shuffle task

classifier.py

@@ -29,6 +29,8 @@
 import optimization
 import tokenization
 import tensorflow as tf
+import numpy as np 
+import networkx as nx
 
 flags = tf.flags
 
@@ -147,6 +149,8 @@
 
 flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
 
+flags.DEFINE_bool("shuffle", False, "Whether to run shuffle experiment.")
+
 tf.flags.DEFINE_string(
     "tpu_name", None,
     "The Cloud TPU to use for training. This should be either the name "
@@ -305,6 +309,17 @@ def _create_examples(self, lines, labels, set_type):
       suffix = "_adj" if set_type=="train" else "_adj_val"
       adj = self.get_adjacency(adj_file, suffix=suffix, idx=i)
 
+      if FLAGS.shuffle:
+        split_text = np.asarray(text.split(' '))
+	shuffle_idx = np.random.permutation(len(split_text))
+      #  print(text, ' '.join(split_text[shuffle_idx]))
+        adj = np.asarray(adj)
+        G = nx.from_numpy_matrix(adj)
+        shuffled_adj = nx.adjacency_matrix(G, nodelist=shuffle_idx).todense()
+        padded_adj = np.eye(64)
+        padded_adj[:len(shuffle_idx), :len(shuffle_idx)] = shuffled_adj
+        #print(adj, list(padded_adj))
+
       examples.append(
           InputExample(guid=guid, text_a=text, adjacency=adj, label=label))
       #train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
@@ -800,6 +815,7 @@ def main(_):
     train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
     if FLAGS.clean_data:
       train_examples = processor.get_train_examples(FLAGS.train_file, FLAGS.train_labels)
+      print(len(train_examples))
       num_train_steps = int(
           len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
       file_based_convert_examples_to_features(