fix the compatibility with Python3

It's come out a very fix. Just convert the `print` from statement to
function calls and open the json dump file in text mode. Nothing else.

Author: grissiom

cnn_1_char.py

@@ -49,14 +49,14 @@ def max_pool_2x2(x):
 def main(_):
     # load data
     meta, train_data, test_data = input_data.load_data(FLAGS.data_dir, flatten=False)
-    print 'data loaded'
-    print 'train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0])
+    print('data loaded')
+    print('train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0]))
 
     LABEL_SIZE = meta['label_size']
     IMAGE_HEIGHT = meta['height']
     IMAGE_WIDTH = meta['width']
     IMAGE_SIZE = IMAGE_WIDTH * IMAGE_HEIGHT
-    print 'label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE)
+    print('label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE))
 
     # variable in the graph for input data
     with tf.name_scope('input'):
@@ -147,15 +147,15 @@ def main(_):
                 test_summary, test_accuracy = sess.run([merged, accuracy], feed_dict={x: test_x, y_: test_y, keep_prob: 1.0})
                 test_writer.add_summary(test_summary, i)
 
-                print 'step %s, training accuracy = %.2f%%, testing accuracy = %.2f%%' % (i, train_accuracy * 100, test_accuracy * 100)
+                print('step %s, training accuracy = %.2f%%, testing accuracy = %.2f%%' % (i, train_accuracy * 100, test_accuracy * 100))
 
         train_writer.close()
         test_writer.close()
 
         # final check after looping
         test_x, test_y = test_data.next_batch(2000)
         test_accuracy = accuracy.eval(feed_dict={x: test_x, y_: test_y, keep_prob: 1.0})
-        print 'testing accuracy = %.2f%%' % (test_accuracy * 100, )
+        print('testing accuracy = %.2f%%' % (test_accuracy * 100, ))
 
 
 if __name__ == '__main__':

cnn_n_char.py

@@ -49,15 +49,15 @@ def max_pool_2x2(x):
 def main(_):
     # load data
     meta, train_data, test_data = input_data.load_data(FLAGS.data_dir, flatten=False)
-    print 'data loaded'
-    print 'train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0])
+    print('data loaded')
+    print('train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0]))
 
     LABEL_SIZE = meta['label_size']
     NUM_PER_IMAGE = meta['num_per_image']
     IMAGE_HEIGHT = meta['height']
     IMAGE_WIDTH = meta['width']
     IMAGE_SIZE = IMAGE_WIDTH * IMAGE_HEIGHT
-    print 'label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE)
+    print('label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE))
 
     # variable in the graph for input data
     with tf.name_scope('input'):
@@ -151,15 +151,15 @@ def main(_):
                 test_summary, test_accuracy = sess.run([merged, accuracy], feed_dict={x: test_x, y_: test_y, keep_prob: 1.0})
                 test_writer.add_summary(test_summary, i)
 
-                print 'step %s, training accuracy = %.2f%%, testing accuracy = %.2f%%' % (i, train_accuracy * 100, test_accuracy * 100)
+                print('step %s, training accuracy = %.2f%%, testing accuracy = %.2f%%' % (i, train_accuracy * 100, test_accuracy * 100))
 
         train_writer.close()
         test_writer.close()
 
         # final check after looping
         test_x, test_y = test_data.next_batch(2000)
         test_accuracy = accuracy.eval(feed_dict={x: test_x, y_: test_y, keep_prob: 1.0})
-        print 'testing accuracy = %.2f%%' % (test_accuracy * 100, )
+        print('testing accuracy = %.2f%%' % (test_accuracy * 100, ))
 
 
 if __name__ == '__main__':

datasets/base.py

@@ -112,14 +112,14 @@ def next_batch(self, batch_size):
 
 
 def display_debug_info(meta, train_data, test_data):
-    print '%s Meta Info %s' % ('=' * 10, '=' * 10)
+    print('%s Meta Info %s' % ('=' * 10, '=' * 10))
     for k, v in meta.items():
-        print '%s: %s' % (k, v)
-    print '=' * 30
+        print('%s: %s' % (k, v))
+    print('=' * 30)
 
-    print 'train images: %s, labels: %s' % (train_data.images.shape, train_data.labels.shape)
+    print('train images: %s, labels: %s' % (train_data.images.shape, train_data.labels.shape))
 
