text: #! /usr/bin/env python
  
  import tensorflow as tf
  import numpy as np
  import os
  import time
  import datetime
  import data_helpers
  import sys
  from text_cnn import TextCNN
  from tensorflow.contrib import learn
  
  # Parameters
  # ==================================================
  did = sys.argv[1]
  f1 = open("log_{}".format(did),"w")
  # 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", 300, "Dimensionality of character embedding (default: 128)")
  tf.flags.DEFINE_string("filter_sizes", "2,3,4", "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", 0.2, "Dropout keep probability (default: 0.5)")
  tf.flags.DEFINE_float("l2_reg_lambda", 0.1, "L2 regularization lambda (default: 0.0)")
  
  # Training parameters
  tf.flags.DEFINE_integer("batch_size", 60, "Batch Size (default: 64)")
  tf.flags.DEFINE_integer("num_epochs", 30, "Number of training epochs (default: 200)")
  tf.flags.DEFINE_integer("evaluate_every", 50, "Evaluate model on dev set after this many steps (default: 100)")
  tf.flags.DEFINE_integer("checkpoint_every", 1000, "Save model after this many steps (default: 100)")
  tf.flags.DEFINE_integer("num_checkpoints", 3, "Number of checkpoints to store (default: 5)")
  # 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._parse_flags()
  # print("\nParameters:")
  # for attr, value in sorted(FLAGS.__flags.items()):
  #     print("{}={}".format(attr.upper(), value))
  # print("")
  
  def preprocess():
      # 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/test set
      # TODO: This is very crude, should use cross-validation
      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:]
  
      del x, y, x_shuffled, y_shuffled
  
      print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
      print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
      return x_train, y_train, vocab_processor, x_dev, y_dev
  
  def train(x_train, y_train, vocabulary, x_dev, 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 = TextCNN(
                  sequence_length=x_train.shape[1],
                  num_classes=y_train.shape[1],
                  vocab_size=len(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)
  
              # Define Training procedure
              global_step = tf.Variable(0, name="global_step", trainable=False)
              optimizer = tf.train.AdamOptimizer(1e-3)
              grads_and_vars = optimizer.compute_gradients(cnn.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", cnn.loss)
              acc_summary = tf.summary.scalar("accuracy", cnn.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 = "models/"
              checkpoint_prefix = os.path.join(checkpoint_dir, "model_{}".format(did))
              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())
              initW = data_helpers.initialize_word2vec(did,FLAGS.embedding_dim)
              print("Initialized with word2vec")
              f1.write("Initialized with word2vec\n")
              sess.run(cnn.W.assign(initW))
  
              def train_step(x_batch, y_batch):
                  """
                  A single training step
                  """
                  feed_dict = {
                    cnn.input_x: x_batch,
                    cnn.input_y: y_batch,
                    cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
                  }
                  _, step, loss, accuracy = sess.run(
                      [train_op, global_step, cnn.loss, cnn.accuracy],
                      feed_dict)
                  #time_str = datetime.datetime.now().isoformat()
                  #print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                  #f1.write("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy))
                  #train_summary_writer.add_summary(summaries, step)
  
              def dev_step(x_batch, y_batch):
                  """
                  Evaluates model on a dev set
                  """
                  feed_dict = {
                    cnn.input_x: x_batch,
                    cnn.input_y: y_batch,
                    cnn.dropout_keep_prob: 1.0
                  }
                  step, loss, accuracy = sess.run(
                      [global_step, cnn.loss, cnn.accuracy],
                      feed_dict)
                  time_str = datetime.datetime.now().isoformat()
                  print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                  f1.write("{}: step {}, loss {:g}, acc {:g}\n".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)
                  #print(len(x_batch),len(y_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:")
                      f1.write("\nEvaluation:")
                      dev_step(x_dev, y_dev)
                      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))
                      f1.write("Saved model checkpoint to {}\n".format(path))
              path = saver.save(sess, "models/model-final_{}".format(did))
              print("Saved model checkpoint to {}\n".format(path))
              f1.write("Saved model checkpoint to {}\n".format(path))
  
  def main(argv=None):
      x_train, y_train, vocabulary, x_dev, y_dev = data_helpers.preprocess(did)
      train(x_train, y_train, vocabulary, x_dev, y_dev)
  
  if __name__ == '__main__':
      tf.app.run()
  