python 2 to 3

.gitignore

@@ -0,0 +1,4 @@
+.idea/
+__pycache__/
+training/
+MNIST_data/

input_data.py

@@ -12,9 +12,9 @@ def maybe_download(filename, work_directory):
         os.mkdir(work_directory)
     filepath = os.path.join(work_directory, filename)
     if not os.path.exists(filepath):
-        filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath)
+        filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
         statinfo = os.stat(filepath)
-        print 'Succesfully downloaded', filename, statinfo.st_size, 'bytes.'
+        print ('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
     return filepath
 
 
@@ -25,7 +25,7 @@ def _read32(bytestream):
 
 def extract_images(filename):
     """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
-    print 'Extracting', filename
+    print ('Extracting', filename)
     with gzip.open(filename) as bytestream:
         magic = _read32(bytestream)
         if magic != 2051:
@@ -52,7 +52,7 @@ def dense_to_one_hot(labels_dense, num_classes=10):
 
 def extract_labels(filename, one_hot=False):
     """Extract the labels into a 1D uint8 numpy array [index]."""
-    print 'Extracting', filename
+    print ('Extracting', filename)
     with gzip.open(filename) as bytestream:
         magic = _read32(bytestream)
         if magic != 2049:

main.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 import numpy as np
-import input_data
+from tensorflow.examples.tutorials.mnist import input_data
 import matplotlib.pyplot as plt
 import os
 from scipy.misc import imsave as ims
@@ -64,16 +64,18 @@ def train(self):
         with tf.Session() as sess:
             sess.run(tf.initialize_all_variables())
             for epoch in range(10):
+                gen_loss = None
+                lat_loss = None
+
                 for idx in range(int(self.n_samples / self.batchsize)):
                     batch = self.mnist.train.next_batch(self.batchsize)[0]
                     _, gen_loss, lat_loss = sess.run((self.optimizer, self.generation_loss, self.latent_loss), feed_dict={self.images: batch})
-                    # dumb hack to print cost every epoch
-                    if idx % (self.n_samples - 3) == 0:
-                        print "epoch %d: genloss %f latloss %f" % (epoch, np.mean(gen_loss), np.mean(lat_loss))
-                        saver.save(sess, os.getcwd()+"/training/train",global_step=epoch)
-                        generated_test = sess.run(self.generated_images, feed_dict={self.images: visualization})
-                        generated_test = generated_test.reshape(self.batchsize,28,28)
-                        ims("results/"+str(epoch)+".jpg",merge(generated_test[:64],[8,8]))
+
+                print ("epoch {}: genloss {} latloss {}".format(epoch, np.mean(gen_loss), np.mean(lat_loss)))
+                saver.save(sess, os.getcwd()+"/training/train",global_step=epoch)
+                generated_test = sess.run(self.generated_images, feed_dict={self.images: visualization})
+                generated_test = generated_test.reshape(self.batchsize,28,28)
+                ims("results/"+str(epoch)+".jpg",merge(generated_test[:64],[8,8]))
 
 
 model = LatentAttention()

utils.py

@@ -7,7 +7,7 @@ def merge(images, size):
 
     for idx, image in enumerate(images):
         i = idx % size[1]
-        j = idx / size[1]
+        j = int(idx / size[1])
         img[j*h:j*h+h, i*w:i*w+w] = image
 
     return img