Parameterized training set vs. dev set distribution

train.py

@@ -27,6 +27,7 @@
 # Misc Parameters
 tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
 tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
+tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
 
 FLAGS = tf.flags.FLAGS
 FLAGS._parse_flags()
@@ -56,8 +57,9 @@
 
 # Split train/test set
 # TODO: This is very crude, should use cross-validation
-x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
-y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
+dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
+x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
+y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
 print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
 print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))