-    print 'test images: %s, labels: %s' % (test_data.images.shape, test_data.labels.shape)
+    print('test images: %s, labels: %s' % (test_data.images.shape, test_data.labels.shape))
 
 
 if __name__ == '__main__':

datasets/gen_captcha.py

@@ -30,7 +30,7 @@ def _gen_captcha(img_dir, num_per_image, n, width, height, choices):
 
     image = ImageCaptcha(width=width, height=height)
 
-    print 'generating %s epoches of captchas in %s' % (n, img_dir)
+    print('generating %s epoches of captchas in %s' % (n, img_dir))
     for _ in range(n):
         for i in itertools.permutations(choices, num_per_image):
             captcha = ''.join(i)
@@ -62,15 +62,15 @@ def gen_dataset():
         'height': height,
     }
 
-    print '%s choices: %s' % (len(choices), ''.join(choices) or None)
+    print('%s choices: %s' % (len(choices), ''.join(choices) or None))
 
     _gen_captcha(build_file_path('train'), num_per_image, n_epoch, width, height, choices=choices)
     _gen_captcha(build_file_path('test'), num_per_image, max(1, int(n_epoch * test_ratio)), width, height, choices=choices)
 
     meta_filename = build_file_path(META_FILENAME)
-    with open(meta_filename, 'wb') as f:
+    with open(meta_filename, 'w') as f:
         json.dump(meta, f, indent=4)
-    print 'write meta info in %s' % meta_filename
+    print('write meta info in %s' % meta_filename)
 
 
 if __name__ == '__main__':

simple_softmax.py

@@ -15,12 +15,12 @@
 def main(_):
     # load data
     meta, train_data, test_data = input_data.load_data(FLAGS.data_dir, flatten=True)
-    print 'data loaded'
-    print 'train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0])
+    print('data loaded')
+    print('train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0]))
 
     LABEL_SIZE = meta['label_size']
     IMAGE_SIZE = meta['width'] * meta['height']
-    print 'label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE)
+    print('label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE))
 
     # variable in the graph for input data
     x = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
@@ -55,11 +55,11 @@ def main(_):
             if i % 100 == 0:
                 # Test trained model
                 r = sess.run(accuracy, feed_dict={x: test_data.images, y_: test_data.labels})
-                print 'step = %s, accuracy = %.2f%%' % (i, r * 100)
+                print('step = %s, accuracy = %.2f%%' % (i, r * 100))
 
         # final check after looping
         r_test = sess.run(accuracy, feed_dict={x: test_data.images, y_: test_data.labels})
-        print 'testing accuracy = %.2f%%' % (r_test * 100, )
+        print('testing accuracy = %.2f%%' % (r_test * 100, ))
 
 
 if __name__ == '__main__':

softmax_with_log.py

@@ -31,13 +31,13 @@ def variable_summaries(var):
 def main(_):
     # load data
     meta, train_data, test_data = input_data.load_data(FLAGS.data_dir, flatten=True)
-    print 'data loaded. train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0])
+    print('data loaded. train images: %s. test images: %s' % (train_data.images.shape[0], test_data.images.shape[0]))
 
     LABEL_SIZE = meta['label_size']
     IMAGE_WIDTH = meta['width']
     IMAGE_HEIGHT = meta['height']
     IMAGE_SIZE = IMAGE_WIDTH * IMAGE_HEIGHT
-    print 'label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE)
+    print('label_size: %s, image_size: %s' % (LABEL_SIZE, IMAGE_SIZE))
 
     # variable in the graph for input data
     with tf.name_scope('input'):
@@ -100,14 +100,14 @@ def main(_):
                 # Test trained model
                 test_summary, r = sess.run([merged, accuracy], feed_dict={x: test_data.images, y_: test_data.labels})
                 train_writer.add_summary(test_summary, i)
-                print 'step = %s, accuracy = %.2f%%' % (i, r * 100)
+                print('step = %s, accuracy = %.2f%%' % (i, r * 100))
 
         train_writer.close()
 
         # final check after looping
         test_summary, r_test = sess.run([merged, accuracy], feed_dict={x: test_data.images, y_: test_data.labels})
         train_writer.add_summary(test_summary, i)
-        print 'testing accuracy = %.2f%%' % (r_test * 100, )
+        print('testing accuracy = %.2f%%' % (r_test * 100, ))
 
 
 if __name__ == '__main__':