Add code

README.md

@@ -1,2 +1,4 @@
 # dirt-t
 A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
+
+A note about MNIST data set: I accidentally divided images by 256 instead of 255. This will cause some differences in how the the images behave upon reshaping. Just keep that in mind.

codebase/datasets.py

@@ -38,16 +38,18 @@ def next_batch(self, bs):
  y = self.labeler(x) if self.labels is None else self.labels[idx]
  return x, y
 
+
 class Mnist(object):
  def __init__(self, shape=(32, 32, 3)):
  """MNIST domain train/test data
 
  shape - (3,) HWC info
  """
  print "Loading MNIST"
- data = np.load(os.path.join(PATH, 'mnist.npz'))
- trainx = np.concatenate((data['x_train'], data['x_valid']), axis=0)
- trainy = np.concatenate((data['y_train'], data['y_valid']))
+ data = np.load(os.path.join(PATH, 'mnist_784.npz'))
+
+ trainx = data['x_train']
+ trainy = data['y_train']
  trainy = np.eye(10)[trainy].astype('float32')
 
  testx = data['x_test']
@@ -75,7 +77,6 @@ def resize_cast(x, shape):
  resized_x = np.tile(resized_x, (1, 1, 1, C))
  return resized_x
 
-
 class Mnistm(object):
  def __init__(self, shape=(28, 28, 3), seed=0, npc=None):
  """Mnist-M domain train/test data

codebase/utils.py

@@ -2,7 +2,6 @@
 import tensorflow as tf
 import shutil
 import os
-import datasets
 import numpy as np
 
 def u2t(x):