text: #! /usr/bin/env python
  
  # TODO: 1. This only does train/dev split. How to do a proper train/dev/test split?
  # TODO: 2. Can you use pre-trained embeddings, e.g., Google word2vec or GloVe, instead of the randomly initialized word embeddings? How do you handle Out-Of-Vocabulary (OOV) words?
  # TODO: 3. TensorBoard visualization.
  
  import tensorflow as tf
  import numpy as np
  import sys
  import os
  import time
  import datetime
  import data_helpers
  from cnn import CNN
  from rnn import RNN
  from mlp import MLP
  from tensorflow.contrib import learn
  
  # Parameters
  # ==================================================
  
  # Data loading params
  tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
  tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
  tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")
  
  # Model Hyperparameters
  tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
  tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")
  tf.flags.DEFINE_integer("hidden_size", 128, "Dimensionality of character embedding (Default: 128)")
  
  # for RNN
  tf.flags.DEFINE_string("cell_type", "vanilla", "Type of rnn cell. Choose 'vanilla' or 'lstm' or 'gru' (Default: vanilla)")
  
  # for CNN
  tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
  tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
  
  tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
  
  
  # Training parameters
  tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
  tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
  tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)")
  tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
  tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")
  tf.flags.DEFINE_float("learning_rate", 1e-3, "Which learning rate to start with. (Default: 1e-3)")
  
  # 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")
  
  FLAGS = tf.flags.FLAGS
  FLAGS(sys.argv)
  print("\nParameters:")
  for attr, value in sorted(FLAGS.__flags.items()):
      print("{}={}".format(attr.upper(), value))
  print("")
  
  
  # Data Preparation
  # ==================================================
  
  # Load data
  print("Loading data...")
  x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)
  
  # Build vocabulary
  max_document_length = max([len(x.split(" ")) for x in x_text])
  vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
  x = np.array(list(vocab_processor.fit_transform(x_text)))
  
  # Randomly shuffle data
  np.random.seed(10)
  shuffle_indices = np.random.permutation(np.arange(len(y)))
  x_shuffled = x[shuffle_indices]
  y_shuffled = y[shuffle_indices]
  
  # Split train/dev set
  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)))
  
  # Training
  # ==================================================
  
  with tf.Graph().as_default():
      session_conf = tf.ConfigProto(
        allow_soft_placement=FLAGS.allow_soft_placement,
        log_device_placement=FLAGS.log_device_placement)
      sess = tf.Session(config=session_conf)
      with sess.as_default():
          # ###################################################################### CNN
          # nn = CNN(
          #     sequence_length=x_train.shape[1],
          #     num_classes=y_train.shape[1],
          #     vocab_size=len(vocab_processor.vocabulary_),
          #     embedding_size=FLAGS.embedding_dim,
          #     filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
          #     num_filters=FLAGS.num_filters,
          #     l2_reg_lambda=FLAGS.l2_reg_lambda)
          # ###################################################################### CNN
  
          ###################################################################### RNN
          # nn = RNN(
          #     sequence_length=x_train.shape[1],
          #     num_classes=y_train.shape[1],
          #     vocab_size=len(vocab_processor.vocabulary_),
          #     embedding_size=FLAGS.embedding_dim,
          #     cell_type=FLAGS.cell_type,
          #     hidden_size=FLAGS.hidden_size,
          #     l2_reg_lambda=FLAGS.l2_reg_lambda
          # )
          ###################################################################### RNN
  
          #################################################################### MLP
          nn = MLP(
              sequence_length=x_train.shape[1],
              num_classes=y_train.shape[1],
              vocab_size=len(vocab_processor.vocabulary_),
              embedding_size=FLAGS.embedding_dim,
              hidden_size = FLAGS.hidden_size,
              l2_reg_lambda=FLAGS.l2_reg_lambda
          )
          ##################################################################### MLP
  
          # Define Training procedure
          global_step = tf.Variable(0, name="global_step", trainable=False)
          optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
          grads_and_vars = optimizer.compute_gradients(nn.loss)
          train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
  
          # Keep track of gradient values and sparsity (optional)
          grad_summaries = []
          for g, v in grads_and_vars:
              if g is not None:
                  grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
                  sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
                  grad_summaries.append(grad_hist_summary)
                  grad_summaries.append(sparsity_summary)
          grad_summaries_merged = tf.summary.merge(grad_summaries)
  
          # Output directory for models and summaries
          timestamp = str(int(time.time()))
          out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
          print("Writing to {}\n".format(out_dir))
  
          # Summaries for loss and accuracy
          loss_summary = tf.summary.scalar("loss", nn.loss)
          acc_summary = tf.summary.scalar("accuracy", nn.accuracy)
  
          # Train Summaries
          train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
          train_summary_dir = os.path.join(out_dir, "summaries", "train")
          train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
  
          # Dev summaries
          dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
          dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
          dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
  
          # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
          checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
          checkpoint_prefix = os.path.join(checkpoint_dir, "model")
          if not os.path.exists(checkpoint_dir):
              os.makedirs(checkpoint_dir)
          saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)
  
          # Write vocabulary
          vocab_processor.save(os.path.join(out_dir, "vocab"))
  
          # Initialize all variables
          sess.run(tf.global_variables_initializer())
  
          def train_step(x_batch, y_batch):
              """
              A single training step
              """
              feed_dict = {
                nn.input_x: x_batch,
                nn.input_y: y_batch,
                nn.dropout_keep_prob: FLAGS.dropout_keep_prob
              }
              _, step, summaries, loss, accuracy = sess.run(
                  [train_op, global_step, train_summary_op, nn.loss, nn.accuracy],
                  feed_dict)
              time_str = datetime.datetime.now().isoformat()
              print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
              train_summary_writer.add_summary(summaries, step)
  
          def dev_step(x_batch, y_batch, writer=None):
              """
              Evaluates model on a dev set
              """
              feed_dict = {
                nn.input_x: x_batch,
                nn.input_y: y_batch,
                nn.dropout_keep_prob: 1.0
              }
              step, summaries, loss, accuracy = sess.run(
                  [global_step, dev_summary_op, nn.loss, nn.accuracy],
                  feed_dict)
              time_str = datetime.datetime.now().isoformat()
              print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
              if writer:
                  writer.add_summary(summaries, step)
  
          # Generate batches
          batches = data_helpers.batch_iter(
              list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
          # Training loop. For each batch...
          for batch in batches:
              x_batch, y_batch = zip(*batch)
              train_step(x_batch, y_batch)
              current_step = tf.train.global_step(sess, global_step)
              if current_step % FLAGS.evaluate_every == 0:
                  print("\nEvaluation:")
                  dev_step(x_dev, y_dev, writer=dev_summary_writer)
                  print("")
              if current_step % FLAGS.checkpoint_every == 0:
                  path = saver.save(sess, checkpoint_prefix, global_step=current_step)
                  print("Saved model checkpoint to {}\n".format(path))
  